Neural Radiance Maps for Extraterrestrial Navigation and Path Planning

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 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.