Fraunhofer IIS has opened paper submissions for The International Conference on Indoor Positioning and Indoor Navigation (IPIN) 2023, which takes place Sept. 25 -28 at the Nordostpark in Nuremberg, Germany.
The event is dedicated to indoor positioning, its applications and recent developments. The last decade has seen tremendous technical advances in indoor positioning. However, unlike the GNSS solutions established in the outdoor environment, there is not yet a technology that is affordable and accurate enough for the general market.
The potential applications of indoor localization are all-encompassing, from the home to vast public areas, from internet of things and personal devices, to monitoring applications.
The conference expects to attract up to 300 industrial and academic experts from the fields of computer science, electronics and surveying to address these challenges and the future of the industry.
To learn more about the conference and paper submissions, visit INIP-Confrence.org.
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.
NavVis, a mobile indoor mapping, visualization and navigation company, released new mapping software that significantly improves the accuracy of simultaneous localization and mapping (SLAM) technology in indoor environments, such as long corridors, the company said.
The software update will be available for users of the NavVis M3 Trolley and will significantly improve the accuracy of the resulting maps and point clouds. NavVis’ mobile mapping system, the M3 Trolley, builds upon SLAM to increase speed and efficiency when scanning buildings.
The images below demonstrate the impact of NavVis Precision SLAM technology. The left image depicts a long corridor mapped with a conventional SLAM system where the above-mentioned drift error has occurred. The green outline shows how the map deviates from the true structure. The image on the right shows the significantly improved map accuracy obtained when mapping the same area using the M3 Trolley with the new Precision SLAM technology.
Image: NavVis
Here is a closer look:
Image: NavVis
SLAM is a technique originally developed by the robotics industry that is now increasingly being used in surveying and autonomous driving technologies. It solves a core problem that long plagued robotics engineers by enabling a device to determine its location while simultaneously mapping an unknown environment. This is done by chaining millions of measurements into a trajectory estimate.
However, even when a device captures highly accurate individual measurements, chaining them will result in an accumulation of noise and tiny measurement uncertainties. Over time, the estimated motion will start to deviate from the true motion (drift error). This can often be observed as a slight bending of long corridors that are actually straight. All available SLAM systems — regardless of whether these use LIDARs or other sensors — are inherently affected by this phenomenon.
The NavVis Precision SLAM technology significantly reduces drift error and improves the SLAM accuracy. This is particularly evident in cases where complementary techniques such as loop closures cannot be deployed, if, for example, the building’s layout does not allow for it.
Precision SLAM even improves accuracy when SLAM anchors are used to incorporate ground control points into the mapping process.
“I am very excited about our new Precision SLAM technology,” said Stefan Romberg, head of mapping and perception at NavVis. “We are always striving for the highest possible map and point-cloud accuracy and improving SLAM is a critical component to being successful. It is widely known among SLAM developers and users that complementary approaches such as loop closures or ground control points are needed to achieve a high accuracy.
“However, with the Precision SLAM technology we have developed an approach that not only nicely complements the former techniques but is especially evident when these have little effect or cannot be used.”
When a platform’s mission requires maneuvering among different environments, transitions between these environments may mean that a single method cannot solve the full positioning, navigation and mapping problem.
This article describes an integrated navigation and mapping design using a GPS receiver, an inertial measurement unit, a monocular digital camera and three short-to-medium range laser scanners.
By Evan T. Dill and Steven D. Young, NASA Langley Research Center, and Maarten Uijt de Haag, Ohio University
An unmanned aircraft system (UAS) traffic management system (UTM) is an ecosystem for coordinating UAS operations in uncontrolled airspace, particularly operations under 400 feet altitude involving small- to mid-sized vehicles. In this domain, information services regarding the state of the airspace will be provided to UAS operators.
In addition, UTM would coordinate and authorize access to airspace for particular time periods based on requests from the operators. The Federal Aviation Administration would maintain regulatory and operational authority, and may for example, issue changes to constraints or airspace configurations to operators via this information service. However, there is no direct control from air traffic control personnel (such as “climb and maintain 300 feet” or “turn left, heading 150”).
As with visual flight rules operations of manned aircraft in uncontrolled airspace, under UTM the onus is on the vehicle operator to assure the flight system provides adequate performance with regard to communication, navigation and surveillance during flight. The vehicle/operator is responsible for avoiding other aircraft, terrain, obstacles and incompatible weather. UTM information services do not yet include, for example, information from an alternative positioning, navigation and timing system that may be needed for operations conducted in GPS-degraded environments (such as near buildings or other structures). This is the challenge being addressed by the integrated navigation concept described in this article. Other concepts are also being considered and developed for alternate, and unique, UAS missions and flight environments.
The method presented here employs a monocular camera as part of a multi-sensor solution that continuously operates throughout and between outdoor and structured indoor environments. For this work, an indoor environment is considered “structured” if its walls are vertical and remain approximately parallel, while the floor is either roughly flat or slanted.
In this type of environment, GPS is typically only sparsely available or not available at all. Hence, in our proposed navigation architecture, additional information from a camera and multiple laser range scanners (not the focus of this article) are used to increase the system’s positioning, navigation and mapping availability and accuracy in a GPS-challenged indoor environment. Figure 1 shows the target operational scenario, and Figure 2 the equipped multi-copter used in this research.
Figure 1. Operational scenario: open-sky environment, transition to indoor and indoor environment.Figure 2. Hexacopter sensors and sensor locations.
Figure 3 shows a block diagram for the methodology implemented in this research, with the elements related to monocular camera methods highlighted. When assessing the capabilities of each of the sensors used in the work, only the inertial sensor produces data that is solely dependent on the motion of the platform and local gravity and is more or less unaffected by its surroundings. Therefore, the inertial is chosen to be the primary sensor for this method.
The mechanization integrates the measurements from GPS, the laser scanners and the monocular camera through a complementary Kalman Filter (CKF) that estimates the errors in the inertial measurements and feeds them back to the inertial strapdown calculations. For this inertial error estimation method to function properly, pre-processing methods must be implemented that relate the sensors’ observables to the inertial measurements.
Here we describe the processing techniques necessary to relate measurements from a monocular camera to measurements from the inertial measurement unit (IMU). Then we show how these techniques are used in the broader GPS/optical/inertial mechanization and present testing results.
Figure 3. Monocular camera components of a broader mechanization.
2D Monocular Camera Methods
To process data from the camera, we first perform feature detection and tracking of both point features and line features. Specifically, elements from Lowe’s Scale Invariant Feature Transforms (SIFT) are used to track point features, which are in turn used to obtain estimates of the camera’s rotational and un-scaled translational motion using structure from motion (SFM) based methods. To resolve the ambiguous scale factor, a novel scale estimation technique is employed that uses data from the platform’s horizontally scanning laser. This technique as well as algorithms that produce a 3D visual odometry solution are presented below.
SIFT Point Feature Extraction. To aid in determining camera motion, SIFT has been used as a way of identifying local features that are invariant to translation, rotation, and image scaling. This technique yields 2D point features that are unique to their surroundings and readily identified and associated across a set of sequential camera images. Each key location and its surroundings are analyzed, resulting in a descriptive 128-element feature vector, known as a SIFT key. Example results of the SIFT key identification process are shown in Figure 4.
Figure 4. SIFT feature identification.
Based on the results of the SIFT feature extraction process from two image frames, a feature association function is performed using the feature vectors. For this work, a two-step procedure is implemented.
First, SIFT keys are associated using a matching procedure. Example results of this process are shown in Figure 5, where it can be observed that incorrectly associated features may result from this process. To remove these artifacts, inertial measurements are utilized to ensure the correctness of the associations.
Figure 5. SIFT matching results between consecutive image frames.
Using a triangulation method, prospective associations are used to crudely estimate each feature’s 3D position with respect to the previous frame. While this triangulation method yields 3D data, it is of poor quality, and is therefore only used to obtain rough approximations that are sufficient for association purposes, but insufficient for navigation purposes.
Once transformed to a 3D reference frame, the projected distances of each feature are compared with one another, and prospective associations that produce significantly different depths than surrounding points are eliminated. Example results of this filtering process can be seen in Figure 6.
Figure 6. Point feature association after inertial based miss-association rejection.
In future implementations, the ORB feature will be evaluated, as its performance is expected to be more than two orders of magnitude faster than SIFT.
Wavelet Line Feature Extraction. To implement the scale factor estimation technique described in a later section, it is necessary to first extract and track vertical line features. To accomplish this, a method using wavelet transforms (WTs) was developed. When applied to a 2D image, WTs can be viewed as filters operating in the x and y directions of an image. By applying either a high- or low-pass filter to both of an image’s channels (that is, x and y directions), four sub-images are formed to represent an image approximation. For this work, a level-one bi-orthogonal 1.3 wavelet was used to decompose each image. An example of the four sub-images produced by this wavelet is shown in Figure 7 along with the original image.
Figure 7. Example results of wavelet decomposition.
Through further processing of the vertical decomposition results, strong line features are identified by first inspecting the illuminated elements along the vertical channels of the decomposed image and identifying clusters of adjacent pixels. Next, a 2D line fit is applied to the groups to estimate residual noise. Pixel collections with low residuals (<3 here) are considered valid line features. Example results of this process are shown in Figure 8.
Figure 8. Example vertical line extraction results.
For association purposes, lines cannot be compared over a sequence of image frames solely based on location as similar line features may not necessarily possess the same endpoint, and, therefore, can be of varying lengths. However, corresponding lines will possess many common points and similar orientations if they are projected into the same frame. Using the inertial reference frame, each line’s orientation, , can be transformed across image frames as given by:
In this manner, lines between frames that contain multiple similar points and have comparable orientations are considered associated.
For a discussion of the projective visual odometry and epipolar geometry methodology as well as the resolution of true metric scale used in this work, download the supplemental PDF.
Metric Scale. As the unscaled translation estimate calculated through the aforementioned visual odometry method is a unit vector, it only indicates the most likely direction of motion of the camera. To obtain the sensor’s actual translational motion, an estimate of the scale factor, m, is required to determine the absolute translation ∆r. This can be accomplished through techniques using a priori knowledge of the operational environment or measurements from other sensors. In this research effort, a new method is employed that makes use of data provided by a horizontally scanning laser.
The proposed method estimates the scale in an image by identifying points in the environment that are simultaneously observed by the camera and the forward-looking laser range scanner.
To enable this estimation method we must identify the correspondences between the pixels in the camera images (each defined by a direction unit vector corresponding to the row x and column y) and the laser scanner measurements (each defined by direction unit vector). A calibration procedure establishes these correspondences. Given the laser range measurements, 2D features located on the scan/pixel intersections can be scaled up to 3D points.
Unfortunately, extracted 2D point features are rarely illuminated by a laser scan in two consecutive frames. This can be resolved by considering the intersection of a laser scan with 2D line features rather than point features. As the laser intersects the camera frame at the same location regardless of platform motion, and the platform does not make excessive roll and pitch maneuvers, vertical line features in the image frame are preferred as they will be relatively orthogonal to the laser scan plane.
Using the previously described vertical line extraction procedure, Figure 9 shows an example image frame overlaid with the points in the image frame illuminated by the laser (indicated by a blue line) and the extracted vertical line features (indicated as green lines). Multiple intersections of 2D vertical lines with laser scan data are calculated (indicated as red points). Inversely, Figure 10 depicts the location of all laser scan points in green, all laser points observable with the camera field-of-view (FoV) in blue, and intersection points in red.
Figure 9. Image frame overlaid with points; Laser (blue), vertical line features (green), multiple intersections (red).Figure 10. 2D vertical line and laser intersections in laser scan data.
For scale factor calculation purposes, it is necessary to track the motion of these 3D laser/vision intersection points, across sequences of camera image frames. As each intersection point uniquely belongs to a line feature in the 2D image frame, it can be stated that if two lines are associated, their corresponding intersection points are also associated. Using the rotation computed from the visual odometry process, the line association method described by (1) is implemented, and provides associations between laser/vision intersection points across frames.
To calculate the desired scale factor based on these associated laser/vision points, geometric relationships are established: unit vectors from the camera center to points located on a 2D line. From these, the line’s normal vector can be derived.
Monocular Camera Results
To assess the performance of the visual odometry processes, multiple experiments were conducted. The results of one such test are discussed here. During each test, the visual odometry results for rotation, shown in blue, were easily evaluated through comparison with the platform’s inertially-measured rotation, displayed in red.
The rotational results for each sensor were decomposed into the Euler angles: pitch, roll and yaw with respect to an established navigation frame. Unfortunately, the inertial sensor itself cannot be used to evaluate the visual odometry translation results due to relatively large inertial drift in the sensor measurements. As no independent measurements were available to evaluate translation with high precision, the truth reference was established by accurately measuring the actual paths taken during each flight.
A test flight was conducted traversing a rectangular indoor hallway loop. This test contained translation in multiple dimensions, large heading changes and a flight duration of four minutes. Moreover, this test allowed for evaluation of the eight-point algorithm and scale estimation method in the presence of rapid scene changes.
The attitude estimation results for this test are shown in Figure 11. Throughout data collection, the maximum separation between the inertial and vision-based attitude estimators for pitch, roll and yaw was 9°,19° and 14°, respectively. Upon comparison to many of the other conducted tests, the maximum attitude errors were larger. There are multiple reasons for this increase. First, the duration of this experiment was greater than that of previous experiments. Errors accumulate as a function as time due to integration of residual bias errors, so increasing flight duration will increase cumulative error.
Next, the looping path observed throughout this test caused the eight-point algorithm and scale estimation procedures to quickly adapt to differing scenery. Drastic scene changes (turning a corner) increase the difficulty of feature association between frames. This directly affects the procedures used for visual odometry in an adverse manner. Finally, there are situations in this flight where features are sparse. In general, a decrease in features will cause a decrease in the estimation capabilities of visual odometry.
Figure 12 shows the visual odometry path calculated for experiment 2. Here, the estimated length of each of the four straight legs of the rectangular loop matches to within 2 meters of the measured hallway lengths. This implies that the scale estimation technique is working reasonably well.
Figure 12. Visual odometry path determination while traveling around an indoor loop.
As for the estimated translational directionality produced by the eight-point algorithm, the first two legs of the loop never divert from the measured path by more than 2 meters; the third leg diverts by 5 meters. This is most likely due to a lack of well dispersed features in that specific hallway.
The cumulative error contained in the third linear leg of the loop also makes evaluation of the final leg difficult. However, if previous errors are removed, the final leg appears to match the measured path well. In total, the landing position calculated through visual odometry is 6.5 meters away from the measured end of the trial.
Integration Methodology
In cases where GPS measurements are available along with the visual odometry solution, the proposed method can extend the GPS/IMU integration mechanization. The structure of the referenced GPS/inertial integration consists of two filters: a dynamics filter that uses GPS carrier-phase measurements to estimate velocity and other IMU errors, and a position filter that uses the velocity output of the dynamics filter and GPS pseudoranges. The dynamics filter can be adapted and extended to include camera data within its mechanization.
The dynamics filter is a CKF designed to estimate the inertial error states: velocity error in the north-east-down (NED) coordinate reference frame, misorientation (including tilt error), gyro bias error, and specific force or accelerometer bias error. This yields a state vector. For a discussion of the state vector, download the supplemental PDF.
Results
To evaluate the proposed algorithms, data was collected through multiple flights of the hexacopter platform shown in Figure 2 through a structured indoor and outdoor environment including transitions between these two environments. The availability of GPS measurements in these environments ranged from fully denied, to substantially degraded, to enough observables for a full solution.
The results of one test flight are discussed in this section. Apart from the data collections with the hexacopter, truth reference maps were created for the indoor operational environment and used for evaluation of the described processes. The results of the full GPS/inertial/laser/camera integrated solution described in Figure 3 are shown in an NED frame in Figure 13.
Figure 13. Path compared to 2D reference map.
The truth reference of the environment, depicted in red (derived from a terrestrial laser scanner), is compared to the flight path obtained from the extended Kalman filter (EKF), displayed in blue. The estimated flight trajectory constantly remains within the hallway truth model, indicating sub-meter level performance. Furthermore, based on an extension of this work for environmental laser mapping produced from the EKF, combined with the accuracy of the map, it is further reinforced that sub-meter-level navigation performance is obtained.
During portions of the described data collection, there was enough visibility (>3 satellites) to calculate a GPS position. The availability of GPS measurements to the position estimation portion of the filter allowed for geo-referencing of the produced flight path and 3D map.
Figure 14 displays the geo-referenced continued flight path based on the integration filter superimposed on Google Earth on the left, while the standalone GPS solution based on pseudoranges only is plotted on the right. The geo-referenced path correctly displays the platform passing through Stocker Center, the Ohio University engineering building.
To demonstrate the contributions of the monocular camera to the above results, laser measurements were removed from the solution for a 20-second period where GPS was unavailable. During the 20-second removal of laser data, the system is forced to operate on integration between visual odometry measurements and the IMU. The cumulative effect caused by this situation can be observed in Figure 15. After coasting on an IMU/camera solution for 20 seconds, the path is subsequently altered by 3 meters, as opposed to the solution with all sensors.
Figure 15. Effect of losing GPS and lasers for 20 seconds.
To further emphasize the contribution of the visual odometry component, both the laser and camera were removed from the integration for the same 20-second period. During this time frame the EKF is forced to coast on calibrated inertial measurements. The effect of losing all secondary sensors for a 20-second period can be observed in Figure 16.
Figure 16. Effect of coasting on the IMU for 20 seconds.
During the forced sensor outage, a 45-meter cumulative difference is introduced between the path using all sensors and the path with denied sensors. Through comparison of the results shown in Figure 15 and Figure 16, the contribution of monocular camera data can be isolated.
When the EKF was forced to operate for 20 seconds using an IMU/camera solution, 3 meters of error were introduced. This is significantly smaller than the 45 meters of error observed when using only the inertial for the same period. Thus, the camera is shown to provide stability to the EKF when neither the laser nor GPS are available.
Conclusions
The visual odometry techniques produced reasonably good attitude estimation and are effective at constraining inertial drift when other sensors are not available. The inclusion of camera measurements to the discussed integrated solution resulted in increases in the accuracy, availability, continuity and reliability of the system.
Acknowledgment
The material in this article was first presented at the ION Pacific PNT conference in Hawaii, May 2017.
Manufacturers
The camera used aboard the UAV in these tests is a Point Grey Firefly MV and the IMU is an XSENS MTi. The GPS receiver is a NovAtel OEMStar with a corresponding NovAtel L1 patch antenna.
EVAN T. DILL is a research scientist in the Safety Critical Avionics Systems Branch at NASA Langley Research Center. He received his Ph.D. in electrical engineering from Ohio University.
STEVEN D. YOUNG is a senior research scientist at NASA with more than 30 years of experience in the related fields of safety assurance, avionics systems engineering and human-machine interaction.
MAARTEN UJIT DE HAAG is the Edmund K. Cheng Professor of Electrical Engineering and Computer Science and a Principal Investigator (PI) with the Avionics Engineering Center at Ohio University, where he earlier earned his Ph.D. in electrical engineering.
Broadcast Date: Thursday, December 13, 2012 Moderator: Alan Cameron, publisher for GPS World Speakers: J. Blake Bullock (Samsung), Manikantan Parameswaran (Spirent), Chris Gates (NextNav), Dave Huntingford (CSR) Summary: The senior product manager responsible for a new chip that fuses input from several sensors, using the best combination at any given time to maximize coverage and accuracy while keeping power draw to a minimum, joins us to describe the new frontiers in indoor navigation. He’ll be joined by other experts in the field, where difficult challenges meet user requirements for continuous position availability.
TomTom has entered a technology collaboration with sensewhere, a provider of indoor positioning technology. According to the companies, the collaboration will enable the two companies to conquer GPS black spots and bring location-based services indoors.
TomTom Indoor delivers accurate customized indoor maps of public and private venues for site operators and other partners that enable increased efficiency, cost savings and an improved customer experience.
sensewhere has developed a proprietary and patented positioning solution for mobile devices. The combination of TomTom’s maps — both indoor and traditional navigation maps — and sensewhere’s accurate indoor positioning will enable a seamless navigation experience indoors and outdoors.
sensewhere algorithms enable location for indoor locations such as shopping malls, using sensors such as Wi-Fi and Bluetooth.
“Access to indoor positioning technology, coupled with highly accurate indoor maps, means that guidance can be integrated into the day-to-day operations of a wide variety of venues, including enterprise facilities, shopping malls, airports, hospitals and more,” said Pieter Gillegot-Vergauwen, vice president, Maps Product Management, TomTom. “With the explosion of the Internet of Things, we believe that by partnering with sensewhere our customers will not only be able to gain efficiencies, but will also deliver a better experience to their own customers.”
“We are excited to help TomTom extend its navigation prowess indoors with this technology collaboration,” said Rob Palfreyman, CEO of sensewhere. “We believe this integration is a perfect fit for enterprises that need to combine location intelligence, resource planning and efficient execution.”
Where’s Waldo? sensewhere uses pinpoint people to illustrate how its system works in a home page video.
The 24th edition of the European Navigation Conference (ENC 2016) will be held May 30 to June 2 at the Finlandia Hall in Helsinki, Finland.
ENC 2016 is co-sponsored by EUGIN, Nordic Institute of Navigation, IEEE Aerospace and Electronic Systems Society.
The conference focus will be on innovations in positioning, navigation and timing technologies and applications for land, sea and air.
Topic areas include GNSS positioning, indoor and urban navigation and position-based applications. Special topics include navigation challenges in the Arctic and positioning solutions using geospatial big data and in intelligent transportation. Furthermore, it promises to be a unique networking event for all participants from academia, the public sector, and industry.
Welcome keynotes will be presented by Anne Berner, Finland’s minister of Transport and Communications, Matthias Petschke, director for European Satellite Navigation Programmes, European Commission, and Tiina Tuurnala, deputy director general, Finnish Transport Agency.
Technical keynotes will be given by Jari Syrjärinne, HERE Ltd., and Gérard Lachapelle, University of Calgary. The closing keynote by Prof. John Raquet, Air Force Institute of Technology. The conference will feature also technical presentations, panels, posters and an industry exhibition.
The social program of ENC 2016 will showcase unique sights that Helsinki has to offer, including the ice-breaker evening onboard the actual ice-breaker vessel Urho and performances by a traditional Finnish Kantele musician.
The preliminary program is now available, and registration is open. Registration options include fees for the whole conference as well as for individual days.
Trusted Positioning has released the Trusted Portable Navigator (T-PN) indoor location software. Available for any operating system, this mobile embedded software allows mobile users to navigate shopping centers, airports, and subway stations. No additional hardware or infrastructure required.
The T-PN combines the use of existing smartphone motion sensors with wireless updates (such as Wi-Fi and GNSS) for a complete solution with no extra hardware or infrastructure needed.
T-PN needs no additional infrastructure: This enables a consumer to navigate through an environment with no existing navigation technology. T-PN integrates with existing sensors such as Wi-Fi and GPS when available and utilizes significantly less power than either GPS or Wi-Fi positioning. The company reports that accurate positioning can be maintained while the phone is swinging in a hand, a call is answered, the phone is in a pocket or purse, or the consumer is texting.
Summary: This is the next frontier for personal and machine navigation — and many are out there now, working diligently on it. In just one example, a new chip fuses input from several sensors, using the best combination at any given time to maximize coverage and accuracy while keeping power draw to a minimum. This produces continuous position availability in indoor environments, as demonstrated by performance measurements in real-world test environments.
The senior product manager responsible for this development joins us to talk about the inner workings and the outer manifestations of this new solution.
Speakers:
J. Blake Bullock, GNSS and indoor positioning, Samsung
J. Blake Bullock was senior product manager responsible for CSR’s next generation of GNSS solutions. He is now at Samsung System LSI Business and is responsible for GNSS and indoor positioning solutions. He holds a M.Sc. degree in geomatics engineering from the University of Calgary, an MBA from Arizona State University, and several patents in LBS and navigation.
Manikantan Parameswaran, Senior Application Specialist, Spirent
Manikantan Parameswaranis currently a Senior Application Specialist for Spirent’s 8100-Location test product segment. Manikantan initially joined Spirent in 2006 as a development engineer and has worked on a variety of different A-GPS protocols and technologies. He holds a B.S in Computer Engineering from Drexel University, Pennsylvania, USA.
Chris Gates, VP Corporate Strategy, NextNav
With more than 15 years of experience in finance and telecommunications, Gates joined NextNav from SkyTerra Communications, an integrated satellite-terrestrial communications company where he served as VP — Strategy; extensive experience in wireless and wireline communications; he began his career with Chase Securities in their M&A group. Christian holds a Bachelor of Arts from Dartmouth College.
Dave Huntingford, Director of Product Management, Location Products, CSR
A 15 year veteran of the location business, Dave is responsible for the location product portfolio at CSR and its expansion into GNSS, Wireless Hybrid and MEMS motion capabilities. Previously he served with SiRF and Motorola GPS. He holds a B.S from University of Hertfordshire, UK.
Moderator:
Alan Cameron, Editor & Publisher, GPS World
