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Highly Accurate Visual Method of Mars Terrain Classification for Rovers Based on Novel Image Features
It is important for Mars exploration rovers to achieve autonomous and safe mobility over rough terrain. Terrain classification can help rovers to select a safe terrain to traverse and avoid sinking and/or damaging the vehicle. Mars terrains are often classified using visual methods. However, the acc...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9498237/ https://www.ncbi.nlm.nih.gov/pubmed/36141190 http://dx.doi.org/10.3390/e24091304 |
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author | Lv, Fengtian Li, Nan Liu, Chuankai Gao, Haibo Ding, Liang Deng, Zongquan Liu, Guangjun |
author_facet | Lv, Fengtian Li, Nan Liu, Chuankai Gao, Haibo Ding, Liang Deng, Zongquan Liu, Guangjun |
author_sort | Lv, Fengtian |
collection | PubMed |
description | It is important for Mars exploration rovers to achieve autonomous and safe mobility over rough terrain. Terrain classification can help rovers to select a safe terrain to traverse and avoid sinking and/or damaging the vehicle. Mars terrains are often classified using visual methods. However, the accuracy of terrain classification has been less than 90% in read operations. A high-accuracy vision-based method for Mars terrain classification is presented in this paper. By analyzing Mars terrain characteristics, novel image features, including multiscale gray gradient-grade features, multiscale edges strength-grade features, multiscale frequency-domain mean amplitude features, multiscale spectrum symmetry features, and multiscale spectrum amplitude-moment features, are proposed that are specifically targeted for terrain classification. Three classifiers, K-nearest neighbor (KNN), support vector machine (SVM), and random forests (RF), are adopted to classify the terrain using the proposed features. The Mars image dataset MSLNet that was collected by the Mars Science Laboratory (MSL, Curiosity) rover is used to conduct terrain classification experiments. The resolution of Mars images in the dataset is 256 × 256. Experimental results indicate that the RF classifies Mars terrain at the highest level of accuracy of 94.66%. |
format | Online Article Text |
id | pubmed-9498237 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-94982372022-09-23 Highly Accurate Visual Method of Mars Terrain Classification for Rovers Based on Novel Image Features Lv, Fengtian Li, Nan Liu, Chuankai Gao, Haibo Ding, Liang Deng, Zongquan Liu, Guangjun Entropy (Basel) Article It is important for Mars exploration rovers to achieve autonomous and safe mobility over rough terrain. Terrain classification can help rovers to select a safe terrain to traverse and avoid sinking and/or damaging the vehicle. Mars terrains are often classified using visual methods. However, the accuracy of terrain classification has been less than 90% in read operations. A high-accuracy vision-based method for Mars terrain classification is presented in this paper. By analyzing Mars terrain characteristics, novel image features, including multiscale gray gradient-grade features, multiscale edges strength-grade features, multiscale frequency-domain mean amplitude features, multiscale spectrum symmetry features, and multiscale spectrum amplitude-moment features, are proposed that are specifically targeted for terrain classification. Three classifiers, K-nearest neighbor (KNN), support vector machine (SVM), and random forests (RF), are adopted to classify the terrain using the proposed features. The Mars image dataset MSLNet that was collected by the Mars Science Laboratory (MSL, Curiosity) rover is used to conduct terrain classification experiments. The resolution of Mars images in the dataset is 256 × 256. Experimental results indicate that the RF classifies Mars terrain at the highest level of accuracy of 94.66%. MDPI 2022-09-15 /pmc/articles/PMC9498237/ /pubmed/36141190 http://dx.doi.org/10.3390/e24091304 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Lv, Fengtian Li, Nan Liu, Chuankai Gao, Haibo Ding, Liang Deng, Zongquan Liu, Guangjun Highly Accurate Visual Method of Mars Terrain Classification for Rovers Based on Novel Image Features |
title | Highly Accurate Visual Method of Mars Terrain Classification for Rovers Based on Novel Image Features |
title_full | Highly Accurate Visual Method of Mars Terrain Classification for Rovers Based on Novel Image Features |
title_fullStr | Highly Accurate Visual Method of Mars Terrain Classification for Rovers Based on Novel Image Features |
title_full_unstemmed | Highly Accurate Visual Method of Mars Terrain Classification for Rovers Based on Novel Image Features |
title_short | Highly Accurate Visual Method of Mars Terrain Classification for Rovers Based on Novel Image Features |
title_sort | highly accurate visual method of mars terrain classification for rovers based on novel image features |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9498237/ https://www.ncbi.nlm.nih.gov/pubmed/36141190 http://dx.doi.org/10.3390/e24091304 |
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