| Citation: |
WANG Li，ZANG Tianxiang，SU Bo. Visual SLAM algorithm for underground robots in coal mines based on point-line features[J]. Coal Science and Technology，2025，53（5）：325−337. DOI: 10.12438/cst.2025-0116
|
In the application of visual SLAM(Simultaneous Localization and Mapping) in coal mines, lighting changes and low-texture scenes seriously affect the extraction and matching of feature points, resulting in the failure of pose estimation and affecting the positioning accuracy. Therefore, a binocular vision localization algorithm SL-SLAM for underground mobile robots in coal mines based on the improved ORB (Oriented Fast and Rotated Brief)-SLAM3 algorithm is proposed. For the lighting change scenario, the original ORB feature point extraction algorithm is replaced by the SuperPoint feature point extraction network with lighting stability at the front end, and a feature point grid limitation method is proposed to effectively eliminate the invalid feature point area and increase the stability of pose estimation. For the low-texture scene, a stable LSD(Line Segment Detector) line feature extraction algorithm is introduced at the front end, and a point-line joint algorithm is proposed, which groups the line features according to the feature point grid, and matches the line features according to the matching results of the feature points, so as to reduce the matching complexity of the line features and save the pose estimation time. The reprojection error model of point features and line features is constructed, the angle constraints are added to the line feature residual model, the Jacobian matrix of the pose increment of point features and line features is derived, the unified cost function of the reprojection error of point features and line features is established, the local mapping thread uses the ORB-SLAM3 classic local optimization method to adjust the pose of points, line features and keyframes, and performs loop correction, subgraph fusion and global BA(Bundle Adjustment) in the back-end thread. The experimental results on the EuRoC dataset show that the APE(Absolute Pose Error) index of SL-SLAM is better than other comparison algorithms, and the trajectory prediction results closest to the true value are obtained, and the root mean square error is reduced by 17.3% compared with ORB-SLAM3. The experimental results of simulating the underground scene of coal mine show that SL-SLAM can adapt to the scene of light change and low texture, and can meet the positioning accuracy and stability of the mobile robot in the underground coal mine.
| [1] |
王国法,张良,李首滨,等. 煤矿无人化智能开采系统理论与技术研发进展[J]. 煤炭学报,2023,48(1):34−53.
WANG Guofa,ZHANG Liang,LI Shoubin,et al. Progresses in theory and technological development of unmanned smart mining system[J]. Journal of China Coal Society,2023,48(1):34−53.
|
| [2] |
王国法,庞义辉,任怀伟,等. 智慧矿山系统工程及关键技术研究与实践[J]. 煤炭学报,2024,49(1):181−202.
WANG Guofa,PANG Yihui,REN Huaiwei,et al. System engineering and key technologies research and practice of smart mine[J]. Journal of China Coal Society,2024,49(1):181−202.
|
| [3] |
林伯强. 以煤矿智能化建设推动煤炭工业协同转型[J]. 人民论坛·学术前沿,2025(2):26−35.
LIN Boqiang. Promoting the collaborative transformation of the coal industry through the intelligentization of coal mines[J]. Frontiers,2025(2):26−35.
|
| [4] |
王国法,任怀伟,富佳兴. 煤矿智能化建设高质量发展难题与路径[J]. 煤炭科学技术,2025,53(1):1−18. doi: 10.12438/cst.2024-0550
WANG Guofa,REN Huaiwei,FU Jiaxing. Challenge and path of high-quality development of coal mine intelligent construction[J]. Coal Science and Technology,2025,53(1):1−18. doi: 10.12438/cst.2024-0550
|
| [5] |
孙凌飞,刘亚,彭继国,等. 基于惯性技术的掘进机组合定位方法[J]. 煤炭科学技术,2024,52(12):300−310.
SUN Lingfei,LIU Ya,PENG Jiguo,et al. Integrated positioning method of roadheader based on inertial technology[J]. Coal Science and Technology,2024,52(12):300−310.
|
| [6] |
张旭辉,陈鑫,杨文娟,等. 基于单激光束信息的掘锚装备视觉定位方法研究[J]. 煤炭科学技术,2024,52(1):311−322. doi: 10.12438/cst.2023-1674
ZHANG Xuhui,CHEN Xin,YANG Wenjuan,et al. Research on visual positioning method of digging and anchoring equipment based on single laser beam information[J]. Coal Science and Technology,2024,52(1):311−322. doi: 10.12438/cst.2023-1674
|
| [7] |
戚群涛. 矿井环境下基于改进卡尔曼滤波的UWB定位算法研究[D]. 包头:内蒙古科技大学,2023.
QI Quntao. Research on UWB positioning algorithm based on improved Kalman filter in mine environment[D]. Baotou:Inner Mongolia University of Science & Technology,2023.
|
| [8] |
毛清华,周庆,安炎基,等. 惯导与视觉信息融合的掘进机精确定位方法[J]. 煤炭科学技术,2024,52(5):236−248. doi: 10.12438/cst.2023-1003
MAO Qinghua,ZHOU Qing,AN Yanji,et al. Precise positioning method of tunneling machine for inertial navigation and visual information fusion[J]. Coal Science and Technology,2024,52(5):236−248. doi: 10.12438/cst.2023-1003
|
| [9] |
程德强,钱建生,郭星歌,等. 煤矿安全生产视频AI识别关键技术研究综述[J]. 煤炭科学技术,2023,51(2):349−365.
CHENG Deqiang,QIAN Jiansheng,GUO Xingge,et al. Review on key technologies of AI recognition for videos in coal mine[J]. Coal Science and Technology,2023,51(2):349−365.
|
| [10] |
KLEIN G,MURRAY D. Parallel tracking and mapping for small AR workspaces[C]//2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality. Piscataway,NJ:IEEE,2007:225-234.
|
| [11] |
MUR-ARTAL R,MONTIEL J M M,TARDÓS J D. ORB-SLAM:A versatile and accurate monocular SLAM system[J]. IEEE Transactions on Robotics,2015,31(5):1147−1163. doi: 10.1109/TRO.2015.2463671
|
| [12] |
MUR-ARTAL R,TARDÓS J D. ORB-SLAM2:An open-source SLAM system for monocular,stereo,and RGB-D cameras[J]. IEEE Transactions on Robotics,2017,33(5):1255−1262. doi: 10.1109/TRO.2017.2705103
|
| [13] |
CAMPOS C,ELVIRA R,RODRÍGUEZ J J G,et al. ORB-SLAM3:An accurate open-source library for visual,visual–inertial,and multimap SLAM[J]. IEEE Transactions on Robotics,2021,37(6):1874−1890. doi: 10.1109/TRO.2021.3075644
|
| [14] |
MU Q,WANG Y H,LIANG X,et al. Autonomous localization and mapping method of mobile robot in underground coal mine based on edge computing[J]. Journal of Circuits,Systems and Computers,2024,33(1):2450018.
|
| [15] |
RUBLEE E,RABAUD V,KONOLIGE K,et al. ORB:An efficient alternative to SIFT or SURF[C]//2011 International Conference on Computer Vision. Piscataway,NJ:IEEE,2011:2564-2571.
|
| [16] |
江松,崔智翔,代碧波,等. 暗环境适应性的基于SLAM的煤矿井下机器人定位方法[J/OL]. 煤炭科学技术,1−14[2024−12−06]. http://kns.cnki.net/kcms/detail/11.2402.td.20240325.1341.004.html.
JIANG Song,CUI Zhixiang,DAI Bibo,et al. SLAM-based positioning method for underground robot in coal mine based on dark environment adaptability[J/OL]. Coal Science and Technology,1−14[2024−12−06]. http://kns.cnki.net/kcms/detail/11.2402.td.20240325.1341.004.html.
|
| [17] |
高毅楠,姚顽强,蔺小虎,等. 煤矿井下多重约束的视觉SLAM关键帧选取方法[J]. 煤炭学报,2024,49(S1):472−482.
GAO Yinan,YAO Wanqiang,LIN Xiaohu,et al. Visual SLAM keyframe selection method with multiple constraints in underground coal mines[J]. Journal of China Coal Society,2024,49(S1):472−482.
|
| [18] |
DETONE D,MALISIEWICZ T,RABINOVICH A. SuperPoint:Self-supervised interest point detection and description[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). Piscataway,NJ:IEEE,2018:337-33712.
|
| [19] |
GROMPONE VON GIOI R,JAKUBOWICZ J,MOREL J M,et al. LSD:A fast line segment detector with a false detection control[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2010,32(4):722−732. doi: 10.1109/TPAMI.2008.300
|
| [20] |
BURRI M,NIKOLIC J,GOHL P,et al. The EuRoC micro aerial vehicle datasets[J]. The International Journal of Robotics Research,2016,35(10):1157−1163. doi: 10.1177/0278364915620033
|
| [21] |
SARLIN P E,DETONE D,MALISIEWICZ T,et al. SuperGlue:Learning feature matching with graph neural networks[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE,2020:4937-4946.
|
| [22] |
ZHANG L L,KOCH R. An efficient and robust line segment matching approach based on LBD descriptor and pairwise geometric consistency[J]. Journal of Visual Communication and Image Representation,2013,24(7):794−805. doi: 10.1016/j.jvcir.2013.05.006
|
| [23] |
ZHANG B S,MA X G,MA H J,et al. DynPL-SVO:A robust stereo visual odometry for dynamic scenes[J]. IEEE Transactions on Instrumentation and Measurement,2024,73:5006510.
|
| [24] |
李用杰,秦广健,武利明,等. 基于点线面特征的无漂移旋转视觉里程计[J]. 计算机应用研究,2023,40(12):3805−3809.
LI Yongjie,QIN Guangjian,WU Liming,et al. Drift-free rotation visual odometry based on point-line-plane feature fusion[J]. Application Research of Computers,2023,40(12):3805−3809.
|
| [25] |
SUN Y Y,WANG Q,TANSEY K,et al. Multi-constrained optimization method of line segment extraction based on multi-scale image space[J]. ISPRS International Journal of Geo-Information,2019,8(4):183. doi: 10.3390/ijgi8040183
|
| [26] |
XU K,HAO Y F,YUAN S H,et al. AirVO:An illumination-robust point-line visual odometry[C]//2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Piscataway,NJ:IEEE,2023:3429-3436.
|
| [27] |
HE Y J,ZHAO J,GUO Y,et al. PL-VIO:Tightly-coupled monocular visual–inertial odometry using point and line features[J]. Sensors,2018,18(4):1159. doi: 10.3390/s18041159
|
| [28] |
KASPER M,MCGUIRE S,HECKMAN C. A benchmark for visual-inertial odometry systems employing onboard illumination[C]//2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Piscataway,NJ:IEEE,2019:5256−5263.
|
| [29] |
GOMEZ-OJEDA R,MORENO F A,ZUÑIGA-NOËL D,et al. PL-SLAM:A stereo SLAM system through the combination of points and line segments[J]. IEEE Transactions on Robotics,2019,35(3):734−746. doi: 10.1109/TRO.2019.2899783
|
| [30] |
LEUTENEGGER S. Okvis2:Realtime scalable visual-inertial slam withloop closure[J]. arXiv preprint arXiv:2202.09199,2022.
|
Supported by: Beijing Renhe Information Technology Co., Ltd. 
DownLoad: