Trimble has introduced a new family of Trimble BD GNSS boards for high-precision guidance and control applications.
The BD boards’ simple connectivity and configuration allow system integrators and OEMs to easily add GNSS positioning and orientation — with the ability to upgrade its capabilities — using the same board footprint, connectors and software interface for specialized and custom hardware solutions, the company said.
The compact Trimble BD boards include a broad range of receiver capabilities, from high-accuracy GNSS only to full GNSS-Inertial features for positioning and 3D orientation. Firmware options are upgradeable, allowing functionality to be added as requirements change.
Product manufacturers in markets such as unmanned aerial vehicles (UAVs), autonomous vehicles, fleet management and aviation now have the ability to offer customers an extensive range of capabilities to meet all their needs.
According to Trimble, the low-power BD family of boards includes the BD940 GNSS and GNSS-Inertial boards and new top-of-the-line BD990 GNSS, GNSS-Heading and GNSS-Inertial boards, enabling customers to choose the most appropriate receiver for their applications.
In addition, the BX940 and BX992 are available in a rugged enclosure for applications used in harsh environments.
Integrating Trimble RTX technology, which enables precise and robust location worldwide without the use of a base station, the BD boards are ideal for flexible positioning. Trimble RTX technology enables users to subscribe to a complete portfolio of real-time correction services that deliver varying levels of accuracy depending on the user’s application requirements.
The new BD family incorporates the latest Trimble Maxwell technology with advances in high-precision GNSS-Inertial positioning. By integrating inertial sensors onto the GNSS boards, users can experience more robust performance in a variety of challenging environments such as urban canyons, tunnels, heavy canopy or other GNSS-denied environments.
Robust centimeter-level, real-time kinematic (RTK) positioning is achieved through the combination of multi-frequency GNSS — full triple-frequency support of all available GNSS satellite constellations—and onboard inertial sensors.
System integrators and OEMS also have the ability to detect interference with the included RF Spectrum Monitoring and Analysis tool embedded in the receiver. The GNSS engine with 336 channels is capable of tracking L1/L2/L5 frequencies from the GPS, GLONASS, Galileo and BeiDou constellations.
“The OEM and system integrator communities demand high performance, reliability and support for their positioning solutions,” said Elmar Lenz, general manager of Trimble’s Integrated Technologies Division. “The new BD family of boards deliver the latest GNSS and inertial technology in an easy-to-integrate form factor.”
The new Trimble BD OEM GNSS family is available now through Trimble’s Integrated Technologies Precision GNSS Sales Channel.
Tallysman, a manufacturer of high-performance GNSS antennas and related products, has released two high-gain (50dB) GNSS antennas: the TW3152 and TW3752.
High-gain GNSS antennas are useful in situations where long cable runs are required, such as in timing systems and GNSS re-radiator systems, the company said.
The TW3152 provides reception of GPS L1. The TW3752 provides reception of GPS L1, GLONASS G1, BeiDou B1 and Galileo E1 signals. Both antennas employ Tallysman’s Accutenna technology, which provides a high degree of multipath signal rejection through the full bandwidth of the antenna.
According to Tallysman, the antennas are triple filtered to prevent the saturation of the front-end LNA by strong near frequency and harmonic signals, which are a growing concern throughout the world.
These antennas are available with a choice of radome shape (flat or conical), color of radome (white or grey), as well as a wide variety of connectors.
Leica Geosystems showed off its Zeno GG04 smart antenna and DS2000 Utility Detection Radar at the 2017 Esri User Conference, which took place July 10-14 in San Diego, California. The Zeno GG04 improve mobile devices’ GNSS accuracy with Real-Time Kinematic (RTK) and precise point positioning (PPP), while the Leica DS2000 Utility Detection Radar detects and positions shallow and deep targets simultaneously.
Applanix is collaborating on advanced research for autonomous vehicle guidance and control systems with the University of Waterloo Centre for Automotive Research (WatCAR) in Ontario, Canada. Applanix is a Trimble company.
Applanix will provide WatCAR with its positioning and orientation system for testing autonomous guidance and control systems in real-world conditions. Applanix will also provide the Trimble GNSS-inertial board set for integration with car systems and sensors to enable precise positioning.
The Applanix POS LV is a robust, reliable and repeatable positioning solution for on- and off-road vehicles. Applanix technology will be used by WatCAR to assess the performance of the guidance and control systems on board their autonomous vehicles.
The testing will take place in challenging weather conditions and environments including on roads under repair, with lane reductions and closures, are wet or covered in snow, and where there is poor visibility.
An SUV in an anechoic chamber at WatCAR.
Applanix will also provide WatCAR with Trimble on-board GNSS-inertial board set designed for high-performance, high-volume original equipment manufacturer applications. These products, currently used in a variety of autonomous vehicle programs, include the Trimble AP GNSS-inertial board set that includes a high-precision inertial measurement unit.
Small, rugged and low powered, the AP board sets provide the precise positioning needed for autonomous vehicle applications as they navigate their environment. Designed for use on all sizes and types of vehicles, the AP boards feature Trimble’s high-performance precision GNSS receivers and Applanix’ IN-Fusion GNSS-inertial integrated technology that produces uninterrupted position, roll, pitch and true heading measurements of moving platforms. Integrating easily with vehicle sensors, the AP board sets provide precise vehicle control when interacting with a constantly changing environment.
The relationship with WatCAR will aid in improving the core technologies that deliver high-end systems capabilities for a variety of Trimble markets.
The Waterloo Centre for Automotive Research in Canada conducts advanced research to further automotive innovation and competitiveness. From active safety to automated driving through lightweighting and advanced powertrains, 130 faculty researchers comprise the largest university-based automotive activity in the country. Leading-edge studies for industry partners around the world enhance vehicles, components and their materials with new approaches and integration of innovative technologies.
“We are excited to collaborate with the University of Waterloo and WatCAR on this leading research in autonomous vehicle technology,” said Louis Nastro, director of land products at Applanix. “Applanix has been committed to meeting the needs of autonomous vehicle manufacturers for more than a decade, as first demonstrated in the early days of the DARPA Grand Challenge. And today, we are also part of many autonomous vehicle programs deployed worldwide in commercial applications.”
“The Trimble AP products, first introduced in 2009, are designed for use in small, mass market vehicles where size, weight and cost factors are important,” Nastro said. “They have also been designed to easily integrate with the industry’s leading sensors, making them an ideal solution for autonomous vehicle navigation systems and sub-systems.”
“We welcome the opportunity to work with Applanix, a leader in reference systems. Their technology identifies, with very high accuracy, the exact location of our vehicle at all times,” said Ross McKenzie, managing director at WatCAR. “Applanix is a valued industry partner and their team is great to work with. Going forward we anticipate a solution that will enable autonomous vehicles to traverse the real world reliably and safely.”
Coastal Zone Mapping and Imaging Lidar System (CZMIL) to be shared at conferences as a critical rapid environmental assessment tool for both natural and manmade disasters
Teledyne Optech’s Coastal Zone Mapping and Imaging Lidar (CZMIL) system is a critical rapid environmental assessment tool for monitoring natural and man-made disasters. From detecting sewage pipe leaks, mapping oil slicks and measuring coastline changes after hurricanes, to counting underwater debris in the Great Pacific Garbage Patch, CZMIL excels at identifying and monitoring oceanic environmental changes, especially in emergency scenarios.
At the Oceans ’17 MTS/IEEE conference in Aberdeen, Scotland, Senior Scientist Viktor Feygels will present “CZMIL as a Rapid Environmental Disaster Response Tool.” Using case studies from CZMIL and its predecessor systems, Feygels will describe four distinct applications of Teledyne Optech lidar bathymeters. Attendees can catch this presentation in Room 15 on June 21 at 12:10 p.m.
Research Scientist Hieu Duong and Marine Business Manager Bob Marthouse will present “Small-Object Detection using Coastal Zone Mapping and Imaging Lidar (CZMIL)” at the Teledyne CARIS International User Group Conference in Ottawa, Canada. Conference attendees can hear about these applications on Thursday, June 22, 10:05 am, in the Rideau Room.
“CZMIL has proved to be ideally suited for rapid environmental assessment and small-object detection,” said Bob Marthouse. “Both the upcoming MTS/IEEE Oceans ‘17 conference and the recent United Nations Ocean Conference during the week of June 5 underline the urgent requirement to more critically monitor our oceans and coastlines. At Teledyne Optech, we were pleased to be part of this ongoing effort.”
The BLK360 is now available for reservation in Europe.
Leica Geosystems’ BLK360 miniaturized 3D imaging laser scanner is now available for reservation within Europe, for delivery in summer. The laser scanner simplifies the collection of as-built reality capture data for work in architecture, design, construction and engineering among other vertical markets.
The Leica BLK360 is an easy-to-use and powerful reality-capture solution that enables professionals to capture 360-degree HDR spherical imagery within minutes. Users place the lightweight BLK360 on a level surface or tripod and, with the push of a button, it captures 360-degree HDR spherical imagery and takes a 360,000 point per second laser scan.
The BLK360 features +-4 mm accuracy at 10 meters and an overall 0.6–60-meter range. Within three minutes, the spherical image and laser scan is completed and ready to view in the Autodesk ReCap Pro for mobile app, which runs on an iPad Pro. From there, users can take measurements, add markup and annotations or share onsite data with their colleagues back in the office.
“If you’ve ever relied on pencil and paper, tape measures, or other laser measuring devices to capture a room’s dimensions and images, you know that there’s always redundancy and missed measurements,” said Steven Gross, architectural engineer, Valley Home Improvement. “With the BLK360 those issues disappear. Everything is captured on the first visit, which streamlines the process, saving us enormous amounts of time. Not to mention that it makes us look that much more professional to our clients.”
“The BLK360 brings together exclusive technologies to deliver outstanding performance, all while simplifying the process of 3D image scanning and reality capture through the touch of a single button,” said Burkhard Boeckem, CTO, Leica Geosystems. “This has enabled us to create new opportunities for scanning experts and introduce entirely new audiences to laser scanning while uncovering possibilities that were previously unimaginable.”
The BLK360 has already earned several prestigious industry awards including the PRISM Award for Photonics, iF Design Award, the Red Dot Design Award, and the Geospatial World Innovation Award, and was also a CES Innovation Award nominee.
Road Navigation Using Multiple Dissimilar Environmental Features
New navigation paradigms combining GNSS and inertial with additional sensors can increase overall reliability and power robust road navigation. A feasibility study tests a barometer, a magnetometer and a camera looking at road signs, and concludes that such sensors examining environmental features can supply the necessary context for frequently traveled or shared routes.
By Debbie Walter, Paul D. Groves, Bob Mason, Joe Harrison, Joe Woodward and Paul Wright
Where a robust and reliable position solution is required, it is necessary to combine GNSS with other technologies. Dead-reckoning is only suitable for bridging short outages. For robustness against longer GNSS outages, alternative position fixing techniques are needed. Radio-based signals have been excluded from this study as they are either not yet mature or are, like GNSS, susceptible to jamming, though they may still play a part in the final navigation solution.
For land navigation in particular, a new approach is therefore needed. Environmental features provide a potential source of location information. These include buildings or parts thereof, signs, roads, rivers, terrain height, sounds, smells and even variations in the magnetic and gravitational fields. Visual navigation technologies are being developed and are likely to be complementary to the feature-matching discussed in this article; however, they will not be directly discussed. The environmental features will be integrated with dead reckoning to provide robust positioning.
The overall solution is to place hardware within a batch of vehicles, comprising multiple sensors, including a GNSS receiver and sensors for dead reckoning. Road map matching could also be included. During normal usage, the GNSS receiver is used for positioning and a database is updated with the feature information from all the sensors accompanied by location stamps from the GNSS-based position solution.
As the multiple vehicles travel around an area, the database is built up for these routes. In the event that the GNSS receiver does not receive sufficient signals to maintain an accurate position, the database is called upon for navigation by environmental feature matching. In this scenario, the sensors continue to take measurements and, by combining the knowledge of the last known location, dead reckoning and the sensor’s outputs, the positioning algorithm draws upon the database to estimate a positioning solution. This method is shown in Figure 1 and Figure 2.
Figure 1. Basic workflow mode for collecting data. Source: GPS World
Figure 2. Basic workflow mode for navigation using collected data.
This navigation system relies upon the roads being travelled on a regular basis so that the “maps” created from the sensor’s outputs are kept up to date and therefore valid. The most likely users of this technology would be fleets of vehicles that can share the mapping information. To focus on a typical system, use in emergency vehicles was considered. Knowing your position is vital in an emergency vehicle, and a system that incorporates a back-up to GNSS would be advantageous. The motivation for maintaining a continuous positioning solution is that, when moving within a complex environment, it is necessary to maintain the integrity of the current position. In emergency situations, delays are not acceptable and integrity is vital. There will be no point in time when the vehicle can be delayed to obtain a position fix.
Although this system will be designed with emergency service vehicles such as ambulances and police cars in mind, it could also be used in wider applications such as fleet management and tracking devices. Ultimately, crowd sourcing or cooperative techniques could be used to pool information from different vehicles equipped with the system. With a very large number of vehicles maintaining the feature database, the system could adapt to changes in the environment very quickly.
To reliably achieve meters-level positioning across a range of different challenging environments, a paradigm shift is needed. We need to use as much information as we can cost-effectively obtain from many different sources in order to determine the best possible navigation solution in terms of both accuracy and reliability.
This new approach to navigation and real-time positioning in challenging environments requires many new lines of research to be pursued.
ROAD EXPERIMENT
A set of sensors with a GNSS receiver were attached to a car and driven in closed loops around Stoke-on-Trent on multiple road types over multiple days. The loops were repeated three times on each day on four road types and then repeated over three consecutive days. The sensors used can be seen in Table 1.
Table 1. Sensors used in the road experiments.
The accelerometer, air quality sensor, barometer, dust sensor, light sensors and microphone interfaced with an Arduino microprocessor which outputted the signals from the sensors to a laptop. The Arduino sensors had a data rate of 20 measurements per second. There was an individual accelerometer (attached to the axel of the vehicle) for use in identifying road texture. There are also accelerometers that form part of the inertial measurement unit (IMU), and these were used for dead reckoning.
The onset of movement as recorded from the IMU was used to assist in identifying the beginning of each circuit. During the car journeys, there were two experimenters, one to drive the car and another to monitor the sensors. There were 5–10 minutes between each round; during this time, the sensors would be turned off and then restarted. The equipment was designed for the outputs of the sensors to be post-processed.
The four classes of road were suburban, urban, rural and high-speed road. The route taken and a view from Google Street View showing the general type of landscape traveled through is shown in Figure 3.
Figure 3. The urban road type used in the road experiment.
Figure 3. The suburban road type used in the road experiment.
Figure 3. The rural road type used in the road experiment.
Figure 3. The high-speed road type used in the road experiment.
A road experiment travelled the routes, using GPS receivers with the Arduino, video camera and the IMU so that GPS time could be used as a constant for the various sensors.
WHOLE ROUTE ANALYSIS
The outputs from the sensors were evaluated initially for their cross correlation over the whole route. This process assessed whether the data from different runs over the same terrain were similar and thus had a high cross correlation. This is vital for this map-building method of navigation. This section deals only with sensors that produce continuous output. The next section discusses discrete features.
Cross-Correlation Coefficients. The correlation coefficient (see online version of this article for derivation equations) is used to calculate the cross-correlation of two rounds of sensor data. The cross correalation coefficient is a normalized value. If a signal is correlated with itself, at zero offset (autocorrelation) this would give a value of 1; entirely uncorrelated data gives 0. Signals 180° out of phase would give a correlation value of –1.
The cross-correlation coefficients are shown in Table 2 for all of the sensors. It shows the coefficients for the four different road types using combinations of rounds (round 1 and 2, round 2 and 3 and round 1 and 3 for each three days) from the same days and the average of the coefficients for all the combinations. The sensors with higher coefficients are discussed in more detail in the following subsections. Road signs do not have cross-correlation coefficients; they are treated differently as this is a discrete measurement.
Accelerometer. The magnitude of the acceleration from a accelerometer triad was used in this experiment as a method of measuring road texture. A zoomed-in section of the acceleration as recorded from the accelerometer against the distance traveled can be seen in Figure 4.
Figure 4. Profile from accelerometer attached to axle.
It is difficult to see similarities in the output from the different rounds, although the accelerometer can show movement from stationary to driving and this was used to initialize the sensor outputs from the XSens IMU. This is shown in Figure 5 at 44s.
Figure 5. Accelerometer data showing vehicle setting off.
Barometer. The barometer measures height change of the vehicle. This sensor consistently produced the highest cross-correlation coefficient, shown in Figure 6.
Figure 6. Comparison of height profile over 3 days with minimums set to zero.
Magnetometer. The magnetometer produced data with distinct spikes caused by various magnetic anomalies in the environment being travelled through. This can be seen in Figure 7 for the high-speed road.
Figure 7. Vertical axis magnetic field profile for a high-speed road.
Figure 8 is a zoomed-in section of the magnetometer data from the high-speed road in Figure 7. It shows correlation with an offset of approximately 44m between round 1 and round 3. This is mostly due to synchronization errors between the magnetometer counter and the GNSS receiver clock. This is the reason a second run of the road experiment was completed.
Figure 8. Zoomed-in section of the vertical axis magnetic field experienced on a high-speed road.
Microphone. The microphone was able to pick up clear signals when the vehicle was stationary, and the signal seems to be dependent on the speed of the vehicle. Figure 9 shows the profile from the microphone.
Figure 9. Profile from microphone attached to axle.
It may be possible to combine this data with the accelerometer or odometer data to develop a clearer picture of what sound is resulting directly from the road surface and what is speed related, although this still may not result in a useful feature for this study.
Thermometer. Temperature can vary particularly in a rural environment, seen in Figure 10. Similarities are not consistent across environments as seen from cross-correlation values in Table 2 and are likely to change with the seasons and due to weather conditions.
Figure 10. Temperature profile for rural roads.
Light Sensor. Four light sensors were used in the experiment: upwards, forwards, left and right facing. Figure 11 shows the data from the upward-facing sensor on the high-speed road. There are distinct events where the light level drops. Many of these instances correspond to gantries (bridge -like structures spanning highways displaying speed limits and other information). These features could be treated as discrete, whereby the sharp dips in light level would be treated as momentary events. Some of the information would be lost in treating the ambient light as discrete, but it would make the feature more robust against changing light levels due to shadowing or cloud cover.
If light is treated as a continuous feature, it can be seen in Table 2 that the cross correlation was inconsistent. This is partly due to the effects of changing light conditions. On the days with direct sunlight, the light sensor would reach its maximum intensity and be saturated. This can be seen in Figure 11, and this affects the cross-correlation coefficient calculated.
Table 2. Cross-correlation coefficients for sensor outputs for the four road types.Figure 11. The upward-facing light sensor profile for the high-speed road in second experiment day two.
Feature outcome. Thermometer data has been discounted as although it gave a cross-correlation coefficient of about 0.5 for the rural route, the other routes had lower cross-correlation values. Similarly, the microphone data had moderate success in high speed and rural environments but not in the other two routes. Therefore it will not be taken forward to the next phase although it could be used in the future if further processing was carried out on the data. As with the microphone, the light sensor had cross-correlation values greater than 0.5 in the rural and urban environments but had lower values in the other two. The success of this sensor is more reliant on the weather conditions than the environment type. At the current point this will not be brought forward to the next stage.
The accelerometer (used to measure road texture) showed no correlation with cross-correlation coefficients of approximately zero (between 0.0 and 0.3). It was useful for use in dead reckoning, but does not illustrate road texture.
The magnetometer and barometer showed the greatest potential for positioning with the highest cross-correlation values consistent over all the environments. These sensors are taken forward into phase 3.
DISCRETE FEATURES
A discrete feature is one where there are environmental events that occur at one position but can repeat multiple times along a route. The discrete feature can either be Boolean (an event occurs or does not) or it can be descriptive (different possible events or the strength of the event). Examples of discrete features include lamp posts, speed humps and shop signage.
In this paper, the discrete feature that will be discussed will be street signs, although the techniques used are applicable to many discrete features. How the signs are identified will also not be discussed in detail in this paper; instead, focus will be on how a sequence of discrete features is used show consistency across a route.
SCANNING METHOD
This section will look at scanning one round to find the region that best matches a region from a different round. Figure 12 illustrates this technique.
Figure 12. Diagram showing the principle used to scan for best match to a pre-set region.
The test data is scanned through the reference data. Cross-correlation coefficients are calculated as the test data is scanned through. The aim is to locate the position of the test data using the reference data for which the position is already known. The output of this exercise gives a cross-correlation profile (cross correlation as a function of position).
This profile can be treated similarly to a probability density distribution of position (although they are not the same) and so gives an idea of the probability of the position at each point in the test data.
Results. Two rounds from the suburban route are shown in this section as an example of the results achieved with the scanning method. Figure 13 and Figure 14 show the cross-correlation profiles for magnetic field and height for day 3 rounds 1 and 2 on the suburban route respectively. The test data region chosen is centered at 1.6 km into the route. The test data region size was 125 m for 4.5 km reference data.
It can be seen that the magnetic field has a number of peaks along the route. The peak with the highest cross-correlation coefficient is at the 1.6-km point (which is the correct position). For the height figure, there are many broad peaks at similar cross-correlation values approximately 700 m apart. The height peaks are broader than the magnetic peaks because the terrain height changes more slowly than the magnetic field.
Ambiguities, Dead Reckoning. The two graphs in Figure 13 and Figure 14 show that there are ambiguities present in both of the features. The majority of the features will have some ambiguities along a route, and so it is important to develop a technique that could mitigate them. One way ambiguities could be mitigated is by using the information available from dead reckoning. The dead-reckoning solution will have a specific position error (which grows with time), and the ambiguities from the features can be reduced by only considering the candidate position within the dead reckoning position uncertainty bounds.
Figure 13. Cross correlation scan for magnetic field on the suburban route with the center of the actual position of the test data shown as blue dotted line.
Figure 14. Cross correlation scan for the barometric height on the suburban route with the center of the actual position shown as blue dotted line.
COMBINING FEATURES
The quality of position information that can be extracted from a particular feature type varies with location. Thus, a better position solution can be obtained if higher weighting is attributed to higher quality features. Factors that will need to be considered include the precision of position that can be extracted from a feature, the level of ambiguity (Are there multiple candidate positions?) and the reliability (how much measurements vary unpredictably with time).
There are multiple ways to combine the scores from different features. Initially, there is the decision as to when in the position estimation process to combine the features. There are two ways to do this: Either combine the scores for each feature, or combine the position estimates for each sensor. The following subsections will describe a number of ways of combining the scores before estimating the position. It will be noted if these techniques could also be used to combine position estimates.
Equal Weighting. A simple combination technique is for each feature score to have equal weighting. The equal weighting used earlier took the two scores and found the average. This way, no single feature will dominate the navigation solution. As the feature scores are not probabilities, the values are not self-weighting, therefore it cannot be presumed that that equal weighting would always provide an optimal position estimation.
Test Data Weighting. This method takes a set of experimental data and empirically determines the weighting coefficients based on the best position solution in this test dataset. The test data would be used to maximize the score of the combined features using weighting at the correct position. This would have the benefit of using real data to determine the weighting, but its strength is based on how representative the test dataset is to the environments that the car will travel in.
Environment Weighting. This would detect the environmental context and use this to select an appropriate set of weights. For example, the presence of many Wi-Fi sources would suggest a suburban or urban environment, while a vehicle speed of 31m/s (70 mph/113 km/h) would suggest that the vehicle is likely to be on a highway. Based on this knowledge, it would possible to use a specific weighting coefficient set is developed for that environmental context.
Cross-Correlation Weighting. This weights each feature according to the characteristics of the cross-correlation coefficients profile obtained using the scanning method described earlier. This enables the weighing to adapt to the quality of the data. Figure 15 shows traits of a set of peaks that affect the confidence in the highest peak being the correct position.
Figure 15. Weighting nomenclature of actual position shown as blue dotted line.
Taking the uncertainty in the current position, only peaks that, for example, fall within 3 standard deviations would be evaluated. The characteristics of the tallest peaks compared with the others would be used to determine a measure of confidence for that feature.
There will be more confidence in the tallest peak (h0) if there is a greater difference between its height and that of the other peaks within the uncertainty range (h1, 2, 3). In Table 3 this is height.
Table 3. Cross-correlation profile weighting showing average distance from true position of the vehicle and the percentage of times the weighted scanning technique calculated the position within 100 m.
The next factor is the number of peaks within the uncertainty range (No. Peaks). The more peaks, the less confidence that the correct peak has been chosen as the position estimate.
The average cross-correlation coefficient within the uncertainty region (γ) would affect the confidence in the estimated position. If the average coefficient value (Av. CC) was similar to that of the highest peak, this suggests insufficient variation in the data being analyzed from that feature.
Finally, the standard deviation could be used. Calculating how many standard deviation (Std Dev) the highest peak was from the mean could provide a weighting value.
Each of these characteristics was looked at separately and compared against the benchmark of equal weighting using the scanning method comparing multiple pairs of rounds on different routes. It can be seen in Table 3 that the standard deviation from the mean provided the best weighting outcomes. To optimize the weighting algorithm, it may be that using a combination of these profile characteristics would provide the best position estimation.
Figure 16 and Figure 17 show examples of cross-correlation profiles; they show high and low confidence respectively. Figure 16 is the cross correlation of data from day three, rounds two and three, on suburban roads. It has a few spaced out peaks over the full profile, and one of the peaks is clearly higher than the others. Figure 17 is the cross correlation of data from day two, round three, and day three, round three, from the high-speed road. It has many similar height peaks all around the value of 0.5.
Figure 16. Good cross correlation profile; few spaced out peaks with one higher than all other peaks.Figure 17. Poor cross correlation profile; many low similar height peaks.
CONCLUSION
Environmental features have sufficient variability spatially and stability temporally for a database of features to be developed to create a map of the environment. This supports the hypothesis that it is feasible to map a space and then create a feature-mapping and navigation algorithm using a combination of environmental feature sensors, a GNSS receiver and sensors for dead reckoning.
FUTURE WORK
The next step of the project is to develop a feature-matching, mapping and navigation algorithm that incorporates inputs from the multiple sensors, a GNSS receiver, map-matching and sensors for dead reckoning. The algorithm will run collecting sensor data while GNSS receiver data is available, and store this in a database along with location stamps until called upon in times of GNSS receiver signal disturbance. The data from the road experiments will be used for a test database in developing the navigation system.
ACKNOWLEDGMENTS
Debbie Walter is funded by Engin-eering and Physical Sciences Research Council (EPSRC) and Terrafix ltd.
The authors thank Paul Neesham for a method of manually recording street signs seen in video footage and Juliusz Romaniuk of Terrafix for advice and creating hardware that contained the sensors’ carrier frequencies.
DEBBIE WALTER is a Ph.D. student at University College London in the Engineering Faculty’s Space Geodesy and Navigation Laboratory, and a software engineer at u-blox.
PAUL GROVES is a lecturer at UCL, where he leads a program of research into robust positioning and navigation. He holds a Ph.D. in physics from the University of Oxford.
BOB MASON is chief scientific officer and director of Terrafix Limited, holding a Ph.D. in communications and neuroscience from Keele University.
JOE HARRISON is principal radio frequency design engineer at Terrafix Ltd.
JOE WOODWARD is a software design engineer at Terrafix Ltd.
PAUL WRIGHT is a development engineer with Terrafix Ltd., with doctoral degrees in physics and electronics.
Precis-BX316R is a GNSS Post-Processing Kinematic (PPK) board for accurate positioning. It supports raw measurement output from two antennas: GPS L1/L2, GLONASS G1/G2 and BDS B1/B2 from primary antenna and GPS L1/L2 from the second.
The SD card on board (up to 32G) makes it convenient for users to collect data for post processing. Working with GNSS antennas, it can output stable measurement in challenging conditions, Tersus GNSS said.
Integrated with versatile interfaces and connectors, Precis-BX316R aims to facilitate applications such as precision navigation, precision agriculture, surveying and UAV, and enforcing effective GNSS data management.
VectorNav Technologies, a provider of embedded navigation solutions, announced at AUVSI’s Xponential that it will supply its surface mount VN-100 inertial measurement unit/attitude and heading reference system (IMU/AHRS) to Neya Systems for a custom version of that company’s UxAB module.
The back-packable Advanced Explosive Ordnance Disposal Robotic System (AEODRS) with integrated Neya Systems’ UxAB module. Photo: VectorNav
Neya Systems will in turn deliver its custom version of the UxAB platform to Northrop Grumman for that company’s Advance Explosive Ordnance Disposal Robotic System (AEODRS) Increment 1 delivery, an autonomous bomb-disposal robot, to the U.S. military. The AEODRS unmanned ground vehicle “back-packable” increment 1 system weighs less than 35 pounds and comprises the handheld operator control unit, communications link, mobility capability module, master capability module, power capability module, manipulator capability module, end effector capability module, visual sensors capability module, autonomous behaviors capability module and other minor components.
The UxAB is a a fully self-contained semi-autonomy and autonomy capability module that includes GPS waypoint navigation, multi-joint manipulator control (with self-collision avoidance), retrotraverse, return-to-comms and optional obstacle avoidance behaviors.
About the size of a postage stamp, VectorNav’s surface mount VN-100 is a temperature calibrated MEMS-based IMU/AHRS that includes 3-axis accelerometers, gyros and magnetometers. The module delivers to users a real-time 3D orientation solution that is continuous over the complete 360 degrees of motion at rates of up to 400 Hz. In addition to calibrated IMU and AHRS functionality, the VN-100 includes VectorNav’s Vector Processing Engine (VPE), a suite of proprietary sensor fusion algorithms running onboard the sensor that deliver real-time magnetic & acceleration disturbance rejection, adaptive signal filtering, dynamic filter tuning, and on-board Hard & Soft Iron compensation.
The VN-100 surface mount module is being integrated directly into the electronics board of Neya Systems’ UxAB platform. Neya is using the calibrated pitch and roll estimates to assist in its controller functionality, for example to provide warning when the robotic module is in danger of tipping. The VN-100 AHRS magnetometer-based heading solution is used for waypoint navigation. VectorNav is providing platform specific hard/soft iron calibration expertise to ensure the magnetometer-based heading solution takes into account the magnetic signature of the UxAB module and provides accurate navigation in a variety of environmental conditions.
AEODRS is the next generation of Explosive Ordnance Disposal robotic systems, designed as a follow-on and capability upgrade to existing deployed platforms. AEODRS is based on an open architecture, and Neya’s Autonomy Module will conform to the logical, electrical, and physical interfaces that are required by this architecture. Neya will be adapting its commercially available UxAB platform to comply with AEODRS Capability Module requirements.
Tallysman, a manufacturer of high-performance GNSS antennas and related products, released its NMO (New Motorola) mounts for its dual- and triple-band GNSS antennas. NMO mounts are used in a variety of applications such as automobiles, railway cars and emergency vehicles.
With the introduction of this mount, customers can now upgrade existing GPS L1-only antennas to dual (L1/L2) and triple (L1/L2/L5) band GNSS antennas.
The NMO mount is available for Tallysman’s TW3872 (GPS L1/L2, GLONASS G1/G2, BeiDou B1, and Galileo E1) and the TW3972 (GPS L1/L2/L5, GLONASS G1/G2/G3, BeiDou B1/B2, Galileo E1/E5a+b + L-band correction) antennas.
The NMO mount is able to accept a ground plane (also available from Tallysman) to increase the gain of the antenna.
Tallysman antennas are housed in an IP67 compliant housing and are REACH and RoHS compliant.
Remote sensing technology has come a long way and is delivering serious benefits across a wide range of industries. Since the early 1970s, when the first LANDSAT satellites were launched, there has been rapid technological innovation in platform architecture and sensor technology used to collect both active and passive spectral information.
These advancements have dramatically changed the way we collate, interpret and act on geographic information system (GIS) data in virtually every discipline and in our day-to-day lives. Efficiencies in data acquisition coupled with revolutionary improvements in analytic platforms have pushed remote sensing technology to the forefront of scientific and business-critical decision making, delivering insights not previously possible.
Let’s examine how new sensor technologies, acquisition platforms and high-performance, cloud-based computing enable greater visibility and provide detailed data that enhances public safety, improves reliability of critical infrastructure and supports proactive planning.
Figure 1. Time-lapse of fixed-wing aircraft collecting near-shore topobathy lidar after Superstorm Sandy near Holden Beach, North Carolina. (Photo: Brett Murphy)
A Sample of the Latest Technologies
Remote sensors work by recording the radiance of specific wavelengths of the electromagnetic (EM) spectrum tuned for particular applications. Today, sensors of all sizes, types and designs — accommodating an almost limitless variety of spectral bands and fusion of these bands — are being deployed for an array of remote sensing applications.
There are two general types of sensors: active, which transmit and record their own light source; and passive, which measure reflected or emitted energy produced from an external source. Most modern sensors are integrated with inertial navigation systems (INS) and global navigation satellite systems (GNSS), which provide high-precision and spatially accurate data. Active sensors also can provide extremely accurate range information for detailed 3-D applications, while 2-D passive sensors, relying on relatively new techniques using structure from motion (SfM), can achieve similar ranging capabilities.
Among the types of active and passive sensors in mainstream use today are:
Topographic lidar. Best known for producing highly accurate 3-D point cloud data, it is used for topographic and above ground analyses, 3-D reconstructions and advanced artificial intelligence applications.
Topobathy lidar. A specialized airborne sensor capable of penetrating water to map underwater surfaces. It also offers the potential to simultaneously map land and sea floor and reaches areas too shallow for survey boats.
Thermal Imaging. It records radiation emitted from objects and differences in temperature across a scene.
Multispectral Imaging. It measures energy within specific bands of the EM spectrum, most commonly visible blue, green and red, as well as near infrared.
Hyperspectral Imaging. Capable of collecting visible to long-wave reflected solar energy across more than 200 bands. With each additional band of information, the data dimensionality grows and increases the potential for discriminating specific materials based on diagnostic spectral features.
When evaluating new remote sensing tools, sensor technology innovation is only one piece of the puzzle. The platforms that carry these sensors are also rapidly evolving. Manufacturers are producing cheaper and lightweight versions of sensors making it possible to mount them on compact satellites, unmanned aerial vehicles (UAVs), automobiles, handheld devices and autonomous robotic vehicles.
How to Use and Interact with Remote Sensing Data
Recent trends in data fusion and multitemporal data analysis are leading to new approaches and solutions to complex geospatial problems. We can now acquire, combine and analyze data in ways that allow us to do even more things. But, users also are faced with challenges in managing the ever-increasing data volumes, and associated storage and processing capabilities, that come with higher spatial resolution, increased point densities, collection of hundreds of spectral bands and fusion with other data sources.
The rise of cloud-based and high-performance computing environments enable new rapid data processing and retrieval techniques. Historically, the volume of data from hyperspectral sensors made it difficult to quickly analyze and derive actionable information. Only recently has computing power caught up to sensor technology, enabling data analysis for vast areas in a reasonable time frame.
Now that large amounts of data can be converted into high-quality analytics, consumers require an organized, intuitive and integrated delivery mechanism to fully leverage the intrinsic advantages of the extracted information. These needs are being addressed by integrated cloud-based platforms that rapidly update and distribute intelligence across organizations.
Remote Sensing in Action
Many applications, like the ones below, historically relied on antiquated collection platforms or time-consuming manual data collection and interpretation. Now, technological advancements in remote sensing are being leveraged to address diverse and complex problems.
Hurricane Sandy: Near Shore, Post-Disaster Survey
In 2012, Superstorm Sandy grew to the largest Atlantic hurricane on record, affecting the entire Eastern Seaboard from Florida to Maine and moving west across the Appalachian Mountains to Michigan and Wisconsin. Damage was estimated at more than $63 billion, the second costliest hurricane in United States history.
Following the storm, the U.S. National Oceanic and Atmospheric Administration (NOAA) National Geodetic Survey required collection and processing of airborne topobathy lidar and multispectral imagery. The data collected would enable accurate and consistent measurement of the national shoreline for coastal zone management, inundation modeling, habitat mapping and restoration purposes. In less than six months, the NOAA project team, of which Quantum Spatial Inc. (QSI) and Dewberry were members, successfully mapped more than 2,772 square miles of shoreline encompassing the outer coastline from New York to South Carolina.
The airborne topobathy lidar enabled the rapid survey of shallow water areas that are difficult, dangerous or impossible to reach using water-borne platforms. They also were able to collect topographic and hydrographic data concurrently to provide seamless data from land to water (see Figure 2).
Figure 2. Lynnhaven Inlet, Virginia Beach: Lidar point cloud collected from a single topobathy acquisition flight. Topographic data shown in grayscale and subsurface water depth in bluescale.
Water Infrastructure: Leak and Corrosion Detection
In 2016, a municipal water district expressed interest in a technology that could help solve ongoing concerns about underground water leaks and infrastructure corrosion. QSI engineered a solution incorporating lightweight thermal and multispectral sensors mounted on a UAV operated by 5-D Robotics in a pilot program.
The plan was to simulate a leak by pouring a bucket of water near the pipeline and image it over the course of a few hours to show the thermal response of soil moisture. The UAV also flew over the rest of the site to build a SfM 3-D point cloud, identify signs of degradation and map leaks on the reservoir. Within 24 hours of data acquisition, not only was the simulated leak detected, but an actual leak was detected from an underground pipe 10 feet from the simulated leak (see Figure 3).
The survey also revealed water leaking on the surface of a reservoir cover, rust on pipes and tanks, and identified a cracked cap on a tank pressure release valve. One limited drone operation generated the exact information that is supposed to be identified in monthly manual inspections, yet had not been noted by the professionals.
Figure 3. Visible multispectral (left) and thermal imagery of active water leak collected from a UAV.
Forest Assessment: Species and Tree Health
Last year, QSI partnered with Davey Resource Group to classify individual tree types and health for a 2,500-acre area in the Louisville, Ky., metro area. Specifically they wanted to identify and assess ash trees because of damage caused by the emerald ash borer.
Individual tree crowns were separated from one another with automated tools using lidar point-based segmentation routines. At the same time, powerful machine-learning algorithms were used on co-acquired hyperspectral data to both classify and assess canopy stress at the pixel scale (see Figure 4). Typically it would take foot patrols months or years to take only a partial sampling of a survey area this size. However, within a matter of weeks, QSI was able to detect individual trees across the entire area, and classify the dominant tree types with an overall accuracy of 83 percent.
Figure 4. Lidar data from Louisville, Kentucky, colored by tree type (above) and health (below).
Beginning in 2014, a leading transportation company began continually collecting 3-D data along along its railways using lidar sensors attached to specially equipped geometry cars. Last year, QSI was tasked with rapidly analyzing the raw data to develop a baseline asset inventory of important infrastructure, including signage, signals, track locations, vegetation encroachment and road crossings. Following the initial inventory, data from serial acquisitions were then leveraged to monitor changes along the railway corridor.
Advanced machine learning algorithms were used in a parallel processing environment to rapidly ingest and classify the lidar point cloud for multiple time frames. Using the same cloud-based processing utilities, QSI provided automated difference reporting a few days after new point lidar data was collected. A web-based platform was then used to distribute and visualize the analytic results in an interactive 3-D environment (see Figure 5).
Most rail companies lack an accurate spatial inventory of assets given the cost of ground-based surveys or methods requiring manual interpretation of imagery. Machine learning, parallel processing and automated 3D change detection offer new ways to catalog and track assets in near real-time to address maintenance and safety along entire corridor networks.
Figure 5. Lidar point cloud viewer showing changes detected along a rail corridor between two years of acquisition flights.
Pipeline Monitoring: Integrity Analysis
On the North Slope of Alaska, above-ground pipeline supports are subject to settlement and heave due to the yearly freeze/thaw cycle, loss of permafrost, as well as water movement and other terrain failures. Routine inspections of pipelines are required to identify areas of stress that exceed established tolerances. However, limited access and rugged terrain make it difficult to do ground surveys and manual inspections.
Since 2014, QSI has conducted annual aerial patrols in Alaska utilizing high-density aerial lidar to map pipelines and support structures in detail. Precise pipeline elevation values at supports are automatically extracted and analyzed to find areas of stress and potential for failures. Recurring surveys monitor changes at specific structure over time, providing integrity managers powerful planning tools to identify risks before significant damage occurs (see Figure 6).
Figure 6. Lidar point cloud of pipeline pumping station near Prudhoe Bay, Alaska.
Conclusion
Innovations in remote sensing technology and platforms, such as UAVs and robots that can carry sensors, have coupled with cloud-based, high-performance computing environments to enable new applications for data collection and analysis. With these advancements, organizations of all types now can quickly access mission-critical, actionable information that enables them to protect critical infrastructure, ensure public safety and improve the reliability of their operations.
Will Fellers is product manager for Quantum Spatial Inc.Since 2006, Will has spearheaded the technical development of a comprehensive set of innovative products utilized across technical platforms at Quantum Spatial. He and his team are focused on state-of-the-art solutions for remote sensing applications using machine learning/artificial intelligence systems, advanced data analytics, high-performance cluster computing, immersive 3-D environments and cloud-based data distribution models.
Swift Navigation is teaming up with Carnegie Robotics LLC to develop a line of navigation products for autonomous vehicles, outdoor robotics and machine control. The first navigation product will be announced May 8 at the AUVSI XPONENTIAL event in Dallas, Texas.
Swift Navigation is a San Francisco-based startup building centimeter-accurate GPS technology for autonomous vehicles, and Carnegie Robotics LLC (CRL), the industry leader in reliable robotic components and systems.
Swift Navigation solutions use real-time kinematics (RTK) technology, providing location solutions that are 100 times more accurate than traditional GPS. In 2016, Swift shipped the Piksi Multi, a multi-band, multi-constellation high-precision GNSS receiver, suitable for autonomous vehicles.
The Piksi Multi.
The Piksi Multi offers advanced precision GNSS capabilities for the mass market. The robotics market, through this partnership with Carnegie Robotics, stands to benefit from Piksi Multi’s improved localization and control, the companies said.
Carnegie Robotics supplies rugged, reliable robotic systems for real-world work. The team at Carnegie Robotics has decades of experience successfully transitioning state-of-the-art technologies from early design into commercial use in precision agriculture, machine control, autonomous vehicles and industrial and military robots. This process requires both a deep knowledge of robotics and best-in-class engineering, but it cannot succeed without also addressing the business case, the needs of the end-user, reliability, maintenance, safety, certifications and the dozens of other essential factors necessary for a product to succeed in the real world.
“Swift’s technology is perfectly suited for the world of robotics, and we couldn’t do better than working with the renowned industry leaders at Carnegie Robotics,” said Timothy Harris, CEO of Swift Navigation. “From their robotics technology expertise to their inertial intellectual property, Carnegie is an ideal partner for Swift. We are looking forward to developing an exciting line of products and making more joint announcements in the near future.”
“Thanks to its focus on high-accuracy and low-cost, Swift Navigation has established itself as a leader and innovator in the world of high-precision GNSS,” said Steve DiAntonio, CEO of Carnegie Robotics. “Swift is an ideal partner to work with us on rapid development of robots and autonomous systems. We’re designing our joint line of products specifically for outdoor robots and autonomous vehicles with the appropriate physical, electrical and software interfaces to enable rapid deployment of precision GNSS and other mission-critical sensors.”
More information about the partnership and the unveiling of this duo’s first joint product will take place at AUVSI XPONENTIAL. Visit the joint Swift Navigation and Carnegie Robotics booth #506 at the Kay Bailey Hutchison Convention Center.
About the size of a postage stamp, VectorNav’s surface mount VN-100 is a temperature calibrated MEMS-based IMU/AHRS that includes 3-axis accelerometers, gyros and magnetometers. The module delivers to users a real-time 3D orientation solution that is continuous over the complete 360 degrees of motion at rates of up to 400 Hz. In addition to calibrated IMU and AHRS functionality, the VN-100 includes VectorNav’s Vector Processing Engine (VPE), a suite of proprietary sensor fusion algorithms running onboard the sensor that deliver real-time magnetic & acceleration disturbance rejection, adaptive signal filtering, dynamic filter tuning, and on-board Hard & Soft Iron compensation.
With the introduction of this mount, customers can now upgrade existing GPS L1-only antennas to dual (L1/L2) and triple (L1/L2/L5) band GNSS antennas.
