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Autonomous valet parking is a specific application for autonomous vehicles.In this task, vehicles need to navigate in narrow, crowded and GPS-deniedparking lots. Accurate localization ability is of great importance. Traditionalvisual-based methods suffer from tracking lost due to texture-less regions,repeated structures, and appearance changes. In this paper, we exploit robustsemantic features to build the map and localize vehicles in parking lots.Semantic features contain guide signs, parking lines, speed bumps, etc, whichtypically appear in parking lots. Compared with traditional features, thesesemantic features are long-term stable and robust to the perspective andillumination change. We adopt four surround-view cameras to increase theperception range. Assisting by an IMU (Inertial Measurement Unit) and wheelencoders, the proposed system generates a global visual semantic map. This mapis further used to localize vehicles at the centimeter level. We analyze theaccuracy and recall of our system and compare it against other methods in realexperiments. Furthermore, we demonstrate the practicability of the proposedsystem by the autonomous parking application.

AVP-SLAM: Semantic Visual Mapping and Localization for Autonomous Vehicles in the Parking Lot