TERRAIN-BASED NAVIGATION: A TOOL TO IMPROVE NAVIGATION AND FEATURE EXTRACTION PERFORMANCE OF MOBILE MAPPING SYSTEMS
Abstract
Terrain-referenced navigation (TRN) techniques are of increasing interest in the research community, as they can provide alternative navigation tools when GPS is not available or the GPS signals are jammed. Some form of augmentation to cope with the lack of GPS signals is typically required in mobile mapping applications in
urban canyons and is of interest for military applications. TRN could provide alternative position and attitude fixes to support an inertial navigation system, since such systems inevitably drift over time if not calibrated by GPS or other methodologies. With improving imaging sensor performance as well as growing
worldwide availability of terrain high-resolution data and city models, terrain-based navigation is becoming a viable option to support navigation in GPS-denied environments. Furthermore, the feedback from the imaging sensors can be used even during GPS availability, which increases the redundancy of the measurement
update step of the navigation filter, enabling more reliable integrity monitoring at this stage. The relevance of TRN to mobile mapping applications is twofold: (1) the process of obtaining real-time position and attitude fixes for the navigation filter is
based on feature extraction, and, in particular, on the capability to separate the static and dynamic objects from the image data, and (2) the use of already available terrain data, including surface models (DSM), raster or vector data in CAD/GIS environments, such as city models, can effectively support the extraction processes. These two tasks could overlap, although the separation of the static and dynamic objects should work without any terrain data, and in fact, this is, to a large extent, the idea behind the removal of vehicles (moving objects) from imagery. The overall TRN concept, where LiDAR and optical imagery are matched with the existing terrain data is discussed and initial performance results are reported.
urban canyons and is of interest for military applications. TRN could provide alternative position and attitude fixes to support an inertial navigation system, since such systems inevitably drift over time if not calibrated by GPS or other methodologies. With improving imaging sensor performance as well as growing
worldwide availability of terrain high-resolution data and city models, terrain-based navigation is becoming a viable option to support navigation in GPS-denied environments. Furthermore, the feedback from the imaging sensors can be used even during GPS availability, which increases the redundancy of the measurement
update step of the navigation filter, enabling more reliable integrity monitoring at this stage. The relevance of TRN to mobile mapping applications is twofold: (1) the process of obtaining real-time position and attitude fixes for the navigation filter is
based on feature extraction, and, in particular, on the capability to separate the static and dynamic objects from the image data, and (2) the use of already available terrain data, including surface models (DSM), raster or vector data in CAD/GIS environments, such as city models, can effectively support the extraction processes. These two tasks could overlap, although the separation of the static and dynamic objects should work without any terrain data, and in fact, this is, to a large extent, the idea behind the removal of vehicles (moving objects) from imagery. The overall TRN concept, where LiDAR and optical imagery are matched with the existing terrain data is discussed and initial performance results are reported.
Keywords
Navigation; Feature Matching; Kalman Filtering; LiDAR.
