New algorithm cuts indoor positioning error by nearly half
Conventional indoor positioning often depends on expensive Wi-Fi or Bluetooth infrastructure, or on inertial sensors that accumulate drift within seconds. Magnetic navigation has emerged as a promising alternative because steel structures and electronics leave buildings with unique, location-specific magnetic signatures.
However, existing map-free methods rely on polynomial models that oversimplify the magnetic field’s spatial variations. They capture the broad trend but miss the sharp, local anomalies caused by metal pipes or distribution boxes.
With these limitations, a more accurate, robust, and physically interpretable approach to magnetic field modeling is urgently needed for practical indoor navigation.
A team from the Aerospace Information Research Institute, Chinese Academy of Sciences, publishing (DOI: 10.1186/s43020-026-00201-3) in the journal Satellite Navigation on June 5, has unveiled a robust magnetic-inertial odometry (MIO) method based on the Fibonacci sphere-sampled equivalent magnetic dipole model, denoted as FSS-EMD-MIO. The system uses an array of 30 small magnetometers and an inertial measurement unit to track movement without any external signals.
The core innovation lies in how the system models the indoor magnetic environment. Instead of drawing smooth curves through the data, it represents the local field as a combination of virtual “equivalent magnetic dipoles” — with 16 dipoles identified as optimal through systematic parameter analysis.
Their positions are determined by the Fibonacci sphere sampling technique, which evenly distributes points in 3D space without any directional bias, preventing overfitting. Each dipole’s magnetic moment is then solved in real time using least squares fitting.
The team also derived the spatial gradient of this model, creating a direct mathematical link between changing magnetic readings and the carrier’s displacement, velocity, and attitude. To handle the inherent nonlinearity and location-dependent noise, an adaptive error state Kalman filter fuses inertial data with magnetic observations. Tested on a public dataset, the method achieved a horizontal positioning root mean square error below 1.27 meters, outperforming the previous state of the art (MAINS) by 46% on average.
“The old polynomial methods look at the magnetic field from far away — they see the hills but not the potholes. Our model places virtual sources exactly where the magnetic perturbations live,” the authors explained. “The Fibonacci sphere sampling ensures that no direction is favored, so whether you tilt the sensor or walk in circles, the system adapts reliably. We essentially gave the building’s chaotic magnetic field a readable 3D structure. This means first responders or warehouse robots can finally have a ‘magnetic compass’ that works even when the lights are off and GNSS is out.”
The research paves the way for truly infrastructure-free indoor navigation. Potential applications include guiding firefighters through smoke-filled buildings, tracking inventory robots in steel-racked warehouses, and providing positioning for autonomous vehicles in parking garages or mines. The authors note that future work will incorporate loop-closure detection to correct long-term drift, akin to how a person recognizes a familiar intersection.
By developing scan-matching algorithms based on overlapping magnetic field regions, the team aims to build a complete magnetic simultaneous localization and mapping (SLAM) system for multi-floor buildings, further closing the gap between outdoor and indoor navigation reliability.
During a recent infrastructure survey, a handheld scanning system captured a multi-acre property in less than 15 minutes. As the operator moved through the site, the device continuously scanned the environment while maintaining centimeter-level positioning using satellite signals, inertial sensors and lidar.
The result was a fully georeferenced three-dimensional dataset containing terrain, buildings, trees and infrastructure — captured in a fraction of the time required by traditional survey workflows. Technologies such as these illustrate how far positioning systems have evolved. What once required multiple instruments, control networks and extended field observation can now be accomplished through integrated sensing systems combining satellite navigation with reality capture.
Yet, the foundation of these capabilities traces back more than six decades. Today, billions of devices depend on GNSS positioning. Smartphones, vehicles, aircraft, agricultural equipment and industrial systems rely on satellite signals to determine location and synchronize time. Within the geospatial industry, GNSS has evolved beyond navigation. It now serves as the spatial framework anchoring a growing ecosystem of sensors and measurement technologies capable of capturing the physical world in extraordinary detail.
Receiver evolution and productivity
While satellite constellations and positioning algorithms have steadily improved, many of the most noticeable changes for surveyors have occurred in the instruments themselves.
Modern GNSS receivers are smaller and more efficient than earlier generations. Advances in electronics, antenna design, signal processing and battery technology have reduced size and power requirements while improving reliability and usability in the field.
According to Chris Pappas, owner of Green Forest Surveys and a geospatial thought leader, recent GNSS receiver development has focused on usability rather than increases in raw positioning accuracy.
“What I’ve seen lately is smaller receivers, longer battery life and smaller antenna sizes on the heads,” Pappas said. “The quality has basically remained the same.” These improvements may appear incremental, but they have meaningful impacts on field operations.
Survey crews work in demanding environments such as steep terrain, construction sites, transportation corridors and remote infrastructure locations where equipment weight and power management affect productivity.
“It’s portability. It’s fatigue from walking up a hill,” Pappas explained. “And the= longer battery life means you don’t have to constantly swap batteries or carry extras. You can take a single set with you and it’ll last all day.”
Modern receivers also have benefited from advancements in satellite signals and correction services. Today’s survey-grade receivers routinely track multiple frequencies from multiple constellations.
Miniaturization is not simply a reduction in size. Achieving multi-constellation tracking, multi-frequency processing and real-time correction required major advances in RF engineering and integrated circuit design.
Capabilities that once required large, power-intensive hardware platforms are now integrated into compact receivers capable of operating an entire day on a single charge.
Signal modernization, algorithms and the RTK engine
While receiver hardware has become smaller and more power-efficient, some of the most significant advancements in GNSS performance have occurred in the algorithms and processing engines operating inside those devices.
Modern receivers are specialized computing platforms designed to process signals from multiple constellations, frequencies and correction sources simultaneously. Tracking multiple constellations enables receivers to observe dozens of satellites while reducing ionospheric and multipath errors.
The real breakthrough, however, has come from improvements in the RTK engine itself.
RTK positioning relies on resolving the carrier-phase ambiguities — the unknown integer number of wavelengths between the satellite and the receiver. Earlier RTK systems often required extended initialization periods.
Modern receivers use more sophisticated ambiguity resolution algorithms that leverage multi-frequency observations and improved statistical modeling. Initialization times have dropped, and solutions are more robust in difficult environments.
Modern RTK engines incorporate advanced filtering techniques, stochastic modeling and automated outlier detection to maintain stable solutions when individual observations become unreliable.
These improvements are particularly important as surveyors increasingly work in environments where GNSS conditions are less than ideal. Urban infrastructure, tree canopy and industrial facilities can obstruct satellite signals and introduce multipath errors.
Advanced filtering architectures allow receivers to reject corrupted observations while maintaining stable positioning using valid measurements.
Many modern receivers incorporate Kalman filtering frameworks that continuously estimate position, velocity, clock bias and measurement uncertainties.
These filters allow GNSS measurements to be integrated with inertial sensors and motion constraints, creating more stable positioning solutions.
Network-based correction services also have become increasingly common. Rather than relying solely on a nearby base station, many surveyors now use network RTK systems that aggregate observations from multiple reference stations across a region.
These networks model atmospheric errors and deliver corrections through cellular or internet connections.
Precise point positioning (PPP) techniques, which use precise orbit and clock information rather than local base stations, also have matured significantly. Modern PPP engines can now resolve centimeter level positioning in real time or near real time, something that only a few years ago could take up to an hour using satellite based augmentation.
These advances have been enabled by the evolution of GNSS chipsets. Modern receivers integrate RF front ends, signal processors and navigation engines into compact system-on-chip architectures capable of tracking dozens of signals while running complex positioning algorithms in real time.
The result is a positioning engine that is no longer confined to a single receiver mounted on a survey pole, but operates as the central reference system for a network of sensors capturing complex environments.
The maturity of the modern positioning engine
One of the less visible but most important developments in GNSS over the past decade is the maturation of the positioning engine itself. Early GNSS receivers were essentially signal trackers paired with simple navigation algorithms. Today’s receivers function more like specialized computing platforms optimized for real time estimation.
At the core of these systems is an estimation framework that continuously evaluates the quality of each observation entering the solution. Carrier phase measurements provide the highest precision available from GNSS, but are highly sensitive to noise, multipath and signal interruptions.
Modern RTK engines must balance precision with reliability. Rather than assuming every observation is equally valid, processing engines assign dynamic weights based on signal strength, satellite geometry, atmospheric models and measurement stability. These approaches allow receivers to maintain accurate positioning even when portions of the satellite environment become unreliable.
Solar storms, such as this one in North Carolina, produce beautiful auroras. They also cause signal disruption and interference for GNSS systems. Many of the modern RTK engines now have the ability to filter out this interference and maintain a fix.
The introduction of multi frequency signals also has changed how ambiguity resolution is performed. Earlier RTK systems relied on dual-frequency measurements to estimate ionospheric delay and resolve integer ambiguities. With additional frequencies across multiple constellations, modern receivers apply more advanced ambiguity resolution strategies that improve convergence speed. In practical terms, this means surveyors spend less time waiting for initialization and more time collecting data.
Modern receivers also incorporate tightly integrated filtering architectures. Extended Kalman filtering frameworks continuously estimate position, velocity, clock bias, atmospheric parameters and measurement noise. These models treat positioning as a dynamic estimation problem rather than a static calculation performed at each epoch. The result is a positioning engine capable of maintaining stable centimeter level solutions even when signal conditions fluctuate. For surveyors working in environments with partial satellite obstruction, intermittent multipath or complex site conditions, these improvements often determine whether a day in the field is productive or not.
GNSS as foundational infrastructure
Today, GNSS occupies a unique position in the technology landscape. It is both a mature infrastructure system and a platform for continued innovation. The fundamental architecture of satellite navigation has remained largely consistent for decades, while the ecosystem built around those signals has expanded dramatically.
In many ways, GNSS has become invisible because it works so well. Surveyors, engineers and geospatial professionals interact with receivers, correction services and data products rather than with the satellites themselves. Positioning is expected to function, much like electricity or cellular connectivity. But under that routine operation lies one of the most sophisticated global infrastructure systems ever constructed.
At the space segment level, multiple international constellations provide overlapping coverage. The United States’ GPS, Russia’s GLONASS, Europe’s Galileo and China’s BeiDou systems transmit modernized signals designed to improve accuracy, reliability and interoperability. Regional systems such as Japan’s QZSS and India’s NavIC further strengthen coverage.
This multi-constellation environment represents one of the most significant changes in the GNSS landscape throughout the past two decades. Early survey grade receivers relied primarily on GPS signals, while modern receivers track four or more global constellations simultaneously.
The impact extends beyond redundancy. Observing more satellites improves geometric strength and allows receivers to maintain robust solutions in environments where single constellation systems would struggle, including urban corridors, forested areas and complex infrastructure sites.
Signal modernization has expanded the range of measurements available to positioning engines. Additional civilian frequencies such as GPS L5 and Galileo E5 allow better modeling of ionospheric effects and reduced measurement noise, contributing to more stable positioning solutions.
The most important shift, however, is not in the satellites themselves, but in GNSS’s role within the broader measurement ecosystem.
In the surveying and geospatial industries, GNSS has evolved from a standalone measurement technique into the spatial reference framework for modern data capture technologies. It now anchors measurement platforms capable of capturing millions of spatial observations.
In traditional surveying, GNSS remains a primary method for establishing control networks and geodetic reference points, with RTK and post-processed kinematic techniques routinely achieving centimeter-level accuracy.
In construction and machine control, GNSS enables automated positioning systems that guide heavy equipment using digital terrain models in real time.
In agriculture, precision farming systems use satellite positioning to guide equipment along exact paths, reducing fuel consumption and optimizing inputs.
GNSS also functions as the primary time synchronization system for critical infrastructure, including telecommunications, financial systems and power grids.
For geospatial professionals, the most significant change is how GNSS interacts with emerging measurement technologies. Rather than acting as a standalone sensor, it now operates as the global reference frame for integrated systems.
The satellite-derived position establishes a coordinate foundation that other sensors use to build dense spatial models. In a typical workflow, GNSS establishes the reference, inertial sensors track motion, lidar captures geometry and cameras record imagery. All observations rely on the GNSS reference frame to maintain spatial consistency.
This enables a shift from discrete point measurement to continuous data capture. Instead of collecting individual points, modern platforms capture millions of observations that can be analyzed and extracted as needed.
GNSS remains the backbone of this process. Even as new sensors emerge, the requirement for a stable global reference frame has not changed. GNSS provides that anchor.
Sensor fusion and the expanding positioning stack
While GNSS technology continues to evolve, some of the most significant advances in positioning are occurring through integration with other sensing technologies.
Trees, such as this 150-year-old tulip poplar, were killers of previous-generation GNSS systems. Robust designs, the modern sensor stack, and powerful algorithms can now fix reliably in heavy canopy, saving hours of traditional work.
Modern positioning systems operate as part of a broader sensor ecosystem. Satellite observations provide the global reference frame, while inertial measurement units track motion and orientation, lidar sensors capture geometry and visual sensors analyze environmental features.
Hybrid platforms extend GNSS capability into environments where satellite signals alone may struggle. Several manufacturers now offer handheld systems that combine GNSS receivers with lidar scanning and inertial navigation. Systems such as the CHC Navigation VLi100 integrate GNSS, lidar, inertial sensing and visual positioning into a single instrument. The VLi100 also incorporates the SureFix 2.0 engine, which uses lidar to stabilize the GNSS position for up to 60 ft after signal loss, extending positioning capability in obstructed environments.
The Tersus S1 SLAM system similarly combines lidar-based mapping with GNSS positioning to capture dense spatial data in complex environments.
The same principles drive mobile mapping systems designed for infrastructure-scale data capture. Trimble’s MX series, including the MX9 and MX90, combines GNSS positioning, high-accuracy inertial navigation and high-density lidar to capture detailed spatial data while in motion.
“Sensor fusion is probably the biggest one right now,” said Justin Brooks, sales manager for reality capture at Trimble. “When you combine GNSS with lidar and inertial sensors, you’re not just collecting points anymore. You’re capturing entire environments.”
Mobile mapping is increasingly used across the energy sector. According to Jason Rosbach, director, energy solutions at Trimble, large renewable energy projects such as utility scale solar and wind developments require rapid spatial documentation across thousands of acres. These systems allow survey teams to capture dense geospatial datasets while maintaining consistent positioning through tightly integrated GNSS and inertial navigation.
Karl Bradshaw, director, product management, reality capture at Trimble, explained that GNSS remains the core reference.
“In the MX systems, that GNSS position is the initial core point,” Bradshaw said. “Then the IMU interpolates the vehicle path between those GNSS fixes and provides heading, pitch and roll orientation. Every lidar pulse gets geolocated using that combined solution.”
Reality capture and the GNSS positioning pyramid
The convergence of GNSS positioning with lidar scanning, inertial navigation, and SLAM-based mapping is driving the broader adoption of reality capture workflows across the geospatial and infrastructure industries.
At the core of these systems remains a GNSS-centric positioning pyramid. Satellite observations provide the spatial reference that anchors all other measurements. The additional sensors extend and stabilize that position when conditions become challenging.
From point measurement to spatial data acquisition
The integration of GNSS with modern sensing technologies has changed the scale of spatial data collection.
For most of the 20th century, surveying workflows were based on discrete point measurements. Whether using optical instruments, total stations or early GNSS receivers, surveyors collected individual observations that were later combined to construct maps and models.
This approach remains essential for control networks and boundary surveys, but many modern applications now operate at a fundamentally different level of data density.
Lidar scanners, mobile mapping systems and handheld SLAM platforms can collect millions of measurements in minutes. Instead of selecting points, operators move through an environment while continuously capturing geometric observations. These datasets provide a far more detailed representation of the physical world.
GNSS enables this transition by providing a stable global reference frame. Without it, large point clouds and reality capture datasets would exist only as isolated local models. GNSS allows these datasets to align with engineering design files, geographic information system (GIS) databases and previous survey measurements.
This spatial consistency makes reality capture practical for large infrastructure projects. Transportation departments can compare roadway conditions over time, utilities can integrate asset models and construction teams can verify progress against design.
In each of these workflows, GNSS provides the coordinate framework that keeps datasets aligned across time, sensors and project stages.
The shift from point measurement to continuous data acquisition is one of the most significant changes in geospatial practice in decades.
Even within these systems, positioning still begins with satellite signals. GNSS remains the foundation. Lidar captures geometry, inertial sensors measure motion and SLAM algorithms track environmental features, all fused with the GNSS position.
These systems collect dense spatial observations continuously, allowing entire corridors, facilities and infrastructure sites to be captured rapidly. Because these datasets are anchored to GNSS positioning, they maintain consistent spatial reference over time.
Looking ahead
Another development drawing increasing attention across the positioning industry is the emergence of low Earth orbit (LEO) satellite constellations as potential complements to traditional GNSS systems.
Unlike GNSS satellites operating at medium-Earth orbit altitudes of roughly 20,000 kilometers, LEO satellites orbit much closer to Earth. This proximity allows their signals to reach receivers with significantly higher signal strength and faster acquisition times.
Because the satellites move rapidly across the sky, they also provide constantly changing geometry that can improve positioning performance in environments where traditional GNSS signals struggle.
A number of research groups and commercial companies are now exploring how LEO constellations might augment existing GNSS infrastructure. Some approaches rely on signals from existing communications constellations, while others involve dedicated navigation payloads designed specifically for positioning.
For surveyors and geospatial professionals, the potential benefit is improved positioning reliability in environments where GNSS signals are degraded. Urban corridors, industrial sites and areas with heavy canopy often limit satellite visibility and introduce multipath interference that complicates carrier-phase measurements.
Additional signals from LEO satellites could provide stronger observations in these environments while also improving the redundancy of positioning solutions.
The integration of LEO signals would not replace GNSS but rather expand the broader positioning ecosystem that already has begun to emerge through sensor fusion.
Modern positioning systems increasingly combine GNSS, inertial navigation, lidar, camera and SLAMbased mapping into tightly integrated sensor stacks. GNSS provides the global reference frame, while the other sensors extend and stabilize the positioning solution when satellite visibility becomes limited.
If LEO navigation signals become widely available, they will likely become another layer within that stack.
The long-term result could be positioning systems capable of maintaining centimeter-level trajectories across environments that would have been extremely difficult for GNSS-only solutions just a decade ago.
For the geospatial industry, this evolution represents a continuation of a trend that began decades ago: positioning systems becoming more robust, more integrated, and increasingly capable of capturing the physical world in unprecedented detail.
GNSS researchers presented hundreds of papers at the 2022 Institute of Navigation (ION) GNSS+ conference, which took place Sept. 19-23, 2022, in Denver, Colorado, and virtually. The following four papers focused on the use of GNSS in urban environments. The papers are available here.
GPS World will be attending this year’s ION conference in Denver, Colorado, on Sept. 11-15.
FGO-based GNSS/INS integration improves performance in urban canyons in Hong Kong
The integration of GNSS and inertial navigation systems (INS) has the potential to improve performance due to their complementariness. In this paper, the authors investigated positioning based on the integration of GNSS and INS using factor graph optimization (FGO). This ultimately showed improved performance in urban canyons in Hong Kong. The effectiveness of the proposed method was verified using challenging datasets collected using two automobile-level GNSS receivers in the urban canyons of Hong Kong.
For the experiment conducted in this paper, only the GNSS pseudorange measurement was utilized in the existing FGO-based GNSS/INS integration. The overall potential of the Doppler frequency and carrier-phase measurements has yet to be explored by the authors. To fill this gap, the authors proposed a tightly coupled GNSS/INS integration, using FGO, by exploiting the potential of diverse raw GNSS measurements. The GNSS pseudorange, Doppler frequency, and time-differenced carrier-phase measurements were integrated with the INS, using FGO.
The authors believe the improved performance using FGO-based GNSS/INS integration positioning was due to the global optimization property and the increased measurement redundancy of FGO, compared with the method based on extended Kalman filtering.
Weisong, Hsu; “Factor Graph Optimization for Tightly-Coupled GNSS Pseudorange/Doppler/Carrier Phase/INS Integration: Performance in Urban Canyons of Hong Kong.”
3D mapping in urban environments aided by surround mask GNSS/lidar SLAM
Automatic driving with coupled GNSS/INS and lidar sensors has been implemented in many urban environments successfully over the years. However, this technology is still prone to errors. These potential errors are especially evident in challenging environments, such as urban canyons with several moving objects and building layouts that provide unexpected and abnormal features for lidar sensors and multi-path for GNSS signals.
To address these error challenges in urban environments, the authors of this paper proposed a surround mask that explores error sources from surrounding environments, which could subsequently improve the performance of an integrated mapping system. The surround mask in this experiment extracted a two-layer factor, including non-line-of-sight detection and static objects detection, to collectively compensate for the specific drawbacks of the lidar-based SLAM and the navigation system.
The authors explain that the surround mask eliminated the need to apply complex post-processing to eliminate the accumulated error for each observing unit.
The experimental results demonstrated that the proposed surround mask detected the represented error sources in the local coordinate and provided environment-awareness information for the integrated mapping system.
Ai, Luo, El-Sheimy; “Surround Mask Aiding GNSS/LiDAR SLAM for 3D Mapping in the Dense Urban Environment.”
Novel process noise model helps GNSS Kalman filter degradation in busy cities
Improving the accuracy of GNSS positioning in urban environments is difficult, especially when using low-cost GNSS receivers. In this paper, the authors showed that if the process noise covariance is turned up in a “naïve” manner for poor satellite geometry, the estimation-error covariance could become unintentionally large in a certain direction.
The unintentional inflation of estimation-error covariance could cause the degradation of accuracy. The authors also proposed a fictitious process noise covariance based on an extension of a novel process noise model, which was proposed in their previous work.
The authors stated that in Kalman filter for GNSS positioning, the process noise covariance is often bumped up to avoid the filter divergence in the presence of unknown model errors, by assuming there is a fictitious process noise in addition to the nominal process noise. In this study, the fictitious noise covariance is determined based on the observation matrix, step-by-step, and it reduced the estimation errors without causing the unintentional inflation of estimation-error covariance.
The effectiveness of the derived process noise model is demonstrated for the data sets that simulate GNSS signals from the antenna that moves from open sky areas to urban areas. The estimation errors with the derived process noise model were significantly reduced, compared to the ones with other two process noise models.
Takayama, Yoji, Urakubo, Takateru, Tamaki, Hisashi; “Avoiding GNSS Kalman Filter Degradation in Urban Canyons with a Novel Process Noise Model.”
3D lidar-aided GNSS RTK positioning for increased accuracy mapping in urban canyons
The GNSS real-time kinematic (RTK) positioning technique has shown centimeter-level absolute results in open-sky areas; however, it can suffer from polluted GNSS measurements and poor satellite geometry in urban environments. This is due to the non-line-of-sight (NLOS) and multipath reception caused by signal blockage and reflection.
In this paper, the authors stated that lidar sensors integrated with odometry systems that include an inertial measurement unit (IMU) provided a precise environment description and short-term accurate relative positioning capabilities that could be utilized for aiding GNSS-RTK to obtain better performance.
While 3D lidar-aided GNSS RTK positioning methods detect the GNSS NLOS receptions via an incrementally built map and improve the satellite geometry using the low-lying virtual satellite from lidar features, the high-elevation angle NLOS receptions cannot be fully detected, and the multipath signals cannot be effectively mitigated.
In response to this, the authors proposed a 3D lidar-aided GNSS RTK positioning method with iterated coarse to fine batch optimization by a global 3D NLOS exclusion aided by a point cloud map, which enables the detection of high-elevation angle NLOS receptions. Additionally, the authors proposed iterated batch optimization based on a devised, tightly coupled, factor graph that fully exploited the global consistency among the constraints of lidar, IMU and GNSS RTK to exclude potential multipath signals.
The proposed method aimed to achieve lifelong accurate positioning performance in deeply urbanized areas. The effectiveness of the proposed method has been proved by the evaluation conducted on the author’s open-source challenging dataset, UrbanNav, which contains various sequences collected by automobile-level low-cost GNSS receivers in urban canyons of Hong Kong.
Liu, Wen, Hsu; “3D LiDAR Aided GNSS Real-time Kinematic Positioning via Coarse-to-fine Batch Optimization for High Accuracy Mapping in Dense Urban Canyons.”
The Mayflower Autonomous Ship (MAS) is set to re-embark on its three-week trans-Atlantic journey in April 2022 equipped with two of Silicon Sensing’s AMU30 inertial measurement units (IMUs). These devices send highly precise motion data to the new ‘AI captain’ that guides the vessel. They also assist in measuring sea surface height as part of detailed scientific analysis of ocean topography.
AMU30 is a micro electro-mechanical system (MEMS) unit with excellent inertial performance, including very good bias stability and low noise characteristics, plus an embedded Kalman Filter-based AHRS (attitude and heading reference system) algorithm. It delivers precise 3-axis outputs of angular rate and acceleration, plus roll, pitch and heading angles, altitude and pressure, and temperature, at 200 Hz — all critical to precise maritime navigation.
“The two AMU30 are used to make real-time, precision measurements of the movement of the Mayflower Autonomous Ship in 6 degrees of freedom (DOF) so that the AI Captain may make minute manoeuvring adjustments to optimise vessel performance in a complex wavefield, while also providing redundant general navigation capability at sea,” said Brett Phaneuf, co-director of the project. “Furthermore, when coupled with optical and RTK (real time kinematics) GPS data, the AMU30 assists the ship in making highly accurate measurements of sea surface height, which are important for studying ocean tides, circulation and the amount of heat the ocean holds.”
The MAS journey across the Atlantic will celebrate the voyage of the original Mayflower some 400 years ago. It is just one element of an extensive scientific data gathering and research programme the vessel will complete in the coming years. The ship is guided by its new AI Captain, built using IBM cloud, artificial intelligence (AI) and edge computing technologies, and uses a hybrid engine that draws on solar power. Working with scientists and other autonomous vessels it provides a flexible platform for deepening understanding of issues such as climate change, ocean plastic pollution and marine mammal conservation. In parallel, the development of marine autonomous systems such as this will transform ocean-related industries such as shipping, oil & gas, telecommunications, security & defence, fishing & aquaculture.
For navigation and control of any robotic or autonomous outdoor system, GNSS and inertial navigation systems (INS) are key components. Inevitably, the question arises: Should you build your own custom solution or integrate an available GNSS/INS combined solution? What would give you the best performance, while keeping the total cost of ownership (TCO) to a minimum? The TCO is also known as the “long-term price” and is defined as the purchase price plus the costs of operation over time.
Xenomatix is a company offering automotive solutions based on lidar technology. With eight years of innovative experience, Xenomatix has installed a pre-integrated GNSS/INS receiver on its latest lidar product, achieving high GNSS/INS performance with minimal TCO.
In an integrated INS/GNSS receiver, the GNSS receiver provides positioning with centimeter-level accuracy. The other component is a micro-electromechanical inertial measurement unit (MEMS IMU), which measures 3D orientation in terms of heading, pitch and roll angles with sub-degree precision. For its latest product XenoTrack, Xenomatix chose an INS called XenoAsterx based on the AsteRx SBi3 from Septentrio, which it integrated alongside its lidar to collect road-quality data to the smallest detail.
From an in-house solution to a pre-integrated system
Three years ago, when Xenomatix started developing its new lidar road-inspection system, the company had a GPS receiver, an IMU and an odometer as accompanying sensors. The company wanted to expand into new markets of road inspection in accordance with international standards, and so it needed to improve its components to take the overall performance of its system to the next level with RTK high-accuracy positioning.
To achieve this, while saving time and costs, Xenomatix acquired an AsteRx SBi3 INS/GNSS receiver, which allowed it to focus on its core lidar technology and sensor-fusion algorithms.
This off-the-shelf INS/GNSS solution provided all the high-accuracy positioning and orientation information Xenomatix needed, while eliminating most costs of development, maintenance and support. The new receiver allowed them to drive for miles, without any offset in positioning, something impossible with the previous GPS receiver.
The unique technology from Xenomatix stitches images by using lidar point-cloud overlays. However, when the car is moving fast, this overlay is smaller. The pre-calibrated GNSS/INS extends system performance by allowing stitching even when driving at higher speeds.
“If we start driving and we stitch the road for tens of kilometers and we come back to the same starting point, then we see an offset of only a few millimeters,” said Filip Geuens, CEO, Xenomatix. “This is for us the strongest proof of accuracy and reliability of the GNSS sensor.“
Why pre-integrated GNSS/INS offers better value
The pre-integrated GNSS/INS allows XenoTrack to collect road data even at higher speeds. (Credit: Septentrio)
A pre-integrated GNSS/INS solution — versatile enough to fit into virtually any autonomous or mapping system — offers the best value in the long run for the following reasons.
Better performance. The manufacturer of a GNSS/INS solution specializes in fusing the GNSS receiver and the INS in an optimal way. To accomplish this, the sensors are synchronized and their output run through a sophisticated Kalman filter algorithm. The fused device is then fine-tuned for optimal operation under various conditions. Finally, it is extensively tested and validated.
While being used by numerous customers and in varying applications, the GNSS/INS solution proves itself on various levels such as accuracy and robustness. This results in superior performance, even in the most demanding environments.
After installing the AsteRx SBi3 GNSS/INS system, XenoTrack was able to extend its functionality to inspect longer distances of roads at higher speeds. The AsteRx SBi3 operates reliably, even in challenging environments, such as when driving near high cliffs or under bridges.
Less development time and lower costs. When building a system, the development time is usually about one year employing two full-time GNSS/INS specialists. Hardware components need to be integrated and synchronized, while various interfaces and the Kalman filter need to be implemented. Additional features may be developed, such as velocity input as well as tools for validation, before the intricate step of performance fine-tuning. Finally, additional testing efforts are needed for verification and validation of the device.
On the other hand, a pre-integrated GNSS/INS system with easily accessible interfaces and flexible configuration ensures quick installation, meaning the product is ready within weeks.
Lower maintenance costs and support. Certain high quality pre-integrated GNSS/INS receivers are future-proof — ready to use new GNSS satellite signals and services as soon as they become available. An example of such upcoming service is the Galileo OSNMA anti-spoofing authentication.
Some receiver manufacturers such as Septentrio also offer continuous product improvement in the form of free firmware updates. A system developed in-house, on the other hand, needs continuous investment to maintain its competitive edge.
When issues occur, Septentrio also offers local worldwide support, with experienced application engineers ready to solve GNSS, INS or coupling issues that could halt the production process. For example, when Xenomatix discovered that its GNSS/INS was not working optimally in a certain environment, the company called Septentrio. Within days application engineering experts who analyzed the logged data found the source of the issue and proposed a solution.
Focus on core technology. When the budget is limited, choices need to be made about where to focus the efforts. When a company saves on GNSS/INS development, more can be invested in core technology. This means avoiding any lost-opportunity costs and optimizing margins.
Building your own is not always the best option
Acquiring a pre-integrated GNSS/INS receiver allowed Xenomatix to have a superior and affordable product with a competitive edge. AsteRx SBi3 increased the performance of the XenoTrack mapping system, while a short integration period allowed a faster time-to-market.
Xenomatix also benefited from low maintenance costs, keeping overall TCO to a minimum. Since the company was not spending time developing a custom GNSS/INS system, it could focus fully on its core technology. This allowed Xenomatix to take its business to the next level at a high pace.
Award-winning technology
In November 2021, the XenoTrack road scanner, with AsteRx SBi3 inside, was announced a winner of the IRF Global Road Achievement Award for its innovative road scanning and surveying solutions.
By Karsten Mueller, Jamal Atman, Nikolai Kronenwett and Gert F. Trommer
Innovation Insights with Richard Langley
IT DOESN’T WORK EVERYWHERE. GPS, that is. Unlike many radio broadcasts and the transmissions from nearby cell-phone towers, the signals from GPS satellites are too weak to be reliably received indoors. They don’t make it into tunnels either. And even outdoors, the signals can be blocked by tall buildings and mountains. This is why the Japanese developed the Quasi-Zenith Satellite System — to provide supplementary signals when an insufficient number of GPS signals are available in the concrete canyons of Tokyo and other high-density cities. Even if a GPS signal can be received, it might be contaminated with multipath interference resulting in a degraded position solution.
While GPS signals can be piped indoors from an antenna on the top of a building and reradiated, a GPS receiver will give its position as that of the rooftop antenna and not where it is in the building. While this might be useful for establishing the approximate whereabouts of the receiver when it’s on a bus in an underground terminal, for example, and allows the receiver to continue to receive up-to-date navigation messages providing a quick time-to-first-fix when it leaves the terminal, it’s far from satisfactory as a general indoor navigation solution.
While there are some improvements in signal reception in degraded environments with modernized signals from GPS and the other GNSS constellations, in many instances where we don’t have an unobstructed line-of-sight view of the satellites, GPS alone won’t cut it. Thankfully, other navigation sensors can be used to supplement or replace GNSS when the going gets tough for GPS alone. These include, among others, inertial measurement units, digital compasses, barometric pressure sensors, cameras and laser rangefinders.
But, even with these, is one better than another in all situations, or do they each have benefits and drawbacks just like GNSS? Would a system composed of multiple sensors be best? Such considerations are important if trying to develop a navigation system that can work well in most any environment both outdoors and indoors and transition gracefully when moving from one type of environment to another. This is the problem that confronted a team of researchers from Germany’s Karlsruhe Institute of Technology when designing a navigation system to allow a micro aerial vehicle to operate continuously and autonomously in almost any environment. In this issue’s “Innovation” column, we learn how they went about it and how well the system worked.
Today, micro aerial vehicles (MAVs) are widely used in outdoor environments. The navigation solution of commercially available products typically relies on the availability and accuracy of GNSS. To expand the field of application of MAVs to autonomous operation in indoor environments, an accurate navigation solution is necessary. Possible scenarios include the support of rescue forces, surveillance tasks and inspection missions. Different algorithms using camera or laser rangefinder measurements for indoor navigation can provide accurate results.
However, application of these algorithms is typically limited to indoor scenarios and will not provide accurate results in outdoor environments. Another drawback of these approaches is that absolute positioning is not achieved. Hence, we sought a navigation system for outdoor and indoor environments that combines the beneficial properties of outdoor and indoor navigation systems. Such a navigation system should provide an accurate navigation solution both outdoors and indoors, as well as during transition phases from outdoor to indoor and vice versa.
THE PROBLEM
Several challenges arise when combining multiple sensors in a single navigation system due to specific sensor characteristics. While an accurate navigation solution is obtained by an inertial navigation system with GNSS aiding in open-sky environments, urban canyons and indoor environments degrade the quality of GNSS signals or lead to GNSS outages such that no accurate navigation solution is available.
On the other hand, laser rangefinder measurements allow for the generation of accurate relative measurements indoors. However, due to the limited range of the laser rangefinder, no or only a few measurements are available outdoors away from buildings. Obviously, it is best to exploit the complementary characteristics of both sensors. To avoid losing information, hard switching between two different navigation systems is undesirable. Hence, two main challenges arise:
Accurate time synchronization is necessary when processing measurements from different sensors.
A method has to be developed for the decision on whether a measurement should be processed or rejected.
Moreover, for aerial vehicles, two more requirements must be met:
Estimation of the 3D position and attitude instead of only the 2D position and heading as provided by 2D simultaneous localization and mapping (SLAM) approaches.
Estimation of the vehicle’s velocity and inertial measurement unit (IMU) biases.
Our goal was to develop a navigation system that provides an accurate navigation solution for large-scale environments. The navigation system needed to provide a frequent navigation solution at the update rate of the IMU with very short delays. The framework needed to seamlessly integrate GNSS and other sensors such as a laser rangefinder or cameras. Additionally, the approach could not be limited to a specific sensor setup except for a mandatory GPS receiver, necessary for absolute positioning.
The results presented in the literature often do not include large-scale, realistic environments. Some investigators only consider short indoor sequences, while others ignore challenging GNSS conditions. In contrast, the focus of our approach is on rejecting outlier measurements in transition zones such as urban-canyon environments occurring between outdoor open sky and indoor environments. The choice of the navigation system architecture depends on the requirements of a specific platform. In the case of a quadrotor helicopter (see FIGURE 1), a high update rate is necessary for vehicle guidance and control. Therefore, we chose a Kalman-filter-based approach because it has the advantage over pure SLAM approaches when providing a navigation solution at a high update rate is required.
FIGURE 1. Components of the quadrotor helicopter. (Photo: K. Mueller, J. Atman, N. Kronenwett & G.F. Trommer)
SYSTEM OVERVIEW
We attached several sensors and two processing platforms to the quadrotor helicopter used in our work. A microcontroller sensor board reads the sensor values from the IMU, digital compass, air pressure sensor and a GPS-only GNSS module. Timestamps are generated for each sensor data type so that accurate synchronization is provided even when delays occur, such as when processing the sensor data. The IMU is mounted close to the center of the vehicle. The air pressure sensor is directly attached to the sensor board, while the three-axis digital compass is attached to the quadrotor’s landing skid to avoid interfering magnetic fields from power electronics. The GPS receiver provides pseudorange and Doppler measurements at a rate of 10 Hz. Moreover, ephemeris data for each satellite and Klobuchar ionospheric parameters are recorded to correct the measurements. Each second, a time pulse is generated by the receiver to precisely determine the time when GPS measurements were taken. Additionally, the time pulse is used to estimate the drift of the real-time clock (RTC) on the sensor board and, therefore, to provide more accurate timestamps.
A two-dimensional laser rangefinder is mounted on top of the helicopter. Distance and angular information of objects within a scan angle of 270° is provided by this sensor. The maximum range is 30 meters. Time synchronization is achieved through a pulse registered by the microcontroller sensor board before every scan. The body of the laser rangefinder is shielded using copper foil to reduce interference with signals received by the GPS antenna. A trigger signal is sent to the camera mounted at the front of the helicopter to provide time synchronization. However, the camera was not used for the results presented in this article. An overview of the sensor setup and time synchronization is depicted in FIGURE 2.
The camera and laser rangefinder data is sent via USB to a powerful computing platform attached to the bottom carbon-fiber sheet. Time synchronization information and additional sensor data is sent from the microcontroller sensor board to the computer for processing the sensor data and calculating the navigation solution.
FIGURE 2. Block diagram showing signal flows among system hardware components. (Photo: K. Mueller, J. Atman, N. Kronenwett & G.F. Trommer)
NAVIGATION SYSTEM
The navigation system presented in this article was developed to provide a navigation solution in both outdoor and indoor environments. Therefore, processing GPS position and velocity estimations must be possible, as well as handling of relative position and heading angle changes resulting from the laser rangefinder scans. Challenges arise due to the different time delays as illustrated in FIGURE 3. IMU measurements are available at a high frequency. Messages with the trigger timestamps are sent from the sensor board to the computer to provide information about when a GPS or laser measurement was taken.
FIGURE 3 Time sequencing of measurements and calculations. (Photo: K. Mueller, J. Atman, N. Kronenwett & G.F. Trommer)
The corresponding measurements are available with significant delays. Since GPS pseudorange and Doppler measurements are not immediately available and processing requires additional time, the typical delay between the point in time when the measurement was taken by the receiver and the time when the estimated position and velocity are available to the navigation filter is between 70 and 90 milliseconds. Even longer delays occur when processing laser rangefinder data. After processing the laser scans, the horizontal position changes and yaw angle changes (in this article, denoted as two-dimensional pose change measurements) are available for analysis. However, these changes are relative to a point in time in the past. Moreover, due to the processing, additional delay occurs and synchronization with the correct laser rangefinder trigger signal is required. The requirement to process measurements with a temporal overlap causes additional complexity, such as having several GPS measurements that are taken in the time period covered by a pose change measurement.
Error-State Kalman Filter with Stochastic Cloning. An error-state Kalman filter with 16 states estimates the vehicle’s 3D position, 3D velocity, attitude, accelerometer and gyroscope biases, and the bias for the barometric altimeter. The prediction step of the filter consists of integrating the specific force and angular rate measurements of the IMU. Measurements of the air pressure sensor and the digital compass have negligible delays, so these measurements are processed in the Kalman filter update step without compensating for delays. As we mentioned, the assumption of insignificant delays does not hold for GPS measurements and pose change measurements. Thus, we implemented stochastic cloning to overcome errors that would be introduced by delays. The idea of stochastic cloning is to augment the state vector and covariance matrix by copies of the state and covariance estimates at a specific point in time. As the augmented covariance matrix contains cross-correlation terms between the state at a previous time instance and the current state, processing of delayed measurements corrects the current state and covariance estimations.
Processing GPS Measurements. The first step when processing GPS measurements is to clone the current filter state. As outlined in the section “System Overview,” the time pulse generated by the receiver is used to determine the time when a measurement is taken. Once the pseudorange measurements are available, corrections are calculated. A weighted least-squares estimation is used to calculate position and velocity. The weight for each pseudorange measurement is the inverse of the estimated variance, which is calculated depending on the carrier-to-noise-density ratio. Weights for Doppler measurements are calculated similarly.
To reduce the errors introduced by satellite signals of low quality, a minimum carrier-to-noise-density ratio of 33 dB-Hz and a minimum elevation angle of 15° are required for the satellite signals. In addition to position and velocity, valuable information is drawn from the estimation: The variance of the calculated position is chosen to be proportional to the weighted root mean square value of the residuals and the position dilution of precision (PDOP). The velocity variance is calculated similarly. In case only four satellites are used, the variance is only proportional to the PDOP as no residuals are available. The position and velocity estimates are processed by the Kalman filter using these estimated variances. Moreover, before the filter update step is executed, the Mahalanobis distance for each measurement is calculated and outliers removed.
Additionally, measurements are not processed if their variance is above a threshold. This typically occurs in the vicinity of buildings as non-line-of-sight signals are tracked by the receiver and, therefore, processing these measurements is not desired.
Laser Rangefinder Processing. As described in the previous section, stochastic cloning is used to treat delayed pose change measurements. To process a measurement, two cloned states are necessary.
A pose change measurement consists of a relative translation and a rotation, both given in coordinates of the body-stabilized frame, which is identical to the body frame but compensated for roll and pitch angles. Hence, the x and y axes of the body-stabilized frame are parallel to the ground. Several methods could be used for generating pose-change measurements, such as camera-based approaches, laser rangefinder approaches or hybrid approaches. In our work, Cartographer, a laser SLAM approach, is used to obtain horizontal position and yaw angle changes. However, the SLAM module could be easily replaced by other laser SLAM approaches.
As laser SLAM approaches build an incremental map, the laser’s pose is given with respect to the map frame. Therefore, the translational and rotational components of the pose-change measurement must be transformed from the map frame to the body-stabilized frame before being processed by the Kalman filter. Different options are possible when choosing the first point in time for a relative measurement (the second point in time is determined by the most recent laser measurement).
We decided to use a keyframe-based aiding technique. A keyframe is defined and the filter state is cloned accordingly. After the processing of a laser measurement by the SLAM algorithm, pose estimations given in map coordinates are transformed to pose change measurements relative to this keyframe. The keyframe is changed depending on the filter status information as outlined in the section “Using the Filter Status Information” of this article. Additionally, the keyframe is changed if the difference between consecutive pose estimations exceeds a threshold. This indicates an erroneous pose estimation by the SLAM module as only small pose changes are expected due to the high update rate of laser scans and the limited velocity of the vehicle. As a result, the influence of errors in the SLAM module on the navigation solution provided by the Kalman filter is reduced.
FILTER STATUS
Above, we described how relative and absolute delayed measurements are processed in an error-state Kalman filter. However, simply processing all available measurements will not lead to the best performance of the filter. For example, the laser SLAM algorithm might not provide accurate and reliable results in open-sky environments free from human-made structures, as mainly vegetation is detected by the laser rangefinder.
To derive a metric for the decision on the necessity of integrating additional relative measurements, we provide a classification scheme based on GPS measurements. The advantage of using only GPS measurements for the filter status determination is the versatility of the approach: A GPS module will be available on almost every platform. The laser rangefinder, however, could be replaced by a camera without modifications in the classification scheme.
Clearly, processing GPS in indoor environments is not an option as no measurements are available. On the contrary, in outdoor open-sky environments, a sensor setup comprising GPS, IMU, digital compass and air pressure sensor results in an accurate navigation solution. Therefore, the interaction of different sensors in transition phases and urban-canyon environments is the most critical part for an accurate navigation solution in large-scale environments. The following paragraphs introduce the classification of single GPS position measurements and the determination of filter status based on the GPS classification.
Classification of Single GPS Position Measurements. The first step for the filter status determination is the classification of single GPS position measurements. The categories for a measurement are very good, good, medium and poor. Two parameters are used for the classification: the number of satellites used for the position calculation and the estimated variance. For a very good measurement, at least six satellites are required; for a good measurement, at least five satellites are necessary. Moreover, thresholds for the estimated position variance are applied. As the variance is proportional to the PDOP and the root mean square of the weighted residuals, this means that a very good or good position measurement must offer a good satellite constellation and small residuals.
Filter Status Determination. The classification of GPS position measurements is used to calculate a filter status. First, a sum over a time interval of one second is computed. The number of positions classified as very good are multiplied by a factor of four, good positions count twice, and the number of medium positions added without a multiplicative factor. In our setup, 10 position measurements are available in one second. The final filter status is determined using two thresholds. If the sum is at least 20, the filter status is “Good GPS.” This means that five measurements classified as being very good or all 10 measurements classified as being good would be sufficient for this status.
The “Medium GPS” status is achieved with a sum between 10 and 20. If no valid GPS measurements have been available over the last five seconds, an additional indoor flag is set, and it is assumed that the vehicle is now indoors. As soon as GPS position measurements become available again, the filter status is re-calculated. The parameters for the filter status are determined empirically and provide robust results for a large variety of scenarios. However, minor changes of the parameter set to classify single measurements might be necessary in case a different GNSS hardware setup is used.
The resulting filter status for an example trajectory is shown in FIGURE 4. As expected, GPS is good in the western part of the trajectory, and the status quickly deteriorates to poor GPS between the high-rise buildings. Just before entering the building, the status changes to “Indoor.” After leaving the building and moving north, the filter status changes mainly between good and medium GPS as signals are blocked due to buildings or mitigated due to foliage. The end of the trajectory in the eastern part offers better GPS conditions since the surrounding buildings are smaller and the status changes to “Good GPS.”
FIGURE 4. The filter status changes from “Good GPS” to “Poor GPS” in the vicinity of high buildings and provides important information on how accurately the filter is aided by processing GPS measurements. (Photo: K. Mueller, J. Atman, N. Kronenwett & G.F. Trommer)
Using the Filter Status Information. The filter status provides valuable information when combining GPS and relative measurements. As outlined in previous sections, the filter status “Good GPS” occurs in open-sky environments where processing of additional relative measurements does not improve the navigation solution. Since the laser SLAM solution might be corrupted in areas without a sufficient number of human-made structures, relative measurements are not processed while the filter status is “Good GPS.” Additionally, the keyframe is changed in short time intervals during this status. The reasoning behind this decision is that it is desired to have a good estimation of the absolute position and orientation with a low uncertainty at the time a keyframe is chosen.
During a period with “Good GPS” conditions, position estimation typically becomes gradually better. For the same reason, it is best to retain a keyframe for a long time when the filter status is “Poor GPS” or “Indoor.” In these scenarios the laser SLAM algorithm provides accurate results as the environment mostly consists of human-made structures. A drawback inside buildings is that the Earth’s magnetic field might become distorted, for example close to elevators. Hence, magnetometer measurements are not processed when the “Indoor” flag is set. If the status “Medium GPS” is set, GPS and relative measurements should be weighted equally. The keyframe is retained until a predefined maximum age is reached or inconsistencies in the SLAM solution are detected.
In contrast to the “Poor GPS” case, the integration of relative measurements is more pessimistic, and the variance is chosen in the range of the typical GPS accuracy. This takes into account that a very accurate laser SLAM solution is not assured. However, the processing of relative measurements improves position accuracy and avoids the growth of filter state covariance, which is beneficial for rejecting faulty measurements. Independent of the filter status, GPS measurements fulfilling the Mahalanobis distance threshold criterion are processed.
RESULTS
The results of three trajectories recorded at the campus of the Karlsruhe Institute of Technology are presented in this section. All trajectories cover outdoor environments with good GPS signal reception as well as urban-canyon and indoor sections. Since flying these challenging trajectories was not possible due to legal reasons and due to small doors that had to be passed through, the quadrotor helicopter was manually carried.
The first trajectory shown in FIGURE 5 starts in an open-sky environment. At position 1, the footpath goes between two 40-meter buildings. Hence, GPS satellite signals are blocked and non-line-of-sight signals are tracked by the receiver that increasingly deteriorate GPS positon and velocity accuracy. The indoor section starts at position 2. After 30 seconds of indoor navigation, the trajectory continues north on the sidewalk. On this section, numbered 4 in Figure 5, a six-story building on the left side and a nearby building on the right side cause medium to poor GPS conditions as was shown in Figure 4. Despite the difficult conditions, the trajectory follows the footpath correctly. Of course, as no GPS correction service or satellite-based augmentation system is used, sub-meter level accuracy is not achieved. At position 2, the trajectory passes along stairs.
FIGURE 5. Trajectory 1 featuring two high buildings of 42-meter height between positions 1 and 2 in the center of the image. After an indoor section the building is left at position 3. The total time of the trajectory is 394 seconds. (Photo: K. Mueller, J. Atman, N. Kronenwett & G.F. Trommer)
Therefore, accuracy in the north direction is very good. In the east direction, however, the error is larger as the trajectory should be farther east within the building. This error remains throughout the indoor section until position 3, as no GPS position measurement is processed to correct for the error. After leaving the building, the error in the east direction becomes smaller by processing accurate GPS position measurements. After heading north on the sidewalk, the error is within the expected accuracy bounds specified by the GPS position accuracy. The smoothness of the trajectory after leaving the building shows that the rejection of GPS position outliers leads to a consistent navigation solution.
The second trajectory is the longest of the three trajectories, covering 400 meters in 9 minutes. The first difficult section is denoted by position 1 in FIGURE 6, when the vehicle moves between two buildings. The walls of the right building are covered by metal plates. It looks like the trajectory is very close to the edge of the right building. However, this effect is from the perspective view of the building in the georeferenced image. We passed below a canopy at position 2 and entered a building at position 3. An accurate position solution is available during the long indoor section with multiple turns. The total time spent indoors was 112 seconds. GPS position measurements becoming available after leaving the building at position 4 improve the accuracy of the navigation solution. However, due to the high accuracy of the position estimation before leaving the building, only small filter innovations occur. The trajectory ends on the sidewalk near the building identified as number 5.
FIGURE 6. Trajectory 2 with a total duration of 9 minutes. An accurate position estimation is obtained during the segment with poor GPS signal reception between positions 1 and 2 and during the indoor section between positions 3 and 4. (Photo: K. Mueller, J. Atman, N. Kronenwett & G.F. Trommer)
Trajectory three, shown in FIGURE 7, is the most challenging, with position errors exceeding those of the previous two trajectories. Already at the start of the trajectory, only six GPS satellites can be used for calculating position and velocity estimates. It is several meters until an accurate position estimate is available at position 1. Between positions 2 and 3, a section with buildings up to 56 meters tall results in no accurate GPS position fixes being available for more than 30 seconds. In this section, the computed trajectory clearly is several meters too far north. Additionally, at position 2 the heading change is smaller than 90 degrees, which results in additional drift. Before entering the building at position 3, GPS position measurements become available and the position is corrected, reducing the error in the north. After 57 seconds indoors, we exited the building at position 4. The position solution is still too far north, but is corrected by additional measurements so that good accuracy is achieved when walking on the sidewalk. The trajectory ends at its start position.
FIGURE 7. Trajectory 3. Poor GPS conditions due to a building of 56-meter height near the north part of the trajectory cause position errors. At position 3 accurate GPS measurements are available and correct the position such that an accurate navigation solution is obtained during the indoor part part of the trajectory. (Photo: K. Mueller, J. Atman, N. Kronenwett & G.F. Trommer)
CONCLUSION
The navigation system presented in this article fuses GPS measurements and relative pose change measurements to provide an accurate navigation solution in both outdoor and indoor scenarios. We show that position errors are small even for challenging scenarios with high buildings and poor GPS signal reception. Currently, the accuracy in outdoor environments is limited by GPS accuracy. Further improvements are expected by including additional GNSS such as GLONASS or Galileo to obtain better satellite geometry, especially in urban-canyon scenarios.
MANUFACTURERS
We used a u-blox LEA-M8T GPS receiver, an Analog Devices ADIS 16448 IMU, a Freescale (now, NXP Semiconductors) MP3H6115A air pressure sensor, a Honeywell HMC5843 digital compass, an Hokuyo UTM-30LX laser rangefinder, an IDS UI-3260CP-C-HQ camera, and an Intel Next Unit of Computing (NUC) platform. We constructed the quadrotor helicopter ourselves. The motors, motor controllers and landing skid are from MikroKopter, while the carbon fiber sheets and the sensor board PCB are our own design. We used a Pixhawk 4 flight controller from Pixhawk.
ACKNOWLEDGMENTS
The authors acknowledge financial support from the Federal Ministry of Transport and Digital Infrastructure of Germany in the framework of mFUND. We also thank the City of Karlsruhe for providing the georeferenced orthophotos. The datasets used for the results presented in this article are available on our project website. This article is based on the paper “A Multi-Sensor Navigation System for Outdoor and Indoor Environments” presented at ION ITM 2020, the 2020 International Technical Meeting of The Institute of Navigation, San Diego, California, Jan. 21–25, 2020.
KARSTEN MUELLER received an M.Sc. from the Karlsruhe Institute of Technology (KIT), Germany, in 2015, after which he started research as a Ph.D. candidate in KIT’s Institute of Systems Optimization.
JAMAL ATMAN received an M.Sc. in electrical engineering and information technology from KIT in 2015. He is a research engineer in KIT’s Institute of Systems Optimization.
NIKOLAI KRONENWETT received an M.Sc. degree in electrical engineering and information technology from KIT in 2015. He is a Ph.D. candidate in KIT’s Institute of Systems Optimization.
GERT F. TROMMER received Dipl.-Ing. and Dr.-Ing. degrees in electrical engineering from the Technical University of Munich, Germany. He is a professor in KIT’s Institute of Systems Optimization.
FURTHER READING
Authors’ Conference Paper
“A Multi-Sensor Navigation System for Outdoor and Indoor Environments” by K. Mueller, J. Atman, N. Kronenwett and G.F. Trommer in Proceedings of ITM 2020, the 2020 International Technical Meeting of The Institute of Navigation, San Diego, California, Jan. 21–24, 2020, pp. 612–625. https://doi.org/10.33012/2020.17165.
“Laser Range Finder Aided Indoor Navigation for a Micro Aerial Vehicle” by P. Crocoll, J. Seibold, M. Popp and G.F. Trommer in European Journal of Navigation, Vol. 11, No. 1, pp. 4–14, 2013.
Keyframe-Based Navigation
“Relative Navigation: A Keyframe-Based Approach for Observable GPS-Degraded Navigation” by D.O. Wheeler, D.P. Koch, J.S. Jackson, T.W. McLain and R.W. Beard in IEEE Control Systems Magazine, Vol. 38, No. 4, 2018, pp. 30–48. https://doi.org/10.1109/MCS.2018.2830079.
Integrated Navigation
“3D Multi-Copter Navigation and Mapping Using GPS, Inertial, and LiDAR” by E.T. Dill and M. Uijt de Haag in NAVIGATION: Journal of The Institute of Navigation, Vol. 63, No. 2, Summer 2016, pp. 205–220. https://doi.org/10.1002/navi.134.
Principles of GNSS, Inertial, and Multisensor Integrated Navigation Systems, 2nd edition, by P.D. Groves. Published by Artech House, Boston, Massachusetts, 2013.
Stochastic Cloning
“Stochastic Cloning: A Generalized Framework for Processing Relative State Measurements” by S.I. Roumeliotis and J. W. Burdick in Proceedings of 2002 IEEE International Conference on Robotics and Automation, Washington, DC, May 11–15, 2002, pp. 1788–1795. https://doi.org/10.1109/ROBOT.2002.1014801.
A new military vehicle navigation system designed and developed by South Africa-based Etion Create is ready for the local and export markets.
Designed for harsh environments and battlefield conditions, the CheetahNAV provides outstanding situational awareness, according to Etion Create. The crew of a light military vehicle can count on highly accurate position information, irrespective of whether they are denied satellite navigation. This is achieved through an advanced inertial measurement system (IMS), comprising several aids, including a gyro-compensated compass and an advanced Kalman filter-based algorithm.
“We are confident that the system provides dead-reckoning horizontal position accuracy of 0.2% of distance travelled in a GNSS denied situation,” said Jan Hurter, senior product manager. “This translates, by way of example, to accuracy of just 200 metres over a distance of 100 kilometers.”
The CheetahNAV can integrate with any number of different inertial navigation systems (INS) and can be aligned with any of the satellite navigation constellations. Combined with GNSS and compass information, the system enables dead-reckoning and accurate positioning of the vehicle in tactical situations. The tactical grade integral inertial measurement unit (IMU) ensures jamming-free operation.
Some of the guidance cues the system provides to the crew during tactical maneuvers include the vehicle’s current position, true heading and desired heading towards the next waypoint, current speed and desired speed to reach the next waypoint or destination on time, and the next waypoint or destination. It also shows the pitch and roll attitude of the vehicle and the track it has travelled.
This data is displayed on a sunlight-readable touch-screen enabled moving map display unit measuring 11.6-inch diagonal, in 16:9 TFT format, with a 1920×1080 resolution. Etion Create is also offering a slave unit for the vehicle driver, as the main display might be positioned elsewhere in a space constrained vehicle. This slave unit, measuring 3.5-inch diagonal TFT, displays information that is specifically required by the driver.
Significant benefits of the CheetahNAV system include ruggedness for extreme battlefield conditions and 28V or 12V DC operation in line with military standards. Moreover, it boasts a high operational reliability.
“It is important to note that Etion Create, as original design manufacturer, is focusing the CheetahNAV on the export market, including the possibility of technology transfer for indigenous manufacturing,” said Hurter. “Besides we offer a multi-language option, which is certainly a key advantage in multinational operations that are almost the norm nowadays.”
The CheetahNAV is non-ITAR controlled, which is the preference of most land forces around the world today to meet their battlefield management requirements.
Having utilized the building blocks of previously developed military off-the-shelf technologies, Etion Create considers the system to be at a high TRL (technology readiness level), and thus available for the export market.
Previously called Parsec, Etion Create is a South African original design manufacturer (ODM) with a long-standing international reach and a professional portfolio of technology offerings and experience across a wide range of business sectors, including defence and aerospace, information security, and mining and industrial sectors.
Earlier this month, Apple applied to the Federal Communications Commission for to a license to install GPS testing equipment on its headquarters campus.
This may be related to an application filed by Apple Inc. with the U.S. Patent Office in August 2019, which describes the company’s “Machine Learning Assisted Satellite Based Positioning.”
From the patent application:
MACHINE LEARNING ASSISTED SATELLITE BASED POSITIONING
A device implementing a system for estimating device location includes at least one processor configured to receive an estimated position based on a positioning system comprising a Global Navigation Satellite System (GNSS) satellite, and receive a set of parameters associated with the estimated position.
The processor is further configured to apply the set of parameters and the estimated position to a machine learning model, the machine learning model having been trained based at least on a position of a receiving device relative to the GNSS satellite.
The processor is further configured to provide the estimated position and an output of the machine learning model to a Kalman filter, and provide an estimated device location based on an output of the Kalman filter.
In 2015, Apple acquired the small enhanced-GPS company Coherent to aid the speed and accuracy of its devices’ location services. Presumably, Apple intends to incorporate its machine-learning positioning method into its navigation software.
Aerial refueling requires highly precise relative navigation. (ILLUSTRATION: Charles Park)
Future UAVs will require relative navigation capability to fulfill a broad range of assisted manned and unmanned missions. A new approach, demonstrated in application to aerial refueling, provides access to accurate relative time-space positioning information (R-TSPI) between platforms.
By Shahram Moafipoor, Jeffrey A. Fayman, Lydia Bock and David Honcik
The advent of unmanned aerial vehicles (UAVs) highlights the importance of precise relative navigation information for safe use of UAVs in many application areas. Future military and civilian UAV applications will increasingly require capabilities such as
sense and avoid
swarming
vehicle-to-vehicle (V2V) platooning
docking
autonomous landing and
autonomous aerial-refueling,
all of which require access to accurate relative time-space positioning information (R-TSPI) between platforms.
In this article, we present the foundation for a generic approach to relative navigation capable of meeting the full range of relative assisted manned and unmanned operations. We present a relative extended Kalman filter (R-EKF) that integrates line-of-sight relative observations from GPS as well as non GPS-based onboard sensors measuring relative bearing and/or relative distance. Multi-sensor fusion provides enhanced system integrity and robustness to partial or total lack of GPS-satellite navigation (GPS-denied). The relative navigation system described here uses these technologies, providing up to 100 Hz R-TSPI with an accuracy of up to ±1.0 m (a function of relative distance), ±0.1 m/s velocity and ±0.5º attitude. The system can be applied to a variety of relative navigation applications; here we focus on its use in aerial refueling.
132d Air Refueling Squadron. A Boeing KC-135R Stratotanker refuels an F-22A Raptor. (Photo: USAF)
AERIAL REFUEL CHALLENGES
Automated aerial refueling for manned and unmanned platforms is a challenging problem requiring accurate R-TSPI. The Geo-RelNAV system provides a key measurement for aerial refueling: the vector closure rate, the differential velocity between the tanker and refueling aircraft. The closure rate is monitored in real time onboard the tanker. The measurement can be used to:
maintain safety-of-flight by ensuring refueling aircraft do not exceed a certain velocity,
determine whether or not a refueling aircraft is approaching the tanker with sufficient velocity, and
provide data to drogue-control engineers to improve control law design.
As a GPS/INS system, Geo-RelNAV can produce a relative navigation solution at a faster sample rate than GPS alone. Solutions are available via serial and/or Ethernet (both TCP and UDP) providing input to external systems as well as the tools for analysis engineers to monitor the data in real time using standard monitoring and recording tools. The system provides R-TSPI in different frames, including the body frame of the platforms, local navigation frame (wander-azimuth) and Earth-fixed frame, as well as transferring the solution to arbitrary points of interest on the aircraft such as the refueling aircraft’s refueling probe.
RELATIVE INERTIAL NAVIGATION
We use the terms primary and secondary in this article to identify the platforms for which R-TSPI data is being generated. R-TSPI is always provided for the primary with respect to the secondary. Referring to Figure 1, the tanker is considered the primary and the refueling aircraft, the secondary (or vice versa, depending on the location of the control segment). Data is always transmitted through the data link from the secondary to the primary. Figure 1 summarizes the geometric relations, where the primary body frame is labeled p-frame and the secondary body frame is labeled s-frame. The body frame fixed to the primary (P) is shown by (xPp,yPp,zPp), and body frame fixed to the secondary (S) is shown by (xSs,ySs,zSs).
Fgure 1. Primary/secondary geometry and corresponding body frames fixed to the vehicle body.
The relative navigation equation is set up for the state of the secondary with respect to the state of the primary in the center of the body frame of the primary, p-frame:
(1)
where xPp is the primary position vector established in the p-frame, and xSp is the secondary position vector defined in the p-frame. Note that these vectors can also be obtained from the primary/secondary strapdown inertial navigation solutions after transferring to the reference (eccentric) point. Equation (1) represents the fundamental equation, from which the relative navigation equations are derived. Once the relative kinematic model of the position and velocity are established, the next step is to develop the relative attitude kinematic model. The relative attitude, denoted by the quaternion qpS, is used to map vectors in the s-frame to vectors in the p-frame:
(2)
where qp and qs are the quaternion attitudes of the primary and secondary with respect to the i-frame, qp* is the conjugate of qp, and is the quaternion multiplication operator.
Hardware for the relative navigation system.
RELATIVE EXTENDED KALMAN FILTER
To establish the R-EKF, we must derive the relative inertial error equations. The R-EKF has 21 basic states including nine for relative position, δΔxpPS , relative velocity, δΔvpPS , and relative attitude, Ψpps, and 12 to model the primary’s gyro and accelerometer bias (non-constant) and non-linear scale factors. Since the relative distance between the secondary and primary is small compared to the radius of the Earth, the gravity terms are negligible. Thus, in the linearized terms, the relative gravitational terms are ignored. It should be noted that the secondary states are assumed to be known for retrieving the absolute primary TSPI information. Since Equations (1) and (2) can only provide the general dynamic model for a nonlinear state model, all these equations must be linearized using Taylor series about nominal values (neglecting the higher-order terms). After perturbation state equations are established, they should be discretized from a continuous-time to a discrete-time sequence. The final solution to the state equation can be expressed as:
(3)
with:
(4)
FPpS is the Jacobian matrix, and the perturbation elements are all related to the primary:
(5)
RELATIVE GPS MEASUREMENT MODEL
When GPS is available, high-accuracy relative positions are derived from the use of carrier-phase differential GPS, a technique commonly used in static positioning applications such as surveying. However, unlike those applications, in this case the reference receiver is not stationary; it is located on a moving platform (secondary) creating a moving baseline. The relative GPS measurement in our system is provided by epoch-by-epoch (EBE) differential carrier-phase processing, which measures accurate relative position between the secondary and primary systems. The EBE relative position has a typical accuracy better than 3 cm (1-sigma horizontal) and 6 cm (1-sigma vertical). Testing of the relative measurement was conducted using two ground vehicles configured with 10-Hz dual-frequency GPS sensors. The mean difference was less than 5 cm. As a conclusion, the GPS relative mode was shown to provide accurate relative positions between the platforms. Once the relative position is measured, the R-EKF observation model can be established as:
(6)
The (ΔxpPS )GPS term is the relative position measured by using GPS data, and the term (ΔxpPS)INS is the relative position, which is predicted by using the last updated inertial solutions. Note that in order to use this relative observation, the lever-arm vector between the GPS and IMU of both the primary and the secondary must be accurately measured and applied (see Figure 2).
Figure 2. Relative observation model.
Here, the observation model is represented on the condition that the vector of observations has yielded certain values based on an assumed linear relationship to:
(7)
Equations (3) and (7) are the fundamental equations of the R-EKF.
SYSTEM ARCHITECTURE
Relative navigation is computed and provided at one of the units, designated the primary unit. This requires data from the secondary unit to be transferred to the primary unit over a data link. The primary unit uses this transmitted data to calculate its position, velocity and attitude relative to the secondary unit. Figure 3 summarizes the architecture and data-flow. Mathematically, the data from the secondary unit used in the relative calculations are assumed to be errorless.
Figure 3. Geo-RelNAV architecture.
OPERATIONAL ENVIRONMENT
We distinguish the following three relative navigation stages, illustrated in Figure 4, where each phase utilizes a unique processing mode.
Fgure 4. Relative navigation phases.
In the Approach phase, the data link between primary and secondary units is not closed. An autonomous navigation solution for both the primary and secondary units is computed on each platform independently. This information will be later used when the system transitions to the Engagement phase to initialize the R-EKF.
In the Engagement phase, the data link between primary and secondary units is closed, and the R-TSPI solution is computed between the platforms. Sensor observations are transmitted across the data link from the secondary unit to the primary unit. The primary unit implements the R‑EKF to produce the R-TSPI solution.
In the Departure phase, the activity requiring R-TSPI (that is, refueling) is complete, and the secondary platform pulls away from the primary platform. In this phase, we transition from the R-EKF back to the autonomous independent navigation system.
The Approach phase is as important as the Engagement phase in attenuating the initialization error in terms of position, velocity and attitude. To initialize the R-EKF, the autonomous TSPI solution from the secondary unit is transferred to the primary unit, where the initial relative position, velocity and attitude are estimated.
There are three conditions under which this initialization must occur:
upon transition from the Approach phase to the Engagement phase,
when in the Engagement phase and the system experiences a data link dropout, and
when there is a large latency in the data link. If the data link latency is too large, the data arriving at the primary can no longer be used.
VALIDATION TESTING
Several system tests were conducted including static bench testing, dynamic ground vehicle testing and flight testing. We discuss the results for the static and bench testing here.
For static bench testing, the system was set up on two points with a measured fixed displacement. The sensor configuration included dual-frequency GPS receivers, ring laser gyro-based IMUs, and a data link operating in the 900-MHz frequency band.
The results show that relative position held to the fixed offset with a standard deviation of less than 0.1 m in North, East and Up. Relative velocity held to zero with a standard deviation less than 0.01 m/s, and relative attitude was also maintained with the accuracy up to the gyro bias stability of the ring laser gyro IMU (1°/hr for a stationary platform).
The overall performance of the system in static bench test confirms the stability of the hardware and software of the system, when it is not exposed to any dynamics, and the sensors are in close proximity (no data link latency or data dropouts).
Dynamic Drive Test. In a more realistic test to simulate the operational phases described in Figure 4, the drive test followed a scripted path. As shown in Figure 5, the two platforms left Geodetics’ facility and drove separately (simulated Approach) until they met each other at the Fiesta Island test site, where the data link was closed for the Engagement phase. The primary and secondary navigation systems operated independently during the Approach phase.
Figure 5. Drive test ground trajectory of the primary (blue) and secondary (red).
Once the data link was closed at the test site, the R-EKF engaged, using initialization information transmitted from the secondary to the primary platform. To provide a “truth source” for evaluating the performance of the relative navigation solution, both autonomous GPS/IMU systems were fed data from an external reference receiver. Table 1 shows the statistical data analysis in the form of mean and standard deviation for the collected data.
Average RMS of fit in the relative position, velocity and attitude of approximately 1.0 m, 0.1 m/s and 0.3º, respectively, were computed for the entire relative navigation period. In this dynamic test, we encountered frequent data link dropouts, data link latency, as well as GPS outages, causing discontinuity in the R-EKF measurement updates until GPS was reacquired. During these periods, the R-EKF prediction model, updated with the last calibrated IMU data, provided the R-TSPI. This test help confirm that system performance is at the expected levels, even in the presence of real-world data link and GPS problems.
Table 1. Statistical analysis of the R-TSPI solution.
GPS-DENIED OPERATIONS
Over-reliance on GPS has exposed vulnerabilities associated with this technology. For example, GPS is easily jammed and spoofed. While spoofing can be addressed with Selective Availability Anti-Spoofing (SAASM) technology, and advances such as M-code will mitigate other vulnerabilities, systems of the future must be robust to partial or total lack of GPS. Advanced sensor-fusion technologies are necessary to provide capabilities in conjunction with, and in the absence of, GPS.
In the context of aerial refueling, sensors such as active and passive vision systems can be used as complimentary observations by the system, providing a GPS-free relative distance observation in situations where GPS is blocked due to airframe masking, jamming, and so on.
Data from both active (lidar) and passive (camera) vision sensors were added to the system, providing significant advantages in the process flow. The use of vision sensors provides the relative distance observation in GPS-denied conditions for continuity in R-EKF updating. In addition, vision-based relative distance allows for the detection of outliers by evaluating the redundancy contribution of the measured GPS-based relative distance, and enables the transfer of the R-TSPI solution from the secondary refueling center to the on-the-fly probe-drogue system, as shown in Figure 6.
Figure 6. Vision sensor aiding increasing the integrity
For the active vision system, we leveraged a fully integrated lidar mapping payload as shown in Figure 7 (left). For the passive sensor, we utilize a stereo camera. Figure 7 (right) shows the test area and the simulated drogue. Imagery observations from the passive camera and the lidar system were processed with independent algorithms appropriate to each data type and the relative distance between each of the two sensors, and the simulated drogue was measured with an RMS error of less than 10 cm.
Figure 7. Geo-MMS (left) and its application (right) for measuring relative distance.
INTEGRITY
While outside the scope of this article, in addition to supplying a GPS-free relative distance observation, the use of vision sensors was applied to the task of increasing system integrity. This includes, in general, the capability to indicate when the system should not be used for the intended operation. We focused on two aspects: outlier detection (inner reliability), and the effect of undetected outliers (outer reliability).
To properly address the reliability and integrity requirements, a quality testing mechanism was designed to assess the estimated/predicted relative distance observations before passing them in to the R-EKF module.
CONCLUSIONS
An autonomous relative navigation, in its application for the aerial refueling problem, places special attention on system architecture so that it can handle most possible real-world scenarios, including frequent data link dropouts, data link latency and GPS outages. The core of the system is a relative extended Kalman filter, which uses GPS and IMU measurements of the primary and secondary platforms to estimate the relative inertial navigation states. The system is able to provide relative TSPI at the IMU sample rate with an accuracy of ±1.0 m position, 0.1 m/s velocity and ±0.5º attitude.
An added benefit of the system architecture is the ability to add observation models that do not rely on GPS. Thus, redundancy can be introduced using sensors such as vision systems.
SHAHRAM MOAFIPOOR is a senior navigation scientist at Geodetics, focusing on new sensor technologies, sensor-fusion architectures, application software, embedded firmware and sensor interoperability in GPS and GPS-denied environments. He holds a Ph.D. in geodetic science from The Ohio State University.
JEFFREY A. FAYMAN serves as Geodetics’ CTO. He holds a Ph.D. in computer science from the Technion Israel Institute of Technology and has published more than 40 papers in robotics, computer vision, computer graphics and navigation systems.
LYDIA BOCK serves as Geodetics’ president and CEO. She has more than 35 years of industry experience spanning a variety of high-tech industries including electronics, semiconductors and telecommunications. She has a Ph.D. from the Massachusetts Institute of Technology.
DAVID HONCIK, Geodetics’ director of engineering, has more than 30 years of experience in software/hardware integration and structured software design for real-time embedded systems, Windows programs, graphics, telecommunications, aerospace, flight simulation and airborne instrumentation.
The integrated lidar mapping payload referenced is Geodetics’ Geo-MMS system.
Jackson Labs Technologies Inc. has made available the M12M Replacement Receiver GNSS module that is form-fit-function compatible to the legacy Motorola M12M and M12+ timing and navigation receivers. It uses an eighth-generation GNSS timing-enabled receiver allowing 72 GNSS-channel reception with any two GNSS systems being received simultaneously.
The M12M adds configurability via USB ports as well as dual in-line package (DIP) switches and various status displays. GPS, GLONASS, BeiDou, QZSS and SBAS (WAAS/EGNOS/MSAS/GAGAN) signals can be received.
The module supports NMEA, Motorola binary and u-blox binary, as well as SCPI (GPIB) communication protocols for easy configuration and monitoring, and is designed to allow plug-and-play retrofit of equipment designed for the legacy Motorola receivers, as well as provide an easy design-in for new customer applications, the company said.
The M12M is certified to operate as a plug-and-play upgrade to legacy equipment such as the Symmetricom/Microsemi XLI server, as well as the Jackson Labs Technologies Fury GPSDO, requiring no setup or configuration to operate in those products, and can thus be used to retrofit products for GLONASS/BeiDou compatibility. In the process, the module enhances all performance parameters such as time to first fix; position, velocity and timing accuracy; tracking sensitivity; the addition of SBAS (differential compensation) capability; and the addition of external interfaces such as USB and a synthesized frequency output.
The module supports a satellite tracking sensitivity of down to -167 dBm, allowing indoor reception in typical environments, a 1PPS output with better than 5-nanosecond real-mean-squared (rms) stability (quantization corrected), and a positioning accuracy of typically better than 0.3 meters rms (survey-in) or better than 0.7-meter rms horizontal even in high-dynamic environments such as aircraft missions.
Dynamic auto Kalman filter configuration software allows using changing Kalman filter parameters in real time for improved accuracy, with filter parameters being automatically set dependent on actual mission dynamics. The GNSS timing receiver also supports Auto Survey (Survey-in) operation with Position Hold mode and TRAIM, allowing single-satellite timing reception in challenged or denied stationary environments.
The module integrates a UTC (GNSS)-synchronized NCO synthesizer with buffered output that can generate a user-adjustable frequency from 0.25 Hz to over 10 MHz with extreme frequency accuracy when locked to the satellites. Additional features include operation from various power sources such as USB, or 3V via the M12M compatible connector, as well as a 7-segment LED status display, and numerous DIP switches for easy software-less configuration of the operating modes and desired GNSS systems to be enabled, Jackson Labs said. The module displays Satellite Status information including signal strengths and systems received, and can thus be used as a handheld antenna- and satellite signal distribution-system monitor.
Various optional programs can be used to configure, control and monitor the unit such as GPSD/NTP, GPSCon, Z38xx, u-blox uCenter, TimeKepper, TeraTerm Pro, WinOncore-12 and others. The industry-standard SCPI software interface supports easy-to-use English-language commands such as GPS?, HELP?, and others to monitor and configure the unit, while all advanced GNSS receiver functions such as capturing carrier phase data, assisted start, satellite setup and gating, and health monitoring features are also supported.
M12M Replacement Receiver module samples ship from stock, and are priced at $220 each.
The VIA-100G GPS/IMU by Karel Electronics. Photo: Karel Electronics
The VIA-100G, an integrated GPS and MEMS-IMU (inertial measurement system), has been added to the ViaNav inertial navigation system family produced by Karel Electronics Corporation.
Featuring a high-accuracy fusion filter running on an embedded processor, the VIA-100G provides all the functions of a vertical reference unit (VRU), an attitude and heading reference system (AHRS) and integrated GPS/IMU system. The system contains GPS, 3D gyroscopes, 3D accelerometers, a magnetometer, a static pressure sensor and temperature sensors in a compact and rugged enclosure. The embedded processor provides driftless and real-time navigation information over a wide range of temperature in dynamic and static conditions, Karel said.
The sensors are integrated with a highly accurate fusion filter. A Kalman filter running on an embedded processor fuses data from the IMU, GPS, magnetometer, altimeter and barometer in an optimal manner to output highly accurate navigation solutions. VIA-100G outputs high-frequency position, velocity and attitude information in addition to calibrated 3D acceleration, rotation, magnetometer and pressure data.
The ViaNav product family includes other navigation products designed to be used in stability, guidance, control and navigation applications in industry. The VIA-100 line includes:
VIA-100I, is an inertial measurement unit with 3D accelerometers and 3D gyroscopes.
VIA-100A, is a 3 DOF AHRS that provides driftless real-time orientation information over the full 360 degrees of angular motion on all three axes. It includes 3D accelerometers, 3D gyroscopes and 3D magnetometers.
VIA-100A+, is a 3 DOF AHRS that provides driftless real-time orientation information. It includes a multi-IMU configuration and employs an optimum filter to lower IMU noise level. It provides 3D orientation with improved accuracy and reliability.
The xOEMcore, now being offered by Oxford Technical Solutions (OxTS), is an inertial navigation system that can also serve as a framework for other positioning systems.
The xOEMcore is a combined six-axis inertial measurement unit and navigation system with sensor fusion in one compact OEM module. In its base form, the xOEMcore measures and outputs raw accelerations and angular rates with small, high-grade MEMS gyros and accelerometers. With a simple upgrade, the xOEMcore is turned into a full inertial navigation system, able to take aiding data from external sources such as GNSS and blend it in the on-board Kalman filter. It is desgined for integration inside any solution that requires robust, high-performance position and orientation.
xOEMcore provides continuity from one point to the next, so detecting unexpected measurements from other devices is easy, the company said. It has deterministic error growth for accuracy, a high update rate and lowdelay, enabling easier control of vehicles and robots.
As a framework, the xOEMcore can be merged other technologies, such as GNSS and vision positioning. The xOEMcore solves conflicts between the two systems, removing timing mismatches, delays, jumps and inconsistencies.
The xOEMcore is small, light and low power. The inertial sensors have low drift rates — less than 5-meters drift after 60 seconds can be achieved in real-time with only odometer aiding. Heading, roll and pitch can be accurate to 0.05 degrees, exceeding magnetic heading and vertical reference system performance.
