Collaborative Mapping and Autonomous Parking for Multi Story Parking Garage
We present a novel collaborative mapping and autonomous parking system for semi structured multi story parking garages, based on cooperative 3 D LiDAR point cloud registration and Bayesian probabilistic updating. First, an inertial enhanced (IE) generalized iterative closest point (G ICP) approach is presented to perform high accuracy registration for LiDAR odometry, which is loosely coupled with inertial measurement unit using multi state extended Kalman filter fusion. Second, the IE G ICP is utilized to reconstruct the 3 D point cloud model for each vehicle, and then the individual model maps are merged and updated into a global probabilistic 2 D grid map. A collaborative multiple layer semantic map is constructed to support autonomous parking. Finally, we propose a collaborative navigation approach for path planning when there are multiple vehicles in the parking garage through vehicle to vehicle communication. A global path planner is designed to explore the minimum cost path based on the semantic map, and local motion planning is performed using a random exploring algorithm for obstacle avoidance and path smoothing. Our pilot experimental evaluation provides a proof of concept for indoor autonomous parking by collaborative perception, map merging, and updating methodologies.
IEEE Transactions on Intelligent Transportation Systems