论文标题
LOC-NERF：使用神经辐射场的蒙特卡洛定位
Loc-NeRF: Monte Carlo Localization using Neural Radiance Fields
论文作者
论文摘要
我们提出了LOC-NERF，这是一种基于实时视觉的机器人定位方法，结合了蒙特卡洛本地化和神经辐射场（NERF）。我们的系统使用预先训练的NERF模型作为环境的地图，可以使用RGB摄像机实时定位自身作为机器人唯一的外部感受传感器。尽管神经辐射场在计算机视觉和图形中看到了重要的视觉渲染应用，但他们发现在机器人技术中使用有限。现有的基于NERF的本地化方法需要良好的初始姿势猜测和重大的计算，这使得它们对于实时机器人技术应用不切实际。通过使用Monte Carlo定位作为使用NERF MAP模型估算姿势的主力，LOC-NERF能够比ART的状态更快地执行本地化，并且不依赖初始姿势估计。除了测试合成数据外，我们还使用ClearPath Jackal UGV收集的实际数据运行系统，并首次证明了使用神经辐射场进行实时全球定位的能力。我们在https://github.com/mit-spark/loc-nerf上公开代码。
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 https://github.com/MIT-SPARK/Loc-NeRF.
