# Neural Radiance Maps for Extraterrestrial Navigation and Path Planning

arXiv: https://arxiv.org/abs/2603.17236 · HTML: https://mlsanity.com/paper/2603.17236/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 25 references that are in the map.
- First posted on arXiv 18 Mar 2026.

## Builds on (most cited first, 11 of 11)

- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [KiloNeRF: Speeding up Neural Radiance Fields with Thousands of Tiny MLPs](https://mlsanity.com/paper/2103.13744/) (2021)
- [Nerfstudio: A Modular Framework for Neural Radiance Field Development](https://mlsanity.com/paper/2302.04264/) (2023)
- [AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles](https://mlsanity.com/paper/1705.05065/) (2017)
- [FastNeRF: High-Fidelity Neural Rendering at 200FPS](https://mlsanity.com/paper/2103.10380/) (2021)
- [BungeeNeRF: Progressive Neural Radiance Field for Extreme Multi-scale Scene Rendering](https://mlsanity.com/paper/2112.05504/) (2021)
- [Vision-Only Robot Navigation in a Neural Radiance World](https://mlsanity.com/paper/2110.00168/) (2021)
- [CATNIPS: Collision Avoidance Through Neural Implicit Probabilistic Scenes](https://mlsanity.com/paper/2302.12931/) (2023)
- [Machine Learning Based Path Planning for Improved Rover Navigation (Pre-Print Version)](https://mlsanity.com/paper/2011.06022/) (2020)
- [MLNav: Learning to Safely Navigate on Martian Terrains](https://mlsanity.com/paper/2203.04563/) (2022)
- [MaRF: Representing Mars as Neural Radiance Fields](https://mlsanity.com/paper/2212.01672/) (2022)

## Abstract (arXiv)

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.