Advance Search

MA Baoliang,CUI Lizhen,LI Minchao,et al. Study on tightly coupled LiDAR-Inertial SLAM for open pit coal mine environment[J]. Coal Science and Technology,2024,52(3):236−244

. DOI: 10.12438/cst.2023-0538
Citation:

MA Baoliang,CUI Lizhen,LI Minchao,et al. Study on tightly coupled LiDAR-Inertial SLAM for open pit coal mine environment[J]. Coal Science and Technology,2024,52(3):236−244

. DOI: 10.12438/cst.2023-0538

Study on tightly coupled LiDAR-Inertial SLAM for open pit coal mine environment

Funds: 

National Natural Science Foundation of China (62261042); Science and Technology Plan Funding Project of Inner Mongolia Autonomous Region (2019GG328); Science and Technology Plan Funding Project of Inner Mongolia Autonomous Region (2022YFSH0051)

More Information
  • Received Date: April 13, 2023
  • Available Online: March 19, 2024
  • With the rapid development of artificial intelligence and unmanned and other related disciplines, the intelligence and unmanned of coal mining equipment has become a new trend. The application of intelligent equipment will greatly improve the productivity of coal mine operations as well as personnel safety. In this environment, the existing LIDAR-based Simultaneous localization and mapping (SLAM) solution is prone to positioning drift and large mapping errors. To address these problems, a tightly coupled SLAM algorithm based on LiDAR (Light Detection and Ranging) and IMU (Inertial Measurement Unit) is proposed, which uses both LiDAR and IMU sensors as data inputs.The front-end uses an iterative extended Kalman filter to fuse the pre-processed LiDAR feature points with the IMU data and uses backward propagation to correct the radar motion distortion, the back-end uses the LiDAR relative positional factor to use the LiDAR inter-frame alignment results as a constraint factor together with the loopback factor to complete the global factor map optimization. The robustness and accuracy of the algorithm are verified using open source dataset and open pit coal mine field dataset. The experimental results show that the accuracy of the proposed algorithm is consistent with the current LiDAR SLAM algorithm in the urban structured environment, while the proposed algorithm improves the localization accuracy by 46.00% and 23.15% with higher robustness than the FAST-LIO2 and LIO-SAM tightly coupled algorithms for the open pit coal mine field environment of more than2000meters long, respectively.

  • [1]
    王国法,任世华,庞义辉,等. 煤炭工业“十三五”发展成效与 “双碳”目标实施路径[J]. 煤炭科学技术,2021,49(9):1−8.

    WANG Guofa,REN Shihua,PANG Yihui,et al. Development achievements of China’s coal industry during the 13th Five-Year Plan period and implementation path of “dual carbon” target[J]. Coal Science and Technology,2021,49(9):1−8.
    [2]
    王 猛,马如英,代旭光,等. 煤矿区碳排放的确认和低碳绿色发展途径研究[J]. 煤田地质与勘探,2021,49(5):63−69.

    WANG Meng,MA Ruying,DAI Xuguang,et al. Confirmation of carbon emissions in coal mining areas and research on low–carbon green development path[J]. Coal Geology & Exploration,2021,49(5):63−69.
    [3]
    王国法. 煤矿智能化最新技术进展与问题探讨[J]. 煤炭科学技术,2022,50(1):1−27.

    WANG Guofa. New technological progress of coal mine intelligence and its problems[J]. Coal Sci. Technol,2022,50(1):1−27.
    [4]
    赵 浩. 露天煤矿高质量安全发展形势分析与对策措施[J]. 煤矿安全,2022,53(7):251−256.

    ZHAO Hao. Analysis of development situation and countermeasures of high quality safety in open -pit coal mines[J]. Safety in Coal Mines,2022,53(7):251−256.
    [5]
    葛世荣,胡而已,李允旺. 煤矿机器人技术新进展及新方向[J]. 煤炭学报,2023,48(1):54−73.

    GE Shirong,HU Eryi,LI Yunwang. New progress and direction of robot technology in coal mine[J]. Journal of China Coal Society,2023,48(1):54−73.
    [6]
    XUE G,LI R,LIU S,et al. Research on Underground Coal Mine Map Construction Method Based on LeGO-LOAM Improved Algorithm[J]. Energies,2022,15(17):6256. doi: 10.3390/en15176256
    [7]
    于海旭,杜志勇,魏志丹,等. 我国矿区无人驾驶技术现状与发展趋势分析[J]. 工矿自动化,2022,48(S2):82−87.

    YU Haixu,DU Zhiyong,WEI Zhidan,et al. Analysis on the current situation and development trend ofunmanned driving technology in mining areas in China[J]. Journal of Minc Automation,2022,48(S2):82−87.
    [8]
    REN Z,WANG L,BI L. Robust GICP-based 3D LiDAR SLAM for underground mining environment[J]. Sensors,2019,19(13):2915. doi: 10.3390/s19132915
    [9]
    SEGAL A,HAEHNEL D,THRUN S. Generalized-icp[C]//Robotics:science and systems. 2009,2(4):435.
    [10]
    XU X,ZHANG L,YANG J,et al. A review of multi-sensor fusion slam systems based on 3D LIDAR[J]. Remote Sensing,2022,14(12):2835. doi: 10.3390/rs14122835
    [11]
    薛光辉,李瑞雪,张钲昊,等. 基于3D激光雷达的SLAM算法研究现状与发展趋势[J]. 信息与控制,2023,52(1):18−36.

    XUE Guanghui,LI Ruixue,ZHANG Zhenghao, et al. State-of-the-art and Tendency of SLAM AlgorithmsBased on 3D LiDAR[J]. Information and Control. 2023,52(1):18−36.
    [12]
    ZHANG J,SINGH S. LOAM:Lidar odometry and mapping in real-time[C]//Robotics:Science and Systems. 2014,2(9):1−9.
    [13]
    SHAN T,ENGLOT B. Lego-loam:Lightweight and ground-optimized lidar odometry and mapping on variable terrain[C]//2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE,2018:4758−4765.
    [14]
    XUE G,WEI J,LI R,et al. LeGO-LOAM-SC:An Improved Simultaneous Localization and Mapping Method Fusing LeGO-LOAM and Scan Context for Underground Coalmine[J]. Sensors,2022,22(2):520. doi: 10.3390/s22020520
    [15]
    YE H,CHEN Y,LIU M. Tightly coupled 3d lidar inertial odometry and mapping[C]//2019 International Conference on Robotics and Automation (ICRA). IEEE,2019:3144−3150.
    [16]
    QIN T,LI P,SHEN S. 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
    [17]
    SHAN T,ENGLOT B,MEYERS D, et al. Lio-sam:Tightly-coupled lidar inertial odometry via smoothing and mapping[C]//2020 IEEE/RSJ international conference on intelligent robots and systems (IROS). IEEE,2020:5135−5142.
    [18]
    QIN C,YE H,PRANATA C E, et al. Lins:A lidar-inertial state estimator for robust and efficient navigation[C]//2020 IEEE international conference on robotics and automation (ICRA). IEEE,2020:8899−8906.
    [19]
    YANG X,LIN X,YAO W,et al. A Robust LiDAR SLAM Method for Underground Coal Mine Robot with Degenerated Scene Compensation[J]. Remote Sensing,2022,15(1):186. doi: 10.3390/rs15010186
    [20]
    XU W,ZHANG F. Fast-lio:A fast,robust lidar-inertial odometry package by tightly-coupled iterated kalman filter[J]. IEEE Robotics and Automation Letters,2021,6(2):3317−3324. doi: 10.1109/LRA.2021.3064227
    [21]
    XU W,CAI Y,HE D,et al. Fast-lio2:Fast direct lidar-inertial odometry[J]. IEEE Transactions on Robotics,2022,38( 4):2053−2073. doi: 10.1109/TRO.2022.3141876
    [22]
    CAI Y,XU W,ZHANG F. ikd-tree:An incremental kd tree for robotic applications[J]. arXiv preprint arXiv:2102.10808.
    [23]
    HERTZBERG C,WAGNER R,FRESE U,et al. Integrating generic sensor fusion algorithms with sound state representations through encapsulation of manifolds[J]. Information Fusion,2013,14(1):57−77. doi: 10.1016/j.inffus.2011.08.003
    [24]
    XU W,HE D,CAI Y,et al. Robots’ state estimation and observability analysis based on statistical motion models[J]. IEEE Transactions on Control Systems Technology,2022,30( 5):2030−2045. doi: 10.1109/TCST.2021.3133080
    [25]
    薛光辉,李瑞雪,张钲昊,等. 基于激光雷达的煤矿井底车场地图融合构建方法研究[J]. 煤炭科学技术,2023,51(8):219−227.

    XUE Guanghui,LI Ruixue,ZHANG Zhenghao, et al. Lidar based map construction fusion method for underground coal mine shaft bottom[ J]. Coal Science and Technology , 2023,51(8):219−227.
    [26]
    YIN J,LI A,LI T,et al. M2dgr:A multi-sensor and multi-scenario slam dataset for ground robots[J]. IEEE Robotics and Automation Letters,2021,7(2):2266−2273.
  • Related Articles

    [1]ZHOU Linna, WU Tihao, HUANG Xinli, YANG Chunyu, ZHANG Xin. Multi-Robot exploration for coal mine rescue based on the extension of undirected graph[J]. COAL SCIENCE AND TECHNOLOGY, 2025, 53(5): 338-348. DOI: 10.12438/cst.2024-0234
    [2]LI Hui, LI Minchao, CUI Lizhen, MA Baoliang, ZHANG Qingyu, PAN Bingbing. 3D LiDAR motion distortion algorithm for open-pit coal mine[J]. COAL SCIENCE AND TECHNOLOGY, 2025, 53(4): 373-382. DOI: 10.12438/cst.2024-0111
    [3]GE Xinbo, HUANG Jun, ZHAO Tongbin, MA Hongling, SHI Xilin. Research progress on underground compressed air energy storage based on knowledge graph[J]. COAL SCIENCE AND TECHNOLOGY, 2025, 53(4): 80-103. DOI: 10.12438/cst.2024-0780
    [4]MAO Qinghua, CHAI Jianquan, CHEN Yanzhang, XUE Xusheng, WANG Chuanwei. A three-dimensional reconstruction method of coal mine tunnel fused with LiDAR and IMU[J]. COAL SCIENCE AND TECHNOLOGY, 2025, 53(2): 351-362. DOI: 10.12438/cst.2024-1386
    [5]YANG Xin, SU Le, CHENG Yongjun, WANG Bo, ZHAO Yuan, YANG Xiongwei, ZHAO Chenglong, CAO Xiangang, ZHAO Jiangbin. Health status identification of scraper conveyer based on fusion of multiple graph structure information[J]. COAL SCIENCE AND TECHNOLOGY, 2024, 52(8): 171-181. DOI: 10.12438/cst.2023-1557
    [6]CAO Bo, WANG Shibo, GE Shirong, LU Cheng. Research on shearer positioning experiment based on IMU and UWB at the end of underground coal mining working face[J]. COAL SCIENCE AND TECHNOLOGY, 2023, 51(6): 217-228. DOI: 10.13199/j.cnki.cst.2022-0992
    [7]WANG Zhenyu, LIU Xiaomin, LIU Tingxi, WANG Wenjuan, XUE Lian. Study on influencing factors of coal-water coordinated mining based on theory of full life cycle[J]. COAL SCIENCE AND TECHNOLOGY, 2021, 49(12): 243-251.
    [8]YANG Junzhe, LEI Shaogang. The influence law of mining intensity on RUSLE slope and length factor[J]. COAL SCIENCE AND TECHNOLOGY, 2021, 49(1): 192-197. DOI: 10.13199/j.cnki.cst.2021.01.014
    [9]LI Sen. Measurement & control and localisation for fully-mechanized working facealignment based on inertial navigation[J]. COAL SCIENCE AND TECHNOLOGY, 2019, (8).
    [10]Analysis and Prediction on Carbon Potential Emission Factors of Combusted Coal[J]. COAL SCIENCE AND TECHNOLOGY, 2011, (1).
  • Cited by

    Periodical cited type(6)

    1. 毛清华,柴建权,陈彦璋,薛旭升,王川伟. 激光雷达和IMU融合的煤矿掘进巷道三维重建方法. 煤炭科学技术. 2025(02): 351-362 . 本站查看
    2. 张全平,任助理,郝英豪,邓浩坤,苏士杰,袁瑞甫,方程. 基于移动式三维激光扫描技术的巷道围岩变形监测研究. 煤矿机械. 2025(05): 205-208 .
    3. 李慧,李敏超,崔丽珍,马宝良,张清宇,潘冰冰. 露天煤矿三维激光雷达运动畸变算法. 煤炭科学技术. 2025(04): 373-382 . 本站查看
    4. 李倩,陈付龙,郑亮,赵法龙,陈智君. IMU紧耦合的多激光雷达定位与建图方法. 电子测量技术. 2024(09): 26-32 .
    5. 崔邵云,鲍久圣,胡德平,袁晓明,张可琨,阴妍,王茂森,朱晨钟. SLAM技术及其在矿山无人驾驶领域的研究现状与发展趋势. 工矿自动化. 2024(10): 38-52 .
    6. 胡泽广,金新宇,王婧. 基于可拓学评价模型的露天煤矿矿山地质环境综合评价. 能源与环保. 2024(12): 140-147 .

    Other cited types(1)

Catalog

    Article views (168) PDF downloads (58) Cited by(7)
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return