MoTI: A Multi-Stage Algorithm for Moving Object Identification in SLAM
Simultaneous localization and mapping (SLAM) algorithms are widely applied in fields such as autonomous driving and target tracking. However, the effect of moving objects on localization and mapping remains a challenge in natural dynamic scenarios. To overcome this challenge, this paper proposes an...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/18/7911 |
| _version_ | 1797576977370054656 |
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| author | Changqing Hu Manlu Liu Su Zhang Yu Xie Liguo Tan |
| author_facet | Changqing Hu Manlu Liu Su Zhang Yu Xie Liguo Tan |
| author_sort | Changqing Hu |
| collection | DOAJ |
| description | Simultaneous localization and mapping (SLAM) algorithms are widely applied in fields such as autonomous driving and target tracking. However, the effect of moving objects on localization and mapping remains a challenge in natural dynamic scenarios. To overcome this challenge, this paper proposes an algorithm for dynamic point cloud detection that fuses laser and visual identification data, the multi-stage moving object identification algorithm (MoTI). The MoTI algorithm consists of two stages: rough processing and precise processing. In the rough processing stage, a statistical method is employed to preliminarily detect dynamic points based on the range image error of the point cloud. In the precise processing stage, the radius search strategy is used to statistically test the nearest neighbor points. Next, visual identification information and point cloud registration results are fused using a method of statistics and information weighting to construct a probability model for identifying whether a point cloud cluster originates from a moving object. The algorithm is integrated into the front-end of the LOAM system, which significantly improves the localization accuracy. The MoTI algorithm is evaluated on an actual indoor dynamic environment and several KITTI datasets, and the results demonstrate its ability to accurately detect dynamic targets in the background and improve the localization accuracy of the robot. |
| first_indexed | 2024-03-10T22:01:24Z |
| format | Article |
| id | doaj.art-6826a47cb2f04554ad9a72bbe8ddd454 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T22:01:24Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-6826a47cb2f04554ad9a72bbe8ddd4542023-11-19T12:55:56ZengMDPI AGSensors1424-82202023-09-012318791110.3390/s23187911MoTI: A Multi-Stage Algorithm for Moving Object Identification in SLAMChangqing Hu0Manlu Liu1Su Zhang2Yu Xie3Liguo Tan4School of Information Engineering, Southwest University of Science and Technology, Mianyang 621010, ChinaSchool of Information Engineering, Southwest University of Science and Technology, Mianyang 621010, ChinaSchool of Traffic Transportation Engineering, Central South University, Changsha 410000, ChinaSchool of Information Engineering, Southwest University of Science and Technology, Mianyang 621010, ChinaLaboratory for Space Environment and Physical Sciences, Harbin Institute of Technology, Harbin 150001, ChinaSimultaneous localization and mapping (SLAM) algorithms are widely applied in fields such as autonomous driving and target tracking. However, the effect of moving objects on localization and mapping remains a challenge in natural dynamic scenarios. To overcome this challenge, this paper proposes an algorithm for dynamic point cloud detection that fuses laser and visual identification data, the multi-stage moving object identification algorithm (MoTI). The MoTI algorithm consists of two stages: rough processing and precise processing. In the rough processing stage, a statistical method is employed to preliminarily detect dynamic points based on the range image error of the point cloud. In the precise processing stage, the radius search strategy is used to statistically test the nearest neighbor points. Next, visual identification information and point cloud registration results are fused using a method of statistics and information weighting to construct a probability model for identifying whether a point cloud cluster originates from a moving object. The algorithm is integrated into the front-end of the LOAM system, which significantly improves the localization accuracy. The MoTI algorithm is evaluated on an actual indoor dynamic environment and several KITTI datasets, and the results demonstrate its ability to accurately detect dynamic targets in the background and improve the localization accuracy of the robot.https://www.mdpi.com/1424-8220/23/18/7911moving object detectionmulti-sensor fusionpoint cloud processingSLAM algorithm |
| spellingShingle | Changqing Hu Manlu Liu Su Zhang Yu Xie Liguo Tan MoTI: A Multi-Stage Algorithm for Moving Object Identification in SLAM Sensors moving object detection multi-sensor fusion point cloud processing SLAM algorithm |
| title | MoTI: A Multi-Stage Algorithm for Moving Object Identification in SLAM |
| title_full | MoTI: A Multi-Stage Algorithm for Moving Object Identification in SLAM |
| title_fullStr | MoTI: A Multi-Stage Algorithm for Moving Object Identification in SLAM |
| title_full_unstemmed | MoTI: A Multi-Stage Algorithm for Moving Object Identification in SLAM |
| title_short | MoTI: A Multi-Stage Algorithm for Moving Object Identification in SLAM |
| title_sort | moti a multi stage algorithm for moving object identification in slam |
| topic | moving object detection multi-sensor fusion point cloud processing SLAM algorithm |
| url | https://www.mdpi.com/1424-8220/23/18/7911 |
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