Neural Radiance Maps for Extraterrestrial Navigation and Path Planning
This work addresses the challenge of efficient and safe path planning for autonomous rovers on extraterrestrial surfaces, which is incremental as it applies an existing method (NeRFs) to a new domain (extraterrestrial navigation).
The paper tackles the problem of autonomous navigation for extraterrestrial rovers by using Neural Radiance Fields (NeRFs) to construct global maps from sparse 2D images, enabling online path planning that integrates local and global information; the result is a planning framework that achieves lower cost and higher success rates in simulations compared to baselines.
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.