We present Loc-NeRF, a real-time vision-based robot localization approach
that combines Monte Carlo localization and Neural Radiance Fields (NeRF). Our
system uses a pre-trained NeRF model as the map of an environment and can
localize itself in real-time using an RGB camera as the only exteroceptive
sensor onboard the robot. While neural radiance fields have seen significant
applications for visual rendering in computer vision and graphics, they have
found limited use in robotics. Existing approaches for NeRF-based localization
require both a good initial pose guess and significant computation, making them
impractical for real-time robotics applications. By using Monte Carlo
localization as a workhorse to estimate poses using a NeRF map model, Loc-NeRF
is able to perform localization faster than the state of the art and without
relying on an initial pose estimate. In addition to testing on synthetic data,
we also run our system using real data collected by a Clearpath Jackal UGV and
demonstrate for the first time the ability to perform real-time global
localization with neural radiance fields. We make our code publicly available
at this https URL

本篇论文介绍了一个结合蒙特卡罗定位 (Monte Carlo localization) 和神经辐射场模型 (Neural Radiance Fields) 的机器人实时视觉定位方法 ——Loc-NeRF，它不依赖于初始位姿估计，可以使用机器人上的 RGB 相机在预训练的 NeRF 模型地图上实现实时本地化，相比现有技术能够更快地完成本地化。我们通过 Clearpath Jackal UGV 的真实数据验证了该方法的有效性，并将代码公开在一个 https URL 上。