Highly Accurate Visual Method of Mars Terrain Classification for Rovers Based on Novel Image Features Article Swipe
Fengtian Lv
,
Chuankai Liu
,
Haibo Gao
,
Liang Ding
,
Zongquan Deng
,
Guang Jun Liu
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.32920/25365370.v1
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.32920/25365370.v1
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%.
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- Type
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- Language
- en
- Landing Page
- https://doi.org/10.32920/25365370.v1
- OA Status
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- References
- 26
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- OpenAlex ID
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Highly Accurate Visual Method of Mars Terrain Classification for Rovers Based on Novel Image FeaturesWork title
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preprintOpenAlex work type
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enPrimary language
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2024Year of publication
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2024-03-07Full publication date if available
- Authors
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Fengtian Lv, Chuankai Liu, Haibo Gao, Liang Ding, Zongquan Deng, Guang Jun LiuList of authors in order
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https://doi.org/10.32920/25365370.v1Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.32920/25365370.v1Direct OA link when available
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Mars Exploration Program, Terrain, Artificial intelligence, Computer science, Computer vision, Exploration of Mars, Image (mathematics), Remote sensing, Pattern recognition (psychology), Geology, Geography, Cartography, Astrobiology, PhysicsTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| referenced_works_count | 26 |
| abstract_inverted_index.A | 57 |
| abstract_inverted_index.a | 23 |
| abstract_inverted_index.By | 70 |
| abstract_inverted_index.RF | 173 |
| abstract_inverted_index.at | 177 |
| abstract_inverted_index.by | 141 |
| abstract_inverted_index.in | 54, 67, 161 |
| abstract_inverted_index.is | 1, 65, 149, 164 |
| abstract_inverted_index.of | 46, 158, 181, 183 |
| abstract_inverted_index.to | 7, 21, 26, 125, 151 |
| abstract_inverted_index.× | 166 |
| abstract_inverted_index.256 | 165 |
| abstract_inverted_index.90% | 53 |
| abstract_inverted_index.The | 133, 156 |
| abstract_inverted_index.and | 10, 28, 96, 119 |
| abstract_inverted_index.are | 37, 101, 104, 123 |
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| abstract_inverted_index.has | 49 |
| abstract_inverted_index.the | 33, 44, 127, 130, 142, 162, 172, 178 |
| abstract_inverted_index.was | 139 |
| abstract_inverted_index.256. | 167 |
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| abstract_inverted_index.(MSL, | 146 |
| abstract_inverted_index.(RF), | 122 |
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| abstract_inverted_index.edges | 84 |
| abstract_inverted_index.image | 76, 135 |
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| abstract_inverted_index.rough | 14 |
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| abstract_inverted_index.using | 40, 129 |
| abstract_inverted_index.(KNN), | 114 |
| abstract_inverted_index.(SVM), | 118 |
| abstract_inverted_index.MSLNet | 137 |
| abstract_inverted_index.and/or | 31 |
| abstract_inverted_index.images | 160 |
| abstract_inverted_index.method | 60 |
| abstract_inverted_index.paper. | 69 |
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| abstract_inverted_index.rovers | 6, 20 |
| abstract_inverted_index.select | 22 |
| abstract_inverted_index.vector | 116 |
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| abstract_inverted_index.Terrain | 16 |
| abstract_inverted_index.achieve | 8 |
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| abstract_inverted_index.conduct | 152 |
| abstract_inverted_index.dataset | 136, 163 |
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| abstract_inverted_index.machine | 117 |
| abstract_inverted_index.results | 169 |
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| abstract_inverted_index.support | 115 |
| abstract_inverted_index.terrain | 25, 47, 63, 73, 108, 128, 153, 176 |
| abstract_inverted_index.However, | 43 |
| abstract_inverted_index.accuracy | 45, 182 |
| abstract_inverted_index.classify | 126 |
| abstract_inverted_index.damaging | 32 |
| abstract_inverted_index.indicate | 170 |
| abstract_inverted_index.methods. | 42 |
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| abstract_inverted_index.symmetry | 94 |
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| abstract_inverted_index.terrains | 36 |
| abstract_inverted_index.traverse | 27 |
| abstract_inverted_index.vehicle. | 34 |
| abstract_inverted_index.K-nearest | 112 |
| abstract_inverted_index.amplitude | 90 |
| abstract_inverted_index.analyzing | 71 |
| abstract_inverted_index.collected | 140 |
| abstract_inverted_index.features, | 77, 82, 86, 91, 95, 100 |
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| abstract_inverted_index.resolution | 157 |
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| abstract_inverted_index.exploration | 5 |
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| abstract_inverted_index.classifiers, | 111 |
| abstract_inverted_index.experiments. | 155 |
| abstract_inverted_index.specifically | 105 |
| abstract_inverted_index.vision-based | 59 |
| abstract_inverted_index.high-accuracy | 58 |
| abstract_inverted_index.classification | 17, 48, 64, 154 |
| abstract_inverted_index.gradient-grade | 81 |
| abstract_inverted_index.strength-grade | 85 |
| abstract_inverted_index.classification. | 109 |
| abstract_inverted_index.amplitude-moment | 99 |
| abstract_inverted_index.characteristics, | 74 |
| abstract_inverted_index.frequency-domain | 88 |
| abstract_inverted_index.94.66%.</p> | 184 |
| cited_by_percentile_year | |
| countries_distinct_count | 2 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile.value | 0.02632303 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |