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.

翻译：视觉定位是移动机器人领域中的关键任务，研究人员持续开发新方法以提升其效率。本文提出一种新颖方法，利用运动恢复结构（SfM）技术提高视觉定位精度。我们指出全局SfM存在高延迟的局限性，而局部SfM在精确重建时需要大型图像数据库的挑战。为解决这些问题，我们提出采用神经辐射场（NeRF）替代图像数据库，以降低存储空间需求。研究表明，在先验查询位置周围采样参考图像可进一步改善性能。我们在实验中对比了所提方法与基于LIDAR及高级实时激光雷达里程计与建图（A-LOAM）获得的真实值之间的精度，并与采用COLMAP的局部SfM进行了存储使用量比较。所提方法达到0.068米的真实值定位精度，略低于最先进的COLMAP方法（精度为0.022米），但COLMAP所需数据库大小为400兆字节，而我们的NeRF模型仅为160兆字节。最后，我们进行消融研究以评估使用NeRF重建的参考图像的影响。