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

TL;DR

This paper introduces LESS-Map, a lightweight, semantic mapping system for parking lots that enables precise, long-term self-localization with continuous map updates using low-cost cameras.

Contribution

It presents a novel semantic mapping and localization framework with a lightweight parameterization and high-quality data association for continuous map refinement.

Findings

Achieves centimeter-level localization accuracy.

Maps are compact, only 450 KB/km in size.

Improves registration accuracy by 5cm on average.

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.