Planetary exploration increasingly relies on autonomous robotic systems capable of perceiving, interpreting, and reconstructing their surroundings in the absence of global positioning or real-time communication with Earth. Rovers operating on planetary surfaces must navigate under sever environmental constraints, limited visual redundancy, and communication delays, making onboard spatial awareness and visual localization key components for mission success. Traditional techniques based on Structure-from-Motion (SfM) and Simultaneous Localization and Mapping (SLAM) provide geometric consistency but struggle to capture radiometric detail or to scale efficiently in unstructured, low-texture terrains typical of extraterrestrial environments. This work explores the integration of radiance field-based methods - specifically Neural Radiance Fields (NeRF) and Gaussian Splatting - into a unified, automated environment reconstruction pipeline for planetary robotics. Our system combines the Nerfstudio and COLMAP frameworks with a ROS2-compatible workflow capable of processing raw rover data directly from rosbag recordings. This approach enables the generation of dense, photorealistic, and metrically consistent 3D representations from minimal visual input, supporting improved perception and planning for autonomous systems operating in planetary-like conditions. The resulting pipeline established a foundation for future research in radiance field-based mapping, bridging the gap between geometric and neural representations in planetary exploration.

翻译：行星探索日益依赖于能够在缺乏全球定位或与地球实时通信的情况下感知、解释并重建其周围环境的自主机器人系统。在行星表面运行的火星车必须在严峻的环境约束、有限的视觉冗余和通信延迟下进行导航，这使得机载空间感知和视觉定位成为任务成功的关键组成部分。基于运动恢复结构（SfM）和同步定位与建图（SLAM）的传统技术虽然能保证几何一致性，但在捕捉辐射度细节或在外星环境中典型的非结构化、低纹理地形中高效扩展方面存在困难。本研究探讨了将基于辐射场的方法——特别是神经辐射场（NeRF）和高斯泼溅——集成到一个统一的、自动化的行星机器人环境重建流程中。我们的系统将Nerfstudio和COLMAP框架与一个兼容ROS2的工作流程相结合，该流程能够直接处理来自rosbag记录的原始火星车数据。这种方法能够从最少的视觉输入中生成密集、逼真且度量一致的三维表示，为在类行星条件下运行的自主系统提供改进的感知和规划支持。所建立的流程为未来基于辐射场的建图研究奠定了基础，弥合了行星探索中几何表示与神经表示之间的差距。