An algorithm for automatic detection of pole-like street furniture objects from Mobile Laser Scanner point clouds

An algorithm for automatic extraction of pole-like street furniture objects using Mobile Laser Scanner data was developed and tested. The method consists in an initial simplification of the point cloud based on the regular voxelization of the space. The original point cloud is spatially discretized and a version of the point cloud whose amount of data represents 20–30% of the total is created. All the processes are carried out with the reduced version of the data, but the original point cloud is always accessible without any information loss, as each point is linked to its voxel. All the horizontal sections of the voxelized point cloud are analyzed and segmented separately. The two-dimensional fragments compatible with a section of a target pole are selected and grouped. Finally, the three-dimensional voxel representation of the detected pole-like objects is identified and the points from the original point cloud belonging to each pole-like object are extracted. The algorithm can be used with data from any Mobile Laser Scanning system, as it transforms the original point cloud and fits it into a regular grid, thus avoiding irregularities produced due to point density differences within the point cloud. The algorithm was tested in four test sites with different slopes and street shapes and features. All the target pole-like objects were detected, with the only exception of those severely occluded by large objects and some others which were either attached or too close to certain features.