基于激光雷达的井下带式输送机边缘提取方法

LiDAR-based edge extraction method for underground belt conveyors

带式输送机是煤矿井下非结构化胶带巷中巡检机器人的巡检对象之一,且其边缘提取可使机器人获取自身相对检测目标的空间位姿,为执行巡检任务提供环境信息支持。目前井下大多采用基于视觉的边缘提取技术,难以有效克服照度低、粉尘大、水雾浓等问题。针对该问题,采用防爆16线激光雷达作为巡检机器人传感器获取巷道点云,以降低环境对提取结果的影响。对获取的原始稀疏点云进行统计离群值移除和直通滤波预处理,以去除噪声和无用点云,采用随机样本一致算法分割带式输送机点云平面,基于投影-四叉树方法提取带式输送机边缘点云。rviz+Gazebo联合仿真结果表明:在机器人不同运动工况下,带式输送机边缘提取的准确率不低于96.33%;雷达遮蔽率低于30%时准确率不低于79.23%。实验室测试结果表明:带式输送机表面水层分布比例为100%且厚度饱和条件下,边缘提取准确率不低于88%,整体优于基于经纬的极值检索法、基于KDTree/OcTree的曲率阈值法、基于KDTree/OcTree的临近点夹角阈值法,且平均计算耗时仅为36 ms,满足井下实时巡检需求。

The belt conveyor is one of the inspection targets of the inspection robot in the unstructured belt roadway of underground coal mines.Extracting its edges allows the robot to obtain its spatial pose relative to the inspection target,providing environmental information to support the execution of inspection tasks.Currently,most underground edge extraction techniques are vision-based,which struggle to overcome challenges such as low illumination,heavy dust,and dense fog.To address this issue,an explosion-proof 16-line LiDAR was used as the sensor for the inspection robot to acquire the roadway point cloud,reducing the environmental impact on the extraction results.The raw sparse point cloud was preprocessed using statistical outlier removal and passthrough filtering to eliminate noise and irrelevant points.The belt conveyor's point cloud plane was segmented using the Random Sample Consensus(RANSAC)algorithm,and the edge point cloud of the belt conveyor was extracted using a projection-quad tree method.The combined rviz and Gazebo simulation results showed that,under different operating conditions of the robot,the accuracy of belt conveyor edge extraction was no less than 96.33%.When the LiDAR shielding rate was below 30%,the accuracy was no less than 79.23%.Laboratory tests showed that,even when the surface of the belt conveyor had a 100%water distribution and saturated thickness,the edge extraction accuracy was no less than 88%.Overall,this method outperforms the latitude and longitude extremum search method,the curvature threshold method based on KDTree/OcTree,and the adjacent point angle threshold method based on KDTree/OcTree,with an average computation time of only 36 ms,meeting the real-time inspection needs of underground environments.