Benchmark Analysis of Semantic Segmentation Algorithms for Safe Planetary Landing Site Selection

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T.Claudetetal.Benchmark_Analysis_of_Semantic_Segmentation_Algorithms_for_Safe_Planetary_Landing_Site_Selection(1).pdf (1.25 MB)
Author(s)
Claudet, Thomas
Tomita, Kento
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Daniel Guggenheim School of Aerospace Engineering
The Daniel Guggenheim School of Aeronautics was established in 1931, with a name change in 1962 to the School of Aerospace Engineering
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http://hdl.handle.net/1853/66806
Abstract
This paper presents an in-depth analysis of state-of-the-art semantic segmentation algorithms applied to spacecraft safe planetary landing via hazard detection and avoidance. Several architectures are trained from binary safety maps and the rich dataset of the High-Resolution Imaging Science Experiment (HiRISE) embedded on Mars Reconnaissance Orbiter for realistic purposes. The study incorporates several metrics comparisons such as recognition accuracy, computational complexity, model complexity, and inference time. The proposed performance indices and combinations are analyzed and discussed. The experiments were performed using a Raspberry Pi 4B, which is a relevant commercial-of-the-shelf microcontroller surrogate of NASA’s High-Performance Spaceflight Computer (HPSC) that will thrive within the next decades in space exploration. This paper allows researchers to know what has been tested on the subject and serves as a catalog for users to pick the most relevant architecture for their own application.
Sponsor
This work was supported in part by the National Aeronautics and Space Administration (NASA) through the NASA Early Career Faculty Program under Grant 80NSSC20K0064.
Date
2022-04
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Article
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Attribution 4.0 International
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http://creativecommons.org/licenses/by/4.0/