LiteVLoc: Map-Lite Visual Localization for Image Goal Navigation

Описание: Authors: Jianhao Jiao, Jinhao He, Changkun Liu, Sebastian Aegidius, Xiangcheng Hu, Tristan Braud, Dimitrios Kanoulas

Abstract: 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.

Project: https://rpl-cs-ucl.github.io/LiteVLoc/
Pdf: https://arxiv.org/abs/2410.04419
Code: https://github.com/RPL-CS-UCL/litevlo...