针对煤矿井下狭长密闭巷道内掘进机难定位、难定向的问题，进行了基于激光雷达的煤矿掘进机井下巷道环境建模与障碍检测方法研究，用于判断巷道边缘形状，预测掘进机行进方向。建立了煤矿井下掘进机运动学模型与激光雷达观测模型，解算掘进机在煤矿井巷内部的掘进距离、位置等相关掘进参数，将狭长密闭的巷道环境建模问题转换为数理上的概率统计与数学模型预测问题。同时，对激光SLAM环境建模算法进行分析，将可行性巷道环境建模算法进行优化改进，进一步提高井下巷道环境建模线条特征提取的精确度。试验结果表明：基于激光雷达的煤矿掘进机井下巷道环境建模与障碍检测方法，可以有效解决掘进机在井下掘进过程无地图指导的问题，当距离阈值参数为0.05，角度阈值参数为0.06时，HectorSLAM算法可以精确提取片帮巷道边缘形状，对巷道中的障碍做出识别，为预测掘进机行进方向和判断巷道形状提供保障。

Aiming at the difficult positioning and difficult orientation of the tunneling machine in the narrow and narrow closed roadway of coal mine,the laser radar-based underground roadway environment modeling and obstacle detection method based on laser radar is studied to judge the shape of the roadway edge and predict the direction of the roadheader.The kinematics model and lidar observation model of coal mine roadheader are established,and the excavation distance and position of the tunneling machine in the coal mine roadway are calculated.The modeling problem of narrow and closed coal mine roadway environment is transformed into mathematical probability and statistics.Predict problems with mathematical models.At the same time,the laser SLAM environment modeling algorithm is analyzed,and the feasible roadway environment modeling algorithm is optimized and improved to further improve the accuracy of line feature extraction in underground roadway environment modeling.The experimental results show that the laser radar-based coal mine roadheader environment modeling and obstacle detection method can effectively solve the problem of no map guidance in the tunneling process of the tunneling machine.When the distance_thresh is 0.05 and the angle_thresh is 0.06,the HectorSLAM algorithm can accurately extract the piece.Help the shape of the edge of the roadway,identify the obstacles in the coal mine roadway,and provide guarantee for predicting the direction of the roadheader and judging the shape of the roadway.