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

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 Experim...

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Bibliographic Details
Published in:IEEE access Vol. 10; pp. 41766 - 41775
Main Authors: Claudet, Thomas, Tomita, Kento, Ho, Koki
Format: Journal Article
Language:English
Published: Piscataway IEEE 2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
aerospace safety
Algorithms
Artificial neural networks
Benchmark testing
Commercial spacecraft
Complexity
Computer architecture
Image resolution
Image segmentation
Microcontrollers
NASA
Performance indices
Planetary landing
Prediction algorithms
Semantic segmentation
Semantics
Site selection
Space exploration
Space flight
Space vehicles
ISSN:2169-3536, 2169-3536
Online Access:Get full text
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Summary: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.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2022.3167763