Human Visual Attention Mechanism-Inspired Point-and-Line Stereo Visual Odometry for Environments with Uneven Distributed Features
Abstract Visual odometry is critical in visual simultaneous localization and mapping for robot navigation. However, the pose estimation performance of most current visual odometry algorithms degrades in scenes with unevenly distributed features because dense features occupy excessive weight. Herein,...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2023-05-01
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| Series: | Chinese Journal of Mechanical Engineering |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s10033-023-00872-y |
| _version_ | 1797827659484364800 |
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| author | Chang Wang Jianhua Zhang Yan Zhao Youjie Zhou Jincheng Jiang |
| author_facet | Chang Wang Jianhua Zhang Yan Zhao Youjie Zhou Jincheng Jiang |
| author_sort | Chang Wang |
| collection | DOAJ |
| description | Abstract Visual odometry is critical in visual simultaneous localization and mapping for robot navigation. However, the pose estimation performance of most current visual odometry algorithms degrades in scenes with unevenly distributed features because dense features occupy excessive weight. Herein, a new human visual attention mechanism for point-and-line stereo visual odometry, which is called point-line-weight-mechanism visual odometry (PLWM-VO), is proposed to describe scene features in a global and balanced manner. A weight-adaptive model based on region partition and region growth is generated for the human visual attention mechanism, where sufficient attention is assigned to position-distinctive objects (sparse features in the environment). Furthermore, the sum of absolute differences algorithm is used to improve the accuracy of initialization for line features. Compared with the state-of-the-art method (ORB-VO), PLWM-VO show a 36.79% reduction in the absolute trajectory error on the Kitti and Euroc datasets. Although the time consumption of PLWM-VO is higher than that of ORB-VO, online test results indicate that PLWM-VO satisfies the real-time demand. The proposed algorithm not only significantly promotes the environmental adaptability of visual odometry, but also quantitatively demonstrates the superiority of the human visual attention mechanism. |
| first_indexed | 2024-04-09T12:51:53Z |
| format | Article |
| id | doaj.art-a4ce6a819c4a4bdc8ebdb17ef2f380d9 |
| institution | Directory Open Access Journal |
| issn | 2192-8258 |
| language | English |
| last_indexed | 2024-04-09T12:51:53Z |
| publishDate | 2023-05-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Chinese Journal of Mechanical Engineering |
| spelling | doaj.art-a4ce6a819c4a4bdc8ebdb17ef2f380d92023-05-14T11:10:40ZengSpringerOpenChinese Journal of Mechanical Engineering2192-82582023-05-0136111410.1186/s10033-023-00872-yHuman Visual Attention Mechanism-Inspired Point-and-Line Stereo Visual Odometry for Environments with Uneven Distributed FeaturesChang Wang0Jianhua Zhang1Yan Zhao2Youjie Zhou3Jincheng Jiang4School of Mechanical Engineering, University of Science and Technology BeijingSchool of Mechanical Engineering, University of Science and Technology BeijingSchool of Mechanical Engineering, University of Science and Technology BeijingSchool of Mechanical Engineering, Shandong UniversitySchool of Mechanical Engineering, Hebei University of TechnologyAbstract Visual odometry is critical in visual simultaneous localization and mapping for robot navigation. However, the pose estimation performance of most current visual odometry algorithms degrades in scenes with unevenly distributed features because dense features occupy excessive weight. Herein, a new human visual attention mechanism for point-and-line stereo visual odometry, which is called point-line-weight-mechanism visual odometry (PLWM-VO), is proposed to describe scene features in a global and balanced manner. A weight-adaptive model based on region partition and region growth is generated for the human visual attention mechanism, where sufficient attention is assigned to position-distinctive objects (sparse features in the environment). Furthermore, the sum of absolute differences algorithm is used to improve the accuracy of initialization for line features. Compared with the state-of-the-art method (ORB-VO), PLWM-VO show a 36.79% reduction in the absolute trajectory error on the Kitti and Euroc datasets. Although the time consumption of PLWM-VO is higher than that of ORB-VO, online test results indicate that PLWM-VO satisfies the real-time demand. The proposed algorithm not only significantly promotes the environmental adaptability of visual odometry, but also quantitatively demonstrates the superiority of the human visual attention mechanism.https://doi.org/10.1186/s10033-023-00872-yVisual odometryHuman visual attention mechanismEnvironmental adaptabilityUneven distributed features |
| spellingShingle | Chang Wang Jianhua Zhang Yan Zhao Youjie Zhou Jincheng Jiang Human Visual Attention Mechanism-Inspired Point-and-Line Stereo Visual Odometry for Environments with Uneven Distributed Features Chinese Journal of Mechanical Engineering Visual odometry Human visual attention mechanism Environmental adaptability Uneven distributed features |
| title | Human Visual Attention Mechanism-Inspired Point-and-Line Stereo Visual Odometry for Environments with Uneven Distributed Features |
| title_full | Human Visual Attention Mechanism-Inspired Point-and-Line Stereo Visual Odometry for Environments with Uneven Distributed Features |
| title_fullStr | Human Visual Attention Mechanism-Inspired Point-and-Line Stereo Visual Odometry for Environments with Uneven Distributed Features |
| title_full_unstemmed | Human Visual Attention Mechanism-Inspired Point-and-Line Stereo Visual Odometry for Environments with Uneven Distributed Features |
| title_short | Human Visual Attention Mechanism-Inspired Point-and-Line Stereo Visual Odometry for Environments with Uneven Distributed Features |
| title_sort | human visual attention mechanism inspired point and line stereo visual odometry for environments with uneven distributed features |
| topic | Visual odometry Human visual attention mechanism Environmental adaptability Uneven distributed features |
| url | https://doi.org/10.1186/s10033-023-00872-y |
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