输电线走廊点云数据抽稀算法适用性分析

Comparative analysis of thinning algorithm of point clouds in transmission line corridor

针对机载LiDAR设备获取的输电线走廊点云数据量庞大,为后续的数据处理带来不便的问题,该文采用随机抽稀、空间抽稀和体素分割抽稀3种点云抽稀算法进行比较和分析,以期选择合适的算法对点云数据进行压缩和消除冗余数据,主要从点云抽稀的质量、简度和速度3方面对4组输电线走廊点云数据进行抽稀实验。研究表明:系统(随机)抽稀方法点云抽稀不能有效保持导线形态完整性和连续性;空间距离抽稀方法的处理效果最佳;在相同抽稀率下,体素分割抽稀速率最快;系统抽稀算法,用时均少于19 s;空间抽稀方法用时最长为447 s,抽稀时间效率相对较低。

Aiming at the problem that the amount of cloud data obtained for airborne LiDAR equipment was huge,which was inconvenient for subsequent data processing,this paper used the random thinning,spatial thinning and voxel thinning point cloud extraction algorithms to compare and analyze,in order to select the appropriate algorithm to compress the point cloud data and eliminate redundant data,and did extraction experiment mainly from the point cloud extraction quality,simplicity and speed of the four sets of transmission line corridor point cloud data.The results showed that the system(random)thinning method point cloud extraction could not effectively maintain the integrity and continuity of the pattern of the wire;at the same dilution rate,the voxel thinning rate was the fastest;for the system thinning algorithm,the time was less than 19 s,and the spatial thinning method was used for up to 447 s,the extraction time efficiency was relatively low.