|Abstract:||Occurrence of autonomous driving introduces high requirement in GNSS positioning performance. GNSS is currently the only source providing absolute positioning information. It is indispensable for initial position estimation for the high definition mapbased localization solution in autonomous driving. Satisfactory positioning accuracy can be obtained in open space or sub-urban areas. However, its performance is heavily challenged in super-urbanized scenarios with the positioning error going up to even 100 meters, due to the well-known NLOS receptions which dominates the GNSS positioning errors. The recent state-of-art rangebased 3D map aided GNSS (3DMA GNSS) can mitigate most of the NLOS receptions. However, ray-tracing simulation is timeconsuming. Therefore, we present a novel method to detect the NLOS caused by surrounding buildings and correct the pseudorange measurements using 3D point clouds and building height without ray-tracing simulation. To estimate the geometry and pose of the building relative to GNSS receiver, a surface segmentation method is employed to detect the surrounding building walls. NLOS errors are estimated by integrating the geometry, pose relative to the GNSS receiver and satellites information. Finally, position estimation of GNSS receiver is implemented by weighted least square (WLS) based on the corrected and healthy pseudorange measurements. Dynamic experiment is conducted to evaluate the errors caused by the NLOS receptions and to verify the effectiveness of the proposed method in a deep urbanized area, Hong Kong.|
Proceedings of the 31st International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2018)
September 24 - 28, 2018
Hyatt Regency Miami
|Pages:||3156 - 3168|
|Cite this article:||
Wen, Weisong, Zhang, Guohao, Hsu, Li-Ta, "Correcting GNSS NLOS by 3D LiDAR and Building Height," Proceedings of the 31st International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2018), Miami, Florida, September 2018, pp. 3156-3168.
ION Members/Non-Members: 1 Download Credit