LocoNeRF: A NeRF-based Approach for Local Structure from Motion for
  Precise Localization
Artem Nenashev Mikhail Kurenkov Andrei Potapov Iana Zhura Maksim Katerishich Dzmitry Tsetserukou
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.