Research on Multi-Sensor Simultaneous Localization and Mapping Technology for Complex Environment of Construction Machinery
Simultaneous localization and mapping (SLAM), as a key task of unmanned vehicles for construction machinery, is of great significance for later path planning and control. Construction tasks in the engineering field are mostly carried out in bridges, tunnels, open fields, etc. The prominent features...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/14/8496 |
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| author | Haoling Ren Yaping Zhao Tianliang Lin Jiangdong Wu |
| author_facet | Haoling Ren Yaping Zhao Tianliang Lin Jiangdong Wu |
| author_sort | Haoling Ren |
| collection | DOAJ |
| description | Simultaneous localization and mapping (SLAM), as a key task of unmanned vehicles for construction machinery, is of great significance for later path planning and control. Construction tasks in the engineering field are mostly carried out in bridges, tunnels, open fields, etc. The prominent features of these environments are high scene similarity, few geometric features, and large-scale repetitive texture information, which is prone to sensor detection degradation. This leads to positioning drift and map building failure. The traditional method of motion estimation and 3D reconstruction uses a single sensor, which lacks enough information, has poor adaptability to the environment, and cannot guarantee good positioning accuracy and robustness in complex environments. Currently, the strategy of multi-sensor fusion is proven to be an effective solution and is widely studied. This paper proposes a SLAM framework that integrates LiDAR, IMU, and camera. It tightly couples the texture information observed by camera, the geometric information scanned by LiDAR, and the measured value of IMU, allowing visual-inertial odometry (VIO) and LiDAR-inertial odometry (LIO) common implementation. The LIO subsystem extracts point cloud features and matches them with the global map. The obtained pose estimation can be used for the initialization of the VIO subsystem. The VIO system uses direct method to minimize the photometric error and IMU measurement error between images to estimate the pose of the robot and the geometric structure of the scene. The two subsystems assist each other to perform pose estimation, and can operate normally even when any subsystem fails. A factor graph is used to combine all constraints to achieve global pose optimization. Keyframe and sliding window strategies are used to ensure real-time performance. Through real-vehicle testing, the system can perform incremental and real-time state estimation and reconstruct a dense 3D point cloud map, which can effectively solve the problems of positioning drift and mapping failure in the lack of geometric features or challenging construction environments. |
| first_indexed | 2024-03-11T01:19:35Z |
| format | Article |
| id | doaj.art-75b129341ef748db860378bbdd10c6c0 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T01:19:35Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-75b129341ef748db860378bbdd10c6c02023-11-18T18:13:55ZengMDPI AGApplied Sciences2076-34172023-07-011314849610.3390/app13148496Research on Multi-Sensor Simultaneous Localization and Mapping Technology for Complex Environment of Construction MachineryHaoling Ren0Yaping Zhao1Tianliang Lin2Jiangdong Wu3College of Mechanical Engineering and Automation, Huaqiao University, Xiamen 361021, ChinaCollege of Mechanical Engineering and Automation, Huaqiao University, Xiamen 361021, ChinaCollege of Mechanical Engineering and Automation, Huaqiao University, Xiamen 361021, ChinaCollege of Mechanical Engineering and Automation, Huaqiao University, Xiamen 361021, ChinaSimultaneous localization and mapping (SLAM), as a key task of unmanned vehicles for construction machinery, is of great significance for later path planning and control. Construction tasks in the engineering field are mostly carried out in bridges, tunnels, open fields, etc. The prominent features of these environments are high scene similarity, few geometric features, and large-scale repetitive texture information, which is prone to sensor detection degradation. This leads to positioning drift and map building failure. The traditional method of motion estimation and 3D reconstruction uses a single sensor, which lacks enough information, has poor adaptability to the environment, and cannot guarantee good positioning accuracy and robustness in complex environments. Currently, the strategy of multi-sensor fusion is proven to be an effective solution and is widely studied. This paper proposes a SLAM framework that integrates LiDAR, IMU, and camera. It tightly couples the texture information observed by camera, the geometric information scanned by LiDAR, and the measured value of IMU, allowing visual-inertial odometry (VIO) and LiDAR-inertial odometry (LIO) common implementation. The LIO subsystem extracts point cloud features and matches them with the global map. The obtained pose estimation can be used for the initialization of the VIO subsystem. The VIO system uses direct method to minimize the photometric error and IMU measurement error between images to estimate the pose of the robot and the geometric structure of the scene. The two subsystems assist each other to perform pose estimation, and can operate normally even when any subsystem fails. A factor graph is used to combine all constraints to achieve global pose optimization. Keyframe and sliding window strategies are used to ensure real-time performance. Through real-vehicle testing, the system can perform incremental and real-time state estimation and reconstruct a dense 3D point cloud map, which can effectively solve the problems of positioning drift and mapping failure in the lack of geometric features or challenging construction environments.https://www.mdpi.com/2076-3417/13/14/8496SLAMmulti-sensor fusiontight couplingfactor graph optimizationconstruction machinery |
| spellingShingle | Haoling Ren Yaping Zhao Tianliang Lin Jiangdong Wu Research on Multi-Sensor Simultaneous Localization and Mapping Technology for Complex Environment of Construction Machinery Applied Sciences SLAM multi-sensor fusion tight coupling factor graph optimization construction machinery |
| title | Research on Multi-Sensor Simultaneous Localization and Mapping Technology for Complex Environment of Construction Machinery |
| title_full | Research on Multi-Sensor Simultaneous Localization and Mapping Technology for Complex Environment of Construction Machinery |
| title_fullStr | Research on Multi-Sensor Simultaneous Localization and Mapping Technology for Complex Environment of Construction Machinery |
| title_full_unstemmed | Research on Multi-Sensor Simultaneous Localization and Mapping Technology for Complex Environment of Construction Machinery |
| title_short | Research on Multi-Sensor Simultaneous Localization and Mapping Technology for Complex Environment of Construction Machinery |
| title_sort | research on multi sensor simultaneous localization and mapping technology for complex environment of construction machinery |
| topic | SLAM multi-sensor fusion tight coupling factor graph optimization construction machinery |
| url | https://www.mdpi.com/2076-3417/13/14/8496 |
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