Real-Time Stochastic Terrain Mapping and Processing for Autonomous Safe Landing
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...
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Main Authors | , |
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Format | Journal Article |
Language | English |
Published |
14.09.2024
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Subjects | |
Online Access | Get full text |
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Summary: | 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. |
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DOI: | 10.48550/arxiv.2409.09309 |