This paper presents LiteVLoc, a hierarchical visual localization framework that uses a lightweight topo-metric map to represent the environment. The method consists of three sequential modules that estimate camera poses in a coarse-to-fine manner. Unlike mainstream approaches relying on detailed 3D representations, LiteVLoc reduces storage overhead by leveraging learning-based feature matching and geometric solvers for metric pose estimation. A novel dataset for the map-free relocalization task is also introduced. Extensive experiments including localization and navigation in both simulated and real-world scenarios have validate the system's performance and demonstrated its precision and efficiency for large-scale deployment. Code and data will be made publicly available.
@article{arxiv.2410.04419,
title = {LiteVLoc: Map-Lite Visual Localization for Image Goal Navigation},
author = {Jianhao Jiao and Jinhao He and Changkun Liu and Sebastian Aegidius and Xiangcheng Hu and Tristan Braud and Dimitrios Kanoulas},
journal= {arXiv preprint arXiv:2410.04419},
year = {2024}
}