A Stochastic Approach to Terrain Maps for Safe Lunar Landing

• URL: http://arxiv.org/abs/2512.12058v1
• Date: Fri, 12 Dec 2025 21:56:08 GMT
• Title: A Stochastic Approach to Terrain Maps for Safe Lunar Landing
• Abstract summary: Traditional vision-based hazard detection methods are not reliable on the lunar surface.<n>The potential existence of valuable resources at the lunar South Pole has made landing in that region a high priority for many space agencies and commercial companies.<n> relying on a LiDAR for hazard detection during descent is risky, as this technology is fairly untested in the lunar environment.<n>There exists a rich log of lunar surface data from the Lunar Reconnaissance Orbiter (LRO), which could be used to create prior maps of the surface before descent.
• Score: 8.1140131376627
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Safely landing on the lunar surface is a challenging task, especially in the heavily-shadowed South Pole region where traditional vision-based hazard detection methods are not reliable. The potential existence of valuable resources at the lunar South Pole has made landing in that region a high priority for many space agencies and commercial companies. However, relying on a LiDAR for hazard detection during descent is risky, as this technology is fairly untested in the lunar environment. There exists a rich log of lunar surface data from the Lunar Reconnaissance Orbiter (LRO), which could be used to create informative prior maps of the surface before descent. In this work, we propose a method for generating stochastic elevation maps from LRO data using Gaussian processes (GPs), which are a powerful Bayesian framework for non-parametric modeling that produce accompanying uncertainty estimates. In high-risk environments such as autonomous spaceflight, interpretable estimates of terrain uncertainty are critical. However, no previous approaches to stochastic elevation mapping have taken LRO Digital Elevation Model (DEM) confidence maps into account, despite this data containing key information about the quality of the DEM in different areas. To address this gap, we introduce a two-stage GP model in which a secondary GP learns spatially varying noise characteristics from DEM confidence data. This heteroscedastic information is then used to inform the noise parameters for the primary GP, which models the lunar terrain. Additionally, we use stochastic variational GPs to enable scalable training. By leveraging GPs, we are able to more accurately model the impact of heteroscedastic sensor noise on the resulting elevation map. As a result, our method produces more informative terrain uncertainty, which can be used for downstream tasks such as hazard detection and safe landing site selection.

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