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

TL;DR

This paper introduces a real-time stochastic terrain mapping algorithm using Gaussian digital elevation maps, enhancing autonomous planetary landing safety by accounting for topographic uncertainties with efficient computation.

Contribution

The paper presents a novel real-time stochastic terrain mapping method combining Delauney triangulation and Gaussian process regression, improving hazard detection during autonomous landings.

Findings

Efficient construction of Gaussian digital elevation maps.

Conservative evaluation of slope and roughness.

Enables stochastic safety assessment under limited sensor data.

Abstract

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.