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基于数量场梯度的露天矿点云台阶线 自动提取算法 被引量:1

Automatic extraction algorithm of step lines from point cloud in open-pit mine based on gradient of scalar field
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摘要 为了高效分析露天矿地形特征,本文利用无人机激光雷达扫描矿山,提出了一种基于数量场梯度的露天矿点云台阶线自动提取算法,即数量场梯度法。首先将点云的高程信息作为各点对应的数量场,计算各点数量场梯度的模,经分析,道路和边坡这两类场景梯度的模范围分别为[0.00,0.03]和[0.28,0.35];然后根据梯度的模范围筛选出台阶线特征点;最后对特征点进行四叉树索引,对每个叶节点进行最小二乘,拟合空间直线,并输出拟合的台阶线。通过与现有软件提取的台阶线对比,本文提出的数量场梯度法具有人工干预少的优势,且提取的台阶线错误率低于10%,位置精度优于0.6 m,可大幅提高露天矿测绘产品的自动化生成效率,有较好的应用前景。 In order to efficiently analyze the terrain characteristics of open-pit mine,using UAV LiDAR to scan mine,this paper proposes an automatic extraction algorithm of step lines from point cloud in open-pit mine based on gradient of scalar field,which is referred to as scalar field gradient method.Firstly,the elevation information in point cloud is used as the scalar field corresponding to each point.Then calculate modulus of gradient of scalar field at each point,after analysis the ranges of modulus of gradient of the road and slope scenes are[0.00,0.03]and[0.28,0.35];and then screen out the feature points of step lines according to the range of modulus of gradient.Finally,the feature points are indexed by a quadtree,and least-squares fit straight line in space for each leaf node,and fitted step lines is output.By comparison with step lines extracted by existing software,scalar field gradient method proposed in this paper has the advantage of less manual intervention,and the error rate of extracted step lines can be lower than 10%,and the position accuracy is better than 0.6 meters.The research results can greatly improve the automatic generation efficiency of open-pit mine surveying and mapping products,and have a good application prospect.
作者 张冰洁 蔡来良 王鑫 吴景动 ZHANG Bingjie;CAI Lailiang;WANG Xin;WU Jingdong(School of Surveying and Land Information Engineering,Henan Polytechnic University,Jiaozuo 454003,China)
出处 《测绘通报》 CSCD 北大核心 2023年第7期63-68,共6页 Bulletin of Surveying and Mapping
基金 国家自然科学基金(41701597) 中国博士后科学基金(2018M642746)。
关键词 露天矿 台阶线 激光雷达 数量场梯度 最小二乘法 open-pit mine step line LiDAR gradient of scalar field least square method
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