Accurate Localization in Underground Garages via Cylinder Feature based Map Matching

Zhongxing Tao, Jianru Xue, Di Wang, Shuyang Zhang, Dixiao Cui, Shaoyi Du

Abstract: Autonomous driving in underground garages usually utilizes a 2D/3D occupancy map for localization. However, the real scene is changing, and may not be consistent with the map. Vehicles and other objects not contained in the map are considered as obstacles, which increase the difficulty of localization and affect the accuracy of result. In this paper, we propose a cylinder rotational projection statistics (Cy-RoPS) feature descriptor, which is a local surface feature descriptor to improve the accuracy of localization. The local surface feature motivated by RoPS feature is invariant to rotation of point set enclosed in a cylinder. We also propose to employ the local surface feature for localization in a real underground garage. The experimental results show that the proposed method is robust to dynamic obstacles in the underground garage, and has a higher accuracy in localization, compared with the state-of-the-art methods.