  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.
