A visual positioning method for tunnel boring machines in underground coal mines based on anchor net features
BackgroundPrecise positioning of tunnel boring machines (TBMs) in underground coal mines plays a fundamental role in the automated and intelligent guidance and control of fully mechanized heading face. However, traditional visual positioning methods show limited application effects in underground ro...
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Editorial Office of Coal Geology & Exploration
2025-06-01
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| Series: | Meitian dizhi yu kantan |
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| Online Access: | http://www.mtdzykt.com/article/doi/10.12363/issn.1001-1986.25.03.0207 |
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| author | Xuhui ZHANG Yunkai CHI Yuyang DU Junying JIANG Wenjuan YANG Youjun ZHAO Jicheng WAN Yanqun WANG Chenhui TIAN |
| author_facet | Xuhui ZHANG Yunkai CHI Yuyang DU Junying JIANG Wenjuan YANG Youjun ZHAO Jicheng WAN Yanqun WANG Chenhui TIAN |
| author_sort | Xuhui ZHANG |
| collection | DOAJ |
| description | BackgroundPrecise positioning of tunnel boring machines (TBMs) in underground coal mines plays a fundamental role in the automated and intelligent guidance and control of fully mechanized heading face. However, traditional visual positioning methods show limited application effects in underground roadways due to their narrow and enclosed spaces, insufficient illumination, and sparse textures. This study proposed a visual positioning method for TBMs in underground coal mines based on anchor net features.MethodsA three-stream depthwise separable convolutional neural network (TSCR-NET) for image enhancement was employed to estimate the reflection, illumination, and noise in images individually. Through illumination adjustment while suppressing noise, images with uniform illumination and clear textures were obtained. This contributed to enhanced adaptability of the visual positioning system under complex illumination conditions. An extraction and matching method for anchor net line features was designed. This method enhanced the extraction capacity using the edge drawing lines (EDLines) with adaptive thresholding and improved the matching accuracy using the structural similarity index measure (SSIM). A pose estimation model with minimized reprojection errors of line features was constructed. In combination with pose graph optimization, this model enabled precise TBM positioning. Furthermore, an experimental platform was established. Accordingly, experiments were designed for quantitative analyses of image enhancement, line feature processing, and positioning performance. Results and ConclusionsThe results indicate that the TSCR-NET yielded higher peak signal-to-noise ratio (PSNR) and SSIM values compared to the multi-scale retinex with color restoration (MSRCR) and zero-reference deep curve estimation (Zero-DCE) algorithms. The line feature processing method designed in this study outperformed traditional algorithms in the quantity of extracted features and matching accuracy, laying a solid foundation for subsequent positioning processes. In terms of positioning experiments, the method proposed in this study was compared to other line feature-based visual positioning methods under the EuRoC dataset and the real roadway scene. The comparison results revealed that the proposed method outperformed the real-time monocular visual SLAM with points and lines (PL-VINS) algorithm under nine EuRoC data sequences. Furthermore, in an anchor net-supported roadway scene, continuous TBM tracking was conducted within a range of 60 m. The proposed method yielded a maximum error of 163 mm, indicating a 23.5% reduction compared to the 213 mm obtained using the PL-VINS algorithm. Additionally, the root mean square error (RMSE) decreased from 0.531 to 0.426, suggesting a reduction of 19.8%. Overall, the visual positioning method proposed in this study enjoys high accuracy and stability, providing a valuable reference for long-distance pose detection of TBMs in underground anchor net-supported roadways. |
| format | Article |
| id | doaj-art-dec42f4ad4ec4503b2b81b361c9353f9 |
| institution | OA Journals |
| issn | 1001-1986 |
| language | zho |
| publishDate | 2025-06-01 |
| publisher | Editorial Office of Coal Geology & Exploration |
| record_format | Article |
| series | Meitian dizhi yu kantan |
| spelling | doaj-art-dec42f4ad4ec4503b2b81b361c9353f92025-08-20T02:22:03ZzhoEditorial Office of Coal Geology & ExplorationMeitian dizhi yu kantan1001-19862025-06-0153625927010.12363/issn.1001-1986.25.03.020725-03-0207zhangxiuhuiA visual positioning method for tunnel boring machines in underground coal mines based on anchor net featuresXuhui ZHANG0Yunkai CHI1Yuyang DU2Junying JIANG3Wenjuan YANG4Youjun ZHAO5Jicheng WAN6Yanqun WANG7Chenhui TIAN8College of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, ChinaCollege of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, ChinaCollege of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, ChinaCollege of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, ChinaCollege of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, ChinaCollege of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, ChinaCollege of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, ChinaCollege of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, ChinaCollege of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, ChinaBackgroundPrecise positioning of tunnel boring machines (TBMs) in underground coal mines plays a fundamental role in the automated and intelligent guidance and control of fully mechanized heading face. However, traditional visual positioning methods show limited application effects in underground roadways due to their narrow and enclosed spaces, insufficient illumination, and sparse textures. This study proposed a visual positioning method for TBMs in underground coal mines based on anchor net features.MethodsA three-stream depthwise separable convolutional neural network (TSCR-NET) for image enhancement was employed to estimate the reflection, illumination, and noise in images individually. Through illumination adjustment while suppressing noise, images with uniform illumination and clear textures were obtained. This contributed to enhanced adaptability of the visual positioning system under complex illumination conditions. An extraction and matching method for anchor net line features was designed. This method enhanced the extraction capacity using the edge drawing lines (EDLines) with adaptive thresholding and improved the matching accuracy using the structural similarity index measure (SSIM). A pose estimation model with minimized reprojection errors of line features was constructed. In combination with pose graph optimization, this model enabled precise TBM positioning. Furthermore, an experimental platform was established. Accordingly, experiments were designed for quantitative analyses of image enhancement, line feature processing, and positioning performance. Results and ConclusionsThe results indicate that the TSCR-NET yielded higher peak signal-to-noise ratio (PSNR) and SSIM values compared to the multi-scale retinex with color restoration (MSRCR) and zero-reference deep curve estimation (Zero-DCE) algorithms. The line feature processing method designed in this study outperformed traditional algorithms in the quantity of extracted features and matching accuracy, laying a solid foundation for subsequent positioning processes. In terms of positioning experiments, the method proposed in this study was compared to other line feature-based visual positioning methods under the EuRoC dataset and the real roadway scene. The comparison results revealed that the proposed method outperformed the real-time monocular visual SLAM with points and lines (PL-VINS) algorithm under nine EuRoC data sequences. Furthermore, in an anchor net-supported roadway scene, continuous TBM tracking was conducted within a range of 60 m. The proposed method yielded a maximum error of 163 mm, indicating a 23.5% reduction compared to the 213 mm obtained using the PL-VINS algorithm. Additionally, the root mean square error (RMSE) decreased from 0.531 to 0.426, suggesting a reduction of 19.8%. Overall, the visual positioning method proposed in this study enjoys high accuracy and stability, providing a valuable reference for long-distance pose detection of TBMs in underground anchor net-supported roadways.http://www.mtdzykt.com/article/doi/10.12363/issn.1001-1986.25.03.0207tunnel boring machine (tbm)visual positioningimage enhancementline feature extraction and matchingmotion estimationanchor net featurecoal mine |
| spellingShingle | Xuhui ZHANG Yunkai CHI Yuyang DU Junying JIANG Wenjuan YANG Youjun ZHAO Jicheng WAN Yanqun WANG Chenhui TIAN A visual positioning method for tunnel boring machines in underground coal mines based on anchor net features Meitian dizhi yu kantan tunnel boring machine (tbm) visual positioning image enhancement line feature extraction and matching motion estimation anchor net feature coal mine |
| title | A visual positioning method for tunnel boring machines in underground coal mines based on anchor net features |
| title_full | A visual positioning method for tunnel boring machines in underground coal mines based on anchor net features |
| title_fullStr | A visual positioning method for tunnel boring machines in underground coal mines based on anchor net features |
| title_full_unstemmed | A visual positioning method for tunnel boring machines in underground coal mines based on anchor net features |
| title_short | A visual positioning method for tunnel boring machines in underground coal mines based on anchor net features |
| title_sort | visual positioning method for tunnel boring machines in underground coal mines based on anchor net features |
| topic | tunnel boring machine (tbm) visual positioning image enhancement line feature extraction and matching motion estimation anchor net feature coal mine |
| url | http://www.mtdzykt.com/article/doi/10.12363/issn.1001-1986.25.03.0207 |
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