Real-Time Incremental Mapping and Degeneration-Aware Localization for Multi-Floor Parking Lots Based on IPM Image

Abstract

In indoor parking lots, the use of RTK/GNSS for vehicle localization is often impractical due to the significantly smaller space compared to outdoor roads, which demands higher precision in both mapping and localization. Although feature point based visual SLAM algorithms have achieved high localization accuracy, they impose significant storage demands on embedded systems, and the visual feature point maps are not time-stable and are sensitive to lighting conditions. In this paper, we propose a real-time mapping and localization system for multi-floor parking lot. For the mapping part, we introduce a map-free SLAM method for precise ego-pose estimation, along with an efficient incremental map update framework that supports loop closure and multi-session mapping tasks. In the localization part, a semantic map is reused for vehicle localization based on bidirectional incentive descriptors. We incorporate degenerate cases into our optimization process, which greatly enhances the localization results. To the best of our knowledge, this is the first comprehensive system proposed for multi-floor parking lots. Experimental results demonstrate that our approach achieves state-of-the-art mapping and localization accuracy in multi-floor environments on embedded platforms.