The Spectra Precision SP60 GNSS receiver has been selected to perform survey work for construction of a new 450-kilometer electric power transmission corridor.
Connecting Mauritania’s two largest cities, the capital Nouakchott and to the south Nouadhibou, the 225/90Kv transmission line parallels the Atlantic Ocean as it traverses the Sahara Desert.
The Mauritanian Electricity Company, SOMELEC, through its contracting company, awarded the sub-contract for surveying the transmission line and infrastructure to ETAFAT, a geospatial data acquisition and processing firm.
Difficult work conditions, including high heat (over 45 degrees Celsius) and the lack of existing control points were key factors in ETAFAT’s selection of the SP60 receiver. Because of the absence of existing benchmarks along the entire corridor, the SP60 RTX feature played a key role to ensure homogeneity in the coordinate reference frame between the two cities.
The RTX technology leverages real-time data from a global tracking station network with innovative positioning and compression algorithms to compute and relay satellite orbit, satellite clock and other system adjustments, transmitted to the SP60 via satellite or IP to deliver real time high-accuracy corrections, even in remote locations, the company said.
ETAFAT tested the SP60 data with RTX corrections and obtained consistently successful results. The geodetic survey was related to several ground control points (GCP) used in airborne survey. The measurement itself was conducted using two methods, dependently: the classical statistical method, and the RTK GNSS method. The SP60 met or exceeded the required +/- 15 cm order of accuracy.
According to baseline processing and adjustment reports, the SP60 delivered superior results under all conditions, and it did especially well under typical high temperatures of the Sahara Desert. Initialization was well within 5 to 10 seconds for RTK survey with radio signal coverage inside a 5 km radius.
Globalstar has launched an automotive division to support connectivity solutions for the next generation of connected and autonomous vehicles and intelligent transport. With Globalstar’s two-way global and broadcast-capable network, automakers will be able to comply with the newest safety regulations, deliver over-the-air (OTA) software updates, increase location accuracy, and improve the reliability for autonomous vehicle operation.
Globalstar’s next-generation global, hybrid network service is designed to leverage both satellite and terrestrial technologies to connect cars. The highly scalable broadcast/multi-cast network delivers common content to multiple users with virtually unlimited scalability.
The network has enhanced GNSS accuracy and integrity with protection levels to increase the safety and reliability of autonomous driving systems.
It is an efficient and secure broadcast service for critical security patches and OTA updates to software and firmware in Telematics Control Units (TCUs), Electronics Control Units (ECUs), and Head Units (HUs), as well as map tile and map layer data. It also provides datacasting of traffic, weather, hazards, and other alerts.
Global connectivity provides optimized routing of content and services.
Telematics. Increased coverage and reliability for ACN/eCall, roadside assistance, vehicle tracking and telemetry. Data can be pulled from vehicles for remote diagnostics, condition-based maintenance, and preventative analytics.
Managed Security. Secure link for global certifcate and key management, audits and compliance monitoring, that aslo enables service to patch vulnerabilities, and update firewalls and intrusion detection systems (IDS).
The vehicle-based system V2X-Locate can identify vehicle position to sub-meter accuracy in environments that degrade GPS accuracy, such as tunnels and underground carparks, and between high-rise buildings.
As well as enhancing current connected vehicles, V2X-Locate delivers a critical component for connected autonomous vehicles (CAV), which will require uninterrupted positioning data to safely navigate on roads. V2X-Locate enables equipped vehicles to identify their location using existing Smart City V2X (vehicle-to-everything) roadside infrastructure from any standards-based manufacturer.
V2X-Locate positions the vehicle with sub-meter accuracy by using existing communications signals produced by V2X Smart City infrastructure deployments. The result is that V2X-Locate can eliminate positioning black spots in city centers.
The In-Car Advertising Platform enables automotive OEMs to generate revenue by delivering ads to cars in a safe, user-friendly and contextually relevant way. The end-to-end offering for OEM partners is powered by Telenav’s In-Car Ads SDK (software development kit) and cloud-based intelligent targeting platform.
To ensure driver safety, ads only appear when the vehicle is stopped, such as at car startup, traffic lights and upon arrival. The ads automatically disappear whenever the car is in motion or when users interact with other in-dash functions such as music or phone calls.
Relevant ads such as coupons and recommendations are delivered to customers based on information from the vehicle, including frequently traveled routes, destinations and time of the day. For instance, when the vehicle is low on gas, the platform points out nearby stations along the driver’s route, potentially with discount offers.
The Through Glass Integrated V2X Antenna is designed for vehicle-to-vehicle and vehicle-to-everything (V2X) communications. The design incorporates an integrated GNSS antenna on the interior coupler. The antenna pairs with dedicated short-range communications (DSRC) devices.
The dual-radio, glass-mounted antenna eliminates the risk of damaging the vehicle by using a coupling pair to pass DSRC signals between the vehicle’s interior and exterior, eliminating the need to pass RF cables through the roof or window opening. It antenna can be mounted on the rear, front or side windows using automotive-grade glass adhesive. Flexible installation allows the shortest cable route to the V2X device, reducing signal losses due to cable length.
Lane errors in a three-lane road, giving lane determination (yellow triangle). (Photo: Pavel Vinnik/Shutterstock.com)
A lane-keeping system uses a sensor-fusion engine integrating GPS and an IMU with a two-stage map-matching algorithm. The system does not require explicit lane-level geo-referencing, saving massive storage required for lane-level spatial reference information, and reduces the computational complexity of the map-matching algorithm.
By Mohamed M. Atia, Carleton University and Allaa Hilal, Intelligent Mechatronics Systems
Lane determination is an important feature of advanced automotive navigation and guidance systems. It can be used in advanced driving assistance systems (ADAS), lane-departure warnings, and self-driving cars to perform lane-level, turn-by-turn guidance and control. It is also valuable information for telematics applications such as usage-based insurance. Lane-estimation systems have been dominated by vision and infrared sensors. Light detection and ranging (lidar) has also been used as a lane-determination technique. Those systems depend on visually recognizable features and landmarks that may not be available in some areas due to weather conditions or unstructured environments.
In addition, visual data processing may need specialized accelerators and parallel computing platforms to satisfy real-time constraints. To explore other alternatives, several research projects have started to investigate the feasibility of using low-cost global positioning and navigation technologies such as GPS, micro-electromechanical systems (MEMS) inertial measurement units (IMU) and geographical information systems (GIS) as an alternate lane-determination technology. However, most current systems have two main drawbacks: they use high-end RTK GPS, which suffers from coverage issues, and they use explicit lane geo-referencing, which leads to increased storage and processing.
Here we investigate the feasibility of using standard GPS fused with low-cost MEMS-IMU and a road network that includes lane information but not explicitly storing geo-referenced lane-level links.
The accuracy of Standard Positioning Service (SPS) GPS is within 3.351 meters (m) with a 95 percent confidence level. Figure 1 shows the results of standard single-point positioning test for a stationary receiver.
FIGURE 1. Standard GPS 2D position accuracy in a stationary test. (Figure: Mohamed M. Atia and Allaa Hilal)
The standard lane width in North America is approximately 3.6 m, requiring an unbiased precise positioning solution of much less than 1.8 m. If a safety margin of 50% is considered, unbiased precise positioning of less than 0.9 m is needed. Therefore, a standard SPS GPS technology may not be precise enough to accurately determine the vehicle’s lane. Advanced precise positioning technology like differential GPS (DGPS) can be used with high-resolution lane-level maps to achieve the lane determination.
However, these techniques may require additional cost/infrastructures and extra processing. To target a lower cost lane-determination system, this work suggests the fusion of measurements from a standard GPS, MEMS IMU and road-level network.
The work includes a sensor-fusion engine that is developed to integrate GPS and IMU using a loosely coupled extended Kalman filter (EKF). Then, a two-stage map-matching algorithm using a Hidden-Markov-Model (HMM) and a least-squares (LS) regression is developed.
The system does not require explicit lane-level geo-referencing; consequently, it saves massive storage required to save explicit lane-level spatial reference information, and it reduces the computational complexity of the HMM algorithm by reducing the number of road segments the HMM needs to decode. The overall system is illustrated in Figure 2.
FIGURE 2. Illustration of the proposed system. (Figure: Mohamed M. Atia and Allaa Hilal)
PROBLEM DEFINITION
A geometric illustration of the problem is shown in Figure 3. The road-network map is represented as a set of connected segments. Each road segment is defined by a straight line segment with a start position and end position. Curved roads are approximated by a sufficiently large number of straight line segments. Based on this notation and geometric illustration, the estimation problem that this article is addressing is the determination of the lane on which the vehicle is moving.
FIGURE 3. Illustration of the lane determination problem. (Figure: Mohamed M. Atia and Allaa Hilal)
Map-Matching with Hidden-Markov Model. The simplest map-matching method, point-to-curve-matching, is performed by searching for the nearest road segments within a threshold from the current vehicle’s position. The distance is calculated between the vehicle’s position and its projection on the map segment. However, this approach is sensitive to state estimation errors, and it fails at intersections, joins, branches or dense parallel roads. For example, Figure 4 shows a situation where biased GNSS position measurements exist, and the wrong map segment is selected because of the pure dependence on the distance metric only (for instance, D1 is less than D2).
FIGURE 4. Wrong map-segment selection in intersection. (Figure: Mohamed M. Atia and Allaa Hilal)
To avoid these errors and to improve map-matching accuracy, the matching criteria must include several constraints such as map topology (connectivity), vehicle dynamics, road geometry and legal direction of motions. In this work, to consider these constraints, we keep a recent portion of the vehicle motion history and use it in the matching criteria. This strategy is known as curve-to-curve matching.
To process a noisy stream of data, the HMM algorithm is used. A Markov model is a stochastic model that describes a sequence of states. The transition from one state to another can be modeled by a conditional transition probability.
If the states are not directly observable (hidden) but can be indirectly observed through a sequence of outputs, the process is called a Hidden Markov Process. The HMM in this case is characterized by the transition probability and an emission probability that represents the probability that a given state generates a certain observable.
Both transition probability and emission probability constitute the Bayesian network of HMM. A fundamental problem of HMM is that, given a sequence of outputs, what is the best sequence of states that explains the observed outputs? This problem is solved by selecting the sequence of states that maximize the HMM probability.
This estimation process, called decoding, is solved using the Viterbi algorithm. In the proposed system, the hidden states represent map links, and the observable outputs are the vehicle poses. To develop a robust map-matching framework, the vehicle pose history, roads geometry, and map topology constraints must be considered. Therefore, the emission and transition probabilities of an HMM are formulated such that they reflect all of these constraints. The Bayesian network of the HMM for our system is shown in Figure 5. The vehicle states (poses) is obtained from the INS/GNSS filter described shortly.
FIGURE 5. Hidden Markov model for vehicle’s state map-matching. (Figure: Mohamed M. Atia and Allaa Hilal)
In the proposed work, the length of the processed buffer of the vehicle’s state is determined based on the traveled distance. The aim is to accumulate a reasonable geometric knowledge about the trajectory segment that enables the HMM to accumulate enough geometric and topological constraints to be able to select the correct sequence of road segments in difficult intersections, joins and exit/entry roads.
EKF GNSS/INS SYSTEM
The navigation problem can be modeled as a dynamic system of states vector x(t) as follows:
(1) (2)
(Figures: Mohamed M. Atia and Allaa Hilal)
where f(.) is a nonlinear dynamic model, w(t) is a stochastic system noise vector, u(t) is a control signal vector that triggers the transition from current state to a future state, y(t) is external measurements vector (observables), h(.) is a nonlinear measurement model and v(t) is a stochastic measurement noise vector. Using first-order Taylor series approximation, (1) and (2) can be linearized as follows:
(3) (4)
(5)
(6)
(Figures: Mohamed M. Atia and Allaa Hilal)
A Kalman filter calculates an optimal estimation of provided that w(t) and v(t) are zero-mean Gaussian noise vectors with covariance matrices defined by:
(7)
(8)
and δx is the error vector with zero-mean and a covariance matrix P defined by:
(9)
Using zero-hold discretization where derivative is approximated by:
(10)
where T is the sampling time, equations involving HMM probability can be written in discrete form as follows:
(11)
(12)
The optimal estimation of the error vector, δxk, given measurements, yk, is calculated using two steps: prediction,
(13)
(14)
and update,
(15)
(16)
(17)
(Figures: Mohamed M. Atia and Allaa Hilal)
In INS/GNSS systems, the dynamic system state transition (x(t)) is triggered by IMU sensors (accelerometer and gyroscopes) while GNSS measurements are used as observables (y(t)). The observables update in our case is GNSS position and velocity. Therefore, the measurement error model is defined as follows:
(18)
where H is defined as follows:
(19)
Lane Estimation. When the road segments have been accurately selected based on the filtered vehicle’s pose, the projection of the vehicle’s positions on segment lanes can be easily calculated knowing the lane widths and number of lanes. The sum of squared errors for each lane is then calculated by:
(20)
where N is number of epochs, and pv is the projection of vehicle’s position on lane. The lane associated with the minimum error is selected as the designated lane.
(Figures: Mohamed M. Atia and Allaa Hilal)
Lane-Change Detection. If a lane change occurred within the processed buffer of data, the least-squares regression will not converge to the correct lane. Therefore, the buffer needs to be partitioned at the lane-switch locations. Thus, a lane-change detection module is developed. In this work, a lane-change detection method is designed based on capturing the patterns of the vehicle’s orientation and raw gyroscope measurements. The heading and raw gyroscope measurements during lane changes are shown in Figure 6 and Figure 7.
FIGURE 6. Vehicle’s heading during lane change to left. (Figure: Mohamed M. Atia and Allaa Hilal)FIGURE 7. Vehicle’s gyroscope measurements during lane change to left. (Figure: Mohamed M. Atia and Allaa Hilal)
The general pattern that the lane-change module detects is a peak or a valley in azimuth accompanied by a peak/valley or valley/peak sequence in the gyroscope measurements. To detect peaks and valleys, the standard deviation of a moving window of data is calculated and compared to a peak/valley threshold. If both gyro and azimuth peak/valley sequence are consistent and matched with the pattern described above, a lane change is declared.
Two algorithm phases of processing are then applied:
Acquisition Phase. GNSS and IMU measurements are fused in the main EKF, and HMM map-matching is performed and a lane is estimated. The innovation sequence of the main EKF, which is the difference between the predicted state and GNSS updates, is calculated over a buffer of data. If the innovation sequence is within a small threshold and no lane change has been detected, the acquisition phase is concluded and the tracking phase begins.
Tracking Phase. Two EKF filters are initiated. One EKF accepts position updates from the projection of the vehicle’s position on the selected lane, and the other EKF accepts GNSS position updates only. A discrepancy measure is evaluated between the two EKF instances for a short window of time. If this discrepancy measure is higher than a threshold, a temporary GNSS deviation is assumed and the system keeps reporting the current lane as the designated lane. If GNSS measurements started to be centered again on the new lane, a lane change is confirmed and the output of the first EKF instance will be the correct state. Otherwise, this lane change is declared as false and the second EKF output is the correct output. The overall block diagram of the proposed system is shown in Figure 8.
FIGURE 8. Overall block diagram of the proposed system. (Figure: Mohamed M. Atia and Allaa Hilal)
TESTS AND RESULTS
The proposed system has been tested on a computer connected to a GNSS receiver and an automotive MEMS-grade IMU, and road-network map data. A GPS-enabled camera was installed to capture video of the experiment, to be used as a ground truth to verify the results of our algorithms. Sensor specifications are given in Table 1 and Table 2. The effect of level arm (distance between IMU and GNSS antenna) was not considered in this implementation.
TABLE 1. GNSS receiver accuracy. (Table: Mohamed M. Atia and Allaa Hilal)TABLE 2. IMU specifications. (Table: Mohamed M. Atia and Allaa Hilal)
Three testing trajectories were collected during July 2015 through Highway 400 from Wilson Avenue in the south to Davis Drive in the north. Approximately 65 kilometers of trip data was collected. The data included some urban areas but was mostly open sky. It also included challenging road intersections and road joining/branching points. The experimental setup was designed such that the system automatically started when the vehicle’s engine was turned on. A Linux OS was installed on the gigabyte computer box, and a data acquisition firmware was configured to automatically begin when the computer starts. Measurements from the GNSS receiver at 1 Hz and the IMU at 50 Hz were synchronized on the computer. The main algorithm including GNSS/INS fusion and map-matching was developed in native ANSI C language for efficient processing. Original raw IMU data was set to 50 Hz down-sampled to 5 Hz. Within this interval, the real-time system could fetch map information from a cached database file, perform basic prediction steps and implement the forward calculation of a Viterbi algorithm (including calculation of emission and transition probabilities) that is needed for the HMM map-matching step.
Lane-Determination Results. The lane estimation results were logged and time-tagged. Using the video recording, the ground truth lane-level solution was visually inspected and manually recorded in a file. Since both the video camera and the proposed INS/GNSS/maps systems log data tagged by GPS time, synchronization between ground truth and the estimated lane were possible. The estimated lanes were visually inspected record by record and results were saved in an Excel sheet. The results were written into a time-tagged file where each row can be easily visually inspected by looking at the portion of images corresponding to the same time-tag. The time-tag used was the UTC-time contained in the NMEA GNSS raw measurements. The overall accuracy of the proposed system in lane determination is shown in Table 3.
TABLE 3. Lane-estimation accuracy. (Table: Mohamed M. Atia and Allaa Hilal)
Figure 9 and Figure 10 show example snapshots of the visual inspection software tool developed to evaluate the accuracy of the system. As can be seen in the figures, an image of the road that indicates the correct lane is displayed in the upper graph, while the estimated lane information is displayed along with road information including lane errors in the lower graph. Figure 10 shows that the system can identify the correct lane when the number of lanes is increased.
FIGURE 9. Lane errors in a three-lane road. (Figure: Mohamed M. Atia and Allaa Hilal)FIGURE 10. Lane errors in a four-lane road. (Figure: Mohamed M. Atia and Allaa Hilal)
CONCLUSION
This work described a low-cost lane-level positioning system using a conventional GNSS receiver, MEMS IMUand commercially available road-level network without the need for explicit spatial storage of lanes. The research used a conventional GNSS receiver and MEMS IMU with a computationally efficient two-stage HMM-based map-matching algorithm that avoids the explicit use of lanes as hidden states, which significantly reduces the size of the HMM network and consequently enhance its real-time performance. The proposed system provides an alternative lane determination method without the need for computationally expensive vision/lidar methods that may fail in dark, foggy or dynamically changing environments. The work showed extensive experiments under different road sections, showing an average lane-determination accuracy of 97.14%.
ACKNOWLEDGMENTS
This work was first presented at ION International Technical Meeting, January 2018.
MANUFACTURERS
The system comprises an Intel Celeron N2807 1.58-GHz Mini PC connected to a u-blox EVK-7P kit GNSS receiver and an automotive MEMS-grade IMU 3D space sensor IMU from YOST Labs, and road-network map data from HERE. A GPS-enabled HP f310 car camcorder captured video.
MOHAMED M. ATIA received a Ph.D. in electrical and computer engineering from Queen’s University at Kingston. He is assistant professor and founder/director of the Embedded Multi-sensor Systems research laboratory in Carleton University, Ontario, Canada.
ALLAA HILAL received a Ph.D. degree in electrical and computer Engineering from the University of Waterloo. She is director of the innovation and emerging technology department at Intelligent Mechatronic Systems, a connected-car company based in Waterloo, Canada.
The UX11 is a lightweight, beyond-visual-line-of-sight (BVLOS)-ready fixed-wing mapping drone.
The combination of on-board processing capabilities, real-time control and centimeter-level precision make it a cost-effective solution for large area surveying and mapping, Delair said.
The Delair Septentrio UX11 mapping UAV. (Image: Septentrio)
By employing the latest high-specification photographic, sensor and communications elements, Delair has kept the weight of the UX11 — including payload — down to 1.4 kilograms (3.1 pounds). Among other design innovations, this allows the UX11 to cover 200 hectares (500 acres) in a single one-hour flight, delivering mapping with ground sample distances below 1 centimeter per pixel (0.4 in/px) with accuracy down to 1.27 cm (0.5 in).
A 3G/4G network link to the UX11 allows the operator to assess in real time the quality and overlap of images during flight and make any necessary adjustments to the settings of the integrated camera. This enables operators to collect as much aerial intelligence as possible in a minimum number of flights.
The UAV also features BTOL (bird-like take-off and landing) for steep-climb take offs and descents in confined areas.
The AsteRx-m2 delivers high-precision multi-frequency quad-constellation GNSS measurements for PPK (post-processed kinematic) for only 28 grams, and consumes very little power.
The combination of high-quality camera images and GNSS measurements from the AsteRx-m2 allows Delair to offer its users PPK survey-grade ground precision down to 1 centimeter. With Delair’s PPK software, powered by Septentrio’s GeoTagZ PPK library, users only pay for the precision they need and on a flexible pay-as-you-go basis.
“With the AsteRx-m2, we can offer wide-area coverage at ultra-high precision,” said Chase Fly, geospatial product manager at Delair. “The Delair UX11 sets a new standard of efficiency, cost and quality in a long-range UAV platform. The drone itself is truly state-of-the-art in its design and construction, and it enables industry-leading performance and flight range, as well as streamlined maintenance, advantages that all reduce costs.
“The integrated processing capabilities are able to ensure image quality in real time and provide users with accurate results that shape critical operational decisions and strategies,” Fly said. “And it’s designed for flexible use in a variety of conditions and use models, further lowering TCO.”
The AsteRx-m2 features Septentrio’s proprietary GNSS+ suite of positioning algorithms to convert difficult environments into good positioning:
LOCK+ technology to maintain tracking during the heavy dynamic vibration typical of UAV flights
APME+ to combat multipath
IONO+ technology to ensure position accuracy during periods of elevated ionospheric activity.
The AsteRx-m2 also features AIM+ interference mitigation and monitoring system that can suppress the widest variety of interferers, from simple continuous narrowband signals to the most complex wideband and pulsed jammers.
AIM+ can diagnose self-interference from other electrical or electronic devices onboard the UAV as well as mitigating external interference during operational flights.
“Driven by the explosion in the number and variety of drone applications, drone technology has advanced leaps and bounds in recent years and Delair have been right at the heart of the action. With their focus on innovation and a commitment to providing the very highest quality products, Delair and Septentrio are true kindred spirits and we’re proud to be part of the UX11 project,” said Gustavo Lopez, product manager at Septentrio.
Intelligent vehicles and smart devices could gain more accurate location awareness by fusing GNSS and Wi-Fi signals. A test for this is the focus of an Innovate UK project led by Spirent Communications and involving the Warwick Manufacturing Group (WMG) at the University of Warwick.
The £694k Enhanced Assured Location Simulator Leveraging Wi-Fi and GNSS Sensor Fusion (ELWAG) project will seek to develop and test the pioneering hybrid Wi-Fi and GNSS location system in a cost-effective, repeatable and safe environment so that manufacturers can verify its performance.
International Manufacturing Centre at WMG. (Photo: WMG)
Researchers at WMG, led by Matthew Higgins, will play a significant role in the project. They will take physical layer measurements of both Wi-Fi and GNSS signals in autonomous vehicle scenarios in and around the University of Warwick campus and the local urban road network.
The measurements will then assist in Spirent’s development of an RF propagation model that will overlay RF effects on its Wi-Fi Access Point simulator.
WMG researchers will then perform RF validation and verification activities around the developed model, to provide a level of assurance on its performance.
“The safety and functional assurance of future autonomous vehicles will be one of the many critical paths to large consumer adoption,” said Higgins, who is an associate professor in the intelligent vehicles group at WMG. “Through this project, we will contribute towards providing innovative solutions to the challenges of using sensor fusion in this testing context.”
“This is a highly technical project, which will require a holistic understanding of the signal propagation characteristics between satellites, infrastructure and vehicles. The results will impact future autonomous testing methodologies,” said Erik Kampert, senior research fellow at WMG.
The ELWAG project will run for 18 months, and also involves Chronos Technology.
Project background. Many devices currently rely on a singular location technology (typically GPS), which is one type of the wider eco system of GNSS. These systems, whilst becoming more capable, still suffer at times from the user’s environment — typically in urban areas where buildings and other cityscape features interfere with the signal.
The urban environment is, however, where most users need to know their location to the highest level of accuracy, due to increasing population or device density. Wi-Fi signals exist almost universally within dense urban areas, so there is a possibility of fusing these signals with the GNSS signals to identify one’s location very accurately.
“Currently Wi-Fi access point plus GNSS simulation can only be achieved in an ad hoc manner and does not allow for the testing of moving vehicles, multipath effects, insertion of data errors, spoofing and above all controlled, repeatable testing,” said Mark Holbrow, director of engineering and product development at Spirent’s positioning business unit.
“In the autonomous vehicle sector location accuracy can vary by up to 5 meters, which is unacceptable from a safety perspective. Bringing that accuracy down to 30 centimeters through sensor fusion will have substantial implications for autonomous navigation.”
Self-aware smart devices. The need for smart devices to have a highly accurate self-awareness of their own location, and the location of other smart devices around is becoming increasingly important.
In applications such as autonomous vehicles and transport systems, accurate location awareness is an obvious operational requirement for their safe operation in and around other vehicles, pedestrians or infrastructure.
In the personal devices space, smartwatches, phones and health monitoring and exercise aids are all striving to be able to make a judgment of the user’s state based upon location.
In the emergency and security services space, knowing the location of people and objects is also increasingly important as to target response capabilities effectively.
LORD Sensing MicroStrain has launched a new line of rugged inertial sensors, which the company said will fill a void in the marketplace.
“The sensors respond to a market need for a sensing solution that offers better attitude and positioning accuracy and dynamic response in locations such as on the boom of an excavator or frame of a wheel loader,” said Chris Arnold, LORD Sensing product manager. “Customers are replacing traditional rotary and linear position sensors that provide less rich data on machine position and motion.”
Designed for use in demanding environments for dynamic inclination and positioning, the MV5-AR inertial sensors are designed for off-highway and military vehicles; marine and mobile robot applications; and the autonomous vehicle market.
The rugged, compact, state-of-the-art inertial sensors utilize LORD Corporation’s proven fifth-generation high-performance industrial-grade solid-state six-degrees-of-freedom (6-DOF) micro-electromechanical (MEMS) accelerometer and gyro inertial sensor technology.
Already successfully deployed on ground robots and heavy-machinery, intended applications include autosteer and terrain compensation; dynamic incline detection (roll, pitch, rotation); vehicle stability and leveling; platform control, alignment and stabilization; operator feedback; and precision navigation.
The MV5-AR model has a compact and rugged reinforced PBT housing fully sealed for immersion, pressure wash (IP67, IP69K) as well as a rugged, reliable molded-in AMPSEAL 16 connector. Each sensor is fully calibrated and temperature compensated, the company said.
The MV5-AR models offer:
Low-cost, compact size that is among the smallest form factor in its class.
Full 360-degree measurement range about all axes; it can be mounted in any orientation.
Full accuracy over the entire operational temperature range of -40°C to 85°C.
CAN J1939 communication.
Auto-adaptive extended Kalman filter for optimal dynamic accuracy.
The MV5-AR provides inertial and slope J1939 messages in its standard configuration. Customized CAN protocols and messages are available.
LORD Sensing has also expanded its GX5 portfolio to offer the 3DM-CX5 inertial sensor. With compact chassis or board mount option for embedded applications, the CV5 and CX5 are interchangeable with the same mounting footprint and communication protocol. Each sensor is fully calibrated and temperature compensated. Models offer:
Low-cost, compact size and full 360-degree measurement range about all axes.
Full accuracy over the entire operational temperature range of -40°C to 85°C.
Auto-adaptive extended Kalman filter for optimal dynamic accuracy and on-vehicle performance.
NovAtel has demonstrated high-accuracy positioning performance using automotive-grade GNSS chipsets Teseo APP and Teseo V from STMicroelectronics. Combining automotive-grade multi-frequency GNSS chipsets with positioning algorithms and correction services from NovAtel improves the achievable positioning accuracy available to automotive users and provides a solution suitable for autonomous operation.
According to the company, these chipsets provide multi-frequency GNSS data for precise point positioning (PPP) and real-time kinematic (RTK) to enable accurate positioning capabilities. Teseo APP features built-in integrity checking for use in safety-critical systems, whereas Teseo V is used for non-safety-critical precise positioning applications.
The collaboration between the two companies is designed to reach car manufacturers and Tier 1 suppliers for future production models.
Test results: Horizontal position errors. Teseo V alone is shown in red, Teseo V + NovAtel in green. Test results: Horizontal cumulative error distribution. Teseo V alone is shown in red, Teseo V + NovAtel in green. (Chart: NovAtel)
Test results: Horizontal cumulative error distribution. Teseo V alone is shown in red, Teseo V + NovAtel in green. (Chart: NovAtel)
Driven Today. “STMicro is one of many chipset manufacturers coming to market with dual-frequency chipsets targeting the automotive sector,” said Jonathan Auld, VP Engineering and Safety Critical Systems for NovAtel. “We are taking advantage of their expertise in automotive measurement engines for high-volume, cost-effective reliable positioning. NovAtel brings high-precision algorithm expertise and integration with global corrections supplied by Hexagon Correction Services to this initiative.”
NovAtel’s positioning engine combines the GNSS measurements from these chipsets with inertial measurement unit (IMU) data and Hexagon Correction Services to deliver centimeter-level PPP positioning solutions in real time.
“Working closely with STMicroelectronics allowed us to innovate and drastically reduce time to market of our assured positioning solution tailored specifically for safe positioning of autonomous vehicles,” added Auld.
Comparison of GNSS Performance possible in automotive today (red), L1 automotive with corrections (green) and L1/L2 automotive with corrections (blue).
Driverless Tomorrow. “Precise absolute positioning is just one piece of the overall autonomous vehicle puzzle and must be done with safety and integrity concepts in mind.” Auld pointed to the partnership announced in 2016 between NovAtel, the Illinois Institute of Technology, and Stanford University to conduct leading-edge research to determine how GNSS technology can deliver a positioning solution that meets both the safety and accuracy requirements of autonomous automotive vehicles.
Previous research by academia and industry into GNSS integrity produced the successful WAAS program for aviation. The new work underway will extend the scope to include the autonomous ground vehicle use case. The research includes updated and expanded concepts for high-integrity carrier-phase algorithms as well as expanded threat models and safety monitors.
At the Automotive Tech.AD in Berlin, Auld added: “Today the primary use case for positioning in navigation is single-frequency GNSS, with up to 2 constellations, using narrowband RF and antennas, obtaining accuracy at the 1–2 meter level. This is primarily done with pseudorange-based positioning techniques, with some carrier-phase assistance. There are no functional safety standards, and so safety data is provided on the output solution.”
Autonomous Requirements. By contrast, he continued, autonomous operation will require lane-level and better accuracy: 3D centimeter to decimeter absolute positioning. This means multi-frequency, multi-constellation receivers and antennas to improve overall accuracy and increase available measurements. It will also require increased availability through sensor fusion with IMUs and other sensors. All of this must be brought together through a functionally safe development process targeted at ISO26262 Automotive Safety Integrity Level (ASIL) B.
Moving from meter to centimeter level position requires additional processing to handle all the added signals coming in; residual monitoring and observation exclusion, and carrier phase, “the key to centimeter-level positioning,” as opposed to code phase. The vehicle’s localization system must include enhanced positioning algorithms for multipath mitigation, a fast converging corrections network, enhanced Kalman Filters, and sophisticated sensor fusion.
Flexible Integration. NovAtel’s positioning engine architecture enables a flexible integration with different GNSS receiver chipsets, augmentation sensors and processor environments, providing automotive manufacturers with additional flexibility when it comes to sourcing of components and subsystems of advanced driver assistance systems (ADAS) and autonomous driving solutions.
The positioning engine is being developed to ASIL-B standards and will include a proprietary GNSS integrity solution to ensure safe positioning within defined protection limits tailored to the customer’s application requirements.
The technological underpinning for stock markets’ techno-darlings doesn’t always work perfectly. That problem produces lost revenue and lost value. So Uber, for one, has done something about it, partly based on research developed by Paul Groves at University College London and featured in the February 2012 cover story of GPS World.
Smartphones finding each other in the urban landscape constitute Uber’s business basis. When driver and rider can’t find each other, because they’re on opposite sides of the street or even opposite sides of the block, a ride can’t happen. In the GPS world, we call this multipath, reflected signals, shadowing or simply urban canyon. In Uber parlance it is “wasted supply.”
To eradicate it, Uber acquired Shadow Maps in 2016 and has integrated the company’s technology into the Uber app. Beta testing now goes on in 15 cities; early results indicate that positioning accuracy has improved twofold.
The Shadow Maps process, derived from Groves’ shadow-matching concept, directs the Uber algorithm to examine a 3D rendering of the cityscape and perform a probabilistic estimate of user location based on — simultaneously — which satellites are in direct line-of-sight and which aren’t, in conjunction with predicted satellite location, or almanac.
The process uses ray tracing, color-coding satellite signals by strength to predict likely locations. Each probability calculation takes 20–100 milliseconds, and can run every four seconds for riders and more frequently for drivers, according to Uber engineers and former Shadow Maps principals Andrew Irish and Danny Iland.
“You just want to have a better, tighter estimate to account for how much faster cars move,” Irish said.
Prior Work. Paul Groves has researched this area for nearly a decade at the Space Geodesy and Navigation Laboratory, University College London, where he is an associate professor. Lei Wang won ION’s Parkinson Award for his Ph.D. thesis on shadow matching and now works at Apple. Marek Ziebart is a professor and vice-dean, research, UCL.
“There are many different approaches to 3D-mapping-aided GNSS and several different research groups around the world working on them,” said Groves. “At UCL, we have been integrating shadow matching with 3D-mapping-aided GNSS ranging algorithms. We now have a real-time demo system running on an Android smartphone, albeit limited to Central London. By making full use of the new Android ‘raw measurements’ capability, we get around a factor of 5 accuracy improvement over conventional single-epoch GNSS in dense urban areas.”
“It’s great to see people actually making use of our research rather than it just languishing in research papers. The more widely that shadow matching and other 3D-mapping-aided GNSS techniques are used, the better.”
In February 2012, Groves and his co-authors presciently wrote:
“A practical shadow-matching algorithm must be implementable in real time on a mobile device. Three models may be considered.
A network-based solution, whereby GNSS measurements are transmitted to a server, which stores the building boundary data, computes a solution and then sends it to the user.
A handset-based solution, where the shadow-matching algorithm is run on the handset, which also stores the building boundary data.
A hybrid model, whereby the shadow-matching algorithm runs on the handset, but the building boundary data is streamed from a server as and when required.
“Using stored or streamed building boundaries, fewer than 50 comparison and addition operations are required to calculate an overall shadow-matching score for one candidate position with two GNSS constellations. Therefore, shadow matching may be performed in real time on a mobile device with several hundred candidate positions, where necessary.”
The magazine article was based on a presentation at the European Navigation Conference 2011 in London. The authors will present their latest research, reflecting significant progress over the last seven years, at ION GNSS+ 2018 in Miami, Sept. 24-28.
As we approach the halfway point of 2018, one cannot help but notice the amount of technology that we use every day and how it affects our daily lives. While George Jetson isn’t whizzing by in a flying car to his glass condo in the clouds, we are utilizing an incredible amount of technology in normal life.
I can sit here typing on a computer or tablet that is many times advanced than the first one I used in junior high school and think nothing of it as futuristic technology has become the norm.
The old standard joke about technology used to be about cell phones and television remote controls; if you needed to figure it out, get your child or even grandchild to help. The youngsters were the majority that could embrace technology because they didn’t have past methods to confuse their ability to figure out how to work the new device.
A funny thing has happened along the way, though; those kids are now grown, and technology has advanced even further.
To help explain the names and timeframes of our generations, I found this chart that explained it all:
To help put this chart in context, the average age of the professional surveyor in the United States is 59 and solidly in the Baby Boomer category. But even with an average that high, there are still a significant number of surveyors in the Silent Generation as the economic downturn of the late 2000s has forced them to continue well into their golden years.
HOW SURVEYORS FIT IN THIS DISCUSSION
The surveying profession has suffered through the same generational challenge as the rest of society. The younger set that started out surveying with electronics have now graduated to much more complex yet capable machinery. Prior to the mid- to late 1970s, electronic technology did not play a role in most surveying operations and tasks. The professional surveyor was widely considered a boundary expert, map maker and establisher of topographic data, with the high-tech mapping work left to the government geodesists (see my July 2017 Survey Scene column).
Most surveyors who learned their craft prior to the electronic age were trained on the job or obtained an engineering degree through a program that may have offered a limited surveying curriculum. Surveying was a career for the outdoor type and required traversing rough terrain at times, as well as being able to withstand weather extremes.
THE NON-TECHNOLOGY GENERATIONS
As a second-generation surveyor, I was fortunate enough to have been exposed to land surveying literally as it was performed by our forefathers. While the tasks performed didn’t utilize a true Gunter’s chain and compass, they were completed with a modern transit and steel tape. The surveys we completed didn’t require high tech equipment as our manual procedures greatly exceeded commonly accepted positional tolerances.
A surveyor maps out boundaries for construction. (Photo: Bureau of Labor Statistics)
Most of the work performed by surveyors leading up to the early Baby Boomer generation was much simpler in theory but rarely easy to accomplish due to terrain, weather and the computations necessary to complete the boundary analysis. Traversing a parcel meant having a field crew of several people, often through brush and woods, and time consuming. A large parcel may be days or weeks of field to traverse around with most of it on foot. Once completed, the professional surveyor was tasked with often days of manual calculations, reduction of notes and determination of traverse closure. All the error from days of field work was then balanced through more hand calculations, usually by compass rule or transit rule, and hand drafted onto the final survey plat.
A similar story is followed with topographic and bathymetric surveys and creation of maps with existing conditions. Data collection performed to obtain locations and elevations of existing sites were by radial angle and distance or by grid method, with water depths being determined manually by use of lead lines. In the office this data is placed by manual drafting onto paper, sepia or vellum. Once elevations were plotted, contour intervals were determined by interpolation between each of these points. The creation of the contours was then drawn in by several methods, each with their own level of creativity by the drafter.
Because of the increased use and importance of electronic technology, data collection and advancements of the profession, today’s surveyor is faced with many more challenges than their predecessors. While the concepts for many tasks do follow the protocol for completing a multitude of survey duties, the way we go about collecting and analyzing the data is much more complex than in the past.
The need for our profession to identify these challenges and create opportunities for modern day surveyors is upon us, as our educational and training needs to be ramped up to stay current with demand. All professional surveyors, regardless of what generation they were born in, have filled or will fill an important role in society as expert measurers.
However, the rapid advancement of technology has exposed the lack of additional education and training necessary to keep our standing in serving the public’s health and welfare.
My point here is not that the work and tasks performed by past generations of surveyors was easier, but it did require more manual labor and less technical education and training. I liken the situation to automotive mechanics and how much more technology goes into working on a modern car versus vehicles of earlier generations.
Many mechanics tuned engines by “feel” with no recordable technology to tell them otherwise. I wouldn’t think of calling the expertise shown by past mechanics as inferior to today’s automotive mechanics; each has been trained to rely on different skills sets to work with completely different engines. Thus, I feel the same way in comparing different generations of surveyors. Different tools and methods require unique and specific training for the surveyor to perform at the highest level.
For example, look at the survey-related equipment, software and services within GPS World magazine; most of the articles, case studies and advertisements are for things not even considered five to 10 years ago. All these items require a different mindset of more technical and analytical processing, so the surveyor’s educational requirements and approach must adjust with the technology.
As time marches forward, the need for more advanced surveyors is reaching a critical point.
HOW TODAY’S SURVEYORS GET THE JOB DONE
Today’s surveying profession, including the field and office technicians, rely heavily on technology more than ever.
Many threads of advancing technology go into weaving the tapestry of modern surveying, with the primary material of GNSS being utilized throughout. I have written in the past regarding my thoughts on the single greatest advancement in surveying (see my May 2016 Survey Scene column) and my argument gets stronger with newer technology adding to the way we measure our world.
Here are some of the tasks in which the surveying profession uses GNSS as a basis of measurement and location, and why specific education and training is critical to proper execution:
Boundary surveys
Photo: Tim Burch
Like the surveyors before us, boundary establishment and re-establishment are the main responsibility of the profession. However, with GNSS, the ability to produce more location data has increased tremendously by reducing the need to perform intricate traverses through places when not necessary. It has also reduced the need to perform tedious traverse computations and adjustments; instead, least square adjustments are made to GNSS observational data to provide accurate results.
Topographic surveys
This data can be acquired by a combination of GNSS and conventional total station methods but is based upon geolocation information determined by primarily geodetic coordinates through GNSS solutions. Relying on GNSS data with no standard procedure for location and elevation verification can lead to major issues if not caught by an educated user.
All these methodologies, also known as remote sensing, have revolutionized mass data collection with the enormous amounts of information that can be acquired in a short amount of time. Each has specific functionality and limitations but rely on geolocation as a main attribute of the data. Because of the large data files that are created, the output is in the form of a point cloud rather than the traditional P,N,E,Z,D format normally utilized by surveyors. Like topographic surveys, this data typically relies on GNSS information for geolocation.
Photo: Simon Batzdorfer, Markus Bobbe, Martin Becker and Ulf Bestmann
Unmanned aerial and terrestrial systems
The newest of the data collection methodologies, the unmanned aerial vehicle (UAV) has taken the surveying world by storm. A good percentage of the new adopters (including me) utilize commercial grade multi-rotor units coupled with a high-resolution camera for orthometric photos and video clips of project sites.
While this method uses photogrammetry as its data collection method, it relies on GNSS for establishing ground control points (GCP) to establish geolocation to a known coordinate system. Higher end models incorporate RTK units to minimize the number of control points as well as utilizing lidar and/or hyperspectral modules for high end remote sensing.
Along with the airborne variety, land-based unmanned vehicles are starting to catch on as additional data collectors of open, navigable terrain. These autonomous devices are being equipped with lidar and cameras to augment aerial data in concert with UAVs to gather redundant information for quality checks.
As stated above, these remote sensing technologies, whether used statically or on an unmanned system, all create large point cloud data files that can be cumbersome to manage.
Bathymetric surveys
Many advancements have been made in producing measuring devices using sonar technology, including side- and multi-beam models for more detailed observations in varying conditions. GNSS plays a big role in this survey method due to the electronic ability to combine the depth readings of sonar instantaneously with geographic location. This improvement in data collection provides much more accurate and reliable information for the mapping of water bodies and passageways.
Bathymetric surveys are also getting in on the unmanned vehicle program as well with shallow draft autonomous watercraft being used in places where regular bathymetric vessels cannot go for survey data. More of these crafts are being implemented as they become more affordable.
What do these categories have in common? Most rely on specialized training and equipment to perform each specific task. Surveying has evolved past a “one size fits all” situation and demands that each sector of surveying have personnel trained for the job and have the right equipment to get it done.
A central figure in all these tasks is also GNSS technology; from survey-grade receivers to UAV’s, the tasks all revolve around geolocation.
HOW DOES THE PAST COEXIST WITH THE FUTURE?
The modern-day surveyor now has many different tools at their disposal that generations of surveyors before us couldn’t begin to fathom. The ability to perform at such levels of production and accuracy using new equipment and software is incredible and humbling. However, I’m afraid the technology is outpacing the profession. How many surveyors have taken the time to educate themselves on these enhancements? Because I think we are stretching ourselves too thin, now is the time for the professional surveying community to pause for a self-assessment of our abilities and what it will take to catch up with reality.
One of the biggest hurdles the surveying profession is facing are the lack of qualified technicians for positions both inside and out. The recession of 2008-2011 reduced the number of technicians in our field due to the lack of work being done in the economic downturn, but it also came at a time when technology was starting its upward run at increasing survey task efficiency. The downturn forced many surveyors and firms to make drastic cuts and reduce their investment in new technology, equipment and training to be more efficient. The surveying profession is now paying the price for that downturn with few adequately trained technicians along with licensed professionals not staying current with technological innovation and advances.
WHERE DO WE GO FROM HERE…?
Tim Burch with seventh-and eighth-grade students.
The professional surveyor must embrace technology by promoting the profession to more places beyond the four-year college. We must start in junior-high and high school in math, science and history classes encouraging students to investigate surveying as a career. We also need to support technical and vocational programs that can help introduce surveying as a possible path beyond their certificate or associate degree. One of the simplest topics I use in presentations is the discussion of GNSS technology and how it is built into almost everything the student sees. From their cellphone to the cars their parent’s drive, GNSS surrounds us with geolocation information to make our lives easier.
These technicians aren’t going to all come from a four-year university programs; they are going to come from those teenagers who spend hours honing their hand-eye coordination with video games and drone racing. They will also be the fluid minds writing code for the next big app, and the surveying profession needs to embrace them to incorporate their work in our geolocation world.
The professional surveying occupation has become much more than establishing boundaries of parcels; it now requires knowledge for mapping literally anything in the world. The challenge now is to find those who want to help us continue this surveying and mapping tradition. Fellow surveyors: are you up to the challenge to find your replacement?
Inertial Sense has announced the availability of a micro-sized rugged version of its combined GNSS-INS module, which has an onboard GNSS receiver as well as a fully fused inertial navigation solution.
Designed to fill autonomous vehicle and sensing needs, the module is also available in AHRS/IMU versions.
At 10 grams and with 1 x 1-inch footprint, the solution provides accuracy of 0.1-degree roll/pitch and 0.3-degree dynamic heading. It is also ITAR-free module.
The modules represent 15 years of inertial navigation and motion measurement experience, according to the company.
“When I set out on this journey to provide an accurate and low-cost navigation solution, I wanted to produce a product that engineers could purchase off the shelf, hassle free,” said company founder Walt Johnson. “In my past as a UAV engineer, I was always looking for ways to save myself time and money. It’s all about convenience. There is no need to spend time choosing IMU sensors and writing the algorithms to fuse navigation data. We provide it all for you.”
Measurement results become quicker and more accurate with the gps 70 T’s permanent tilt compensation, resistance to magnetic interferences and calibration-free usage.
With the iCON gps 70 T, construction professionals can measure and stake out points without having to keep the pole vertical to level the bubble. According to the company, it allows the user to look at the immediate environment — for other people, machines, excavations, motor vehicles and structures — rather than on the bubble.
The combination of the latest GNSS technology and inertial measurement unit (IMU) equips the gps 70 T with its true tilt compensation. The tilt compensation extends the measurement possibilities, improves quality and accuracy of the collected data, and reduces errors.
The iCON gps 70 series is seamlessly integrated into the version 4.0 of the iCON field software. By keeping the core central interface, users will benefit from the simple-to-use workflows that require less training and avoid costly downtime.
Sharper Shape, a provider of unmanned aerial utility inspection solutions, has released the Heliscope 2.0, an onboard payload system that expands the company’s aerial sensing portfolio into the manned helicopter industry.
According to the company, the Heliscope 2.0 integrates multiple sensor systems into a single, lightweight helicopter payload, capable of simultaneously collecting a range of data types required for utility maintenance and vegetation management inspections.
Deployment of the Heliscope 2.0 enables optimized inspection and maintenance schedules, offering potential cost savings in those operational activities by as much as 50 percent.
The Heliscope 2.0 also stands out with its flexible mounting configurations and ability to adapt for mounting on many different helicopter types.
For example, the system can be mounted on most Bell Jet/Long Ranger helicopters using its FAA-approved nose mount, or attached to numerous other typical helicopter models using its unique Glider aerodynamic sled.
The U.S. Federal Aviation Administration (FAA) permits mounting the Heliscope 2.0 to helicopters by using the cargo hook found on many helicopter models; this user-friendly method is approved by FAA under a classification for gliders.
“While drones are a very flexible and safe method for performing utility inspections, there are situations where manned helicopters are the preferred vehicle to host sensors during certain utility inspections,” said Mikko Saarisalo, Sharper Shape’s vice president of drones and project lead for the Heliscope 2.0 project. “The new Heliscope 2.0 provides a solution for those situations where we need to operate over greater distances or in harsher environments than the drones can easily accommodate. This system takes our data harvesting efficiency and productivity up to a level unprecedented in the industry.”
CORE includes algorithms to automatically analyze lidar point clouds and quickly generate utility vegetation management reports. Further, its unique automatic issue detection (AID) machine vision software uses artificial intelligence (AI) to eliminate the daunting task of performing frame-by-frame image data inspection, allowing personnel to focus on other aspects of inspection compliance.
CORE applications work equally well with either Sharper Shape’s proven unmanned aerial inspection services, or with the new Heliscope 2.0 manned aircraft solution.
“The fact that the Heliscope 2.0 integrates fully with our CORE software suite is a huge benefit,” said Sharper Shape CEO Ilkka Hiidenheimo. “We can collect all the key inspection assets and measurements in one high-speed pass, and then easily pass these files to our CORE suite for automatic processing. Sharper Shape is the only company on the market that offers this range of options for collecting aerial data and for processing this data automatically into a wide range of digital report formats.”
The Heliscope 2.0 system is now available for immediate contract services in the U.S., South America and Europe.
Difficult work conditions, including high heat (over 45 degrees Celsius) and the lack of existing control points were key factors in ETAFAT’s selection of the SP60 receiver. Because of the absence of existing benchmarks along the entire corridor, the SP60 RTX feature played a key role to ensure homogeneity in the coordinate reference frame between the two cities.
