Advance Search
CHEN Wei,WU Shuaida,TIAN Zijian,et al. Research on coal mine robot positioning algorithm based on integration of ORB-SLAM3 vision and inertial navigation[J]. Coal Science and Technology,2025,53(S1):297−307. DOI: 10.12438/cst.2023-1915
Citation: CHEN Wei,WU Shuaida,TIAN Zijian,et al. Research on coal mine robot positioning algorithm based on integration of ORB-SLAM3 vision and inertial navigation[J]. Coal Science and Technology,2025,53(S1):297−307. DOI: 10.12438/cst.2023-1915

Research on coal mine robot positioning algorithm based on integration of ORB-SLAM3 vision and inertial navigation

More Information
  • Received Date: December 14, 2023
  • Available Online: May 09, 2025
  • Due to the narrow underground environment of coal mine, dark and changeable light, the mine image has the characteristics of low illumination, low contrast map and uneven color, which affects the matching result of visual SLAM feature points extraction and makes the positioning performance drop sharply. In order to improve the positioning accuracy of monocular visual positioning algorithm of coal mine mobile robot in low illumination, weak texture and unstructured environment features, the ORB-SLAM3 positioning algorithm is improved. On the basis of the front-end feature point extraction (ORB) algorithm, histogram equalization, non-maximum suppression, adaptive threshold method and feature point homogenization based on quadtree strategy are introduced. In feature point matching, LK optical flow method based on image pyramid is introduced to reduce the number of optimization iterations. After the feature point matching is completed, the RANSAC algorithm is added to remove the mismatched feature points and improve the matching accuracy of the feature points. Through the method of triangulation at the back end, the pixel depth information is obtained, and the 2D-2D pose solving problem is transformed into 3D-2D (pnp) pose solving problem. According to the principle of tight coupling of visual inertial navigation, the residual function of the whole positioning system is constructed by fusing visual residual error and IMU residual error, and the sliding window BA algorithm based on nonlinear optimization is used to iteratively optimize the residual function to obtain accurate pose estimation of the mobile robot. The improved algorithm is compared with ORB-SLAM3 algorithm and VSIN-Mono algorithm in four data sets. The results show that: (1) Compared with the ORB-SLAM3 algorithm and the VMS-MONO algorithm, the motion trajectory of the proposed positioning system is the closest to the true value trajectory; (2) All indexes of APE of the positioning system are better than ORB-SLAM3 algorithm and VMS-MONO algorithm; The root-mean-square error of the positioning system is 0.049m (the mean value of four experiments), which is 31.1% lower than that of ORB-SLAM3 (the mean value of four experiments).

  • [1]
    王凤超,刘应科,张利瑶,等. 淹没射流冲击煤体应力模型及破煤临界条件研究[J]. 中国安全科学学报,2023,33(12):122−130.

    WANG Fengchao,LIU Yingke,ZHANG Liyao,et al. Study on stress model and critical conditions for coal breaking by submerged jet[J]. China Safety Science Journal,2023,33(12):122−130.
    [2]
    SONG M L,WANG J L,ZHAO J J,et al. Production and safety efficiency evaluation in Chinese coal mines:Accident deaths as undesirable output[J]. Annals of Operations Research,2020,291(1):827−845.
    [3]
    曾一凡,武强,赵苏启,等. 我国煤矿水害事故特征、致因与防治对策[J]. 煤炭科学技术,2023,51(7):1−14.

    ZENG Yifan,WU Qiang,ZHAO Suqi,et al. Characteristics,causes,and prevention measures of coal mine water hazard accidents in China[J]. Coal Science and Technology,2023,51(7):1−14.
    [4]
    范京道,徐建军,张玉良,等. 不同煤层地质条件下智能化无人综采技术[J]. 煤炭科学技术,2019,47(3):43−52.

    FAN Jingdao,XU Jianjun,ZHANG Yuliang,et al. Intelligent unmanned fully-mechanized mining technology under conditions of different seams geology[J]. Coal Science and Technology,2019,47(3):43−52.
    [5]
    张立亚,李晨鑫,刘斌,等. 基于子图像分割映射点云空间的机器人避障算法[J]. 煤炭科学技术,2024,52(S2):368−374. doi: 10.12438/cst.2023-0948

    ZHANG Liya,LI Chenxin,LIU Bin,et al. Robot obstacle avoidance algorithm based on sub-image segmentation mapping point cloud hosting[J]. Coal Science and Technology,2024,52(S2):368−374. doi: 10.12438/cst.2023-0948
    [6]
    程健,李昊,马昆,等. 矿井视觉计算体系架构与关键技术[J]. 煤炭科学技术,2023,51(9):202−218. doi: 10.12438/cst.2023-0152

    CHENG Jian,LI Hao,MA Kun,et al. Architecture and key technologies of coalmine underground vision computing[J]. Coal Science and Technology,2023,51(9):202−218. doi: 10.12438/cst.2023-0152
    [7]
    ZHAI G D,ZHANG W T,HU W Y,et al. Coal mine rescue robots based on binocular vision:A review of the state of the art[J]. IEEE Access,2020,8:130561−130575. doi: 10.1109/ACCESS.2020.3009387
    [8]
    刘瑞军,王向上,张晨,等. 基于深度学习的视觉SLAM综述[J]. 系统仿真学报,2020,32(7):1244−1256.

    LIU Ruijun,WANG Xiangshang,ZHANG Chen,et al. A survey on visual SLAM based on deep learning[J]. Journal of System Simulation,2020,32(7):1244−1256.
    [9]
    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. Selection method of visual SLAM key frames with multiple constraints in coal mine underground[J]. Journal of China Coal Society,2024,49(S1):472−482.
    [10]
    DAVISON A J,REID I D,MOLTON N D,et al. MonoSLAM:Real-time single camera SLAM[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2007,29(6):1052−1067. doi: 10.1109/TPAMI.2007.1049
    [11]
    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.
    [12]
    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
    [13]
    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
    [14]
    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
    [15]
    权美香. 基于多传感器信息融合的单目视觉SLAM算法研究[D]. 哈尔滨:哈尔滨工业大学,2021:23−112.

    QUAN Meixiang. Research on SLAM algorithm of monocular vision based on multi-sensor information fusion[D]. Harbin:Harbin Institute of Technology,2021:23−112.
    [16]
    LIU H M,CHEN M Y,ZHANG G F,et al. ICE-BA:Incremental,consistent and efficient bundle adjustment for visual-inertial SLAM[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway,NJ:IEEE,2018:1974−1982.
    [17]
    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.
    [18]
    ZHU D X,JI K K,WU D,et al. A coupled visual and inertial measurement units method for locating and mapping in coal mine tunnel[J]. Sensors,2022,22(19):7437. doi: 10.3390/s22197437
    [19]
    张帆,葛世荣. 矿山数字孪生构建方法与演化机理[J]. 煤炭学报,2023,48(1):510−522.

    ZHANG Fan,GE Shirong. Construction method and evolution mechanism of mine digital twins[J]. Journal of China Coal Society,2023,48(1):510−522.
    [20]
    SIBLEY G,MATTHIES L,SUKHATME G. A sliding window filter for incremental SLAM[M]//KRAGIC D,KYRKI V,eds. Unifying perspectives in computational and robot vision. Boston,MA:Springer US,2008:103−112.
    [21]
    QIN T,LI P L,SHEN S J. VINS-mono:A robust and versatile monocular visual-inertial state estimator[J]. IEEE Transactions on Robotics,2018,34(4):1004−1020. doi: 10.1109/TRO.2018.2853729
  • Cited by

    Periodical cited type(2)

    1. 张天军,田嘉伟,张磊,庞明坤,潘红宇,孟伟,贺绥男. 循环载荷下破碎煤体渗透率及迂曲度演化研究. 岩土力学. 2025(05): 1409-1418+1428 .
    2. 王磊,钟浩,范浩,邹鹏,商瑞豪,晋康. 循环荷载下含瓦斯煤力学特性及应变场演化规律研究. 煤炭科学技术. 2024(06): 90-101 . 本站查看

    Other cited types(0)

Catalog

    Article views PDF downloads Cited by(2)
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return