LiteVLoc: Map-Lite Visual Localization for Image Goal Navigation
Fuente:
arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
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| _version_ | 1866909356278153216 |
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| author | Jiao, Jianhao He, Jinhao Liu, Changkun Aegidius, Sebastian Hu, Xiangcheng Braud, Tristan Kanoulas, Dimitrios |
| author_facet | Jiao, Jianhao He, Jinhao Liu, Changkun Aegidius, Sebastian Hu, Xiangcheng Braud, Tristan Kanoulas, Dimitrios |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_04419 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LiteVLoc: Map-Lite Visual Localization for Image Goal Navigation Jiao, Jianhao He, Jinhao Liu, Changkun Aegidius, Sebastian Hu, Xiangcheng Braud, Tristan Kanoulas, Dimitrios Robotics Computer Vision and Pattern Recognition 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. |
| title | LiteVLoc: Map-Lite Visual Localization for Image Goal Navigation |
| topic | Robotics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2410.04419 |