# Real-Time Stochastic Terrain Mapping and Processing for Autonomous Safe Landing

arXiv: https://arxiv.org/abs/2409.09309 · HTML: https://mlsanity.com/paper/2409.09309/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 63 references that are in the map.
- First posted on arXiv 14 Sep 2024.

## Builds on (most cited first, 4 of 4)

- [Bayesian Deep Learning for Segmentation for Autonomous Safe Planetary Landing](https://mlsanity.com/paper/2102.10545/) (2021)
- [Multi-Resolution Elevation Mapping and Safe Landing Site Detection with Applications to Planetary Rotorcraft](https://mlsanity.com/paper/2111.06271/) (2021)
- [Hazard Detection And Avoidance For The Nova-C Lander](https://mlsanity.com/paper/2204.00660/) (2022)
- [Deep Monocular Hazard Detection for Safe Small Body Landing](https://mlsanity.com/paper/2301.13254/) (2023)

## Abstract (arXiv)

Onboard terrain sensing and mapping for safe planetary landings often suffer from missed hazardous features, e.g., small rocks, due to the large observational range and the limited resolution of the obtained terrain data. To this end, this paper develops a novel real-time stochastic terrain mapping algorithm that accounts for topographic uncertainty between the sampled points, or the uncertainty due to the sparse 3D terrain measurements. We introduce a Gaussian digital elevation map that is efficiently constructed using the combination of Delauney triangulation and local Gaussian process regression. The geometric investigation of the lander-terrain interaction is exploited to efficiently evaluate the marginally conservative local slope and roughness while avoiding the costly computation of the local plane. The conservativeness is proved in the paper. The developed real-time uncertainty quantification pipeline enables stochastic landing safety evaluation under challenging operational conditions, such as a large observational range or limited sensor capability, which is a critical stepping stone for the development of predictive guidance algorithms for safe autonomous planetary landing. Detailed reviews on background and related works are also presented.