Volume 48 Issue 11
Nov.  2022
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QI Yuhao, GUAN Shiyuan. Real-time 3D mapping method of fully mechanized working face based on laser SLAM[J]. Journal of Mine Automation，2022，48(11)：139-144. DOI: 10.13272/j.issn.1671-251x.2022060047
Citation: QI Yuhao, GUAN Shiyuan. Real-time 3D mapping method of fully mechanized working face based on laser SLAM[J]. Journal of Mine Automation，2022，48(11)：139-144. DOI: 10.13272/j.issn.1671-251x.2022060047
shu

Real-time 3D mapping method of fully mechanized working face based on laser SLAM

  • 1.

    Shandong Energy Group Co., Ltd., Jinan 250013, China

  • 2.

    Beijing Tianma Intelligent Control Technology Co., Ltd., Beijing 101399, China

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  • Received Date: June 12, 2022
  • Revised Date: November 01, 2022
  • Available Online: August 29, 2022
    Graphical Abstract
    • Abstract
  • The mobile mapping method relies on fiber optic inertial navigation with high precision and odometer to calculate the position and attitude. But in the actual engineering practice, the precision of odometer is difficult to meet the application requirements, resulting in incomplete 3D laser point cloud of working face. In order to solve this problem, a real-time 3D mapping method of fully mechanized working face based on laser SLAM is proposed. The method mainly comprises the steps of distortion removal of laser point cloud, feature extraction , position and attitude estimation and optimization mapping. The distortion of laser point cloud is eliminated through the inertial navigation data. The inertial navigation data is retrieved according to the time stamp of each point in the point cloud to obtain the attitude angle corresponding to each point. If the corresponding attitude angle is not retrieved, the quaternion method is adopted for interpolation. The geometric tensor feature of the point cloud is extracted by principal component analysis. Firstly, the covariance matrix of the point set is solved. Secondly, the eigenvalue decomposition is performed to obtain the geometric tensor feature. The distance between the feature points in two adjacent frames is calculated to construct an objective function. The Levenberg-Marquardt algorithm is used to solve the objective function and obtain the transformation matrix, so as to realize position and attitude estimation. The incremental optimization algorithm is adopted. The GTSAM optimization library is used for carrying out joint optimization on the historical keyframe and the current keyframe. All obtained keyframe point clouds are superposed together to obtain the global 3D real-time map. The results of the underground industrial test show that this method can construct the 3D map of the whole working face in real-time, completely and accurately. The maximum mean absolute error is 0.19 m, which meets the precision requirements of monitoring of fully mechanized working face and straightening of the scraper conveyor.
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