| Citation: |
LI Hui，LI Minchao，CUI Lizhen，et al. 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
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In recent years, the coal mining industry in China has been experiencing rapid growth, resulting in an increasing adoption of intelligent technologies. Among these advancements, precise positioning and navigation technology for open-pit coal mining environments have become crucial. However, Simultaneous Localization and Mapping (SLAM), a key technology for unmanned driving, is currently facing significant challenges in open-pit coal mines. Limited environmental feature points and environmental degradation have necessitated SLAM to rely solely on sparse feature points for localization and mapping, thus increasing its complexity. Furthermore, sensor jitter caused by slopes and uneven roads can lead to motion distortion during robot operation. To address these challenges, a novel solution is proposed in this paper. Firstly, the external parameters of the sensors are being recalibrated. Secondly, the integration of inertial guidance and LiDAR is being utilized to improve data consistency and accuracy. This approach aims to enhance the performance of SLAM in open-pit coal mines, improving localization accuracy and mapping effectiveness. Building upon this foundation, our approach is leveraging full feature point matching to directly down sample and extract the point cloud from the LiDAR data. To enrich the preprocessed laser point cloud data, Iterative Closest Point (ICP) matching is being incorporated at the front-end of the algorithm, facilitating the extraction of the key-frame point cloud X. Subsequently, this data is being integrated with inertial guidance information to correct aberrations in the point cloud, leading to the formation of the refined point cloud P. ICP matching is once again being employed to align X and P. Furthermore, a factor graph is being incorporated into our back-end to enhance loopback detection, strengthening constraints and further improving localization accuracy and mapping effectiveness in open-pit coal mine environments. Experimental results demonstrate the high localization precision and undistorted map building capabilities of our proposed algorithm. Notably, the sidewall texture remains clear, exhibiting a certain degree of robustness, effectively enhancing both robustness and accuracy in open-pit coal mine settings.
| [1] |
李浩荡,佘长超,周永利,等. 我国露天煤矿开采技术综述及展望[J]. 煤炭科学技术,2019,47(10):24−35.
LI Haodang,SHE Changchao,ZHOU Yongli,et al. Summary and prospect of open-pit coal mining technology in China[J]. Coal Science and Technology,2019,47(10):24−35.
|
| [2] |
张瑞新,毛善君,赵红泽,等. 智慧露天矿山建设基本框架及体系设计[J]. 煤炭科学技术,2019,47(10):1−23.
ZHANG Ruixin,MAO Shanjun,ZHAO Hongze,et al. Framework and structure design of system construction for intelligent open-pit mine[J]. Coal Science and Technology,2019,47(10):1−23.
|
| [3] |
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):2835−2861.
|
| [4] |
CHOU C C,CHOU C F. Efficient and accurate tightly-coupled visual-lidar SLAM[J]. IEEE Transactions on Intelligent Transportation Systems,2022,23(9):14509−14523.
|
| [5] |
王金科,左星星,赵祥瑞,等. 多源融合SLAM的现状与挑战[J]. 中国图象图形学报,2022,27(2):368−389.
WANG Jinke,ZUO Xingxing,ZHAO Xiangrui,et al. Review of multi-source fusion SLAM:Current status and challenges[J]. Journal of Image and Graphics,2022,27(2):368−389.
|
| [6] |
LI W Y,LIU G,CUI X W,et al. Feature-aided RTK/LiDAR/INS integrated positioning system with parallel filters in the ambiguity-position-joint domain for urban environments[J]. Remote Sensing,2021,13(10):2013.
|
| [7] |
LI X X,YU H,WANG X B,et al. FGO-GIL:Factor graph optimization-based GNSS RTK/INS/LiDAR tightly coupled integration for precise and continuous navigation[J]. IEEE Sensors Journal,2023,23(13):14534−14548.
|
| [8] |
LI X X,WANG S W,LI S Y,et al. Enhancing RTK performance in urban environments by tightly integrating INS and LiDAR[J]. IEEE Transactions on Vehicular Technology,2023,72(8):9845−9856. doi: 10.1109/TVT.2023.3257874
|
| [9] |
ZHANG J,SINGH S. Loam:Lidar odometry and mapping in real-time[C]// Robotics:Science and Systems,2014,2(9):1−9.
|
| [10] |
SHAN T X,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). Piscataway,NJ:IEEE,2018:4758−4765.
|
| [11] |
SHAN T X,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). Piscataway,NJ:IEEE,2020:5135−5142.
|
| [12] |
王铉彬,李星星,廖健驰,等. 基于图优化的紧耦合双目视觉/惯性/激光雷达SLAM方法[J]. 测绘学报,2022,51(8):1744−1756.
WANG Xuanbin,LI Xingxing,LIAO Jianchi,et al. Tightly-coupled stereo visual-inertial-LiDAR SLAM based on graph optimization[J]. Acta Geodaetica et Cartographica Sinica,2022,51(8):1744−1756.
|
| [13] |
张清宇,崔丽珍,李敏超,等. 倾斜地面3D点云快速分割算法[J]. 无线电工程,2024,54(2):447−456.
ZHANG Qingyu,CUI Lizhen,LI Minchao,et al. A fast segmentation algorithm for 3D point cloud on inclined ground[J]. Radio Engineering,2024,54(2):447−456.
|
| [14] |
马宝良,崔丽珍,李敏超,等. 露天煤矿环境下基于LiDAR/IMU的紧耦合SLAM算法研究[J]. 煤炭科学技术,2024,52(3):236−244.
MA Baoliang,CUI Lizhen,LI Minchao,et al. Tightly coupled LiDAR-Inertial SLAM for open pit coal mine environment[J]. Coal Science and Technology,2024,52(3):236−244.
|
| [15] |
王忠鑫,辛凤阳,宋波,等. 论露天煤矿智能化建设总体设计[J]. 煤炭科学技术,2022,50(2):37−46.
WANG Zhongxin,XIN Fengyang,SONG Bo,et al. Overall design of intelligent construction in open pit coal mines[J]. Coal Science and Technology,2022,50(2):37−46.
|
| [16] |
REN Z L,WANG L G,BI L. Robust GICP-based 3D LiDAR SLAM for underground mining environment[J]. Sensors,2019,19(13):2915. doi: 10.3390/s19132915
|
| [17] |
XUE G H,WEI J B,LI R X,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
|
| [18] |
YANG L,MA H W,NIE Z,et al. 3D LiDAR point cloud registration based on IMU preintegration in coal mine roadways[J]. Sensors,2023,23(7):3473.
|
| [19] |
邓鹏,罗静. 复杂环境下机器人多传感器融合定位方法[J]. 电子测量与仪器学报,2023,37(12):48−57.
DENG Peng,LUO Jing. Robot multi-sensor fusion localization method in complex environment[J]. Journal of Electronic Measurement and Instrumentation,2023,37(12):48−57.
|
| [20] |
蒲文浩,刘锡祥,陈昊,等. 多传感器融合的激光雷达点云矫正与定位方法[J]. 激光与光电子学进展,2023,60(24):3788/LOP230762.
PU Wenhao,LIU Xixiang,CHEN Hao,et al. LiDAR point cloud correction and location based on multisensor fusion[J]. Laser & Optoelectronics Progress,2023,60(24):3788/LOP230762.
|
| [21] |
CENSI A. An ICP variant using a point-to-line metric[C]//2008 IEEE International Conference on Robotics and Automation. Piscataway,NJ:IEEE,2008:19−25.
|
| [22] |
LI X Y,LI X,LI Z Y,et al. Construction of a 3D distribution network environment based on multi-source data fusion[J]. Journal of Physics:Conference Series,2023,2614(1):012011. doi: 10.1088/1742-6596/2614/1/012011
|
| [23] |
WU H W,ZHU L C,CHENG J D,et al. Research on motion distortion optimization algorithm of laser SLAM[J]. Journal of Physics:Conference Series,2022,2330(1):012013. doi: 10.1088/1742-6596/2330/1/012013
|
| [24] |
王雪,李登峰,黄杉杉,等. 激光雷达运动畸变去除的算法设计[J]. 自动化仪表,2021,42(5):89−91.
WANG Xue,LI Dengfeng,HUANG Shanshan,et al. Design of algorithm for removal of lidar motion distortion[J]. Process Automation Instrumentation,2021,42(5):89−91.
|
| [25] |
饶启鹏,凌铭,王鑫,等. 基于连续时间样条约束的改进激光里程计[J]. 激光与光电子学进展,2023,60(22):248−255.
RAO Qipeng,LING Ming,WANG Xin,et al. Improved lidar odometry based on continuous-time spline constraints[J]. Laser & Optoelectronics Progress,2023,60(22):248−255.
|
| [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,2022,7(2):2266−2273.
|
| [27] |
Ethz-asl. Lidar_align:A simple method for finding the extrinsic calibration between a 3D lidar and a 6-dof pose sensor[EB/OL]. (2018−03−26) [2024−03−27]. https://github.com/ethz-asl/lidar_align.2019.
|
| [28] |
GAO Wenliang. Imu_utils:A ROS package tool to analyze the IMU performance[EB/OL]. (2018−03−30)[2024−03−27]. https://github. com/gaowenliang/imu_utils. 2018.
|
| [29] |
GRUPP M. Evo:Python package for the evaluation of odometry and SLAM[EB/OL]. (2017−07−25) [2024−03−27]. https://github.com/MichaelGrupp/evo.2017.
|
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