The absolute three-dimensional position of a longwall shearer is fundamental to longwall mining automation. The positioning of the longwall shearer is usually realized by the inertial navigation system (INS) and odometer (OD). However, the position accuracy of this positioning approach gradually decreases over time due to the gyro drift. To further increase the positioning accuracy of the shearer, this paper proposes a positioning approach based on the INS and light detection and ranging (LiDAR). A Kalman filter (KF) model based on the observation provided by detecting hydraulic supports which are part of the longwall face, using the LiDAR, is established. The selection scheme of the point features is studied through a set of simulations. In addition, compared with that of the approach based on the INS and OD, the shearer positioning accuracy obtained using the proposed approach is higher. When the shearer moves along a 350 m track for 6 cutting cycles and lasts about 7.1 h, both east and north position errors can be maintained within 0.2 m and the height error within 0.1 m.

中文翻译：

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地下煤矿中长壁采煤机的激光雷达惯性定位方法

长壁采煤机的绝对三维位置是长壁采煤自动化的基础。长壁采煤机的定位通常通过惯性导航系统（INS）和里程表（OD）来实现。然而，由于陀螺仪漂移，该定位方法的位置精度随着时间而逐渐降低。为了进一步提高采煤机的定位精度，本文提出了一种基于INS和光检测与测距（LiDAR）的定位方法。建立基于卡尔曼滤波器（KF）的模型，该模型基于通过使用LiDAR检测作为长壁面一部分的液压支架而提供的观察结果。通过一组模拟研究了点特征的选择方案。此外，与基于INS和OD的方法相比，使用该方法获得的采煤机定位精度较高。当采煤机沿350 m履带移动6个切削周期并持续约7.1 h时，东和北位置误差均可以保持在0.2 m之内，而高度误差则可以保持在0.1 m之内。