TY - JOUR
AU - Li, Liang
AB - Abstract: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.
TI - LESS-Map: Lightweight and Evolving Semantic Map in Parking Lots for Long-term Self-Localization
JF - Computing Research Repository
DO - 10.48550/arxiv.2310.07390
DA - 2023-10-11
UR - https://www.deepdyve.com/lp/arxiv-cornell-university/less-map-lightweight-and-evolving-semantic-map-in-parking-lots-for-g2p3EKol3k
VL - 2023
IS - 2310
DP - DeepDyve
ER -