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.


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    Title :

    Collaborative Mapping and Autonomous Parking for Multi-Story Parking Garage


    Contributors:
    Li, Bing (author) / Yang, Liang (author) / Xiao, Jizhong (author) / Valde, Rich (author) / Wrenn, Michael (author) / Leflar, Jim (author)


    Publication date :

    2018-05-01


    Size :

    3058527 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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