A Clutter-Resistant SLAM Algorithm for Autonomous Guided Vehicles in Dynamic Industrial Environment
In dynamic and complicated industrial environments, feature-based SLAM based on laser scanner is a popular choice to achieve localization of autonomous guided vehicles. However, there are many clutters and dynamic objects degrading SLAM performance. This paper proposes a clutter-resistant SLAM solut...
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9115027/ |
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| author | Wei Wang Yaohua Wu Zhenyu Jiang Jiahui Qi |
| author_facet | Wei Wang Yaohua Wu Zhenyu Jiang Jiahui Qi |
| author_sort | Wei Wang |
| collection | DOAJ |
| description | In dynamic and complicated industrial environments, feature-based SLAM based on laser scanner is a popular choice to achieve localization of autonomous guided vehicles. However, there are many clutters and dynamic objects degrading SLAM performance. This paper proposes a clutter-resistant SLAM solution where both point features generated from reflectors and line features are employed to improve SLAM robustness. First, a point feature recognition method based on geometrical characteristics of reflectors is developed to filter out clutters and identify true reflector landmarks; Then a dual-map based map management scheme is proposed for EKF-SLAM to further eliminate both types of fallacious landmarks and enhance its clutter resistance capability. The proposed methods eliminate adverse impact of clutters and thus improve SLAM performance in terms of accuracy, consistency and efficiency. The effectiveness of the proposed clutter-resistant SLAM solution is validated through real-time experiments. The absolute localization error is controlled within 19 mm and 31mm in X-axis and Y-axis respectively. The improved SLAM algorithm is proved to be accurate and efficient enough for practical application in dynamic and complicated industrial environments. |
| format | Article |
| id | doaj-art-70ee7b1f48084a828f5508bfb0bee78c |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-70ee7b1f48084a828f5508bfb0bee78c2025-08-20T03:22:00ZengIEEEIEEE Access2169-35362020-01-01810977010978210.1109/ACCESS.2020.30017569115027A Clutter-Resistant SLAM Algorithm for Autonomous Guided Vehicles in Dynamic Industrial EnvironmentWei Wang0https://orcid.org/0000-0002-2418-6490Yaohua Wu1https://orcid.org/0000-0002-5561-8049Zhenyu Jiang2https://orcid.org/0000-0002-5989-0584Jiahui Qi3https://orcid.org/0000-0002-0512-7911School of Control Science and Engineering, Shandong University, Ji’nan, ChinaSchool of Control Science and Engineering, Shandong University, Ji’nan, ChinaSchool of Control Science and Engineering, Shandong University, Ji’nan, ChinaSchool of Control Science and Engineering, Shandong University, Ji’nan, ChinaIn dynamic and complicated industrial environments, feature-based SLAM based on laser scanner is a popular choice to achieve localization of autonomous guided vehicles. However, there are many clutters and dynamic objects degrading SLAM performance. This paper proposes a clutter-resistant SLAM solution where both point features generated from reflectors and line features are employed to improve SLAM robustness. First, a point feature recognition method based on geometrical characteristics of reflectors is developed to filter out clutters and identify true reflector landmarks; Then a dual-map based map management scheme is proposed for EKF-SLAM to further eliminate both types of fallacious landmarks and enhance its clutter resistance capability. The proposed methods eliminate adverse impact of clutters and thus improve SLAM performance in terms of accuracy, consistency and efficiency. The effectiveness of the proposed clutter-resistant SLAM solution is validated through real-time experiments. The absolute localization error is controlled within 19 mm and 31mm in X-axis and Y-axis respectively. The improved SLAM algorithm is proved to be accurate and efficient enough for practical application in dynamic and complicated industrial environments.https://ieeexplore.ieee.org/document/9115027/EKFautonomous guided vehicleSLAMindustrial environment |
| spellingShingle | Wei Wang Yaohua Wu Zhenyu Jiang Jiahui Qi A Clutter-Resistant SLAM Algorithm for Autonomous Guided Vehicles in Dynamic Industrial Environment IEEE Access EKF autonomous guided vehicle SLAM industrial environment |
| title | A Clutter-Resistant SLAM Algorithm for Autonomous Guided Vehicles in Dynamic Industrial Environment |
| title_full | A Clutter-Resistant SLAM Algorithm for Autonomous Guided Vehicles in Dynamic Industrial Environment |
| title_fullStr | A Clutter-Resistant SLAM Algorithm for Autonomous Guided Vehicles in Dynamic Industrial Environment |
| title_full_unstemmed | A Clutter-Resistant SLAM Algorithm for Autonomous Guided Vehicles in Dynamic Industrial Environment |
| title_short | A Clutter-Resistant SLAM Algorithm for Autonomous Guided Vehicles in Dynamic Industrial Environment |
| title_sort | clutter resistant slam algorithm for autonomous guided vehicles in dynamic industrial environment |
| topic | EKF autonomous guided vehicle SLAM industrial environment |
| url | https://ieeexplore.ieee.org/document/9115027/ |
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