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.
@article{arxiv.2406.00312,
title = {NuRF: Nudging the Particle Filter in Radiance Fields for Robot Visual Localization},
author = {Wugang Meng and Tianfu Wu and Huan Yin and Fumin Zhang},
journal= {arXiv preprint arXiv:2406.00312},
year = {2025}
}
Comments
Accepted for Publication in IEEE Transactions on Cognitive and Developmental Systems