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

arXiv: https://arxiv.org/abs/2310.07390 · HTML: https://mlsanity.com/paper/2310.07390/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Oct 2023.

## Built on by (most cited first, 2 of 2)

- [ParkDiffusion: Heterogeneous Multi-Agent Multi-Modal Trajectory Prediction for Automated Parking using Diffusion Models](https://mlsanity.com/paper/2505.00586/) (2025)
- [LT-Gaussian: Long-Term Map Update Using 3D Gaussian Splatting for Autonomous Driving](https://mlsanity.com/paper/2508.01704/) (2025)

## Abstract (arXiv)

Precise and long-term stable localization is essential in parking lots for tasks like autonomous driving or autonomous valet parking, textitetc. 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.