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）已成为一种强大的三维场景表示范式，能够从稀疏且非结构化的传感器数据中提供高保真度的渲染与重建。在自主机器人领域，环境感知与理解至关重要，NeRF 展现出提升系统性能的巨大潜力。本文对利用 NeRF 增强自主机器人能力的前沿技术进行了全面综述与分析。我们特别聚焦于自主机器人的感知、定位与导航以及决策模块，深入探讨对自主运行至关重要的任务，包括三维重建、分割、位姿估计、同步定位与建图（SLAM）、导航与规划以及交互。本综述细致地对现有基于 NeRF 的方法进行了基准测试，剖析其优势与局限。此外，我们探索了该领域未来研究与发展的潜在方向，重点讨论了三维高斯溅射（3DGS）、大语言模型（LLM）及生成式人工智能等先进技术的融合，展望其在提升重建效率、场景理解与决策能力方面的前景。本综述旨在为寻求利用 NeRF 赋能自主机器人的研究者提供路线图，为在复杂环境中实现无缝导航与交互的创新解决方案铺平道路。