LiteVLoc: Map-Lite Visual Localization for Image Goal Navigation
<|reference_start|>LiteVLoc: Map-Lite Visual Localization for Image Goal Navigation: 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.<|reference_end|>
@article{jiao2024litevloc:,
    title={LiteVLoc: Map-Lite Visual Localization for Image Goal Navigation},
    author={Jianhao Jiao, Jinhao He, Changkun Liu, Sebastian Aegidius, Xiangcheng
  Hu, Tristan Braud, Dimitrios Kanoulas},
    journal={arXiv preprint arXiv:2410.04419},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.04419},
    primaryClass={cs.RO cs.CV}
}