Jiao, Jianhao; He, Jinhao; Liu, Changkun; Aegidius, Sebastian; Hu, Xiangcheng; Braud, Tristan; Kanoulas, Dimitrios; (2025) LiteVLoc: Map-Lite Visual Localization for Image Goal Navigation. In: 2025 IEEE International Conference on Robotics and Automation (ICRA). (pp. pp. 5244-5251). IEEE: Atlanta, GA, USA.

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Abstract

This paper presents Lite VLoc, a hierarchical vi-sual 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 dense 3D mapping methods, LiteVLoc reduces storage by avoiding geometric reconstruction. It uses a learning-based feature matcher to establish dense correspondences between sparse keyframes and observations, and then refines poses with a geometric solver, enabling robustness to viewpoint changes. The system assumes depth sensors or stereo camera for deployment. 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 at the webpage:https://rpl-cs-ucl.github.io/LiteVLoc.

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