Lidar-based Localization Algorithm of Vehicle in Parking Lot

Abstract:

Under the limit of car localization sensors such as GPS and Wi-Fi in parking lots and tunnels， an autonomous self-localization method of vehicle based on lidar is proposed. The lidar simultaneous localization and mapping （SLAM） algorithm is used to obtain the estimated pose of vehicle through three-dimensional lidar point cloud matching， and all poses are adjusted according to the graph optimization method and the nonlinear optimization method. Then， a planar grid map of environmental information with controllable resolution is obtained. Based on the Monte Carlo method， a particle filter is adopted for real-time vehicle localization， and an improved method of particle sampling is proposed to achieve real-time high-precision autonomous localization of vehicle. Experimental results show that the particle filter can effectively realize the localization of vehicles in parking lot and other non-GPS environments， and the localization accuracy is within 10 cm.