车载LiDAR场景中路灯的提取与识别

为解决车载LiDAR城市场景中路灯提取和识别问题,文中提出基于样本的路灯提取算法。先通过人机交互的方式提取路灯的样本参数;再依据数学形态学闭运算提取点云场景中疑似路灯的位置,根据样本参数确定疑似路灯范围并提取疑似路灯点云;然后进行路灯样本和疑似路灯点云的匹配;最后,通过建立路灯样本缓冲区实现路灯判断和提取。通过试验验证,算法不仅可以快速自动提取路灯点云,还能完成路灯单一种类的识别。

Streetlamp extraction and identification from mobile LiDAR point cloud scenes

To solve the problem of streetlamp extraction and category identification from mobile LiDAR data in urban scenes,an algorithm based on streetlamp sample has been proposed.Firstly,extract the sample from streetlamps by human-computer interaction;secondly,based on morphological closing operation, extract the location of suspected streetlamps,and determine the scope of suspected streetlamps by the streetlamp sample parameters before extracting the suspected streetlamp point cloud;thirdly,realize the alignment of streetlamp samples and suspected streetlamp point cloud;finally,realize the streetlamp j udgment and extraction by establishing sample buffer.Experimental results show that the algorithm can not only automatically extract streetlamp point cloud,but also be able to identify streetlamp category which is in accordance with the sample.