In recent years, Neural Radiance Fields (NeRF) have emerged as a powerful tool for 3D reconstruction and novel view synthesis. However, the computational cost of NeRF rendering and degradation in quality due to the presence of artifacts pose significant challenges for its application in real-time and robust robotic tasks, especially on embedded systems. This paper introduces a novel framework that integrates NeRF-derived localization information with Visual-Inertial Odometry(VIO) to provide a robust solution for robotic navigation in a real-time. By training an absolute pose regression network with augmented image data rendered from a NeRF and quantifying its uncertainty, our approach effectively counters positional drift and enhances system reliability. We also establish a mathematically sound foundation for combining visual inertial navigation with camera localization neural networks, considering uncertainty under a Bayesian framework. Experimental validation in the photorealistic simulation environment demonstrates significant improvements in accuracy compared to a conventional VIO approach.

翻译：近年来，神经辐射场（NeRF）已成为三维重建和新视角合成的强大工具。然而，NeRF渲染的计算成本以及伪影导致的质量退化，对其在实时和鲁棒机器人任务（尤其是嵌入式系统）中的应用构成了重大挑战。本文提出了一种新颖框架，将NeRF导出的定位信息与视觉惯性里程计（VIO）相结合，为实时机器人导航提供鲁棒解决方案。通过训练基于NeRF渲染增强图像数据的绝对位姿回归网络并量化其不确定性，本方法有效抑制了位置漂移，增强了系统可靠性。此外，我们还在贝叶斯框架下建立了融合视觉惯性导航与相机定位神经网络的数学基础，考虑了不确定性。在逼真仿真环境中的实验验证表明，与传统VIO方法相比，本方法在精度上取得了显著提升。