“We know our diverse new partners will help us address a broad range of complex drone integration challenges,” said FAA Acting Administrator Dan Elwell. “The fields that could see immediate opportunities from the program include commerce, photography, emergency management, public safety, precision agriculture and infrastructure inspections.”
The 10 programs are:
Choctaw Nation of Oklahoma, Durant, Oklahoma
City of San Diego, California
Innovation and Entrepreneurship Investment Authority, Herndon, Virginia
Kansas Department of Transportation
Lee County Mosquito Control District
Memphis-Shelby County Airport Authority
North Carolina Department of Transportation
North Dakota Department of Transportation
City of Reno, Nevada
University of Alaska-Fairbanks
Over the next two and a half years, the selectees will collect drone data involving night operations, flights over people and beyond the pilot’s line of sight, package delivery, detect-and-avoid technologies and the reliability and security of data links between pilot and aircraft.
The data collected from these operations will help the FAA:
craft new enabling rules that allow more complex low-altitude operations,
identify ways to balance local and national interests related to UAS integration,
improve communications with local, state and tribal jurisdictions,
address security and privacy risks, and
accelerate the approval of operations that currently require special authorizations.
First announced in October 2017, the White House initiative partners the FAA with local, state and tribal governments, which then partner with private industry to safely explore the further integration of drone operations.
The program will help tackle the most significant challenges to integrating drones into the national airspace and will reduce risks to public safety and security.
Brian Wynne, president and CEO of the Association for Unmanned Vehicle Systems International (AUVSI), issued the following statement on the announcement of the participants selected for the FAA’s Unmanned Aircraft Systems (UAS) Integration Pilot Program:
“The participants selected for the FAA’s UAS Integration Pilot Program represent a commitment by governments at all levels to safely and efficiently integrate UAS into the national airspace. As more and more businesses and public institutions embrace UAS, it is more important than ever to have a process in which states, municipalities and tribal governments can provide input on federal policy without infringing on the U.S. government’s jurisdiction over the airspace.
“The data the participants will collect on UAS operations will help shape a national UAS policy framework, including for a UAS traffic management system and expanded UAS operations such as flying over people or beyond line of sight,” Wynne said. “We look forward to seeing the results of their work and the contributions these groups will make to keeping our skies safe.”
According to AUVSI, the potential economic benefit of drones in the nation’s air space, in less than a decade, is estimated at $82 billion and could create 100,000 jobs.
Drone maker DJI issued a statement saying it looks forward to the advances in drone regulatory procedures that will be enabled by the innovative proposals offered by the 10 state, local and tribal governments.
“Regulators and governments want to develop safe systems that encourage the beneficial uses of drones while addressing concerns about them, and today’s announcement is a major step forward in this effort,” said Brendan Schulman, DJI vice president of Policy & Legal Affairs. “By connecting state, local and tribal governments with industry partners and federal support, the Integration Pilot Program makes it easier to find ways for American businesses, governments and individuals to put drones to good uses all across the country.”
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.
The PwrPak7D-E1 is a robust, high-precision receiver that has multi-frequency, dual-antenna inputs and provides GNSS multi-constellation heading and position data. These capabilities make the PwrPak7D-E1 suitable for ground vehicle, marine or aircraft-based systems. NovAtel’s Synchronous Position, Attitude and Navigation (SPAN) technology brings together GNSS positioning and inertial navigation to provide an exceptional 3D navigation solution that is stable and continuously available. The PwrPak7D-E1 has a powerful OEM7 GNSS engine, integrated Epson G320N micro electromechanical (MEMS) inertial measurement unit (IMU), built-in Wi-Fi and 16 GB of internal storage.
A roundup of recent products in the GNSS and inertial positioning industry from the May 2018 issue of GPS World magazine.
SURVEY & MAPPING
Rugged handhelds
Operate in harsh environments
The UT series of GNSS-capable rugged handheld devices support industries such as construction, survey, GIS, mapping, asset/logistics management, public safety, utilities and military. The UT10 6-inch rugged phone and UT30 8-inch rugged tablet both feature Android 8.0 operating systems with Qualcomm octa-core 2.2 GHz processors, 4 GB of RAM and 32GB onboard storage.The UT50 10.1-inch full-rugged tablet features the Windows 10 operating system with an Intel Core Skylake i5 processor up to 2.8 GHz, 8 GB RAM and 128 GB of onboard storage. All three new UT models provide the latest high-resolution, capacitive touchscreen and direct sunlight-readable display technology for ease of visibility in all situations. The UT50 also has a 10-finger multi-touchscreen and supports wet hands and gloves operation. The devices have dual built-in cameras. They are designed to be drop-resistant from heights of 1.2 meters (1.5 meters for the UT10), are rated at IP67 (IP68 for UT50), and are certified to both MIL-STD-810G and MIL-STD-461F military standards to ensure durability in most outdoor or challenging environments.
The Trimble TSC7 controller is a new field solution for land and civil construction surveyors. Equipped with GPS, it provides a tablet experience with a physical keyboard and a sunlight-readable 7-inch touchscreen that supports pinch, tap and slide gestures. Front- and rear-facing cameras allow users to video conference their office from the field for on-the-job support, and capture high-definition videos and images that provide valuable context to their data and clients. The TSC7 uses Windows 10 Professional with an Intel Pentium 64-bit quad-core processor. The processor and operating system make it easy to process data in spreadsheets and run office software programs. An ergonomic form factor, IP68-certified rugged design and optional, user-interchangeable modules make the TSC7 a flexible solution for all surveying applications.
The AsteRx-i combines a multi-frequency multi-constellation GNSS engine with an external industrial-grade MEMS-based inertial measurement unit (IMU) to deliver positioning to the centimeter level as well as full 3D attitude at high update rates and low latency. The AsteRx-i is suitable for optical inspection and photogrammetry. Accompanied by a UAS-tailored carrier board, it integrates seamlessly into light UAVs. It also features Septentrio’s AIM+ interference monitoring and mitigation system.
UAS startup DropCopter has initiated a drone pollination service that uses multi-rotor drones to dust almonds, pistachios and cherries, boosting crops by up to 15 percent. Dropcopter’s patent-pending Worker-Bee pollinator helps growers overcome environmental factors like bee shortages, as well as wind, cold, and night time that would prevent honeybee activity. The company is partnered with GENIUS NY and The NUAIR Alliance.
Sensor Optimized for Drone Applications (S.O.D.A.)
Photo: senseFly
The senseFly S.O.D.A. camera is built for professional drone photogrammetry work. It captures sharp aerial images across a range of light conditions, allowing users to produce detailed, vivid orthomosaics and ultra-accurate 3D digital surface models. It has a 1-inch 20 megapixel RGB sensor that provides ground resolution of 2.9 centimeters per pixel flying at 400 feet (122 meters) above ground level. It has built-in dust and shock protection, enabling mapping across challenging terrain.
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 International Technical Symposium on Navigation and Timing, also known as the ITSNT, is an annual event organized by Centre National d’Etudes Spatiales (CNES) and Ecole Nationale de l’Aviation Civile (ENAC) for professionals and researchers working with or interested in navigation and timing technologies and their use.
The 2018 edition of the ITSNT will take place Nov. 13-16 in Toulouse, France, on the campus of ENAC.
This event is composed of two types of sessions:
Invited Guest sessions: special guests are invited by the scientific committee to give a presentation related to the generic topic of the session. Typically, there are four guest speakers per session, and the session ends with a round table. The programme of the “invited guest” sessions is available on the website.
Peer-Reviewed Paper sessions: the presenters of these sessions are selected by the scientific committee based on a call for abstract. The deadline for the call for abstracts is May 25. The final programme of these sessions will be available in October.
The ITSNT also provides a great environment for networking and visiting sponsors’ exhibition stands.
The symposium includes tutorials given by some of the invited guest speakers on their topic of excellence.
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.
Five organizations that flew drones on critical, life-saving missions are winners of the inaugural XCELLENCE Humanitarian Award by the Association for Unmanned Vehicles Systems International (AUVSI).
“We are thrilled to recognize and reward organizations who have utilized drone technology to make great contributions to their communities and the environment, through AUVSI’s inaugural Humanitarian Awards,” said Michael Perry, managing director of North America at DJI.
“We congratulate the winners and thank all those who have participated for sharing the innovative ways they use drones to support humanitarian and life-saving efforts around the world,” Perry said. “We hope this award will inspire more organizations and drone operators to accomplish great feats and help others in their community.”
In Rwanda, fresh blood is launched to a hospital using a Zipline drone. (Image: CNN video)
These first recipients of the AUVSI XCELLENCE Humanitarian Award were recognized for using drones for disaster management, medical assistance and search-and-rescue operations at locations around the world:
Aeryon Labs Inc.: Aeryon SkyRanger UAS provides critical aerial intelligence to first responders in Sint Maarten in the wake of Hurricane Irma (Canada).
DroneSAR, DroneSAR UAV Search & Rescue (SAR) Solution: Executing autonomous aerial search and delivering live drone data to augment first response efforts (Ireland).
Nepal Flying Labs: drone hazard and vulnerability mapping in Nepal (Nepal).
ONG DroneSAR Chile: Emergency response team and humanitarian aid through the use of drones (Chile).
Zipline International: Zipline’s medical drone delivery operation in Rwanda (Rwanda).
“As these organizations have shown, unmanned aircraft systems that are typically flown for commercial purposes are also capable of accomplishing vital humanitarian missions,” said Brian Wynne, president and CEO of AUVSI. “With sophisticated on-board cameras and sensors, drones can quickly fly to remote locations or areas that are inaccessible to ground vehicles because of roads blocked by storm debris or flooding.”
The five organizations will equally divide a $25,000 donation as prizes for their ground-breaking humanitarian and philanthropic efforts.
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.
Harxon showcased high-precision positioning GNSS antennas and its latest wireless data-transmission technologies for UAV applications at AUVSI Xponential, which was held April 30-May 4 in Denver.
The Harxon D-Helix Antenna.
Harxon’s D-Helix is a patented D-QHA (dual-quadrifilar helix antenna) multi-constellation antenna supports excellent reception of GPS, Galileo, BeiDou and GLONASS, as well as L-band signals. Harxon D-QHA technology ensures the ability of low elevation satellites tracking while maintaining 4-dBi high gain, which makes the D-Helix antenna an excellent choice for any applications where the sky is partially visible, the company said.
The antenna’s low noise amplifier (LNA) with out-of-band rejection performance can suppress electromagnetic interference. Moreover, the D-Helix features the latest low wind resistance design with ruggedized IP67 protection for UAV inspection and monitoring, survey and mapping or agricultural UAVs.
Photo: Harxon
The HX-DU2017D is a 5-gram frequency-hopping OEM transceiver supporting frequencies between 840 MHz and 900 MHz. It provides strong anti-jamming and signal receiving capability for complex data intensive applications. Its full duplex mode ensures data secure transmission, more stable long-range communication and short latency of data transmission.
Watch this video to learn more about the HX-DU2017D.
Other showcased Harxon GNSS products, such as Helix Antenna HX-CH7603A, HX-CH4601A and HX-CH6601A, are all featured with patented D-QHA technology. Moreover, the showcased Survey Antenna GPS 500, OEM Modem HX-DU1018D and Smart Antenna are also appropriate for surveying and mapping, as well as precision agriculture.
Ten state, local and tribal governments have been named to conduct flight tests as part of the Federal Aviation Administration’s (FAA’s) Unmanned Aircraft Systems (UAS) Integration Pilot Program.
