NuRF: ラディアンスフィールドにおけるパーティクルフィルタ誘導によるロボット視覚自己位置推定
NuRF: Nudging the Particle Filter in Radiance Fields for Robot Visual Localization
単眼視のみでロボットの6自由度自己位置推定を行うため、ラディアンスフィールドで生成した新規視点とパーティクルフィルタを組み合わせた適応的フレームワークを提案し、従来のモンテカルロ法より7倍速く収束して1m以内の精度を達成した。
著者: Wugang Meng, Tianfu Wu, Huan Yin, Fumin Zhang
分類: cs.RO
原文アブストラクト
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.
