3D LiDAR Aided GNSS for Unmanned Autonomous System in Urban Canyons

 GNSS positioning is an indispensable source of data for providing absolute positioning for unmanned autonomous systems. Satisfactory GNSS accuracy can be obtained in open-sky areas. However, the performance of GNSS can be significantly degraded by signal reflections from buildings, causing multipath effects and non-line-of-sight (NLOS) receptions. State-of-the-art 3D mapping aided (3DMA) GNSS can significantly mitigate the effects of signal reflections caused by static buildings. However, this approach relies heavily on an initial guess of the GNSS receiver, and on the availability of 3D building models. Moreover, the NLOS reception caused by surrounding dynamic objects, such as double-decker buses, cannot be mitigated. The research group in PolyU Intelligent Positioning and Navigation Laboratory proposes a novel 3D LiDAR aided (3DLA) GNSS positioning method which makes use of an onboard 3D LiDAR sensor to detect and correct NLOS reception caused by both buildings and dynamic objects. A novel sliding window map surrounding 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 a real-time sliding window map using a proposed fast searching method.  Finally, the pseudorange measurements are tightly integrated with an inertial navigation system (INS) using factor graph optimization (FGO). The proposed 3DLA GNSS evaluated the performance of the proposed method in two typical urban canyons in Hong Kong and found that improved accuracy is obtained even in such environments.