Precise and long-term stable localization is essential in parking lots for tasks like autonomous driving or autonomous valet parking, \textit{etc}. Existing methods rely on a fixed and memory-inefficient map, which lacks robust data association approaches. And it is not suitable for precise localization or long-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 lightweight parameterization method to establish improved data association and achieve accurate localization at centimeter-level. Furthermore, we propose a novel map update approach by implementing high-quality data association for parameterized semantic features, allowing continuous map update and refinement during re-localization, while maintaining centimeter-level accuracy. We validate the performance of the proposed method in real-world experiments and compare it against state-of-the-art algorithms. The proposed method achieves an average accuracy improvement of 5cm during the registration process. The generated maps consume only a compact size of 450 KB/km and remain adaptable to evolving environments through continuous update.

翻译：精确且长期稳定的定位对于停车场中的自动驾驶或自动代客泊车等任务至关重要。现有方法依赖固定且内存效率低的地图，缺乏鲁棒的数据关联方法，难以适用于精确定位或长期地图维护。本文提出一种基于地面语义特征的新型建图、定位与地图更新系统，利用低成本相机实现。我们提出一种精确且轻量化的参数化方法，以建立更优的数据关联，并实现厘米级精确定位。进一步，我们提出一种新颖的地图更新方法，通过对参数化语义特征实施高质量数据关联，使得在重定位过程中能够持续进行地图更新与精化，同时保持厘米级精度。我们通过真实场景实验验证了所提方法的性能，并将其与最先进算法进行对比。所提方法在配准过程中实现了平均5厘米的精度提升。生成的地图仅占用450 KB/km的紧凑存储空间，并通过持续更新保持对动态环境的适应性。