摘要
针对复杂大场景的点云分割问题,提出了一种快速、准确的分割方法.采用基于扫描线的分割算法,利用点云的有序性和地面几何特征提取地面点,在坡路等复杂地面情况下也能正确分割地面;基于扫描系统的性能确定初始阈值,实现了对非地面点的逐点快速分割;提出了基于体量的自适应算法对过分割的点云进行合并.实验结果表明,在复杂场景下,该分割方法的准确率在90%以上,并且运算复杂度低,逐点处理速度为平均每个点用时14.5μs,可在点云数据采集过程中进行实时处理.
A fast and accurate segmentation method for point cloud of large-scale scenes is proposed. A scan-line based ground filter algorithm is designed based on the ordering of point cloud and the geometrical characteristic of the ground, complex ground conditions such as slopes can be handled. Non-ground points are fast segmented point- by-point based on the initial threshold which takes the performance of the scanning system into consideration. Then over-segmented points are merged through the volume-based adaptive algorithm. The accuracy rate of the proposed method is over 90% and the point-by-point processing speed is 14. 5 μs per point, real-time processing can be achieved.
出处
《微电子学与计算机》
CSCD
北大核心
2016年第11期45-49,共5页
Microelectronics & Computer
基金
深圳市科技计划(JCYJ20150331151358146)
清华大学自主科研计划(2013089223)
关键词
点云分割
激光扫描
大场景
聚类
point cloud segmentation
laser scanning
large-scale scene
cluster
