UPL-SLAM: Unconstrained RGB-D SLAM With Accurate Point-Line Features for Visual Perception
In mainstream simultaneous localization and mapping (SLAM) algorithms, feature points are commonly utilized to represent image features. However, the quantity and quality of these feature points are contingent upon the environmental texture, lighting conditions, and motion speed. Although existing a...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10818667/ |
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| author | Xianshuai Sun Yuming Zhao Yabiao Wang Zhigang Li Zhen He Xiaohui Wang |
| author_facet | Xianshuai Sun Yuming Zhao Yabiao Wang Zhigang Li Zhen He Xiaohui Wang |
| author_sort | Xianshuai Sun |
| collection | DOAJ |
| description | In mainstream simultaneous localization and mapping (SLAM) algorithms, feature points are commonly utilized to represent image features. However, the quantity and quality of these feature points are contingent upon the environmental texture, lighting conditions, and motion speed. Although existing algorithms enhance adaptability by extracting point-line features simultaneously, the presence of trivial short lines resulting from environmental noise and object occlusion can adversely affect system robustness. Therefore, in this study, we propose a line feature fusion strategy along with a model incorporating an adaptive length suppression parameter for line features. A new line feature residual model is defined, and the mathematical analytical form of line feature Jacobian matrix is derived in detail. Additionally, the point features are organized into a lattice structure and utilized to construct a global pointcloud map in a dedicated thread, aiming to enhance the semantic comprehension of environmental information. Finally, our algorithm is compared against state-of-the-art algorithms on the publicly available datasets TUM RGB-D and ICL-NUIM. Through quantitative trajectory error analysis and qualitative trajectory effect and mapping quality analysis, the final results indicate that the algorithm proposed in this paper achieves superior positioning accuracy and mapping quality, enabling robust 3D reconstruction of indoor scenes. |
| format | Article |
| id | doaj-art-e15eedb2ce0845bea5ade0bbe0bb990b |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-e15eedb2ce0845bea5ade0bbe0bb990b2025-01-21T00:02:28ZengIEEEIEEE Access2169-35362025-01-01138676869010.1109/ACCESS.2024.352446510818667UPL-SLAM: Unconstrained RGB-D SLAM With Accurate Point-Line Features for Visual PerceptionXianshuai Sun0https://orcid.org/0009-0001-4811-052XYuming Zhao1https://orcid.org/0009-0004-8445-8365Yabiao Wang2https://orcid.org/0009-0005-4193-8411Zhigang Li3https://orcid.org/0000-0003-4160-6626Zhen He4https://orcid.org/0000-0002-9030-0386Xiaohui Wang5State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, ChinaState Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, ChinaState Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, ChinaState Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, ChinaState Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, ChinaState Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, ChinaIn mainstream simultaneous localization and mapping (SLAM) algorithms, feature points are commonly utilized to represent image features. However, the quantity and quality of these feature points are contingent upon the environmental texture, lighting conditions, and motion speed. Although existing algorithms enhance adaptability by extracting point-line features simultaneously, the presence of trivial short lines resulting from environmental noise and object occlusion can adversely affect system robustness. Therefore, in this study, we propose a line feature fusion strategy along with a model incorporating an adaptive length suppression parameter for line features. A new line feature residual model is defined, and the mathematical analytical form of line feature Jacobian matrix is derived in detail. Additionally, the point features are organized into a lattice structure and utilized to construct a global pointcloud map in a dedicated thread, aiming to enhance the semantic comprehension of environmental information. Finally, our algorithm is compared against state-of-the-art algorithms on the publicly available datasets TUM RGB-D and ICL-NUIM. Through quantitative trajectory error analysis and qualitative trajectory effect and mapping quality analysis, the final results indicate that the algorithm proposed in this paper achieves superior positioning accuracy and mapping quality, enabling robust 3D reconstruction of indoor scenes.https://ieeexplore.ieee.org/document/10818667/Point-lineRGB-D camerasemi-dense pointcloudSLAM |
| spellingShingle | Xianshuai Sun Yuming Zhao Yabiao Wang Zhigang Li Zhen He Xiaohui Wang UPL-SLAM: Unconstrained RGB-D SLAM With Accurate Point-Line Features for Visual Perception IEEE Access Point-line RGB-D camera semi-dense pointcloud SLAM |
| title | UPL-SLAM: Unconstrained RGB-D SLAM With Accurate Point-Line Features for Visual Perception |
| title_full | UPL-SLAM: Unconstrained RGB-D SLAM With Accurate Point-Line Features for Visual Perception |
| title_fullStr | UPL-SLAM: Unconstrained RGB-D SLAM With Accurate Point-Line Features for Visual Perception |
| title_full_unstemmed | UPL-SLAM: Unconstrained RGB-D SLAM With Accurate Point-Line Features for Visual Perception |
| title_short | UPL-SLAM: Unconstrained RGB-D SLAM With Accurate Point-Line Features for Visual Perception |
| title_sort | upl slam unconstrained rgb d slam with accurate point line features for visual perception |
| topic | Point-line RGB-D camera semi-dense pointcloud SLAM |
| url | https://ieeexplore.ieee.org/document/10818667/ |
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