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Precise and long-term stable localization is essential in parking lots fortasks like autonomous driving or autonomous valet parking, \textit{etc}.Existing methods rely on a fixed and memory-inefficient map, which lacks robustdata association approaches. And it is not suitable for precise localization orlong-term map maintenance. In this paper, we propose a novel mapping,localization, and map update system based on ground semantic features,utilizing low-cost cameras. We present a precise and lightweightparameterization method to establish improved data association and achieveaccurate localization at centimeter-level. Furthermore, we propose a novel mapupdate approach by implementing high-quality data association for parameterizedsemantic features, allowing continuous map update and refinement duringre-localization, while maintaining centimeter-level accuracy. We validate theperformance of the proposed method in real-world experiments and compare itagainst state-of-the-art algorithms. The proposed method achieves an averageaccuracy improvement of 5cm during the registration process. The generated mapsconsume only a compact size of 450 KB/km and remain adaptable to evolvingenvironments through continuous update.

LESS-Map: Lightweight and Evolving Semantic Map in Parking Lots for Long-term Self-Localization