LUOJIA Explorer PMS: Panoramic Odometry and Mapping with Structural Information
Accurate dense reconstruction of unknown spatial environments is crucial for applications such as underground exploration and planetary missions. Existing methods face challenges like observing blind spots and the difficulty of edge feature extraction in point clouds with non-repetitive scanning LiD...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2025-01-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://isprs-archives.copernicus.org/articles/XLVIII-2-2024/485/2025/isprs-archives-XLVIII-2-2024-485-2025.pdf |
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| author | Y. Xu Y. Xu Y. Xu C. Chen C. Chen C. Chen B. Yang B. Yang B. Yang L. Li Z. Wang Z. Wang Z. Wang S. Sun S. Sun S. Sun Z. Yan Z. Yan Z. Yan S. Wu S. Wu S. Wu |
| author_facet | Y. Xu Y. Xu Y. Xu C. Chen C. Chen C. Chen B. Yang B. Yang B. Yang L. Li Z. Wang Z. Wang Z. Wang S. Sun S. Sun S. Sun Z. Yan Z. Yan Z. Yan S. Wu S. Wu S. Wu |
| author_sort | Y. Xu |
| collection | DOAJ |
| description | Accurate dense reconstruction of unknown spatial environments is crucial for applications such as underground exploration and planetary missions. Existing methods face challenges like observing blind spots and the difficulty of edge feature extraction in point clouds with non-repetitive scanning LiDARs. This paper first uses a novel odometry and mapping system integrating two solid-state LiDARs and an IMU to obtain distortion-compensated point clouds and corresponding poses, which are utilized to generate submaps. Our approach then leverages these accumulated submaps to efficiently extract edge features. Experimental results demonstrate that our submap-based method effectively identifies edge features within point clouds, which can be used for association with panoramas for joint optimization in the future. |
| format | Article |
| id | doaj-art-a2e176ea74f94fc4b823e8021a0c120d |
| institution | Kabale University |
| issn | 1682-1750 2194-9034 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj-art-a2e176ea74f94fc4b823e8021a0c120d2025-01-23T14:35:10ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342025-01-01XLVIII-2-202448549010.5194/isprs-archives-XLVIII-2-2024-485-2025LUOJIA Explorer PMS: Panoramic Odometry and Mapping with Structural InformationY. Xu0Y. Xu1Y. Xu2C. Chen3C. Chen4C. Chen5B. Yang6B. Yang7B. Yang8L. Li9Z. Wang10Z. Wang11Z. Wang12S. Sun13S. Sun14S. Sun15Z. Yan16Z. Yan17Z. Yan18S. Wu19S. Wu20S. Wu21State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaEngineering Research Center for Spatio-temporal Data Smart Acquisition and Application, Ministry of Education of China, Wuhan University, Wuhan 430079, ChinaInstitute of Geo-spatial intelligence, Wuhan University, Wuhan 430079, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaEngineering Research Center for Spatio-temporal Data Smart Acquisition and Application, Ministry of Education of China, Wuhan University, Wuhan 430079, ChinaInstitute of Geo-spatial intelligence, Wuhan University, Wuhan 430079, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaEngineering Research Center for Spatio-temporal Data Smart Acquisition and Application, Ministry of Education of China, Wuhan University, Wuhan 430079, ChinaInstitute of Geo-spatial intelligence, Wuhan University, Wuhan 430079, ChinaInstitute of Artificial Intelligence, School of Computer Science, Wuhan University, Wuhan 430079, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaEngineering Research Center for Spatio-temporal Data Smart Acquisition and Application, Ministry of Education of China, Wuhan University, Wuhan 430079, ChinaInstitute of Geo-spatial intelligence, Wuhan University, Wuhan 430079, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaEngineering Research Center for Spatio-temporal Data Smart Acquisition and Application, Ministry of Education of China, Wuhan University, Wuhan 430079, ChinaInstitute of Geo-spatial intelligence, Wuhan University, Wuhan 430079, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaEngineering Research Center for Spatio-temporal Data Smart Acquisition and Application, Ministry of Education of China, Wuhan University, Wuhan 430079, ChinaInstitute of Geo-spatial intelligence, Wuhan University, Wuhan 430079, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaEngineering Research Center for Spatio-temporal Data Smart Acquisition and Application, Ministry of Education of China, Wuhan University, Wuhan 430079, ChinaInstitute of Geo-spatial intelligence, Wuhan University, Wuhan 430079, ChinaAccurate dense reconstruction of unknown spatial environments is crucial for applications such as underground exploration and planetary missions. Existing methods face challenges like observing blind spots and the difficulty of edge feature extraction in point clouds with non-repetitive scanning LiDARs. This paper first uses a novel odometry and mapping system integrating two solid-state LiDARs and an IMU to obtain distortion-compensated point clouds and corresponding poses, which are utilized to generate submaps. Our approach then leverages these accumulated submaps to efficiently extract edge features. Experimental results demonstrate that our submap-based method effectively identifies edge features within point clouds, which can be used for association with panoramas for joint optimization in the future.https://isprs-archives.copernicus.org/articles/XLVIII-2-2024/485/2025/isprs-archives-XLVIII-2-2024-485-2025.pdf |
| spellingShingle | Y. Xu Y. Xu Y. Xu C. Chen C. Chen C. Chen B. Yang B. Yang B. Yang L. Li Z. Wang Z. Wang Z. Wang S. Sun S. Sun S. Sun Z. Yan Z. Yan Z. Yan S. Wu S. Wu S. Wu LUOJIA Explorer PMS: Panoramic Odometry and Mapping with Structural Information The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | LUOJIA Explorer PMS: Panoramic Odometry and Mapping with Structural Information |
| title_full | LUOJIA Explorer PMS: Panoramic Odometry and Mapping with Structural Information |
| title_fullStr | LUOJIA Explorer PMS: Panoramic Odometry and Mapping with Structural Information |
| title_full_unstemmed | LUOJIA Explorer PMS: Panoramic Odometry and Mapping with Structural Information |
| title_short | LUOJIA Explorer PMS: Panoramic Odometry and Mapping with Structural Information |
| title_sort | luojia explorer pms panoramic odometry and mapping with structural information |
| url | https://isprs-archives.copernicus.org/articles/XLVIII-2-2024/485/2025/isprs-archives-XLVIII-2-2024-485-2025.pdf |
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