Positioning method for roadheaders based on fusion of LiDAR and inertial navigation
Accurate positioning of roadheaders in coal mines is fundamental to intelligent tunneling. However, harsh working conditions, such as low illumination and high dust levels in underground mines, often degrade the accuracy and stability of single-source positioning methods. To improve the positioning...
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Editorial Department of Industry and Mine Automation
2025-03-01
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| Series: | Gong-kuang zidonghua |
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| Online Access: | http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2025010021 |
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| author | LIU Jing WEI Zhiqiang CAI Chunmeng LIU Yang |
| author_facet | LIU Jing WEI Zhiqiang CAI Chunmeng LIU Yang |
| author_sort | LIU Jing |
| collection | DOAJ |
| description | Accurate positioning of roadheaders in coal mines is fundamental to intelligent tunneling. However, harsh working conditions, such as low illumination and high dust levels in underground mines, often degrade the accuracy and stability of single-source positioning methods. To improve the positioning accuracy of the roadheaders in these harsh conditions, a new positioning method based on the fusion of LiDAR and inertial navigation using error state kalman filter (ESKF) was developed. First, the center of the spherical target suspended from the tunnel roof was defined as the origin of the tunnel coordinate system. A density-based spatial clustering of applications with noise (DBSCAN) and a shape-feature-based spherical target point cloud extraction algorithm were designed to address the problem that conventional methods relying on reflection intensity for distinguishing spherical targets fail in environments with dust accumulation. The coordinate transformation method is then used to build a radar position measurement system to obtain a reference for the fusion positioning. Next, position and attitude information of the roadheader were obtained through inertial navigation integration. Subsequently, an error-state model was formulated based on a first-order Gaussian-Markov process, and the ESKF algorithm was applied to fuse the outputs of LiDAR and the inertial navigation, providing the fusion positioning results of the roadheader within the tunnel. The fusion positioning results were then fed back into the inertial navigation to correct accumulated errors, achieving precise positioning. Experimental results demonstrated that, under static conditions, the position error of the LiDAR-based positioning system remained below 10 cm across different positions and attitude angles, and the inertial navigation system exhibited a position error of less than 70 cm. In dynamic conditions, the fusion positioning system achieved a position error of 5.8 cm, reducing the LiDAR system's error by 12.1%. The proposed LiDAR and inertial navigation fusion-based roadheader positioning method meets the positioning requirements for automated cutting operations of roadheaders in complex tunneling conditions. |
| format | Article |
| id | doaj-art-af0a5f38d3ff44da8485901bf0d2435a |
| institution | OA Journals |
| issn | 1671-251X |
| language | zho |
| publishDate | 2025-03-01 |
| publisher | Editorial Department of Industry and Mine Automation |
| record_format | Article |
| series | Gong-kuang zidonghua |
| spelling | doaj-art-af0a5f38d3ff44da8485901bf0d2435a2025-08-20T02:29:24ZzhoEditorial Department of Industry and Mine AutomationGong-kuang zidonghua1671-251X2025-03-015137885, 9510.13272/j.issn.1671-251x.2025010021Positioning method for roadheaders based on fusion of LiDAR and inertial navigationLIU Jing0WEI Zhiqiang1CAI Chunmeng2LIU Yang3Sany Intelligent Equipment Co., Ltd., Xi'an 712000, ChinaSchool of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an 710049, ChinaSany Intelligent Equipment Co., Ltd., Xi'an 712000, ChinaSany Intelligent Equipment Co., Ltd., Xi'an 712000, ChinaAccurate positioning of roadheaders in coal mines is fundamental to intelligent tunneling. However, harsh working conditions, such as low illumination and high dust levels in underground mines, often degrade the accuracy and stability of single-source positioning methods. To improve the positioning accuracy of the roadheaders in these harsh conditions, a new positioning method based on the fusion of LiDAR and inertial navigation using error state kalman filter (ESKF) was developed. First, the center of the spherical target suspended from the tunnel roof was defined as the origin of the tunnel coordinate system. A density-based spatial clustering of applications with noise (DBSCAN) and a shape-feature-based spherical target point cloud extraction algorithm were designed to address the problem that conventional methods relying on reflection intensity for distinguishing spherical targets fail in environments with dust accumulation. The coordinate transformation method is then used to build a radar position measurement system to obtain a reference for the fusion positioning. Next, position and attitude information of the roadheader were obtained through inertial navigation integration. Subsequently, an error-state model was formulated based on a first-order Gaussian-Markov process, and the ESKF algorithm was applied to fuse the outputs of LiDAR and the inertial navigation, providing the fusion positioning results of the roadheader within the tunnel. The fusion positioning results were then fed back into the inertial navigation to correct accumulated errors, achieving precise positioning. Experimental results demonstrated that, under static conditions, the position error of the LiDAR-based positioning system remained below 10 cm across different positions and attitude angles, and the inertial navigation system exhibited a position error of less than 70 cm. In dynamic conditions, the fusion positioning system achieved a position error of 5.8 cm, reducing the LiDAR system's error by 12.1%. The proposed LiDAR and inertial navigation fusion-based roadheader positioning method meets the positioning requirements for automated cutting operations of roadheaders in complex tunneling conditions.http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2025010021roadheader positioninglidarinertial navigationerror state kalman filterdensity-based spatial clustering of applications with noise (dbscan)spherical targe |
| spellingShingle | LIU Jing WEI Zhiqiang CAI Chunmeng LIU Yang Positioning method for roadheaders based on fusion of LiDAR and inertial navigation Gong-kuang zidonghua roadheader positioning lidar inertial navigation error state kalman filter density-based spatial clustering of applications with noise (dbscan) spherical targe |
| title | Positioning method for roadheaders based on fusion of LiDAR and inertial navigation |
| title_full | Positioning method for roadheaders based on fusion of LiDAR and inertial navigation |
| title_fullStr | Positioning method for roadheaders based on fusion of LiDAR and inertial navigation |
| title_full_unstemmed | Positioning method for roadheaders based on fusion of LiDAR and inertial navigation |
| title_short | Positioning method for roadheaders based on fusion of LiDAR and inertial navigation |
| title_sort | positioning method for roadheaders based on fusion of lidar and inertial navigation |
| topic | roadheader positioning lidar inertial navigation error state kalman filter density-based spatial clustering of applications with noise (dbscan) spherical targe |
| url | http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2025010021 |
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