LocoNeRF: NeRFを用いた局所Structure from Motionによる高精度な自己位置推定
LocoNeRF: A NeRF-based Approach for Local Structure from Motion for Precise Localization
画像データベースの代わりにNeRFモデルを利用して局所的なSfMを行い、ストレージを削減しつつ高精度な視覚的自己位置推定を実現する手法を提案した。
著者: Artem Nenashev, Mikhail Kurenkov, Andrei Potapov, Iana Zhura, Maksim Katerishich, Dzmitry Tsetserukou
分類: cs.CV, cs.RO
原文アブストラクト
Visual localization is a critical task in mobile robotics, and researchers are continuously developing new approaches to enhance its efficiency. In this article, we propose a novel approach to improve the accuracy of visual localization using Structure from Motion (SfM) techniques. We highlight the limitations of global SfM, which suffers from high latency, and the challenges of local SfM, which requires large image databases for accurate reconstruction. To address these issues, we propose utilizing Neural Radiance Fields (NeRF), as opposed to image databases, to cut down on the space required for storage. We suggest that sampling reference images around the prior query position can lead to further improvements. We evaluate the accuracy of our proposed method against ground truth obtained using LIDAR and Advanced Lidar Odometry and Mapping in Real-time (A-LOAM), and compare its storage usage against local SfM with COLMAP in the conducted experiments. Our proposed method achieves an accuracy of 0.068 meters compared to the ground truth, which is slightly lower than the most advanced method COLMAP, which has an accuracy of 0.022 meters. However, the size of the database required for COLMAP is 400 megabytes, whereas the size of our NeRF model is only 160 megabytes. Finally, we perform an ablation study to assess the impact of using reference images from the NeRF reconstruction.
