Simultaneous identification, modeling and registration refinement of poles using laser scanning point clouds

Abstract

The method presented herein performs the tasks of identification, modeling, and registration refinement of poles using Light Detection And Ranging (LiDAR) point clouds. This method starts with a preliminary segmentation followed by eliminating outliers by integrating Fourier series and robust estimators. Poles’ cross sectional shapes are then identified by a customized template matching. Afterwards, points belonging to poles are extracted and the as-built model and registration-refined version of poles are generated. The algorithm was tested on five datasets including point clouds of three industrial sites, an urban environment, and a pole-like monument with very different volumes, resolutions, and configurations. The developed method works robustly despite the challenges that are often present in LiDAR data comprising outliers, non-uniform point sampling, gaps, and registration error. The obtained results indicate that 116 out of 117 poles in all five datasets were identified with no false positives at the object level. At point level, it reached greater than 98% average precision and 98% average accuracy in all datasets. Poles were modeled with a sub-millimeter precision in all datasets except for the third dataset in which poles were modeled with millimeter precision.