PERSONAL NAVIGATION: EXTENDING MOBILE MAPPING TECHNOLOGIES INTO INDOOR ENVIRONMENTS

This paper discusses some unconventional methods for indoor-outdoor navigation, based on the integration of self-contained sensors, including GPS, IMU, digital barometer, magnetometer compass, and a human locomotion model. The human locomotion model is used as navigation  sensor and it is handled by Artificial Intelligence (AI) techniques that form an adaptive knowledge-based system (KBS), which is trained during the GPS signal reception, and is used to support navigation under GPS-denied conditions. A complementary technique used in our solution, which facilitates indoor navigation, is the image-based method (Flash LADAR). In this paper, the system design and an example performance analysis in the mixed indoor-outdoor environment are presented

Experimental evaluation of a UWB-based cooperative positioning system for pedestrians in GNSS-denied environment

Cooperative positioning (CP) utilises information sharing among multiple nodes to enable positioning in Global Navigation Satellite System (GNSS)-denied environments. This paper reports the performance of a CP system for pedestrians using Ultra-Wide Band (UWB) technology in GNSS-denied environments. This data set was collected as part of a benchmarking measurement campaign carried out at the Ohio State University in October 2017. Pedestrians were equipped with a variety of sensors, including two different UWB systems, on a specially designed helmet serving as a mobile multi-sensor platform for CP. Different users were walking in stop-and-go mode along trajectories with predefined checkpoints and under various challenging environments. In the developed CP network, both Peer-to-Infrastructure (P2I) and Peer-to-Peer (P2P) measurements are used for positioning of the pedestrians. It is realised that the proposed system can achieve decimetre-level accuracies (on average, around 20 cm) in the complete absence of GNSS signals, provided that the measurements from infrastructure nodes are available and the network geometry is good. In the absence of these good conditions, the results show that the average accuracy degrades to meter level. Further, it is experimentally demonstrated that inclusion of P2P cooperative range observations further enhances the positioning accuracy and, in extreme cases when only one infrastructure measurement is available, P2P CP may reduce positioning errors by up to 95%. The complete test setup, the methodology for development, and data collection are discussed in this paper. In the next version of this system, additional observations such as the Wi-Fi, camera, and other signals of opportunity will be included

An analytical study of PPP-RTK corrections: precision, correlation and user-impact

PPP-RTK extends the PPP concept by providing single-receiver users, next to orbits and clocks, also information about the satellite phase and code biases, thus enabling single-receiver ambiguity resolution. It is the goal of the present contribution to provide an analytical study of the quality of the PPP-RTK corrections as well as of their impact on the user ambiguity resolution performance. We consider the geometry-free and the geometry-based network derived corrections, as well as the impact of network ambiguity resolution on these corrections. Next to the insight that is provided by the analytical solutions, the closed form expressions of the variance matrices also demonstrate how the corrections depend on network parameters such as number of epochs, number of stations, number of satellites, and number of frequencies. As a result we are able to describe in a qualitative sense how the user ambiguity resolution performance is driven by the data from the different network scenarios

SURFACE COMPLEXITY COMPONENT OF LIDAR POINT CLOUD ERROR CHARACTERIZATION

There are several data product characterization methods to describe LiDAR data quality. Typically based on guidelines developed by government or professional societies, these techniques require the statistical analysis of vertical differences at known checkpoints (surface patches) to obtain a measure of the vertical accuracy. More advanced methods attempt to also characterize the horizontal accuracy of the LiDAR point cloud, using measurements at LiDAR-specific targets or other man-made objects that can be distinctly extracted from both horizontal and vertical representation in the LiDAR point cloud. There are two concerns with these methods. First, the number of check points/features is relatively small with respect to the point cloud size that is typically measured, at least, in millions. Second, these locations are usually selected in relatively benign areas, such as hard flat surfaces at easily accessible locations. The problem with this characterization is that it is not likely that a statistically representative analysis can be obtained from a limited number of points at locations that may not properly represent the overall object space composition. There is an ongoing effort to address these issues, and some of the newer methods to characterize LiDAR data include an average points spacing measure, computed from the LiDAR point cloud. Clearly, it is an important step forward but it ignores the surface complexity. The objective of this study is to elaborate only on the requirements for adequate surface representation in combination with the LiDAR error characterization techniques to identify the relation between the two surfaces, the measured and reference (ideal), and thus, to support better LiDAR or, in general, point cloud error characterization

UAS TOPOGRAPHIC MAPPING WITH VELODYNE LiDAR SENSOR

Unmanned Aerial System (UAS) technology is nowadays willingly used in small area topographic mapping due to low costs and good quality of derived products. Since cameras typically used with UAS have some limitations, e.g. cannot penetrate the vegetation, LiDAR sensors are increasingly getting attention in UAS mapping. Sensor developments reached the point when their costs and size suit the UAS platform, though, LiDAR UAS is still an emerging technology. One issue related to using LiDAR sensors on UAS is the limited performance of the navigation sensors used on UAS platforms. Therefore, various hardware and software solutions are investigated to increase the quality of UAS LiDAR point clouds. This work analyses several aspects of the UAS LiDAR point cloud generation performance based on UAS flights conducted with the Velodyne laser scanner and cameras. The attention was primarily paid to the trajectory reconstruction performance that is essential for accurate point cloud georeferencing. Since the navigation sensors, especially Inertial Measurement Units (IMUs), may not be of sufficient performance, the estimated camera poses could allow to increase the robustness of the estimated trajectory, and subsequently, the accuracy of the point cloud. The accuracy of the final UAS LiDAR point cloud was evaluated on the basis of the generated DSM, including comparison with point clouds obtained from dense image matching. The results showed the need for more investigation on MEMS IMU sensors used for UAS trajectory reconstruction. The accuracy of the UAS LiDAR point cloud, though lower than for point cloud obtained from images, may be still sufficient for certain mapping applications where the optical imagery is not useful