LocoNeRF: A NeRF-based Approach for Local Structure from Motion for Precise Localization

TL;DR

LocoNeRF introduces a NeRF-based method for visual localization that reduces storage needs while maintaining competitive accuracy compared to traditional SfM approaches, enhancing efficiency in mobile robotics.

Contribution

The paper presents a novel NeRF-based localization approach that minimizes storage requirements and improves accuracy by sampling reference images around the query position.

Findings

Achieves 0.068m localization accuracy, close to COLMAP's 0.022m.

Uses only 160MB of storage compared to COLMAP's 400MB.

Demonstrates effective localization with reduced storage in real-world tests.

Abstract

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.