基于固态激光雷达的露天矿非结构化运输道路小尺寸落石检测方法  

Small-Scale Rockfall Detection Method Based on Solid-State Lidar for Unstructured Transportation Roads in Open-Pit Mines

作　　者：顾清华[1,2] 李佳威 陈露[1,2] 祝河杰 Gu Qinghua;Li Jiawei;Chen Lu;Zhu Hejie(School of Resources Engineering,Xi’an University of Architecture and Technology,Xi’an 710055,Shaanxi,China;Xi’an Key Laboratory of Intelligent Industrial Perception Computing and Decision Making,Xi’an University of Architecture and Technology,Xi’an 710055,Shaanxi,China)

机构地区：[1]西安建筑科技大学资源工程学院,陕西西安710055 [2]西安建筑科技大学西安市智慧工业感知、计算与决策重点实验室,陕西西安710055

出　　处：《激光与光电子学进展》2024年第8期219-224,共6页Laser & Optoelectronics Progress

基　　金：国家自然科学基金(52074205);陕西省自然科学基础研究计划(2020JC-44)。

摘　　要：针对露天矿无人矿卡运输作业中路况差、光照强和灰尘大等原因导致小尺寸落石检测困难和实时性差的问题,提出一种使用固态激光雷达检测露天矿非结构化运输道路小尺寸落石的方法。首先使用具有双回波技术的激光雷达进行数据采集,减少灰尘干扰并提取车辆前方可行驶区域;然后采用基于扇面的直线拟合地面分割算法分割地面,实现对有坡度的非结构化粗糙路面的彻底分割;之后引入八叉树的分层网格树模型进行邻域查找,提高邻域查找的速度,引入双色最近对构建图,快速生成聚类簇,引入自适应聚类半径ε,进行聚类并输出小尺寸落石的盒模型。实验结果表明:相较于使用k-d树加速的DBSCAN算法,所提方法的正检率提升9.61个百分点,检测时间缩短379.77 ms。To address the challenges faced in the realtime detection of smallsize rockfalls in openpit mines during the transportation of ores using unmanned carts owing to suboptimal road conditions,intense lighting,and heavy dust,this study proposes a method for detecting smallsize rockfalls in openpit mines based on solidstate lidar.The proposed method employed a doubleecho lidar for data acquisition,effectively reducing dust interference and extracting the driving area in front of the vehicle.Subsequently,a ground segmentation algorithm(straightline fitting)based on fan surfaces was employed to segment the rough and unstructured terrains having slopes.Moreover,a hierarchical grid tree model known as octree was introduced to enhance the efficiency of neighborhood search.Furthermore,the twocolor nearest pair method was applied to construct a graph,rapidly generating the clusters.Finally,the concept of adaptive clustering radiusεwas adopted for clustering and obtaining the box models of smallsize rockfalls.The experimental results demonstrate that the proposed method outperforms the kd treeaccelerated DBSCAN algorithm,increasing the positive detection rate by 9.61 percentage points and reducing the detection time by 379.77 ms.

关 键 词：露天矿 无人驾驶 障碍物检测 激光雷达点云 密度聚类 

分 类 号：TD57[矿业工程—矿山机电]