Optimization of a Dense Mapping Algorithm with Enhanced Point-Line Features for Open-Pit Mining Environments
This study introduces an enhanced ORB-SLAM3 algorithm to address the limitations of traditional visual SLAM systems in feature extraction and localization accuracy within the challenging terrains of open-pit mining environments. It also tackles the issue of sparse point cloud maps for mobile robot n...
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| Format: | Article |
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MDPI AG
2025-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/7/3579 |
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| author | Yuanbin Xiao Bing Li Wubin Xu Weixin Zhou Bo Xu Hanwen Zhang |
| author_facet | Yuanbin Xiao Bing Li Wubin Xu Weixin Zhou Bo Xu Hanwen Zhang |
| author_sort | Yuanbin Xiao |
| collection | DOAJ |
| description | This study introduces an enhanced ORB-SLAM3 algorithm to address the limitations of traditional visual SLAM systems in feature extraction and localization accuracy within the challenging terrains of open-pit mining environments. It also tackles the issue of sparse point cloud maps for mobile robot navigation. By combining point-line features with a Micro-Electro-Mechanical System (MEMS) Inertial Measurement Unit (IMU), the algorithm improves the feature matching’s reliability, particularly in low-texture areas. The method integrates dense point cloud mapping and an octree structure, optimizing both navigation and path planning while reducing storage demands and improving query efficiency. The experimental results using the TUM dataset and conducting tests in a simulated open-pit mining environment show that the proposed algorithm reduces the absolute trajectory error by 44.33% and the relative trajectory error by 14.34% compared to the ORB-SLAM3. The algorithm generates high-precision dense point cloud maps and uses an octree structure for efficient 3D spatial representation. In simulated open-pit mining scenarios, the dense mapping outperforms at reconstructing complex terrains, especially in low-texture gravel and uneven surfaces. These results highlight the robustness and practical applicability of the algorithm in dynamic and challenging environments, such as open-pit mining. |
| format | Article |
| id | doaj-art-2aeef7cd17e74a7c9f69ff120431dccb |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-2aeef7cd17e74a7c9f69ff120431dccb2025-08-20T02:09:13ZengMDPI AGApplied Sciences2076-34172025-03-01157357910.3390/app15073579Optimization of a Dense Mapping Algorithm with Enhanced Point-Line Features for Open-Pit Mining EnvironmentsYuanbin Xiao0Bing Li1Wubin Xu2Weixin Zhou3Bo Xu4Hanwen Zhang5Guangxi Electrical Polytechnic Institute, Nanning 530007, ChinaSchool of Mechanical and Automotive Engineering, Guangxi University of Science and Technology, Liuzhou 545006, ChinaGuangxi Electrical Polytechnic Institute, Nanning 530007, ChinaSchool of Mechanical and Automotive Engineering, Guangxi University of Science and Technology, Liuzhou 545006, ChinaSchool of Mechanical and Automotive Engineering, Guangxi University of Science and Technology, Liuzhou 545006, ChinaSchool of Mechanical and Automotive Engineering, Guangxi University of Science and Technology, Liuzhou 545006, ChinaThis study introduces an enhanced ORB-SLAM3 algorithm to address the limitations of traditional visual SLAM systems in feature extraction and localization accuracy within the challenging terrains of open-pit mining environments. It also tackles the issue of sparse point cloud maps for mobile robot navigation. By combining point-line features with a Micro-Electro-Mechanical System (MEMS) Inertial Measurement Unit (IMU), the algorithm improves the feature matching’s reliability, particularly in low-texture areas. The method integrates dense point cloud mapping and an octree structure, optimizing both navigation and path planning while reducing storage demands and improving query efficiency. The experimental results using the TUM dataset and conducting tests in a simulated open-pit mining environment show that the proposed algorithm reduces the absolute trajectory error by 44.33% and the relative trajectory error by 14.34% compared to the ORB-SLAM3. The algorithm generates high-precision dense point cloud maps and uses an octree structure for efficient 3D spatial representation. In simulated open-pit mining scenarios, the dense mapping outperforms at reconstructing complex terrains, especially in low-texture gravel and uneven surfaces. These results highlight the robustness and practical applicability of the algorithm in dynamic and challenging environments, such as open-pit mining.https://www.mdpi.com/2076-3417/15/7/3579point-line featuresopen-pit miningdense mappingoctree mapvisual SLAM |
| spellingShingle | Yuanbin Xiao Bing Li Wubin Xu Weixin Zhou Bo Xu Hanwen Zhang Optimization of a Dense Mapping Algorithm with Enhanced Point-Line Features for Open-Pit Mining Environments Applied Sciences point-line features open-pit mining dense mapping octree map visual SLAM |
| title | Optimization of a Dense Mapping Algorithm with Enhanced Point-Line Features for Open-Pit Mining Environments |
| title_full | Optimization of a Dense Mapping Algorithm with Enhanced Point-Line Features for Open-Pit Mining Environments |
| title_fullStr | Optimization of a Dense Mapping Algorithm with Enhanced Point-Line Features for Open-Pit Mining Environments |
| title_full_unstemmed | Optimization of a Dense Mapping Algorithm with Enhanced Point-Line Features for Open-Pit Mining Environments |
| title_short | Optimization of a Dense Mapping Algorithm with Enhanced Point-Line Features for Open-Pit Mining Environments |
| title_sort | optimization of a dense mapping algorithm with enhanced point line features for open pit mining environments |
| topic | point-line features open-pit mining dense mapping octree map visual SLAM |
| url | https://www.mdpi.com/2076-3417/15/7/3579 |
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