Achieving efficient and consistent localization a prior map remains challenging in robotics. Conventional keyframe-based approaches often suffers from sub-optimal viewpoints due to limited field of view (FOV) and/or constrained motion, thus degrading the localization performance. To address this issue, we design a real-time tightly-coupled Neural Radiance Fields (NeRF)-aided visual-inertial navigation system (VINS). In particular, by effectively leveraging the NeRF's potential to synthesize novel views, the proposed NeRF-VINS overcomes the limitations of traditional keyframe-based maps (with limited views) and optimally fuses IMU, monocular images, and synthetically rendered images within an efficient filter-based framework. This tightly-coupled fusion enables efficient 3D motion tracking with bounded errors. We extensively compare the proposed NeRF-VINS against the state-of-the-art methods that use prior map information and demonstrate its ability to perform real-time localization, at over 10 Hz, on a resource-constrained Jetson AGX Orin embedded platform.

翻译：在机器人领域中，利用先验地图实现高效且一致的定位仍是一项挑战。传统的基于关键帧的方法常因视场角（FOV）受限和/或运动约束导致视点不理想，从而降低定位性能。为解决这一问题，我们设计了一种基于神经辐射场（NeRF）辅助的实时紧耦合视觉-惯性导航系统（VINS）。具体而言，通过有效利用NeRF合成新视角的潜力，所提出的NeRF-VINS克服了传统基于关键帧地图（视角有限）的局限性，并在高效的滤波框架内最优地融合了惯性测量单元（IMU）、单目图像以及合成渲染图像。这种紧耦合融合可实现具有有界误差的高效三维运动跟踪。我们将所提出的NeRF-VINS与使用先验地图信息的最新方法进行了广泛比较，并证明了其能够在资源受限的Jetson AGX Orin嵌入式平台上以超过10 Hz的频率实现实时定位。