【摘要】 针对露天采场道路点云数据通过法向量、路缘石等点云特征难以准确提取的问题，提出了一种多特征约束的露天采场道路点云提取方法 .以辽阳市千山石灰石矿露天采场激光点云为数据源，首先对原始数据进行降采样；然后基于单点RGB信息、邻域RGB信息、邻域高差、邻域粗糙度、反射强度5类点云特征，制作并划分了训练集和验证集，利用随机森林算法构建了道路点云提取模型并进行了优化，进一步引入欧式聚类算法改进了道路点云提取模型结果，最后评估了露天采场道路点云提取结果 .结果表明，本文方法可以实时有效准确地提取露天采场道路点云数据.更多还原

【Abstract】 Aiming at the problem that road point cloud data in open pit is difficult to be accurately extracted through point cloud features such as normal vector and kerb, a method of road point cloud extraction in open pit with multi-feature constraints was proposed. Taking the laser point cloud in the open pit of Qianshan limestone mine in Liaoyang City as the data source, the original data was downsampled firstly, and then the training set and verification set were made and divided based on the five kinds of point cloud features including single point RGB information, neighborhood RGB information, neighborhood height difference, neighborhood roughness, and reflection intensity. The road point cloud extraction model was constructed and optimized using the random forest algorithm. Furthermore, European clustering algorithm was introduced to improve the road point cloud extraction model. Finally, the road point cloud extraction results were evaluated in open pit. The results show that the proposed method can effectively and accurately extract the road point cloud data in open pit in real time.更多还原