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

2024-09-14

arXiv.org

AI Summary

To address the problem of undetected micro-scale hazards—such as small rocks—due to sparse point clouds in long-range planetary autonomous landing, this paper proposes a high-confidence, real-time terrain modeling and safety assessment method. The approach innovatively integrates Delaunay triangulation with local Gaussian process regression to construct a stochastic digital elevation map (SDEM), enabling rigorous quantification of terrain uncertainty. It further introduces a conservative slope and roughness estimation algorithm that avoids local planar fitting, with formal geometric proof of its conservativeness. Under constraints of large standoff distances and low-resolution sensors, the method achieves millisecond-level terrain reconstruction and probabilistic landing safety evaluation. Experimental results demonstrate significantly improved hazard detection completeness, thereby providing critical support for predictive safe-landing guidance systems.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Planning under Uncertainty
• Reasoning under Uncertainty: Uncertainty Representations

Application Category

• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

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.

Problem

• Real-time mapping of hazardous terrain features for safe landings
• Addressing uncertainty in sparse 3D terrain measurements
• Enabling stochastic safety evaluation under challenging operational conditions

Innovation

• Real-time stochastic terrain mapping algorithm
• Gaussian digital elevation map construction
• Efficient lander-terrain interaction evaluation