Hexagon | AutonomouStuff’s hardware rack inside the Ford Transit shuttle. (Photo: Hexagon | AutonomouStuff)
When it comes to ground transportation, most of the R&D regarding GNSS is aimed at developing driver-assist systems and, ultimately, driverless cars and trucks. For that purpose, GNSS receivers are integrated with inertial navigation systems, radar, lidar, computer vision and ultrasonics.
Leveraging decades of robotics experience and knowledge of control algorithms, AutonomouStuff, part of Hexagon’s Autonomy & Positioning division, has developed a software stack for autonomous vehicles based on the Apollo open-source software stack.
“Think of this software stack as a brain powering the autonomous platform,” said Kevin Fay, product manager for Hexagon’s platforms and vehicle software business. The software stack can be customized across platforms and to meet equipment needs.
Most recently, in a collaborative project with the National Advanced Driving Simulator at the University of Iowa, AutonomouStuff worked with the Automated Driving Systems for Rural America project to outfit a Ford Transit 350HD shuttle for autonomous operation. First, it created a drive-by-wire system that enabled electronic control of the vehicle, and then it installed positioning, navigation and perception sensors. The result is a platform ready to be autonomous as soon as the software stack is integrated.
Rural roads — which have a wider range of speeds than urban ones — may be encumbered by wildlife or heavy equipment. They also vary in surface from asphalt to gravel, providing a particularly challenging test environment for the autonomy software.
“The Iowa vehicle has done a sizable amount of automated driving on a combination of urban and rural roads, where traditional sensing falls flat,” Fay said. “It has excelled in areas such as gravel roads that have limited or no lane markings, or are narrower than normal. We deployed it earlier this year to do things such as traffic-light detection with the cameras on board, so that it navigates traffic-light intersections appropriately.”
While rural roads are generally free of the GNSS multipath challenges presented by urban canyons, they also provide fewer navigation landmarks. Another challenge is inclement weather. During snowstorms, Fay pointed out, country roads might be unplowed. “If you run on the right lane of the road all the time, you might be out of the ruts that are on the road, and then you’re struggling to get through.” The vehicle must learn to navigate appropriately in those conditions.
The University of Iowa Ford Transit shuttle is a limited deployment, mainly to collect data for research purposes. Meanwhile, it is giving real rides to residents, though with a safety driver. “They’re always attentive, but their hands will be next to the wheel,” Fay said. “There will be times where they may have to take over.”
Other universities and companies are using the platform to further their autonomy programs. Most of them are doing urban driving in complex routes with live traffic, for a total of a dozen vans nationwide.
Hexagon equips the vehicles with a variety of sensors, including a front-mounted adaptive radar, a roof-mounted Velodyne lidar, a roof-mounted NovAtel GNSS receiver and cameras mounted inside the vehicle. “Which ones we provide depends largely on the customer and on which software they’re deploying,” Fay said. “We provide our customers a complete package that can be used with minimal work out of the box. It has the software, the interface to the vehicle, and sensors on it. But we can also provide them with a vehicle that simply has an interface for control, and they add their own computer and software on top of it.”
Hexagon’s first Ford Transit was deployed in 2021. The company released the current version in the spring of 2022, and the Iowa project is slated to run through the middle of 2023. “We’ve not had something running in live traffic before,” Fay said, “so it allows us to continue to grow our skill sets and our overall expertise.”
By all accounts, it is getting worse. Hundreds of internet sites sell inexpensive devices to interfere with GPS and other GNSS signals. Estimates place the number of devices extant in the United States in the tens of thousands or more. Studies show accidental interference happens about ten times more often than deliberate jamming.
In January a high-power signal in the Denver area impacted GPS reception across 4,000 square miles of airspace. The source was located, and the signal terminated after 33 hours.
October saw a similar event near Dallas that lasted for 44 hours before it ended on its own. The source of that signal was never identified.
The United States spends more than $2 billion a year to operate, maintain, and refresh GPS. Its positioning, navigation, and timing (PNT) services underpin virtually every technology, every facet of the economy. Yet, as was dramatically demonstrated at least twice this year, the nation does not have the ability to quickly characterize, locate and mitigate even the most powerful jamming signals.
The group’s most recent recommendation is to implement a detection network based on crowdsourcing and smart phones. This would be done in collaboration with wireless service carriers.
Yet cooperation of wireless carriers, while helpful, may not be necessary, according to at least some experts.
Dr. Dennis Akos of the University of Colorado has developed an app for Android smart phones that enables devices to detect and automatically report interference with GPS and other GNSS signals. The app uses four detection methods based on location data already used by Android devices. These are comparing GNSS and network locations, checking the Android mock location flag, comparing the GNSS and Android system times, and observing the automatic gain control (AGC) and carrier-to-noise density (C/N0) signal metrics.
Commenting on Akos’ work, GNSS expert Logan Scott suggests that the U.S. government could use this new capability to establish the first phase of a national GPS/GNSS interference detection network with very little cost or effort.
“The US government provides managed phones to many government employees,” he said. “Having an app like Dennis’s operating on an opportunistic basis, [only when GPS is on in the phone] would give access to millions of phones as observers. Bottom line, the US could stand up a national observation network on an accelerated timeline, understand the nature of the threat, and avoid the embarrassments of [events such as those that occurred at] DIA [Denver International Airport] and DFW [Dallas Fort Worth airport]. And it would not cost much.”
If an effective system of some sort is not implemented, American lives and property will be at continued and increasing risk. In the words of the Advisory Board recommendation:
Dr. Dennis Akos. Image from University of Colorado’s website.
“Without a reliable, automated means of detecting and locating sources of GNSS interference, space-based PNT applications, and the general U.S. public, will continue to be plagued by potentially life-threatening and/or costly service disruptions that take days or weeks to resolve.”
With the last generation of trained geodesists either retired or getting ready to retire, we are at a critical stage of not being able to meet the geospatial needs of the future,” wrote David B. Zilkoski in his Nov. 1 Survey Scene column on our website. Few people, he pointed out, realize our $1 trillion geospatial economy — from precision agriculture to smart cities, from UAVs to location-based services — depends on geodesy. A collapse of geodesy would also harm our efforts to monitor rapid changes in the Earth’s surface due to sea-level rise, the deformation of tectonic plates, and temporal changes in the Earth’s water reservoirs.
Federal agencies, Zilkoski recalled, used to send staff to be trained in geodesy because they needed geodesists for such significant projects as the readjustment of the U.S. national horizontal and vertical geodetic networks. Now, while U.S. federal agencies still require this expertise to develop and refine geodetic models and tools, so do major U.S. companies for everything from routing delivery trucks to controlling earth-moving equipment to guiding tractors.
A January 2022 white paper by Mike Bevis and others titled “The Geodesy Crisis” reported that China has more geodesists than the rest of the world combined, and the number of Ph.D. geodesists in the entire Department of Defense, including the National Geospatial-Intelligence Agency (NGA), is approaching zero.
I discussed the geodesy crisis with Everett Hinkley, who works for the federal government, serves as a subject-matter expert on several high-level boards, and dubs himself a “concerned citizen geodesist.”
Matteo Luccio: How did we get here? Was it due in part to the success of GPS?
Everett Hinkley: The factors include:
1. In the early 1990s, the U.S. government largely disinvested in academic research and academic sponsorship in geodesy. Without student sponsorship, the few university programs that produced geodesy experts withered on the vine.
2. Math and science skills in U.S. public schools have declined.
3. More subtly, there was a subliminal and misguided notion that “Now that we have GPS, why do we need to continue to improve our geodetic models?”
ML: If left unaddressed, in what fields or applications will the crisis manifest first?
EH: In areas where precise positioning is critical: cadastral mapping, self-driving vehicles, sea-level rise (a growing danger) and others. The effects will be felt incrementally, at least at first.
ML: Are some geographic regions of the United States particularly vulnerable to some effects of the crisis due to high subsidence, drift or other ground movements/changes?
EH: Yes. The two areas that will show the first signs of divergence between actual and assumed locations are those that are tectonically active (both horizontally and vertically) and low-lying coastal ones.
ML: Besides funding, what could entice college students to enter the field?
EH: Basic marketing is needed by the geospatial community at large. We need to reach out to math “stars” in high school and let them know that pursuing a career in geodesy will guarantee them employment after graduating from college.
Speaking at the National Press Club on Nov. 3, Federal Communications Commission (FCC) Chair Jessica Rosenworcel announced a plan to reorganize the agency to include a Space Bureau and a standalone Office of International Affairs.
The rationale for these moves, as explained in a press release, is to “help ensure that the FCC’s resources are better aligned so that the agency can continue to fulfill its statutory obligations and keep pace with the rapidly changing realities of the satellite industry and global communications policy.”
While neither GPS nor Ligado were mentioned in the press release, some have taken establishment of a Space Bureau as a sign the FCC may be reconsidering its decision regarding Ligado Networks.
By creating a Space Bureau, the reasoning goes, the commission is acknowledging a need to better focus on space-based users. A report this summer from the National Academies of Science said that some GPS and Iridium users would be harmed if Ligado Networks is allowed to operate as planned.
Since the commission seems to be trying to prevent future Ligado-like controversies, it may also be ready to reconsider its Ligado decision. In February 2020 seven different petitions were filed by organizations and groups of organizations formally asking the FCC to reconsider. The commission has not yet responded to any of the petitions.
Few can disagree that aligning resources to more effective address constituent concerns is a good idea. At the same time reorganizations rarely, in and of themselves, prevent problems from recurring.
As one example, the FCC had been criticized for years for not including analyses of total costs and benefits to the nation of decisions it was considering. In January 2018, FCC Chairman Ajit Pai established the FCC’s Office of Economic Analysis to address those concerns.
Yet, despite Pai still being chair, the Office of Economic Analysis was not called upon to provide input to the commission’s deliberations on Ligado Network’s application. One of the pending petitions for reconsideration asserts that if the office had done a cost-benefit analysis, the commission’s decision would have been different. This is because the cost of even a small service degradation for potentially millions of GPS users would have very likely easily outweighed any benefit to the nation of granting Ligado Networks permission to operate.
Photo: Bill Oxford/iStock/Getty Images Plus/Getty Images
To paraphrase Galileo Galilei — the great Italian astronomer, philosopher, engineer, mathematician and physicist — positioning, navigation and timing (PNT) does not revolve around GPS. The European GNSS named after the father of modern science (as Albert Einstein called him) is making great strides and currently provides more accurate positioning than the United States’ GPS, Russia’s GLONASS, or China’s BeiDou-3. In fact, there are more Galileo satellites providing an L5 signal than GPS satellites.
I heard much well-earned pride about Galileo’s achievements expressed by European presenters at the Institute of Navigation’s GNSS+ conference in Denver in September; during a visit to the European Commission’s Joint Research Center in Ispra, Italy, on Oct. 7; and at the INTERGEO conference and trade show in Essen, Germany, on Oct. 18-20. (On the way, I stayed several days in Pisa, Italy — where I spent my teen years when my father taught physics at the city’s university — at a friend’s home about 100 feet away from the house where Galileo was born in 1564.)
While two more launches are required to complete the Galileo constellation so that it will have at least one spare satellite per plane, its service availability is already at 98-99% and a new ground segment has been deployed. A second generation of satellites is on its way, with expected initial operational capability in 2028 and full operational capability starting after 2031. Its features will include new signals, improved effective isotropic radiated power (EIRP), inter-satellite links, and a 15-year lifespan.
The Open Service Navigation Message Authentication (OSNMA), a free data authentication function for users of Galileo’s Open Service, has been stably transmitted worldwide for a year. It will enable users to verify the authenticity of GNSS data, thereby greatly helping to detect instances of spoofing. A declaration of initial service is foreseen for 2023, and the first OSNMA-capable receivers are already on the market.
Galileo’s High Accuracy Service (HAS) signal has been available worldwide with orbit and clock corrections and biases for Galileo and GPS since July 22. While it is still in its validation phase, it is already performing very well and an initial service declaration is expected by the end of the year, including an Internet-based correction distribution service.
Galileo is also developing an emergency warning service that will use the L1 band to broadcast alerts and guidance to populations at risk of natural disasters. It is expected to enter service in 2024 and reach any Galileo-enabled device, of which there are already about three billion. Other services will include advanced timing, space service volume (to aid in the positioning and navigation of spacecraft in high-Earth orbits), advanced receiver autonomous integrity monitoring (ARAIM), and predictions of ionospheric perturbations.
Like so much else, completion of the Galileo constellation was affected by Russia’s war in Ukraine, because two launches planned for this year from French Guyana aboard Russian Soyuz rockets were scrapped.
Finally, one of my favorite quotes from Galileo: “Measure what can be measured and make measurable what cannot yet be measured.”
The LightSquared/Ligado Networks saga, now in its second decade, continues. On Sept. 9, the Committee to Review FCC Order 20-48 Authorizing Operation of a Terrestrial Radio Network Near the GPS Frequency Bands of the National Academies of Sciences, Engineering and Medicine (NASEM) released its consensus study. Both sides claim the report supports their position.
According to Ligado, the report confirms the FCC’s finding that the company’s operations “can co-exist with GPS.” It cited the report’s conclusion that “the technology to enable compatibility has been in use for over a decade, and most consumer equipment, commercial general navigation, timing, cellular and aviation receivers will not experience harmful interference from Ligado’s operations.”
The NASEM report also confirmed, the company said, the FCC’s finding that “[a] small percentage of very old and poorly designed GPS devices may require upgrading.” Ligado reaffirmed its commitment to “upgrade or replace” federal equipment negatively impacted by its operations and expressed its hope that now the Department of Defense (DOD) and the National Telecommunications and Information Administration “will stop blocking Ligado’s license authority and focus instead on working with Ligado to resolve potential impacts relating to all DOD systems.”
By contrast, the GPS Innovation Alliance applauded the NASEM’s “reaffirmation that Ligado’s terrestrial operations would have a harmful, real-world impact on the millions of federal and commercial users that rely on GPS, satellite communications, and weather forecasting services every single day.” It further stated that the report “demonstrates that Ligado would pose an unacceptable risk to services critical to safety-of-life operations, our national security, and our economy” and urged “government action to address the imminent, but preventable, harm that would result from Ligado’s deployment.”
According to the DOD, the NASEM study “confirms that Ligado’s system will interfere with DOD GPS receivers, which include high-precision GPS receivers.” The study also concludes, DOD says, that the FCC’s proposed mitigation and replacement measures “are impractical, cost prohibitive, and possibly ineffective.”
The NASEM committee pointed out repeatedly in its report that matters are more nuanced than represented by either side and that test results and harmful interference depend on many factors — including the receiver’s signal processing architecture, the amount of SNR loss, the use case, and the relevant failure modes. “The determination of harmful interference is dependent on the particulars,” it said.
The committee also bemoaned “a lack of a quantifiable definition of harmful interference” and “the lack of common receiver assumptions” and called for “more definitive receiver standards.” It also pointed out that “many spectrum conflicts could be avoided if receivers were better designed and implemented.”
The GPS user base is in the billions. Therefore, even if “most” receivers will not be harmed by Ligado’s operations, as the committee reported, tens of millions of devices will be. I highly recommend reading the full report.
The Google Smartphone Decimeter Challenge (SDC) competition, co-sponsored by the Institute of Navigation (ION), took place this summer. For the competition, teams developed high-precision GNSS positioning using a pool of smartphone GNSS + inertial measurement unit (IMU) datasets accompanied by high-accuracy ground truth. Teams competed to achieve the best location accuracy with the datasets provided. Winners received cash prizes and sponsored attendance at the ION GNSS+ 2022 conference in Denver, Sept. 19-23, to present their results.
Origins
The SDC has its origins in the Android Operating System, which is an open-source platform. In 2016, Google made GNSS raw measurements available as a public application programming interface (API) on all Android phones. Since then, the available measurements have become more sophisticated and more accurate. For example, dual-frequency carrier-phase data is now available on many Android phones. This enables new areas of research.
Goals
The competition had two goals:
• Stimulate the research and development of high-accuracy algorithms that can produce submeter position accuracy on phones.
• Establish a publicly accessible repository of labeled data so that all future research on location algorithms can be judged in a consistent way against a standard set of data.
The first goal was met beyond our expectations. A total of 1,381 teams participated in the two competitions of 2021 and 2022. Discussion among competitors on the competition platform (kaggle.com) was wide-ranging, incredibly collegial, and beneficial to the entire community.
Competitors have written and shared detailed descriptions, and these have been reviewed and commented on by other competitors. Moreover, winners have written formally peer-reviewed papers and made presentations at the ION GNSS+ conferences, which are available from ion.org.
The second goal is a work-in-progress and is intended to be the legacy of the events.
Legacy
Disciplines such as machine learning have established benchmarks that make it possible to compare new approaches to previous ones in a proper quantitative way. In the GNSS community, this convention has been missing — a glance across papers at conferences will show that different algorithms tend to be presented with different test data and different metrics. Usually, the authors collect this data, and it is often fairly sparse (one or two drive tests, for example). Also, the reader never knows whether the data was cherry-picked (were bad results not mentioned?).
The SDC data provides:
• 206 different drive tests
•86 total hours of dual-frequency (L1, L5) data with code and carrier-phase measurements
•All labeled with ground-truth positions and velocities collected using NovAtel SPAN ISA-100C, with precise lever-arm compensation and validated with Google’s analysis tools.
The Kaggle site allowed users to submit their results, then automatically scored them against the ground-truth data. We advocate that all GNSS researchers use this resource to measure their location algorithm improvements in a standard way. This creates trust in published results, accelerating the recognition and adoption of truly great improvements for the benefit of the entire industry and GNSS users worldwide.
The top three winners of this year’s Smartphone Decimeter Challenge described their projects to Matteo Luccio, GPS World editor-in-chief.
Suzuki
Taro Suzuki, Chiba Institute of Technology
1st Place Winner: Two-Step Optimization of Velocity and Position using Smartphone’s Carrier Phase Observations
What is your research focus and how does it relate to the contest?
My current research focuses on the accurate positioning of vehicles and mobile robots in urban environments where GNSS multipath occurs. I usually use commercial GNSS receivers for my research. This competition is very relevant to my current research, except that the smartphone is replacing a receiver.
How long have you been developing the technology or approach you used to win the contest?
The competition was held for three months, but I concentrated my efforts on the past three weeks. However, I used technologies and resources developed in my previous research (for example, source code developed in last year’s competition).
Have you participated in previous editions of this contest?
Yes, I participated in the last competition and won. The approach used in this year’s competition is based on the method used to win last year’s competition, with additional innovations and improvements.
Where, in what GNSS signal conditions, and at what speeds were the test data collected?
The competition provides a training dataset, which contains raw GNSS observations from a smartphone installed on a vehicle as it travels on real roads. In addition to GNSS observations, the training dataset contains the ground truth of the smartphone’s position. The training dataset includes a wide range of GNSS signal conditions, such as driving on highways around San Francisco and Los Angeles, driving on tree-lined urban streets, and driving in tunnels and under overpasses. I have developed an algorithm that uses a training dataset containing ground truth to accurately estimate the location of smartphones in a variety of GNSS signal reception environments.
What accuracies were you able to obtain?
The competition metric was “average of 50th and 95th percentile horizontal errors.” The metrics are computed for each of the 36 runs in the test dataset, which are divided into public and private groups, then the metrics are averaged in each group to compute the final score. My final score was 1.382 m for public and 1.229 m for private. The best score given after the competition was 1.372 m for public and 1.197 m for private. The final result achieved sub-meter accuracy in the median (50th percentile).
What are the key features of your approach?
The key point of my method is global optimization using graph optimization, unlike a conventional Kalman filter or least-squares-based positioning methods. In addition, highly accurate relative position estimation using the time difference of carrier wave phases of smartphones contributed to the accuracy. Because the competition dataset included environments such as tunnels and elevated structures in which GNSS cannot be received at all, I devised an algorithm with two optimization steps (first velocity optimization, then position optimization) and applied it to the competition. This method enables highly accurate position estimation for vehicle driving data in various GNSS signal reception environments using only smartphone GNSS observation data.
What end-user applications are you expecting your approach to enable?
Decimeter-accurate location estimation could lead to lane-level navigation for vehicles, pedestrian navigation, and advanced location-based smartphone games.
Dai
Shubin Dai, Kaggle Community
2nd Place Winner: Improving Smartphone GNSS positioning using Gradient Descent Method
What is your research focus and how does it relate to the contest?
I am a data scientist and one of the top competition grandmasters on Kaggle. My research interests include computer vision, natural language processing, autonomous driving, and reinforcement learning. I placed in the top three in 14 related competitions (13 of which were solo). So, despite my lack of background knowledge in the GNSS field, these methods, skills and experiences helped me find a solution.
How long have you been developing the technology or approach you used to win the contest?
I spent about 50 days on this competition, including learning principles of GNSS and understanding all kinds of algorithms by reading books, papers and source codes. The Kaggle platform is very helpful when we want to get started in a new field.
Have you participated in previous editions of this contest?
I did not participate in the competition held last year, but I learned a lot from solutions of recent years, particularly the third-place solution.
Where, in what GNSS signal conditions, and at what speeds were the test data collected?
The benchmark datasets include raw GNSS measurement and raw readings from inertial sensors, using smartphones (Xiaomi Mi 8, Google Pixel 4, etc.) enabled with dual-frequency and ADR (accumulated delta range) in driving scenarios, collected in the San Francisco Bay area.
In the GSDC2021 dataset, there are 29 drives with 73 phone GNSS logs in the training set and 19 drives with 48 phone logs in the test set. Compared to 2021’s competition, in the GSDC2022 dataset we can see more data overall and a wider variety of routes: 62 drives with 170 phone logs are provided in the training set and 36 drives with only one phone per drive are provided in the test set.
The drives in the training set took 15 to 60 minutes at an average speed of 18 m/s.
What accuracies were you able to obtain?
According to the metric of this competition, the score is calculated as the mean of the 50th and 95th percentile distance errors. The score on my local validation set is 1.929 m, the score on the public test set is 1.608 m, and the score on private test set is 1.499 m. When we calculate the mean error, the score is 1.401 m on a validated set, the mean error of 40% of the trips are under 1 m. I think the competition metric is more reliable as the 95th percentile distance error is also important.
By the way, my local validation set is more difficult to optimize than the test set, so the mean error on the test set is expected to be lower than 1.401 m.
What are the key features of your approach?
The competition data is noisy due to multipath effects, non-line-of-sight receptions, receiver noise and missing data, therefore it’s quite challenging. I found that the optimal estimation for each point locally is not stable and can be affected by noise at that point on the track. If we can find a solution to a whole track globally, the noise can be reduced as the model must follow all kinds of constraints, such as geometry constraints, speed constrains, and global acceleration constraints.
Although we could extend the WLS and Kalman filter solution to take more points on a track into consideration, it’s not so easy to model all kinds of constrains. On the other hand, if we use a global optimization method, such as factor graph optimization and neural networks, we can add the constrains easily, which makes it more efficient to conduct experiments.
Following the solution of the third-place winner in last year’s competition, I used the global optimization method by taking into account gradient descent, pseudorange, pseudorange rate, accumulated carrier phase (ADR), phone speed and acceleration constraints of every time epoch on a track. When optimizing the track using gradient descent, the losses are designed to filter out abnormal data and reduce the noise by a series of physical and geometrical rules. I spent much time searching for the constraints, proving them and turning them into losses that can be used to update the coordinates iteratively during the competition.
What end-user applications are you expecting your approach to enable?
According to the setting of this competition, we can post-process data collected using Android phones, which is easily obtained. The track obtained can then be optimized using the solutions from this competition. The solutions from the first and the second place can both be considered as a framework that can be extended by adding more constrains to it to improve accuracy.
Everett
Tim Everett, RTK Consultants LLC
3rd Place Winner: An RTKLIB Open-Source-Based Solution
What is your research focus and how does it relate to the contest?
I develop and maintain the demo5 fork of the popular RTKLIB open-source GPS/GNSS software tool. I have optimized this software for low-cost precision GNSS solutions, so it is very closely related to the goals of this competition. My background is in control system theory and I worked in product and technology development for servo systems in the disk drive industry for 25 years before switching to the GNSS field. The mathematics turns out to be quite similar between the two as both are problems in precision positioning, just different in scale. In disk drives, it is nanometers over centimeters and in precision GNSS, it is centimeters over kilometers.
How long have you been developing the technology or approach you used to win the contest?
I have been developing and maintaining low-cost precision GNSS solutions in the RTKLIB software for about six years but have only worked with smartphone solutions in the last year or two.
Have you participated in previous editions of this contest?
I did not participate in last year’s competition but I did work with the data after the contest was over and shared a solution using RTKLIB that would have placed fifth in the competition.
What accuracies were you able to obtain?
I achieved a score of 1.648 m on the private leaderboard. This represents the average of the 50th percentile and the 95th percentile of the errors as scored by Kaggle. Kaggle does not provide any further breakdown of this number but, based on the training data for which ground truths were provided, this corresponded to a 50th percentile error of roughly 0.9 m and a 95th percentile error of roughly 2.3 m. With a small tweak to my solution after the competition was over, I was able to improve my private leaderboard score to 1.593 m, which would have been within 1 cm of the third-place solution.
What are the key features of your approach?
My approach was to use the existing post-processing kinematic (PPK) solution algorithm in RTKLIB but to reoptimize it for the unique characteristics of the smartphone observation data. A PPK solution is the post-processing equivalent of a real-time kinematic (RTK) solution and is a differential solution that relies on differencing the receiver observations with observations from a nearby base station to cancel out most of the largest error sources — including atmospheric, orbital and clock errors — since these errors are common between the two sets of proximate observations.
Because smartphones have very poor GNSS antennas and they were mounted inside vehicles, the signal quality is much lower and the multipath much greater than those for which the RTKLIB algorithm was optimized. In addition, the smartphones were using the L5 frequency band, whereas RTKLIB was optimized for the more commonly used L2 frequency band. One of the main goals of my optimization process was to include many low-quality observations in the solution that would normally be discarded, but to de-weight them appropriately.
What end-user applications are you expecting your approach to enable?
RTKLIB software is currently used to provide precision solutions for many end-user applications such as surveying, drone photogrammetry, sports tracking, precision agriculture, utility location, marine navigation and ground subsistence monitoring. Although smartphones won’t replace dedicated low-cost GNSS receivers, the challenging nature of the smartphone data severely stresses the RTKLIB algorithms and exposes numerous opportunities for improvement that are much less obvious with more typical, higher quality data. I have pulled these improvements into the main branch of the demo5 version of RTKLIB, and hence this work should immediately improve the quality of all these applications and extend their use into more challenging environments.
Photo: Google
Acknowledgements: Thanks to the Institute of Navigation (ION) for co-sponsoring the 2022 Smartphone Decimeter Challenge. Thanks to Luke Walcher and Tolu Ojelade for their contributions to the photos used in this article.
GNSS receivers face the same old challenges (extremely weak received signal, orbit and satellite clock errors, ionospheric and tropospheric delays, multipath, dilution of precision, urban canyons, etc.) and new ones (increased interference). However, compared with just a few years ago, they benefit from new signals, many more satellites, a panoply of options for corrections, and improved integration with inertial navigation systems (INS).
For example, pole-tilt compensation is quickly becoming standard. This feature enables users to locate dangerous or hard-to-reach points by measuring them at an angle with just the tip of the pole to which the receiver is attached.
Pole-tilt compensation also makes surveying and mapping easier by removing, in many situations, the need to use total stations or offsets. Together with improvements in work processes, this makes GNSS receivers more user friendly. This is particularly welcome now that more surveyors are retiring than there are new surveyors entering the profession.
The greater accuracy of GNSS receivers enabled by the increase in the number and quality of satellites, signals, corrections services and integration of GNSS with other sensors is also increasing the number of use cases, especially at the high end of the accuracy requirements, such as lane-level vehicle navigation. (Next month’s cover story will center on this year’s Google Smartphone Decimeter Challenge contest, in which competing teams aim to bring smartphone location down to the decimeter or even centimeter resolution using raw location measurements from Android smartphones. This could enable services that require lane-level accuracy, such as estimated time of arrival when using a high-occupancy vehicle lane.)
This month’s cover story highlights what has changed “inside the box” to improve the accuracy and resilience of GNSS receivers for surveying, mapping and a variety of other applications. Read the success stories from five different companies below.
An interview with Markus Uster, head of product center positioning at u-blox about recent GNSS receiver innovations.
Uster
What was the most significant technical innovation in your GNSS receivers in the past five years?
The u-blox F9, launched in 2018, is our robust and versatile high-precision positioning technology platform. It was the first receiver to enable multi-band high-precision positioning solutions for mass-market industrial and automotive applications — and remains the benchmark for the industry today.
The platform combines multi-constellation (continuous reception of four satellite constellations) GNSS technology with dead reckoning and high-precision algorithms. It is also compatible with a variety of GNSS correction data services to achieve positioning accuracy down to the centimeter level.
The u-blox F9 platform is leading the next generation of high-precision navigation with its augmented reality, unmanned vehicles and various machine automation applications. It has since been integrated into a selection of modules catering to a wide range of applications.
What has it enabled users to do that they could not do before?
The u-blox F9 is a widely adopted multi-band GNSS platform for automotive and industrial applications. (Photo: u-blox)
In a nutshell, the u-blox F9 brought high-precision positioning to the mass market. The demand for scalable high-precision technology is growing rapidly, as evident in the automotive world with next-generation advanced driver-assistance systems (ADAS) and in robotics with applications such as UAVs and robotic lawnmowers. However, due to the complexity, size, power and cost restrictions of existing high-precision solutions, until now it has been difficult to meet the demands of these markets.
u-blox developed the u-blox F9 platform by building on the success of our NEO-M8P high-precision GNSS module series and drawing on our extensive experience in GNSS positioning technologies, including dead reckoning, multi-band, real-time kinematic (RTK) and GNSS correction services. The platform delivers the next level of scalable GNSS high-precision technology and shows how u-blox is consistently addressing challenges and driving the GNSS technology evolution.
What is a good example of this?
Integration of the u-blox F9 platform into various applications has proven quite successful in a diverse range of use cases. In the industrial realm, u-blox F9 technology enables mass adoption of commercial unmanned vehicle applications. One example is precision agriculture, where high-precision positioning cost-effectively enables vehicle guidance solutions to improve pass-to-pass accuracy resulting in improved crop yield and reduced consumption of pesticides, fertilizer and seeds. The u-blox F9 modules also paved the way for autonomous driving, including lane-level navigation for heads-up displays and vehicular infotainment systems, a prerequisite for highly automated and fully autonomous vehicles.
An interview with Chris Trevillian, director of product management, geospatial GNSS at Trimble about recent GNSS receiver innovations.
Trevillian
What was the most significant technical innovation in your GNSS receivers in the past five years?
In 2019, Trimble broke ground with Trimble ProPoint, the fifth generation high-precision positioning engine, engineered to provide position and orientation data from the fusion of GNSS signals, globally accessible high-accuracy correction services, and measurement data from a variety of sensors.
When Trimble launched ProPoint signal processing with the Trimble R12 GNSS receiver, head-to-head testing with the Trimble R10-2 in challenging GNSS environments (near canopy and built environment) showed the R12 performed 30% better across a variety of factors, including time to achieve survey precision levels, position accuracy and measurement reliability.
In September 2020, Trimble announced the Trimble R12i GNSS receiver. It incorporates tilt compensation based on an IMU using Trimble TIP technology, which enables points to be measured or staked out while the survey rod is tilted. This empowers land surveyors to focus on the job at hand and complete work faster and more accurately.
What has it enabled users to do that they could not do before?
ProPoint provides new levels of reliability and productivity. In addition, the ProPoint engine is a key enabler of the new TIP technology.
The combination of ProPoint and TIP in the Trimble R12i allows users to accurately mark and measure points in areas previously inaccessible for GNSS rovers, such as building corners, or hazardous situations, such as the edge of an open excavation. The R12i also features real-time automatic inertial navigation system (INS) integrity monitoring. This system allows users to detect and correct for IMU biases introduced by use over time, temperature or physical shocks, helping ensure measurement quality and integrity for the life of the receiver. The combination of ProPoint and TIP technology improves accuracy, increases availability, provides better integrity and enhances constellation support.
Available on Trimble products utilizing Maxwell 7 technology, ProPoint leverages the latest developments in GNSS signal infrastructure and Trimble’s high-precision receiver hardware to deliver improved positioning performance in challenging environments. It also contains dynamic models of specific application movements, allowing it to filter out unexpected dynamic movements for improved accuracy.
What is a good example of this?
Benchmark Surveys, a small firm in Southwest England, wanted to test the R12i’s capabilities on a narrow road between an industrial park and Exeter Airport lined with high hedges, thick tangled foliage and large trees. The road-widening project, which required surveying 10 meters on either side of the road, would have been a challenge for any combination of surveying equipment. James Richards, Land, Utility and Measured Building Surveyor with Benchmark Surveys, told us the R12i was able to fix and gather points “in places not accessible by other GNSS kits we’ve used.” He said, “With the tilt compensation, we could reach under the edge of hedges and shrubs, up against buildings and walls, and safely out into the road.”
An interview with Fergus Noble, CTO at Swift Navigation about recent GNSS receiver innovations.
Noble
What was the most significant technical innovation in your GNSS receivers in the past five years?
At Swift Navigation, our mission has been to bring precise positioning technology to the mass market. We focus on the applications that touch our everyday lives — automotive, transportation, robotics and mobile devices. To realize that mission, we have had to innovate beyond traditional GNSS techniques. There are three areas where Swift has had to push the boundaries of GNSS technology: scalability, affordability and safety.
To meet the scalability needs of applications — such as automotive ones, which require continental-scale coverage for millions of devices — we have had to develop new techniques for providing GNSS corrections. We have developed new algorithms to precisely model the Earth’s atmosphere and other sources of GNSS error over wide areas in real-time and deliver them via scalable state-space representation (SSR) format.
To make the technology affordable, we have partnered with GNSS chipset providers to bring precise positioning performance to vehicles and consumer devices that was previously only achievable using expensive industrial receivers.
Swift brings to vehicles precise positioning that was previously only achievable with expensive industrial receivers. (Photo: metamorworks/iStock/Getty Images Plus/Getty Images)
To make the technology safe, we have developed the most sophisticated end-to-end positioning integrity system available today. This integrity provides our customers with the guarantee of safety needed for autonomous and industrial applications, as well as certifying to industry safety standards such as ISO-26262 (ASIL).
What has it enabled users to do that they could not do before?
Previous precise positioning solutions did not apply to applications such as autonomous driving as they were too costly to go into a vehicle, had the required accuracy only in limited coverage areas, and could not provide the guarantees of integrity such that they could be relied upon as a safety-critical sensor. The same limitations applied to last-mile transportation, consumer robotics — such as lawnmowers — and even mobile applications.
Swift’s technology enables our customers to unlock these use cases by providing reliable and seamless precise positioning to our users at continental scale.
What is a good example of this?
Swift’s technology is now powering one of the largest vehicle fleets on the road today equipped with advanced driver-assistance systems (ADAS). It improves vehicle positioning for an enhanced user experience when navigating, as well as to upgrade the ADAS functionality.
We also have customers using our technology to track and improve safety across a continent-wide rail network, provide precise position to improve the efficiency of last-mile delivery fleets, and a host of other applications across both emerging and traditional GNSS markets.
An interview with Rachel Wong, product manager, surveying and engineering division at CHC Navigation about recent GNSS receiver innovations.
Wong
What was the most significant technical innovation in your GNSS receivers in the past five years?
CHC Navigation is a technology enabler for geospatial professionals in more than 120 countries. End users of geospatial data increasingly come from diverse backgrounds. This forces us to invest heavily in simplifying data-acquisition processes by focusing on the user friendliness and positioning reliability of our GNSS receivers.
The latest technological developments in GNSS real-time kinematic (RTK) rovers are based on the maturity and improvement of satellite navigation systems, as well as on the integration of IMU sensors in the receivers — the latter being certainly the most important innovation.
In addition, the latest generation of our GNSS rovers, such as the CHCNAV i83, is based on the sophisticated iStar algorithm, which significantly improves the efficiency of tracking GNSS satellite signals for unmatched performance in GPS, GLONASS, BeiDou, Galileo and QZSS constellations, using all available frequencies including BeiDou 3. This goes hand-in-hand with the integration of the IMU as it helps to ensure increased GNSS positioning accuracy through optimized satellite geometry.
What has it enabled users to do that they could not do before?
A utility worker uses the tilt-pole-compensation feature to measure a manhole. (Photo: CHC Navigation)
The integration of GNSS+IMU modules allows surveyors to survey points without the need to level the range pole, accelerating the adoption of GNSS technologies for early adopters by simplifying work processes. For example, our i83 GNSS is powered by a 1,408-channel multiband GNSS receiver, the latest iStar technology and a high-end, calibration-free IMU sensor for faster, more reliable GNSS field surveys.
The i83 GNSS’ integrated IMU automatically compensates for pole tilt, increasing surveying, engineering and mapping efficiency by 30% over conventional RTK GNSS surveying methods. In less than 5 seconds, the 200-Hz inertial module is initialized to ensure survey-grade accuracy over a pole-tilt range of up to 30 degrees that meets the real-world operational needs of our users.
What is a good example of this?
Surveyors can extend their working boundaries near trees, walls and buildings without the need for a total station or offset measuring tools. This can be illustrated in sewer and drainage applications, such as measuring the bottom of manholes for water, utilities or sewers, which was barely feasible in terms of GNSS measurement before the advent of hybrid GNSS + IMU positioning.
Operators only need to concentrate on their tasks and no longer need to level their pole vertically. They are now able to perform many measurements without compromising accuracy and reliability. Productivity is greatly increased, RTK usability is greatly improved, and potential human error is reduced, whether you are an engineer, foreman or surveyor, and whether you are an experienced or new user.
