AVP-SLAM: Semantic Visual Mapping and Localization for Autonomous Vehicles in the Parking Lot
This addresses the challenge of reliable navigation in narrow, crowded parking lots for autonomous vehicles, representing an incremental improvement over traditional visual methods.
The paper tackles the problem of accurate localization for autonomous vehicles in GPS-denied parking lots by using robust semantic features like guide signs and parking lines, achieving centimeter-level localization in real experiments.
Autonomous valet parking is a specific application for autonomous vehicles. In this task, vehicles need to navigate in narrow, crowded and GPS-denied parking lots. Accurate localization ability is of great importance. Traditional visual-based methods suffer from tracking lost due to texture-less regions, repeated structures, and appearance changes. In this paper, we exploit robust semantic features to build the map and localize vehicles in parking lots. Semantic features contain guide signs, parking lines, speed bumps, etc, which typically appear in parking lots. Compared with traditional features, these semantic features are long-term stable and robust to the perspective and illumination change. We adopt four surround-view cameras to increase the perception range. Assisting by an IMU (Inertial Measurement Unit) and wheel encoders, the proposed system generates a global visual semantic map. This map is further used to localize vehicles at the centimeter level. We analyze the accuracy and recall of our system and compare it against other methods in real experiments. Furthermore, we demonstrate the practicability of the proposed system by the autonomous parking application.