Neural Radiance Fields (NeRF) have emerged as a powerful paradigm for 3D scene representation, offering high-fidelity renderings and reconstructions from a set of sparse and unstructured sensor data. In the context of autonomous robotics, where perception and understanding of the environment are pivotal, NeRF holds immense promise for improving performance. In this paper, we present a comprehensive survey and analysis of the state-of-the-art techniques for utilizing NeRF to enhance the capabilities of autonomous robots. We especially focus on the perception, localization and navigation, and decision-making modules of autonomous robots and delve into tasks crucial for autonomous operation, including 3D reconstruction, segmentation, pose estimation, simultaneous localization and mapping (SLAM), navigation and planning, and interaction. Our survey meticulously benchmarks existing NeRF-based methods, providing insights into their strengths and limitations. Moreover, we explore promising avenues for future research and development in this domain. Notably, we discuss the integration of advanced techniques such as 3D Gaussian splatting (3DGS), large language models (LLM), and generative AIs, envisioning enhanced reconstruction efficiency, scene understanding, decision-making capabilities. This survey serves as a roadmap for researchers seeking to leverage NeRFs to empower autonomous robots, paving the way for innovative solutions that can navigate and interact seamlessly in complex environments.

翻译：神经辐射场（NeRF）已成为三维场景表示的有力范式，能够从稀疏且非结构化的传感器数据中实现高保真渲染与重建。在自主机器人领域——其核心在于环境感知与理解——NeRF对提升性能展现出巨大潜力。本文对利用NeRF增强自主机器人能力的最新前沿技术进行了全面综述与分析。我们特别聚焦自主机器人的感知、定位与导航以及决策模块，深入探究对自主操作至关重要的任务，包括三维重建、分割、姿态估计、同步定位与地图构建（SLAM）、导航规划及交互。我们的综述对现有基于NeRF的方法进行了严谨的基准测试，揭示了其优势与局限性。此外，我们探索了该领域未来研究与发展中有前景的方向。尤其值得关注的是，我们讨论了三维高斯泼溅（3DGS）、大语言模型（LLM）及生成式AI等先进技术的融合，展望了在重建效率、场景理解及决策能力上的提升。本综述为旨在利用NeRF赋能自主机器人的研究者提供了路线图，为在复杂环境中实现无缝导航与交互的创新方案铺平了道路。