Semantic-Assisted LIDAR Tightly Coupled SLAM for Dynamic Environments
The Simultaneous Localization and Mapping (SLAM) environment is evolving from static to dynamic. However, traditional SLAM methods struggle to eliminate the influence of dynamic objects, leading to significant deviations in pose estimation. Addressing these challenges in dynamic environments, this p...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10444510/ |
| _version_ | 1797243364658118656 |
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| author | Peng Liu Yuxuan Bi Jialin Shi Tianyi Zhang Caixia Wang |
| author_facet | Peng Liu Yuxuan Bi Jialin Shi Tianyi Zhang Caixia Wang |
| author_sort | Peng Liu |
| collection | DOAJ |
| description | The Simultaneous Localization and Mapping (SLAM) environment is evolving from static to dynamic. However, traditional SLAM methods struggle to eliminate the influence of dynamic objects, leading to significant deviations in pose estimation. Addressing these challenges in dynamic environments, this paper introduces a semantic-assisted LIDAR tightly coupled SLAM method. Specifically, to mitigate interference from dynamic objects, a scheme for calculating static semantic probability is proposed. This enables the segmentation of static and dynamic points while eliminating both stationary dynamic objects and moving environmental blocking objects. Additionally, in point cloud feature extraction and matching processes, we incorporate constraint conditions based on semantic information to enhance accuracy and improve pose estimation precision. Furthermore, a semantic similarity constraint is included within the closed-loop factor module to significantly enhance positioning accuracy and facilitate the construction of maps with higher global consistency. Experimental results from KITTI and M2DGR datasets demonstrate that our method exhibits generalization ability towards unknown data while effectively mitigating dynamic interference in real-world environments. Compared with current state-of-the-art methods, our approach achieves notable improvements in both accuracy and robustness. |
| first_indexed | 2024-04-24T18:53:57Z |
| format | Article |
| id | doaj.art-5fb75d97cd9844e6bd11a0e629d0e0a5 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-24T18:53:57Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-5fb75d97cd9844e6bd11a0e629d0e0a52024-03-26T17:47:02ZengIEEEIEEE Access2169-35362024-01-0112340423405310.1109/ACCESS.2024.336918310444510Semantic-Assisted LIDAR Tightly Coupled SLAM for Dynamic EnvironmentsPeng Liu0https://orcid.org/0009-0008-9347-9511Yuxuan Bi1Jialin Shi2Tianyi Zhang3Caixia Wang4School of Electronic Information Engineering, Changchun University of Science and Technology, Changchun, ChinaSchool of Electronic Information Engineering, Changchun University of Science and Technology, Changchun, ChinaSchool of Electronic Information Engineering, Changchun University of Science and Technology, Changchun, ChinaSchool of Electronic Information Engineering, Changchun University of Science and Technology, Changchun, ChinaSchool of Electronic Information Engineering, Changchun University of Science and Technology, Changchun, ChinaThe Simultaneous Localization and Mapping (SLAM) environment is evolving from static to dynamic. However, traditional SLAM methods struggle to eliminate the influence of dynamic objects, leading to significant deviations in pose estimation. Addressing these challenges in dynamic environments, this paper introduces a semantic-assisted LIDAR tightly coupled SLAM method. Specifically, to mitigate interference from dynamic objects, a scheme for calculating static semantic probability is proposed. This enables the segmentation of static and dynamic points while eliminating both stationary dynamic objects and moving environmental blocking objects. Additionally, in point cloud feature extraction and matching processes, we incorporate constraint conditions based on semantic information to enhance accuracy and improve pose estimation precision. Furthermore, a semantic similarity constraint is included within the closed-loop factor module to significantly enhance positioning accuracy and facilitate the construction of maps with higher global consistency. Experimental results from KITTI and M2DGR datasets demonstrate that our method exhibits generalization ability towards unknown data while effectively mitigating dynamic interference in real-world environments. Compared with current state-of-the-art methods, our approach achieves notable improvements in both accuracy and robustness.https://ieeexplore.ieee.org/document/10444510/LIDAR odometrysemantic SLAMdynamic removal |
| spellingShingle | Peng Liu Yuxuan Bi Jialin Shi Tianyi Zhang Caixia Wang Semantic-Assisted LIDAR Tightly Coupled SLAM for Dynamic Environments IEEE Access LIDAR odometry semantic SLAM dynamic removal |
| title | Semantic-Assisted LIDAR Tightly Coupled SLAM for Dynamic Environments |
| title_full | Semantic-Assisted LIDAR Tightly Coupled SLAM for Dynamic Environments |
| title_fullStr | Semantic-Assisted LIDAR Tightly Coupled SLAM for Dynamic Environments |
| title_full_unstemmed | Semantic-Assisted LIDAR Tightly Coupled SLAM for Dynamic Environments |
| title_short | Semantic-Assisted LIDAR Tightly Coupled SLAM for Dynamic Environments |
| title_sort | semantic assisted lidar tightly coupled slam for dynamic environments |
| topic | LIDAR odometry semantic SLAM dynamic removal |
| url | https://ieeexplore.ieee.org/document/10444510/ |
| work_keys_str_mv | AT pengliu semanticassistedlidartightlycoupledslamfordynamicenvironments AT yuxuanbi semanticassistedlidartightlycoupledslamfordynamicenvironments AT jialinshi semanticassistedlidartightlycoupledslamfordynamicenvironments AT tianyizhang semanticassistedlidartightlycoupledslamfordynamicenvironments AT caixiawang semanticassistedlidartightlycoupledslamfordynamicenvironments |