用NeRF构建外星地形地图，实现自主避障路径规划
Neural Radiance Maps for Extraterrestrial Navigation and Path Planning
- 用稀疏图像训练NeRF生成全局3D地形图
- 基于地形特征插值实现在线重规划，避开不可通行区域
- 仿真测试显示成功率更高、路径成本更低
当前火星车等自主探测器在地外天体表面探索中处于前沿地位。为加速科学目标达成，亟需为这些车辆规划安全高效的路径。然而，现有自主系统受限于难以快速构建并存储的全局地图。最近提出的神经辐射场（NeRF）可从稀疏2D图像中训练出高精度3D场景表示，并具备高效存储能力。本文提出利用NeRF构建可用于在线导航的地图，并设计了一套融合局部与全局信息的规划框架。该方法基于从NeRF地图中提取的地形特征，使用核岭回归对局部代价进行跨区域插值，使探测车能在运行过程中发现不可通行区域后自主改道。我们在高保真仿真环境中验证了该方法，在多种基线对比下展现出更低路径代价和更高成功率。
原文摘要 · Abstract (English)
Autonomous vehicles such as the Mars rovers currently lead the vanguard of surface exploration on extraterrestrial planets and moons. In order to accelerate the pace of exploration and science objectives, it is critical to plan safe and efficient paths for these vehicles. However, current rover autonomy is limited by a lack of global maps which can be easily constructed and stored for onboard re-planning. Recently, Neural Radiance Fields (NeRFs) have been introduced as a detailed 3D scene representation which can be trained from sparse 2D images and efficiently stored. We propose to use NeRFs to construct maps for online use in autonomous navigation, and present a planning framework which leverages the NeRF map to integrate local and global information. Our approach interpolates local cost observations across global regions using kernel ridge regression over terrain features extracted from the NeRF map, allowing the rover to re-route itself around untraversable areas discovered during online operation. We validate our approach in high-fidelity simulation and demonstrate lower cost and higher percentage success rate path planning compared to various baselines.
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