3D LiDAR Aided GNSS NLOS Mitigation in Urban Canyons
This addresses positioning inaccuracies for autonomous vehicles in urban canyons, representing an incremental improvement over existing methods.
The paper tackles GNSS positioning errors from non-line-of-sight signals in urban areas by using 3D LiDAR to detect and correct these signals, achieving improved accuracy in real-world tests in Hong Kong.
In this paper, we propose a 3D LiDAR aided global navigation satellite system (GNSS) non-line-of-sight (NLOS) mitigation method caused by both static buildings and dynamic objects. A sliding window map describing the surrounding of the ego-vehicle is first generated, based on real-time 3D point clouds from a 3D LiDAR sensor. Then, NLOS receptions are detected based on the sliding window map using a proposed fast searching method which is free of the initial guess of the position of the GNSS receiver. Instead of directly excluding the detected NLOS satellites from further positioning estimation, this paper rectifies the pseudorange measurement model by (1) correcting the pseudorange measurements if the reflecting point of NLOS signals is detected inside the sliding window map, and (2) remodeling the uncertainty of the NLOS pseudorange measurement using a novel weighting scheme. We evaluated the performance of the proposed method in several typical urban canyons in Hong Kong using an automobile-level GNSS receiver. Moreover, we also evaluate the potential of the proposed NLOS mitigation method in GNSS and inertial navigation systems integration via factor graph optimization.