LI-SLAM: Lightweight and Incremental Semantic Visual Localization and Mapping for Autonomous Valet Parking

Abstract

Autonomous valet parking enables vehicles to identify parking spaces and park without human intervention, with accurate localization being a fundamental prerequisite. Existing methods typically rely on visual feature maps or semantic maps for localization. However, visual feature maps often lack robustness in underground parking environments due to similar structures, weak textures, and fluctuating lighting conditions. Semantic maps require complex post-processing and suffer from heterogeneous data association problems. In this paper, we propose directly to regress the semantic corner points to build the semantic map. Furthermore, we introduce a novel map update and merge method, which is unaffected by environmental and temporal changes, allowing continuous update and refinement of the map. To establish a global semantic map, we use four fisheye cameras to synthesize surround-view images, combined with an IMU (Inertial Measurement Unit) and wheel encoders. Real-world experiments validate the localization accuracy of the proposed system. The experimental results demonstrate the robustness and practicability of the proposed system.