NuRF: Nudging the Particle Filter in Radiance Fields for Robot Visual Localization
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866909552932290560 |
|---|---|
| author | Meng, Wugang Wu, Tianfu Yin, Huan Zhang, Fumin |
| author_facet | Meng, Wugang Wu, Tianfu Yin, Huan Zhang, Fumin |
| contents | Can we localize a robot on a map only using monocular vision? This study presents NuRF, an adaptive and nudged particle filter framework in radiance fields for 6-DoF robot visual localization. NuRF leverages recent advancements in radiance fields and visual place recognition. Conventional visual place recognition meets the challenges of data sparsity and artifact-induced inaccuracies. By utilizing radiance field-generated novel views, NuRF enhances visual localization performance and combines coarse global localization with the fine-grained pose tracking of a particle filter, ensuring continuous and precise localization. Experimentally, our method converges 7 times faster than existing Monte Carlo-based methods and achieves localization accuracy within 1 meter, offering an efficient and resilient solution for indoor visual localization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_00312 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | NuRF: Nudging the Particle Filter in Radiance Fields for Robot Visual Localization Meng, Wugang Wu, Tianfu Yin, Huan Zhang, Fumin Robotics Can we localize a robot on a map only using monocular vision? This study presents NuRF, an adaptive and nudged particle filter framework in radiance fields for 6-DoF robot visual localization. NuRF leverages recent advancements in radiance fields and visual place recognition. Conventional visual place recognition meets the challenges of data sparsity and artifact-induced inaccuracies. By utilizing radiance field-generated novel views, NuRF enhances visual localization performance and combines coarse global localization with the fine-grained pose tracking of a particle filter, ensuring continuous and precise localization. Experimentally, our method converges 7 times faster than existing Monte Carlo-based methods and achieves localization accuracy within 1 meter, offering an efficient and resilient solution for indoor visual localization. |
| title | NuRF: Nudging the Particle Filter in Radiance Fields for Robot Visual Localization |
| topic | Robotics |
| url | https://arxiv.org/abs/2406.00312 |