Neural Radiance Maps for Extraterrestrial Navigation and Path Planning
Adam Dai, Shubh Gupta, Grace Gao

TL;DR
This paper introduces a novel method using Neural Radiance Fields (NeRFs) to create detailed 3D maps for extraterrestrial vehicle navigation, enabling efficient online path planning and obstacle avoidance.
Contribution
It presents a new framework that leverages NeRFs for real-time global map construction and integrates local and global planning for autonomous extraterrestrial navigation.
Findings
Lower path planning cost in simulations
Higher success rate in obstacle avoidance
Effective online re-routing around untraversable areas
Abstract
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsRobotic Path Planning Algorithms · Robotics and Sensor-Based Localization · Advanced Neural Network Applications
