摘要
井下斜坡道的定位与建图是实现井下斜坡道无人驾驶的关键技术之一,矿山井下斜坡道区域为典型非结构化环境特征,且道路具有一定倾斜角度,采用传统SLAM算法无法获得精确里程计信息,导致定位与建图精度难以满足无人矿卡行驶需求。针对上述问题,通过研究激光SLAM(Simultaneous Localization And Mapping)算法LeGO-LOAM,笔者提出一种适用于矿山井下斜坡道环境的定位与建图方法。首先,针对井下斜坡道口两侧均为光滑水泥墙壁,特征点稀少问题,设计了基于人工路标的辅助增强定位方法,有效增加点云特征数量,从而优化位姿估计结果,避免建图漂移现象;然后在特征预处理阶段,提出了一种基于激光点云高度差与坡度信息融合的提取地面点高效算法,通过改善地面地点的选取策略,针对倾斜坑洼路面仍能有效识别地面点,解决了井下斜坡道定位与建图倾斜角度大、误差大等问题;其次,基于CVC(Curved-Voxel Clustering)聚类算法设计了一种斜坡道点云曲率体素聚类算法,采用曲率体素和基于哈希的数据结构对点云进行分割,大幅提高在井下稀疏、噪声环境下点云聚类的鲁棒性;最后,运用Scan-To-Map进行点云匹配,同时兼顾点云配准的性能与速度。在中钢集团山东某井下斜坡道的现场实验证明:与原算法相比精度提升13.15%,Z轴误差降低22.3%,地图质量明显提升,能有效解决井下无人驾驶建图及定位的难题。
Localization and mapping of the underground ramps is one of the key technologies for achieving unmanned driving of underground mining trucks.The underground ramp is a typical unstructured environmental feature,and the road has a certain inclination angle.However,the traditional SLAM algorithm cannot obtain accurate odometer information,which makes the accuracy of positioning and mapping difficult to meet the driving requirements of unmanned mine trucks.To overcome above problems,this paper proposes a positioning and mapping method suitable for underground ramp environments in mines by studying the laser SLAM(Simultaneous Localization And Mapping)algorithm LeGO-LOAM.Firstly,in terms of the characteristics of smooth cement walls and sparse features on both sides of the underground ramp,a manual landmark-based auxiliary enhancement positioning method is designed,effectively increase the number of point cloud features,to optimize the pose estimation results and avoid the phenomenon of mapping drift.Then,in the feature preprocessing stage,a highly efficient algorithm for extracting ground points based on the fusion of laser point cloud height difference and slope information is proposed,by improving the selection strategy of ground points,which can effectively be identified for sloped potholes,to solve the problem of large inclination angle and error in underground ramps.Next,a slope point cloud surface voxel clustering algorithm based on the CVC(Curved-Voxel Clustering)clustering algorithm is designed,using the curvature voxels and hash-based data structures to segmentation of point clouds,which significantly improve the robustness of point cloud clustering in sparse and noisy environments underground.Finally,the Scan-To-Map is used for point cloud registration,while balancing the performance and speed of point cloud alignment.The on-site experiment in an underground ramp of Shandong,Sinosteel Group proved that the proposed method has improved the accuracy by 13.15%and reduced the Z-axis error by 22.3%compared with the original algorithm.The quality of the map has been significantly improved,which can effectively solve the problem of mapping and positioning in underground autonomous driving.
作者
顾清华
白昌鑫
陈露
李萌
付明宇
王维
GU Qinghua;BAI Changxin;CHEN Lu;LI Meng;FU Mingyu;WANG Wei(School of Resources Engineering,Xi’an University of Architecture and Technology,Xi’an 710055,China;Xi’an Key Laboratory of Smart Industry Perception Computing and Decision Making,Xi’an University of Architecture and Technology,Xi’an 710055,China;Sinosteel Shandong Mining Co.,Ltd.,Linyi 277700,China)
出处
《煤炭学报》
EI
CAS
CSCD
北大核心
2024年第3期1680-1688,共9页
Journal of China Coal Society
基金
国家自然科学基金资助项目(52074205)
陕西省杰出青年基金资助项目(2020JC-44)。
关键词
井下斜坡道
无人驾驶
激光SLAM
地面点分割
点云聚类
underground ramps
unmanned driving
laser SLAM
ground point segmentation
point cloud clustering
作者简介
顾清华(1981—),男,山东诸城人,教授,博士。E-mail:qinghuagu@126.com;通讯作者:白昌鑫(1999—),男,陕西西安人,硕士研究生。E-mail:1277457656@qq.com。
