“Seen & Heard” is a monthly feature of GPS World magazine, traveling the world to capture interesting and unusual news stories involving the GNSS/PNT industry.
Photo: welcomia/ iStock / Getty Images Plus / Getty Images
COVID brings better measurements
A new study shows that the quality of GNSS reflectometry measurements may have improved significantly during the pandemic because of the lack of cars parked near the ground station. The study, carried out by geodesists from the University of Bonn, investigated the location of a precise GNSS antenna in Boston, Massachusetts, where parked cars near the ground station decreased accuracy from 2 to 4 centimeters. GNSS reflectometry is used for earthquake early warning systems, determining flood risks, and many other geodesy applications. Read more about the study.
Photo: Daniel Leeb/Iceland Space Agency
But can you press the right button?
A Riegl long-range terrestrial laser scanner helped field test the newly designed MS1 Mars Analog Spacesuit. The test simulated how the new spacesuit design would perform in a polar, Mars-like environment ‚ in this case atop the Grimsvötn Volcano on the Vatnajokull Glacier in Iceland. The field test was part of a research expedition conducted by the Iceland Space Agency. The team included NASA Coordinator and Rhode Island School of Design (RISD) Professor Michael Lye, who led the team that designed the MS1, and doctoral students from the University of Iceland.
In June, a fleet of 300 Chinese fishing vessels entered the waters of the Galápagos Islands, reports environmental news website Mongabay. The ships had ostensibly turned off their GNSS-based automatic identification system (AIS) transponders to engage in illegal activities. Their presence was detected by their overhead lights and industrial jigging machines to attract and catch squid. An analysis of radio-signal data also detected unidentified ships within Ecuador’s Galápagos exclusive economic zone (EEZ).vThe new data provide additional, but still inconclusive, evidence that the Chinese fleet may have entered Ecuador’s EEZ.
A pilot program in Shenzhen will use China’s BeiDou to regulate bike-sharing and address the problem of bikes parked chaotically or in unpermitted areas, according to Chinese news service Caixin. The program is part of Beijing’s push for wider adoption and commercialization of BeiDou. With guidance from the city’s transportation bureau, BeiDou modules on shared bikes will display parking spots. Users will have to park them within geofenced parking spots.
BroadGNSS will procure innovative solutions for applications, synchronization and monitoring of critical mobile broadband communication infrastructure and information assets for public protection and disaster recovery (PPDR) Operations.
The project brings together three members of a joint procurement team:
French Ministry of Interior (lead procurer)
Estonian Infocommunication Foundation
Finnish Erillisverkot
The team will be supported by PSCE and Austrian GNSS company OHB Digital.
The agreement provides for a total budget of €3.6 million over 40 months, beginning this Dec. 1. Of that, €2.5 million is reserved for pre-commercial procurement of innovation solutions. This budget will be made available to industry following an open and competitive process.
BroadGNSS will seek innovative solutions to apply European GNSS to further improve the overall capability of trustworthy information exchange, enabled by new broadband mobile communication.
BroadGNSS will consult key government stakeholders across Europe to scope the specific objectives of the PCP procurement. A “prior information notice” will be released to initiate consultation with innovative industry to contribute their knowledge and capabilities to help our team to prepare a formal request for tender to be released later next year.
BroadGNSS will build upon the success of the BroadWay PCP, which is currently in the prototyping phase. Three innovative prototypes are now in development to address the challenge to enable a pan-European mobile broadband system for PPDR. Prototypes will be evaluated in April 2021, with interim demonstrations to the BroadWay group of procurers this month (November). These prototypes are developed under the leadership of key industry heavyweights, Airbus, Frequentis and Leonardo.
The combination of the results of BroadWay and BroadGNSS will contribute to provide improved mission critical services to public safety responders.
Which recent GNSS/INS innovations have been most helpful in advancing bathymetry? Which upcoming ones will be?
Miguel Amor
“Development of PPP removed reliance on shore-based RTK base stations, allowing operation almost anywhere on the oceans. Continued performance improvement in FOG and MEMS INS, along with bathymetric sensors, provide cost-effective solutions while also providing more accurate seabed maps. The future will see increased PPP accuracy with faster convergence and continued improvement in INS, coupled with increased resolution of bathymetric sensors, leading to more of the oceans mapped using autonomous platforms.” Miguel Amor, Hexagon Positioning
Bernard Gruber
“While GNSS has been a clear contributor to Earth mapping, it is an altogether different dilemma to solve ‘submarine topography’ mapping. Given recent developments in the IMU and lidar markets, one can readily utilize these sensors to correct for roll, pitch, and yaw, and produce digital maps, respectively. Combining these sensors with GNSS receivers, mounted on a drone for example, can allow for precise measurements in areas of tidal shifts or dynamic variations of water depth.” Bernard Gruber, Northrop Grumman
By Karsten Mueller, Jamal Atman, Nikolai Kronenwett and Gert F. Trommer
Innovation Insights with Richard Langley
IT DOESN’T WORK EVERYWHERE. GPS, that is. Unlike many radio broadcasts and the transmissions from nearby cell-phone towers, the signals from GPS satellites are too weak to be reliably received indoors. They don’t make it into tunnels either. And even outdoors, the signals can be blocked by tall buildings and mountains. This is why the Japanese developed the Quasi-Zenith Satellite System — to provide supplementary signals when an insufficient number of GPS signals are available in the concrete canyons of Tokyo and other high-density cities. Even if a GPS signal can be received, it might be contaminated with multipath interference resulting in a degraded position solution.
While GPS signals can be piped indoors from an antenna on the top of a building and reradiated, a GPS receiver will give its position as that of the rooftop antenna and not where it is in the building. While this might be useful for establishing the approximate whereabouts of the receiver when it’s on a bus in an underground terminal, for example, and allows the receiver to continue to receive up-to-date navigation messages providing a quick time-to-first-fix when it leaves the terminal, it’s far from satisfactory as a general indoor navigation solution.
While there are some improvements in signal reception in degraded environments with modernized signals from GPS and the other GNSS constellations, in many instances where we don’t have an unobstructed line-of-sight view of the satellites, GPS alone won’t cut it. Thankfully, other navigation sensors can be used to supplement or replace GNSS when the going gets tough for GPS alone. These include, among others, inertial measurement units, digital compasses, barometric pressure sensors, cameras and laser rangefinders.
But, even with these, is one better than another in all situations, or do they each have benefits and drawbacks just like GNSS? Would a system composed of multiple sensors be best? Such considerations are important if trying to develop a navigation system that can work well in most any environment both outdoors and indoors and transition gracefully when moving from one type of environment to another. This is the problem that confronted a team of researchers from Germany’s Karlsruhe Institute of Technology when designing a navigation system to allow a micro aerial vehicle to operate continuously and autonomously in almost any environment. In this issue’s “Innovation” column, we learn how they went about it and how well the system worked.
Today, micro aerial vehicles (MAVs) are widely used in outdoor environments. The navigation solution of commercially available products typically relies on the availability and accuracy of GNSS. To expand the field of application of MAVs to autonomous operation in indoor environments, an accurate navigation solution is necessary. Possible scenarios include the support of rescue forces, surveillance tasks and inspection missions. Different algorithms using camera or laser rangefinder measurements for indoor navigation can provide accurate results.
However, application of these algorithms is typically limited to indoor scenarios and will not provide accurate results in outdoor environments. Another drawback of these approaches is that absolute positioning is not achieved. Hence, we sought a navigation system for outdoor and indoor environments that combines the beneficial properties of outdoor and indoor navigation systems. Such a navigation system should provide an accurate navigation solution both outdoors and indoors, as well as during transition phases from outdoor to indoor and vice versa.
THE PROBLEM
Several challenges arise when combining multiple sensors in a single navigation system due to specific sensor characteristics. While an accurate navigation solution is obtained by an inertial navigation system with GNSS aiding in open-sky environments, urban canyons and indoor environments degrade the quality of GNSS signals or lead to GNSS outages such that no accurate navigation solution is available.
On the other hand, laser rangefinder measurements allow for the generation of accurate relative measurements indoors. However, due to the limited range of the laser rangefinder, no or only a few measurements are available outdoors away from buildings. Obviously, it is best to exploit the complementary characteristics of both sensors. To avoid losing information, hard switching between two different navigation systems is undesirable. Hence, two main challenges arise:
Accurate time synchronization is necessary when processing measurements from different sensors.
A method has to be developed for the decision on whether a measurement should be processed or rejected.
Moreover, for aerial vehicles, two more requirements must be met:
Estimation of the 3D position and attitude instead of only the 2D position and heading as provided by 2D simultaneous localization and mapping (SLAM) approaches.
Estimation of the vehicle’s velocity and inertial measurement unit (IMU) biases.
Our goal was to develop a navigation system that provides an accurate navigation solution for large-scale environments. The navigation system needed to provide a frequent navigation solution at the update rate of the IMU with very short delays. The framework needed to seamlessly integrate GNSS and other sensors such as a laser rangefinder or cameras. Additionally, the approach could not be limited to a specific sensor setup except for a mandatory GPS receiver, necessary for absolute positioning.
The results presented in the literature often do not include large-scale, realistic environments. Some investigators only consider short indoor sequences, while others ignore challenging GNSS conditions. In contrast, the focus of our approach is on rejecting outlier measurements in transition zones such as urban-canyon environments occurring between outdoor open sky and indoor environments. The choice of the navigation system architecture depends on the requirements of a specific platform. In the case of a quadrotor helicopter (see FIGURE 1), a high update rate is necessary for vehicle guidance and control. Therefore, we chose a Kalman-filter-based approach because it has the advantage over pure SLAM approaches when providing a navigation solution at a high update rate is required.
FIGURE 1. Components of the quadrotor helicopter. (Photo: K. Mueller, J. Atman, N. Kronenwett & G.F. Trommer)
SYSTEM OVERVIEW
We attached several sensors and two processing platforms to the quadrotor helicopter used in our work. A microcontroller sensor board reads the sensor values from the IMU, digital compass, air pressure sensor and a GPS-only GNSS module. Timestamps are generated for each sensor data type so that accurate synchronization is provided even when delays occur, such as when processing the sensor data. The IMU is mounted close to the center of the vehicle. The air pressure sensor is directly attached to the sensor board, while the three-axis digital compass is attached to the quadrotor’s landing skid to avoid interfering magnetic fields from power electronics. The GPS receiver provides pseudorange and Doppler measurements at a rate of 10 Hz. Moreover, ephemeris data for each satellite and Klobuchar ionospheric parameters are recorded to correct the measurements. Each second, a time pulse is generated by the receiver to precisely determine the time when GPS measurements were taken. Additionally, the time pulse is used to estimate the drift of the real-time clock (RTC) on the sensor board and, therefore, to provide more accurate timestamps.
A two-dimensional laser rangefinder is mounted on top of the helicopter. Distance and angular information of objects within a scan angle of 270° is provided by this sensor. The maximum range is 30 meters. Time synchronization is achieved through a pulse registered by the microcontroller sensor board before every scan. The body of the laser rangefinder is shielded using copper foil to reduce interference with signals received by the GPS antenna. A trigger signal is sent to the camera mounted at the front of the helicopter to provide time synchronization. However, the camera was not used for the results presented in this article. An overview of the sensor setup and time synchronization is depicted in FIGURE 2.
The camera and laser rangefinder data is sent via USB to a powerful computing platform attached to the bottom carbon-fiber sheet. Time synchronization information and additional sensor data is sent from the microcontroller sensor board to the computer for processing the sensor data and calculating the navigation solution.
FIGURE 2. Block diagram showing signal flows among system hardware components. (Photo: K. Mueller, J. Atman, N. Kronenwett & G.F. Trommer)
NAVIGATION SYSTEM
The navigation system presented in this article was developed to provide a navigation solution in both outdoor and indoor environments. Therefore, processing GPS position and velocity estimations must be possible, as well as handling of relative position and heading angle changes resulting from the laser rangefinder scans. Challenges arise due to the different time delays as illustrated in FIGURE 3. IMU measurements are available at a high frequency. Messages with the trigger timestamps are sent from the sensor board to the computer to provide information about when a GPS or laser measurement was taken.
FIGURE 3 Time sequencing of measurements and calculations. (Photo: K. Mueller, J. Atman, N. Kronenwett & G.F. Trommer)
The corresponding measurements are available with significant delays. Since GPS pseudorange and Doppler measurements are not immediately available and processing requires additional time, the typical delay between the point in time when the measurement was taken by the receiver and the time when the estimated position and velocity are available to the navigation filter is between 70 and 90 milliseconds. Even longer delays occur when processing laser rangefinder data. After processing the laser scans, the horizontal position changes and yaw angle changes (in this article, denoted as two-dimensional pose change measurements) are available for analysis. However, these changes are relative to a point in time in the past. Moreover, due to the processing, additional delay occurs and synchronization with the correct laser rangefinder trigger signal is required. The requirement to process measurements with a temporal overlap causes additional complexity, such as having several GPS measurements that are taken in the time period covered by a pose change measurement.
Error-State Kalman Filter with Stochastic Cloning. An error-state Kalman filter with 16 states estimates the vehicle’s 3D position, 3D velocity, attitude, accelerometer and gyroscope biases, and the bias for the barometric altimeter. The prediction step of the filter consists of integrating the specific force and angular rate measurements of the IMU. Measurements of the air pressure sensor and the digital compass have negligible delays, so these measurements are processed in the Kalman filter update step without compensating for delays. As we mentioned, the assumption of insignificant delays does not hold for GPS measurements and pose change measurements. Thus, we implemented stochastic cloning to overcome errors that would be introduced by delays. The idea of stochastic cloning is to augment the state vector and covariance matrix by copies of the state and covariance estimates at a specific point in time. As the augmented covariance matrix contains cross-correlation terms between the state at a previous time instance and the current state, processing of delayed measurements corrects the current state and covariance estimations.
Processing GPS Measurements. The first step when processing GPS measurements is to clone the current filter state. As outlined in the section “System Overview,” the time pulse generated by the receiver is used to determine the time when a measurement is taken. Once the pseudorange measurements are available, corrections are calculated. A weighted least-squares estimation is used to calculate position and velocity. The weight for each pseudorange measurement is the inverse of the estimated variance, which is calculated depending on the carrier-to-noise-density ratio. Weights for Doppler measurements are calculated similarly.
To reduce the errors introduced by satellite signals of low quality, a minimum carrier-to-noise-density ratio of 33 dB-Hz and a minimum elevation angle of 15° are required for the satellite signals. In addition to position and velocity, valuable information is drawn from the estimation: The variance of the calculated position is chosen to be proportional to the weighted root mean square value of the residuals and the position dilution of precision (PDOP). The velocity variance is calculated similarly. In case only four satellites are used, the variance is only proportional to the PDOP as no residuals are available. The position and velocity estimates are processed by the Kalman filter using these estimated variances. Moreover, before the filter update step is executed, the Mahalanobis distance for each measurement is calculated and outliers removed.
Additionally, measurements are not processed if their variance is above a threshold. This typically occurs in the vicinity of buildings as non-line-of-sight signals are tracked by the receiver and, therefore, processing these measurements is not desired.
Laser Rangefinder Processing. As described in the previous section, stochastic cloning is used to treat delayed pose change measurements. To process a measurement, two cloned states are necessary.
A pose change measurement consists of a relative translation and a rotation, both given in coordinates of the body-stabilized frame, which is identical to the body frame but compensated for roll and pitch angles. Hence, the x and y axes of the body-stabilized frame are parallel to the ground. Several methods could be used for generating pose-change measurements, such as camera-based approaches, laser rangefinder approaches or hybrid approaches. In our work, Cartographer, a laser SLAM approach, is used to obtain horizontal position and yaw angle changes. However, the SLAM module could be easily replaced by other laser SLAM approaches.
As laser SLAM approaches build an incremental map, the laser’s pose is given with respect to the map frame. Therefore, the translational and rotational components of the pose-change measurement must be transformed from the map frame to the body-stabilized frame before being processed by the Kalman filter. Different options are possible when choosing the first point in time for a relative measurement (the second point in time is determined by the most recent laser measurement).
We decided to use a keyframe-based aiding technique. A keyframe is defined and the filter state is cloned accordingly. After the processing of a laser measurement by the SLAM algorithm, pose estimations given in map coordinates are transformed to pose change measurements relative to this keyframe. The keyframe is changed depending on the filter status information as outlined in the section “Using the Filter Status Information” of this article. Additionally, the keyframe is changed if the difference between consecutive pose estimations exceeds a threshold. This indicates an erroneous pose estimation by the SLAM module as only small pose changes are expected due to the high update rate of laser scans and the limited velocity of the vehicle. As a result, the influence of errors in the SLAM module on the navigation solution provided by the Kalman filter is reduced.
FILTER STATUS
Above, we described how relative and absolute delayed measurements are processed in an error-state Kalman filter. However, simply processing all available measurements will not lead to the best performance of the filter. For example, the laser SLAM algorithm might not provide accurate and reliable results in open-sky environments free from human-made structures, as mainly vegetation is detected by the laser rangefinder.
To derive a metric for the decision on the necessity of integrating additional relative measurements, we provide a classification scheme based on GPS measurements. The advantage of using only GPS measurements for the filter status determination is the versatility of the approach: A GPS module will be available on almost every platform. The laser rangefinder, however, could be replaced by a camera without modifications in the classification scheme.
Clearly, processing GPS in indoor environments is not an option as no measurements are available. On the contrary, in outdoor open-sky environments, a sensor setup comprising GPS, IMU, digital compass and air pressure sensor results in an accurate navigation solution. Therefore, the interaction of different sensors in transition phases and urban-canyon environments is the most critical part for an accurate navigation solution in large-scale environments. The following paragraphs introduce the classification of single GPS position measurements and the determination of filter status based on the GPS classification.
Classification of Single GPS Position Measurements. The first step for the filter status determination is the classification of single GPS position measurements. The categories for a measurement are very good, good, medium and poor. Two parameters are used for the classification: the number of satellites used for the position calculation and the estimated variance. For a very good measurement, at least six satellites are required; for a good measurement, at least five satellites are necessary. Moreover, thresholds for the estimated position variance are applied. As the variance is proportional to the PDOP and the root mean square of the weighted residuals, this means that a very good or good position measurement must offer a good satellite constellation and small residuals.
Filter Status Determination. The classification of GPS position measurements is used to calculate a filter status. First, a sum over a time interval of one second is computed. The number of positions classified as very good are multiplied by a factor of four, good positions count twice, and the number of medium positions added without a multiplicative factor. In our setup, 10 position measurements are available in one second. The final filter status is determined using two thresholds. If the sum is at least 20, the filter status is “Good GPS.” This means that five measurements classified as being very good or all 10 measurements classified as being good would be sufficient for this status.
The “Medium GPS” status is achieved with a sum between 10 and 20. If no valid GPS measurements have been available over the last five seconds, an additional indoor flag is set, and it is assumed that the vehicle is now indoors. As soon as GPS position measurements become available again, the filter status is re-calculated. The parameters for the filter status are determined empirically and provide robust results for a large variety of scenarios. However, minor changes of the parameter set to classify single measurements might be necessary in case a different GNSS hardware setup is used.
The resulting filter status for an example trajectory is shown in FIGURE 4. As expected, GPS is good in the western part of the trajectory, and the status quickly deteriorates to poor GPS between the high-rise buildings. Just before entering the building, the status changes to “Indoor.” After leaving the building and moving north, the filter status changes mainly between good and medium GPS as signals are blocked due to buildings or mitigated due to foliage. The end of the trajectory in the eastern part offers better GPS conditions since the surrounding buildings are smaller and the status changes to “Good GPS.”
FIGURE 4. The filter status changes from “Good GPS” to “Poor GPS” in the vicinity of high buildings and provides important information on how accurately the filter is aided by processing GPS measurements. (Photo: K. Mueller, J. Atman, N. Kronenwett & G.F. Trommer)
Using the Filter Status Information. The filter status provides valuable information when combining GPS and relative measurements. As outlined in previous sections, the filter status “Good GPS” occurs in open-sky environments where processing of additional relative measurements does not improve the navigation solution. Since the laser SLAM solution might be corrupted in areas without a sufficient number of human-made structures, relative measurements are not processed while the filter status is “Good GPS.” Additionally, the keyframe is changed in short time intervals during this status. The reasoning behind this decision is that it is desired to have a good estimation of the absolute position and orientation with a low uncertainty at the time a keyframe is chosen.
During a period with “Good GPS” conditions, position estimation typically becomes gradually better. For the same reason, it is best to retain a keyframe for a long time when the filter status is “Poor GPS” or “Indoor.” In these scenarios the laser SLAM algorithm provides accurate results as the environment mostly consists of human-made structures. A drawback inside buildings is that the Earth’s magnetic field might become distorted, for example close to elevators. Hence, magnetometer measurements are not processed when the “Indoor” flag is set. If the status “Medium GPS” is set, GPS and relative measurements should be weighted equally. The keyframe is retained until a predefined maximum age is reached or inconsistencies in the SLAM solution are detected.
In contrast to the “Poor GPS” case, the integration of relative measurements is more pessimistic, and the variance is chosen in the range of the typical GPS accuracy. This takes into account that a very accurate laser SLAM solution is not assured. However, the processing of relative measurements improves position accuracy and avoids the growth of filter state covariance, which is beneficial for rejecting faulty measurements. Independent of the filter status, GPS measurements fulfilling the Mahalanobis distance threshold criterion are processed.
RESULTS
The results of three trajectories recorded at the campus of the Karlsruhe Institute of Technology are presented in this section. All trajectories cover outdoor environments with good GPS signal reception as well as urban-canyon and indoor sections. Since flying these challenging trajectories was not possible due to legal reasons and due to small doors that had to be passed through, the quadrotor helicopter was manually carried.
The first trajectory shown in FIGURE 5 starts in an open-sky environment. At position 1, the footpath goes between two 40-meter buildings. Hence, GPS satellite signals are blocked and non-line-of-sight signals are tracked by the receiver that increasingly deteriorate GPS positon and velocity accuracy. The indoor section starts at position 2. After 30 seconds of indoor navigation, the trajectory continues north on the sidewalk. On this section, numbered 4 in Figure 5, a six-story building on the left side and a nearby building on the right side cause medium to poor GPS conditions as was shown in Figure 4. Despite the difficult conditions, the trajectory follows the footpath correctly. Of course, as no GPS correction service or satellite-based augmentation system is used, sub-meter level accuracy is not achieved. At position 2, the trajectory passes along stairs.
FIGURE 5. Trajectory 1 featuring two high buildings of 42-meter height between positions 1 and 2 in the center of the image. After an indoor section the building is left at position 3. The total time of the trajectory is 394 seconds. (Photo: K. Mueller, J. Atman, N. Kronenwett & G.F. Trommer)
Therefore, accuracy in the north direction is very good. In the east direction, however, the error is larger as the trajectory should be farther east within the building. This error remains throughout the indoor section until position 3, as no GPS position measurement is processed to correct for the error. After leaving the building, the error in the east direction becomes smaller by processing accurate GPS position measurements. After heading north on the sidewalk, the error is within the expected accuracy bounds specified by the GPS position accuracy. The smoothness of the trajectory after leaving the building shows that the rejection of GPS position outliers leads to a consistent navigation solution.
The second trajectory is the longest of the three trajectories, covering 400 meters in 9 minutes. The first difficult section is denoted by position 1 in FIGURE 6, when the vehicle moves between two buildings. The walls of the right building are covered by metal plates. It looks like the trajectory is very close to the edge of the right building. However, this effect is from the perspective view of the building in the georeferenced image. We passed below a canopy at position 2 and entered a building at position 3. An accurate position solution is available during the long indoor section with multiple turns. The total time spent indoors was 112 seconds. GPS position measurements becoming available after leaving the building at position 4 improve the accuracy of the navigation solution. However, due to the high accuracy of the position estimation before leaving the building, only small filter innovations occur. The trajectory ends on the sidewalk near the building identified as number 5.
FIGURE 6. Trajectory 2 with a total duration of 9 minutes. An accurate position estimation is obtained during the segment with poor GPS signal reception between positions 1 and 2 and during the indoor section between positions 3 and 4. (Photo: K. Mueller, J. Atman, N. Kronenwett & G.F. Trommer)
Trajectory three, shown in FIGURE 7, is the most challenging, with position errors exceeding those of the previous two trajectories. Already at the start of the trajectory, only six GPS satellites can be used for calculating position and velocity estimates. It is several meters until an accurate position estimate is available at position 1. Between positions 2 and 3, a section with buildings up to 56 meters tall results in no accurate GPS position fixes being available for more than 30 seconds. In this section, the computed trajectory clearly is several meters too far north. Additionally, at position 2 the heading change is smaller than 90 degrees, which results in additional drift. Before entering the building at position 3, GPS position measurements become available and the position is corrected, reducing the error in the north. After 57 seconds indoors, we exited the building at position 4. The position solution is still too far north, but is corrected by additional measurements so that good accuracy is achieved when walking on the sidewalk. The trajectory ends at its start position.
FIGURE 7. Trajectory 3. Poor GPS conditions due to a building of 56-meter height near the north part of the trajectory cause position errors. At position 3 accurate GPS measurements are available and correct the position such that an accurate navigation solution is obtained during the indoor part part of the trajectory. (Photo: K. Mueller, J. Atman, N. Kronenwett & G.F. Trommer)
CONCLUSION
The navigation system presented in this article fuses GPS measurements and relative pose change measurements to provide an accurate navigation solution in both outdoor and indoor scenarios. We show that position errors are small even for challenging scenarios with high buildings and poor GPS signal reception. Currently, the accuracy in outdoor environments is limited by GPS accuracy. Further improvements are expected by including additional GNSS such as GLONASS or Galileo to obtain better satellite geometry, especially in urban-canyon scenarios.
MANUFACTURERS
We used a u-blox LEA-M8T GPS receiver, an Analog Devices ADIS 16448 IMU, a Freescale (now, NXP Semiconductors) MP3H6115A air pressure sensor, a Honeywell HMC5843 digital compass, an Hokuyo UTM-30LX laser rangefinder, an IDS UI-3260CP-C-HQ camera, and an Intel Next Unit of Computing (NUC) platform. We constructed the quadrotor helicopter ourselves. The motors, motor controllers and landing skid are from MikroKopter, while the carbon fiber sheets and the sensor board PCB are our own design. We used a Pixhawk 4 flight controller from Pixhawk.
ACKNOWLEDGMENTS
The authors acknowledge financial support from the Federal Ministry of Transport and Digital Infrastructure of Germany in the framework of mFUND. We also thank the City of Karlsruhe for providing the georeferenced orthophotos. The datasets used for the results presented in this article are available on our project website. This article is based on the paper “A Multi-Sensor Navigation System for Outdoor and Indoor Environments” presented at ION ITM 2020, the 2020 International Technical Meeting of The Institute of Navigation, San Diego, California, Jan. 21–25, 2020.
KARSTEN MUELLER received an M.Sc. from the Karlsruhe Institute of Technology (KIT), Germany, in 2015, after which he started research as a Ph.D. candidate in KIT’s Institute of Systems Optimization.
JAMAL ATMAN received an M.Sc. in electrical engineering and information technology from KIT in 2015. He is a research engineer in KIT’s Institute of Systems Optimization.
NIKOLAI KRONENWETT received an M.Sc. degree in electrical engineering and information technology from KIT in 2015. He is a Ph.D. candidate in KIT’s Institute of Systems Optimization.
GERT F. TROMMER received Dipl.-Ing. and Dr.-Ing. degrees in electrical engineering from the Technical University of Munich, Germany. He is a professor in KIT’s Institute of Systems Optimization.
FURTHER READING
Authors’ Conference Paper
“A Multi-Sensor Navigation System for Outdoor and Indoor Environments” by K. Mueller, J. Atman, N. Kronenwett and G.F. Trommer in Proceedings of ITM 2020, the 2020 International Technical Meeting of The Institute of Navigation, San Diego, California, Jan. 21–24, 2020, pp. 612–625. https://doi.org/10.33012/2020.17165.
“Laser Range Finder Aided Indoor Navigation for a Micro Aerial Vehicle” by P. Crocoll, J. Seibold, M. Popp and G.F. Trommer in European Journal of Navigation, Vol. 11, No. 1, pp. 4–14, 2013.
Keyframe-Based Navigation
“Relative Navigation: A Keyframe-Based Approach for Observable GPS-Degraded Navigation” by D.O. Wheeler, D.P. Koch, J.S. Jackson, T.W. McLain and R.W. Beard in IEEE Control Systems Magazine, Vol. 38, No. 4, 2018, pp. 30–48. https://doi.org/10.1109/MCS.2018.2830079.
Integrated Navigation
“3D Multi-Copter Navigation and Mapping Using GPS, Inertial, and LiDAR” by E.T. Dill and M. Uijt de Haag in NAVIGATION: Journal of The Institute of Navigation, Vol. 63, No. 2, Summer 2016, pp. 205–220. https://doi.org/10.1002/navi.134.
Principles of GNSS, Inertial, and Multisensor Integrated Navigation Systems, 2nd edition, by P.D. Groves. Published by Artech House, Boston, Massachusetts, 2013.
Stochastic Cloning
“Stochastic Cloning: A Generalized Framework for Processing Relative State Measurements” by S.I. Roumeliotis and J. W. Burdick in Proceedings of 2002 IEEE International Conference on Robotics and Automation, Washington, DC, May 11–15, 2002, pp. 1788–1795. https://doi.org/10.1109/ROBOT.2002.1014801.
New service provides PPP convergence for centimeter-level accuracy on land, air and marine applications around the world
Research from Hexagon’s Autonomy & Positioning division has resulted in breakthrough innovations in precise point positioning (PPP) that enable nearly instant global centimeter-level accuracy. These developments pave the way to bring “RTK from the Sky” performance to worldwide users through correction service products and GNSS receivers from Hexagon.
RTK from the Sky technology provides the quick accuracy of an RTK solution with the high accessibility and availability of PPP. Users will no longer have geographic or regional infrastructure restrictions — they will be free to operate anywhere around the world with the same premium level of positioning performance.
RTK from the Sky technology removes the traditional PPP barrier of long convergence times as well as internet and radio communication limitations, delivering instantaneous convergence anywhere in the world. This breakthrough establishes the foundation for assured positioning with no downtime in marine, agriculture, and autonomous applications.
To achieve these results, there must be masterful attention to detail throughout the entire positioning ecosystem: no errors conveniently cancelled and no errors ignored. All errors are carefully estimated and removed from the final GNSS position faster and more reliably than ever before.
This end-to-end fine-tuning of measurement quality and error mitigation establishes the foundation for RTK from the Sky performance. No matter the location or application, users will be able to rely upon the highest availability and accuracy of corrections anywhere in the world, without the convergence time, Hexagon said.
“In 2020, PPP has become RTK — without the mobility limitations,” said Sandy Kennedy, VP of Innovation at Hexagon’s Autonomy & Positioning division. “RTK from the Sky has been a very satisfying development. To see this kind of positioning performance available anywhere in the world is the realization of the next step of innovation for GNSS.”
RTK from the Sky technology will be the foundation for future correction service products and applications from Hexagon built for diverse applications.
Flexible, resilient military PNT designed for every military environment
Orolia, through its Orolia Defense & Security business, has announced the availability of M-code military GPS receivers in its resilient PNT products and solutions, including M-code-enabled mobile mission timing and synchronization platforms.
M-code capabilities further enhance Orolia’s Versa mobile PNT platform for rugged, small SWaP-C requirements and Orolia’s flagship SecureSync resilient time and frequency reference solution — the first Defense Information Systems Agency (DISA) approved time server.
M-code is a military signal used in the L1 and L2 GPS bands and is required by congressional mandate for U.S. Department of Defense (DoD) military operations. It is designed to enhance positioning, navigation and timing (PNT) capabilities and improved resistance to existing and emerging GPS threats, such as jamming and spoofing.
M-code offers several operational benefits, including a higher power signal with improved resistance to jamming and interference; advanced security features to prevent unauthorized access or exploitation; and improved message formats and signal modulation techniques for faster and more accurate performance.
“As threats against GPS increase, military forces will need M-code capabilities on mobile PNT systems to ensure continuous operations wherever they go,” said Hironori Sasaki, president of Orolia Defense & Security. “Orolia is proud to continue to support Department of Defense initiatives to ensure that warfighters have the most secure, reliable and accurate positioning, timing and synchronization solutions in any environment.”
From resilient PNT solutions to GPS/GNSS simulation, interference detection and mitigation, Orolia provides end-to-end NAVWAR and resilient PNT solutions to protect, augment and strengthen military systems for GPS-denied environments.
“In some specific cases, e.g., for critical infrastructures and applications requiring both continuous availability and fail-safe operations, GNSS cannot be the sole means of positioning and timing information.” European Radionavigation Plan, 2018
The Joint Research Center in Ispra, Italy, is the preferred demonstration site. (Photo: European Commission)
The European Commission is undertaking a GNSS backup technology demonstration, much like the one completed by the U.S. Department of Transportation earlier this year. Companies from many countries outside the European Union, including the United States, are eligible to participate. Responses are due by Jan. 13, 2021.
A tender issued on Oct. 26 says that the goal is for the commission to better understand available non-GNSS PNT technologies. Also, they are interested in services that can provide positioning and navigation, and/or time.
Completely Independent from GNSS
Since the intent is to provide a backup for GNSS during an outage, all offered technologies must be completely independent. Specifically, they must have “no common points of failure with GNSS.”
Some industry observers have opined that this eliminates any space-based capabilities from consideration. Coronal mass ejections from the sun have long been considered a threat to satellites. Others have wondered if networked-based solutions could be also excluded because of frequent use of GNSS for synchronization, billing and other applications.
Another requirement is that offered technologies be capable of covering the entire EU territory, including inland waters. While this might seem to rule out fiber-based timing systems, advocates say that is not necessarily the case. They contend a fiber network supporting dispersed transmitters would serve both fixed and mobile applications, and reach users for whom connecting to a fiber node is not feasible.
Other requirements listed in the tender for offered technologies include:
Resilience to GNSS jamming, spoofing, and unintentional interference
Technical readiness levels of 5 or more for positioning and navigation, 6 or more for timing
Able to perform for at least a day during a loss of GNSS
Positioning accuracy < 100 m horizontal, or timing accuracy < 1 microsecond relative to UTC
If timing is included, it must be traceable to UTC
The Demonstration
A webinar for potential offerors was held on Nov. 4. Although it was not recorded, the slides shown are available at the RNT Foundation website. One update to the slides is a new email replacing the one of the first slide. All inquiries should be sent to the project leader at [email protected].
Up to seven companies, presumably each demonstrating different technologies, will be accepted into the program.
The preferred demonstration site is the European Commission’s Joint Research Center in Ispra, Italy. Recognizing that transporting equipment and traveling to Italy might be a challenge for many companies, the tender states’ commission personnel are willing to travel to other locations to see systems demonstrated.
The JRC Ispra campus covers 170 hectares with 100 buildings and 36 km of roads. It provides state-of-the-art laboratories, smart city infrastructure (grids, homes, mobility), and varied topography with urban, semi-urban, rural and woodland areas. (Image: EC)
Information on All Technologies Sought
Unlike the European Space Agency’s Navigation Innovation and Support Programme (NAVISP), companies from outside of the EU are invited to respond to the tender and could be selected. This reflects the commission’s desire to include as many technologies and collect as much information as possible.
Limited funding for the demonstration, pandemic travel restrictions, the need for infrastructure to support wide-area signals, and other obstacles may prevent some companies from participating in this effort. The commission’s overall goal, though, is to get information about as many technology options as possible.
So, while not stated in the tender, the commission is eager to hear from technology companies, even if they do not want to be considered as a part of demonstration project. All are invited to contact project leader Ignacio Alcantrailla-Medina. All information is welcome, though most important are a technology’s performance, technical readiness level (TRL), and if it can be deployed in the European Union.
We understand that, as is the case in the United States, solutions delivering timing are of particular interest.
Combining the data from the demonstration project with other information gathered, the commission hopes to be able to identify a way forward with alternative PNT in Europe by the end of 2021.
M10 receiver platform can track four GNSS constellations, even in challenging environments
Photo: u-blox
U-blox’s new M10 GNSS platform is designed for ultra-low-power high-performance positioning applications such as sport watches and asset trackers.
The M10 positioning platform can track up to four GNSS constellations at once to deliver positioning data even in challenging environments such as deep urban canyons. The receiver’s Super-S technology helps distinguish positioning signals from background noise to capture positioning data even when satellite signals are weak.
Its high RF sensitivity also enables it to work well with small antennas, making it suitable for compact product designs. In sport watches, for instance, u-blox M10 guarantees highly dynamic positioning accuracy during a run in cities, woods or under an open sky, while preserving battery life.
Low power consumption. The u-blox M10 is designed to consume 12mW in continuous tracking mode, five times less than the power consumed by previous u-blox meter-level GNSS technology, making it beneficial for battery-powered applications.
U-blox M10’s enhanced RF sensitivity also cuts the time it takes for the platform to achieve a first position fix when initialized, further reducing systemic power consumption. And switching to the improved Super-E mode can extend battery life even more.
This new GNSS platform will be supported by AssistNow, u-blox’s assisted GNSS service, to accelerate positioning and improve accuracy. Depending on the required level of assistance, the service is available free of charge or for a recurring fee.
Jamming detection. The u-blox M10 platform benefits from u-blox’s experience in building robust GNSS receivers, incorporating proven techniques for detecting spoofed signals through the analysis of raw GNSS data, jamming-detection strategies, and embedded filters to mitigate the effects of in-band RF interference.
“U-blox can be proud of over 20 years of experience with GNSS technology, and with u-blox M10 we are setting a new benchmark in ultra-low power high performance positioning applications,” said Bernd Heidtmann, product manager, Product Center Positioning, u-blox. “We have increased concurrent reception of satellite signals by a GNSS platform from three to four constellations and improved the power consumption level five-fold compared to previous generations while shrinking the chip size by 35 percent.”
UPDATE: The U.S. Space Force, Space and Missile Systems Center (SMC) and its mission partners successfully launched the fourth GPS III satellite at 6:24 p.m. EST Nov. 5 from Space Launch Complex 40 at Cape Canaveral Air Force Station, Florida.
The Lockheed Martin-built satellite was carried to orbit aboard a Space Exploration Technologies Corporation (SpaceX) Falcon 9 launch vehicle.
“The launch of GPS III SV04 is a testament to SMC’s ability to rapidly and safely deliver new capabilities on orbit,” said Cordell DeLaPena, Air Force program executive officer for SMC’s Space Production Corps. “At SMC, we are proud to deliver our fourth GPS III satellite and will continue to operate at an accelerated pace to enhance the capabilities of the billions of users worldwide.”
“I’m proud of my team’s 83rd successful National Security Space Launch and look forward to our future missions with SpaceX,” said Col. Robert Bongiovi, SMC’s Launch Enterprise director. “Ultimately, our ability to embrace innovation with our launch providers advances warfighter’s capabilities while lowering costs to the U.S. Government and its taxpayers.”
GPS III SV04 separated from its upper stage approximately 90 minutes after launch. Engineers and operators at Lockheed Martin’s Waterton Facility will now begin on-orbit checkout and tests, which are estimated to complete in approximately one month. Operational use is expected to begin in a few months.
“The GPS III program continues to make strides in modernizing the GPS constellation for the U. S. Space Force while maintaining the gold standard for position, navigation and timing,” said Col. Edward Byrne, Medium Earth Orbit Space Systems Division chief.
GPS III SV04 will join the current GPS constellation comprised of 31-operational spacecraft. GPS III, the newest generation of GPS satellites, brings new capabilities to users, including three times greater accuracy and up to eight times improved anti-jamming capabilities.
A Falcon 9 carrying GPS III SV04 lifts off from Cape Canaveral Air Force Station, Florida, Nov 5. (Photo: SpaceX via USAF)
GPS constellation status
According to the U.S. Space Force Second Space Operations Squadron (2 SOPS), the satellite is designated SVN-77/PRN-14 in the GPS almanac. GPS III SV04 (SVN-77/PRN-14) will replace SVN-44/PRN-28 in the B plane at slot 03. 2 SOPS will issue a Launch NANU after on-orbit checkout when control of SVN-77 is transferred from Lockheed Martin to 2 SOPS for insertion into the GPS control segment.
GPS III SV-2 (SVN 75), launched Aug. 22, 2019, replaced SVN 45/PRN-21 at D3 and was set healthy on April 1, 2020. As a result, SVN 45 is being re-phased from D3 to D2F replacing SVN 46/PRN 11 and will arrive sometime in November of this year. SVN 46 will be taken out of the operational constellation before the January 2021 launch of GPS III SV05 (SVN-78) and sent to Launch, Anomaly, Resolution, and Disposal Operations (LADO), making PRN-11 available.
GPS III SV-03 (SVN 76, PRN-23) launched June 30, 2020, and was set operational and healthy on October 1.
SVN-46, launched October 7, 1999, has been an “iron bird” workhorse in the D-plane and has successfully served the world’s GPS users for more than 20 years, 12 years past its designed service life. It outlasted (and in many cases, outperformed) many of its peers on-orbit, testament to quality engineering and the diligent efforts of the men and women of the U.S. Air Force.
Screenshot: SpaceX
The fourth GPS III satellite (GPS III SV04) is scheduled to launch today at 06:24 p.m. EST (~15 minute launch window) from Cape Canaveral Air Force Station, Florida, on a SpaceX Falcon 9 rocket. The new launch window follows an aborted launch with two seconds to go on Oct. 2.
Built by Lockheed Martin, GPS III satellites are designed to help the U.S. Space Force modernize the current GPS constellation with new technology and advanced capabilities. GPS III provides three times greater accuracy and up to eight times improved anti-jamming power over satellites in the current constellation. GPS III also adds a new L1C civil signal compatible with Europe’s Galileo global navigation satellite system, which will provide greater civil user connectivity in the future.
After adding GPS III SV04, the four GPS III satellites on orbit will represent about 12 percent of the 31 satellites in the GPS constellation.
GPS III SV04 is the 23rd M-code-enabled satellite in the constellation, only one short now of the 24 needed for global coverage. M-code is a more-secure, harder-to-jam or spoof signal invaluable to U.S. and allied military forces.
GPS III SV03, which lifted off from the Cape on June 30, was set operational on Oct. 1. The next satellite — GPS III SV05 — was declared “Available for Launch” in May 2020. The satellite is now waiting to be called up for a launch date in 2021. Five more GPS III satellites are in production, three of which are fully assembled and in testing.
Lockheed Martin is also under contract to build up to 22 additional GPS III Follow On (GPS IIIF) satellites, which add additional technology and advanced capabilities to this warfighting system, including a new Regional Military Protection Capability, which will increase anti-jam support in theater to ensure U.S. and allied forces cannot be denied access to GPS in hostile environments; an accuracy-enhancing laser retroreflector array; a fully digital navigation payload; and a new search and rescue payload.
In July, the Space Force declared that the GPS IIIF program had fulfilled Milestone C, which means the start of the production phase. Lockheed Martin has introduced augmented reality tools into the GPS IIIF production process to drive even-greater efficiency into the production process.
Continued investment in GPS is important. Besides the military applications, the U.S. economic benefit of GPS is estimated to be over $300 billion per year and $1.4 trillion since inception.
TDK Corp. has announced that Trusted Positioning Inc. (TPI), a TDK Group Company focused on creating and selling positioning software, has joined the New Business Promotion Center of TDK Corporation as an independent business unit.
With the expanding positioning and location tracking market, this move signals TDK’s commitment to developing TPI as an independent software solutions business, according to a TDK press release.
TPI has been developing integrated positioning solutions for decades, with software deployments in more than 50 million systems worldwide. The company’s innovation team is comprised of experts in inertial navigation, dynamic motion mechanics, geomagnetic positioning, GNSS, Bluetooth low energy, Wi-Fi and other wireless positioning techniques.
TPI’s inertial navigation solutions provide highly accurate positioning for the autonomous vehicle, automotive infotainment/telematics, robotics, two wheeled micro-mobility and indoor positioning markets.
VENUE (previously Coursa Venue) is TPI’s flagship indoor positioning solution based on inertial, geomagnetic and other wireless technology. The indoor positioning market is exploding with the now-ubiquitous GPS everywhere, except indoors. TPI’s indoor positioning requires minimal infrastructure investment, which reduces costs, and is well suited to scale for large venues.
RIDE is TPI’s two wheeled micro-mobility solution (previously called MML) for the burgeoning rental bike and electric scooter industry. This software solution enables the return and location identification of vehicles in urban areas where GPS is less accurate, and facilitates correct orientation of parked bikes to ensure city standards are met.
TPI’s TRACK product (previously named IPL), fuses GNSS and an IMU to provide accurate dead reckoning for automobile infotainment and telematics systems during GNSS outages in tunnels, underground parking and other sheltered areas.
TPI’s AUTO solution (previously known as Coursa Drive) improves reliability in autonomous vehicles and robots utilizing onboard radar and inertial sensors. AUTO provides all weather decimeter level positioning accuracy in urban areas with limited GPS signal availability.
With the introduction of TPI’s new structure and product names, today TPI also launches a new dedicated website: www.trustedpositioning.com.
“Relaunching our business and brand while leveraging a SaaS business model, partnering with major companies around the world and keeping them competitive, marks a strategic move for TPI”, says Chris Goodall, managing director and founder of TPI.
To prevent the further spread of COVID-19, the world is shifting to a “new normal” in which social distancing is practiced and contact between people is avoided. Due to early evidence suggesting the spread of COVID-19 is much more aggressive indoors than outdoors, many companies have begun efforts to monitor workers’ movements and trace contacts to keep offices and factories from becoming new epicenters of infection.
The Need to Monitor
Keeping a safe distance from others and avoiding contact is essential to prevent getting infected with COVID-19. However, there are many situations where avoiding contact with others at indoor locations such as offices and factories is difficult. Hence, there is a growing need for technologies that monitor contact between workers and their movement histories in real time.
Indoor location information can be obtained using various wireless communication technologies including Wi-Fi, Bluetooth and ultra-wideband (UWB). For example, Bluetooth beacons have been deployed at commercial facilities to enable services that provide location-relevant information to customers with smartphones. The positioning accuracy of Bluetooth, however, is only around 3 to 10 meters and is dependent on infrastructure installation.
To be useful for contact tracing of infectious diseases, the number of beacons must be increased to achieve an adequate level of accuracy. UWB technology features high positioning accuracy, but deployment in a wide area would require installation of a large number of radio transceivers and repeaters, putting it at a cost disadvantage.
Solution Based on Geomagnetism
Given this background, a solution using geomagnetism is attracting attention. TDK has developed VENUE, which displays the real-time locations of people by utilizing geomagnetic sensors found in today’s smartphones. Each indoor location has a geomagnetic signature that can be used to ascertain the position of the phone.
There are several approaches to indoor positioning, but geomagnetism, tightly coupled with inertial navigation, optimally balances accuracy, reliability and cost of deployment and maintenance.
“The beauty of geomagnetic positioning is that it works in all large venues whose structures interfere with Earth’s magnetic field, making this an infrastructure-free approach to indoor positioning that is accurate to better than 2 meters,” said Chris Goodall, founder and managing director of Trusted Positioning Inc., a TDK Group Company based in Calgary, Alberta, Canada.
VENUE provides a position display with even higher accuracy by combining geomagnetic information with information from accelerometers and gyroscopic sensors inside smartphones.
VENUE requires only the creation of a geomagnetic map that combines an indoor layout map with the geomagnetic data of that particular location acquired through a survey, with no need to install new devices and terminals. This leads to low installation cost. The accuracy of positioning using geomagnetism is better than two meters (6 feet) — sufficient for tracing contact with infected persons. In addition, VENUE provides a position display with even higher accuracy by combining geomagnetic information with information from accelerometers and gyroscopic sensors inside smartphones.
“People may hold their smartphones while walking or put them in their pockets or bags,” Goodall said. “Since the orientation to the user changes constantly, the movements and pedestrian use cases need to be corrected using inertial sensors. Solving these issues was the greatest challenge for practical applications such as tracking, and took our team many years to create, perfect and protect.”
Comparison of indoor location information technologies. (Chart: Trusted Positioning)
Real-World Trial Under Way
Beginning in August, a contact tracing trial among workers is being conducted at TDK’s headquarters in Nihonbashi, Tokyo, using VENUE. Employees carry smartphones with a special app installed, and their positions and movement histories on the floor are combined with anonymous identification information. If an employee is found to be infected, the data will be analyzed to identify people who had contact with that employee within the preceding two weeks, and measures such as stay-at-home instruction will be taken.
This solution not only can identify those who were in close contact with the infected person as primary contacts, but also trace those who stayed in areas where the infected person had been shortly before as potential “area contacts.” Analysis that combines location and elapsed time enables more effective contact tracing by improving primary contact tracing indoors and enabling area-based contact tracing over time.
Ongoing Trial at TDK Headquarters: VENUE displays an individual worker’s tracking data on the dashboard. (Conceptual illustration: Trusted Positioning)Ongoing Trial at TDK Headquarters: A worker’s durations of stay and positions can be visualized in the form of a heat map. (Conceptual illustration: Trusted Positioning)
New Possibilities Opened
Because VENUE can display the positions and histories of people and objects using not only workers’ smartphones but special tags containing geomagnetic and inertial sensors (under development), it can be deployed for a wide range of applications beyond contact-tracing of infectious diseases. Possible uses include monitoring the flow of employees to improve operational efficiency or tracking positions of equipment to manage their operational statuses. TDK is working with a number of companies on solutions to improve business efficiencies using location information.
Many offices have introduced open seating, so maintaining a “real-time seating chart” using VENUE is a real advantage so staff can more easily find one another in large office settings, encouraging more collaboration between staff and departments.
Companies have been using Bluetooth low-energy (BLE) beacons to manage the movement of workers, materials and equipment indoors in warehouses, factories and construction sites. VENUE can reduce the installation and maintenance costs of such systems, especially in large-scale facilities.
VENUE is also useful for other contact-tracing applications that do not focus on viral transmission, such as human-to-machine, human-to-vehicle and human-to-robot contacts. The future work environment will undoubtedly change with more automation, and the interaction of humans and machines poses safety concerns. VENUE’s designers hope it can improve safety in many types of contact-tracing applications.
Similar to the expansion of GPS for outdoor positioning applications, indoor positioning technologies will likely grow in our everyday lives. VENUE is one indoor location information solution that enables highly accurate location information to be obtained while keeping infrastructure costs down.
The U.S. Coast Guard Public Affairs YouTube channel is hosting streaming files for virtual meetings of the U.S. Government’s Civil GPS Service Interface Committee (CGSIC), held Sept. 21-22.
This link leads to the CGSIC page of GPS.gov. On that page are additional links for the Sept. 22 morning and afternoon speakers at the CGSIC Plenary Session as well as links for the three subcommittee sessions held Sept 22. The subcommittees are:
International Information Subcommittee
Timing Subcommittee
Survey, Mapping and Geoscience Subcommittee
Anyone can access the briefings, which include slides, embedded video of the speakers and comments made during the presentations.
