Sonar-Based Simultaneous Localization and Mapping Using the Semi-Direct Method
The SLAM problem is a common challenge faced by ROVs working underwater, with the key issue being the accurate estimation of pose. In this work, we make full use of the positional information of point clouds and the surrounding pixel data. To obtain better feature extraction results in specific dire...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/12/12/2234 |
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| _version_ | 1846104164739842048 |
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| author | Xu Han Jinghao Sun Shu Zhang Junyu Dong Hui Yu |
| author_facet | Xu Han Jinghao Sun Shu Zhang Junyu Dong Hui Yu |
| author_sort | Xu Han |
| collection | DOAJ |
| description | The SLAM problem is a common challenge faced by ROVs working underwater, with the key issue being the accurate estimation of pose. In this work, we make full use of the positional information of point clouds and the surrounding pixel data. To obtain better feature extraction results in specific directions, we propose a method that accelerates the computation of the two-dimensional SO-CFAR algorithm, with the time cost being only a very slight increase compared to the one-dimensional SO-CFAR. We develop a sonar semi-direct method, adapted from the direct method used in visual SLAM. With the initialization from the ICP algorithm, we apply this method to further refine the pose estimation. To overcome the deficiencies of sonar images, we preprocess the images and reformulate the sonar imaging model in imitation of camera imaging models, further optimizing the pose by minimizing photometric error and fully leveraging pixel information. The improved front end and the accelerated two-dimensional SO-CFAR are assessed through quantitative experiments. The performance of SLAM in large real-world environments is assessed through qualitative experiments. |
| format | Article |
| id | doaj-art-b12d5af2da6f4fdd8c533f8ef0e7b32f |
| institution | Kabale University |
| issn | 2077-1312 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-b12d5af2da6f4fdd8c533f8ef0e7b32f2024-12-27T14:33:20ZengMDPI AGJournal of Marine Science and Engineering2077-13122024-12-011212223410.3390/jmse12122234Sonar-Based Simultaneous Localization and Mapping Using the Semi-Direct MethodXu Han0Jinghao Sun1Shu Zhang2Junyu Dong3Hui Yu4Faculty of Computer Science and Technology, Ocean University of China, Qingdao 266000, ChinaFaculty of Computer Science and Technology, Ocean University of China, Qingdao 266000, ChinaFaculty of Computer Science and Technology, Ocean University of China, Qingdao 266000, ChinaFaculty of Computer Science and Technology, Ocean University of China, Qingdao 266000, ChinaFaculty of Creative and Cultural Industries, School of Creative Technologies, University of Portsmouth, Portsmouth PO1 2DJ, UKThe SLAM problem is a common challenge faced by ROVs working underwater, with the key issue being the accurate estimation of pose. In this work, we make full use of the positional information of point clouds and the surrounding pixel data. To obtain better feature extraction results in specific directions, we propose a method that accelerates the computation of the two-dimensional SO-CFAR algorithm, with the time cost being only a very slight increase compared to the one-dimensional SO-CFAR. We develop a sonar semi-direct method, adapted from the direct method used in visual SLAM. With the initialization from the ICP algorithm, we apply this method to further refine the pose estimation. To overcome the deficiencies of sonar images, we preprocess the images and reformulate the sonar imaging model in imitation of camera imaging models, further optimizing the pose by minimizing photometric error and fully leveraging pixel information. The improved front end and the accelerated two-dimensional SO-CFAR are assessed through quantitative experiments. The performance of SLAM in large real-world environments is assessed through qualitative experiments.https://www.mdpi.com/2077-1312/12/12/2234SLAMSO-CFARsemi-direct methodsonarfeature extraction |
| spellingShingle | Xu Han Jinghao Sun Shu Zhang Junyu Dong Hui Yu Sonar-Based Simultaneous Localization and Mapping Using the Semi-Direct Method Journal of Marine Science and Engineering SLAM SO-CFAR semi-direct method sonar feature extraction |
| title | Sonar-Based Simultaneous Localization and Mapping Using the Semi-Direct Method |
| title_full | Sonar-Based Simultaneous Localization and Mapping Using the Semi-Direct Method |
| title_fullStr | Sonar-Based Simultaneous Localization and Mapping Using the Semi-Direct Method |
| title_full_unstemmed | Sonar-Based Simultaneous Localization and Mapping Using the Semi-Direct Method |
| title_short | Sonar-Based Simultaneous Localization and Mapping Using the Semi-Direct Method |
| title_sort | sonar based simultaneous localization and mapping using the semi direct method |
| topic | SLAM SO-CFAR semi-direct method sonar feature extraction |
| url | https://www.mdpi.com/2077-1312/12/12/2234 |
| work_keys_str_mv | AT xuhan sonarbasedsimultaneouslocalizationandmappingusingthesemidirectmethod AT jinghaosun sonarbasedsimultaneouslocalizationandmappingusingthesemidirectmethod AT shuzhang sonarbasedsimultaneouslocalizationandmappingusingthesemidirectmethod AT junyudong sonarbasedsimultaneouslocalizationandmappingusingthesemidirectmethod AT huiyu sonarbasedsimultaneouslocalizationandmappingusingthesemidirectmethod |