Stochastic Hazard Detection For Landing Under Topographic Uncertainty

TL;DR

This paper presents a novel stochastic hazard detection algorithm for landing in uncertain terrains, utilizing Gaussian random field regression to handle imperfect and sparse sensor data, enhancing safety in unknown surface conditions.

Contribution

The paper introduces a new hazard detection method that extends stochastic algorithms to handle more general topographic uncertainties using Gaussian random field regression.

Findings

Effective hazard detection on Mars terrain models

Handles imperfect and sparse sensor measurements

Improves safety assessment in unknown environments

Abstract

Autonomous hazard detection and avoidance is a key technology for future landing missions in unknown surface conditions. Current state-of-the-art stochastic algorithms assume simple Gaussian measurement noise on dense, high-fidelity digital elevation maps, limiting the algorithm's applicability. This paper introduces a new stochastic hazard detection algorithm capable of more general topographic uncertainty by leveraging the Gaussian random field regression. The proposed approach enables the safety assessment with imperfect and sparse sensor measurements, which allows hazard detection operations under more diverse conditions. We demonstrate the performance of the proposed approach on the existing Mars digital terrain models.