Automated Recognition of Electrical Substation Components from 3D LiDAR Point Clouds

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This study presents an innovative automated methodology for identification of electrical substations’ key elements from 3D LiDAR point clouds acquired by terrestrial laser scanners. The developed methodology is composed of nine algorithms that identify objects of interest with respect to their physical shape and topological relationships among them. The objects of interest in this contribution are ground, fence, cables, circuit breakers, bushings, bus pipes, insulators, and three types of poles with circular, octagonal, and square cross sectional shape. The developed methodology incorporates a computationally-efficient algorithm for detection of ground within electrical substations; two separate algorithms for identifying well-sampled and poorly-sampled fences; robust algorithms for detecting cables, circuit breakers, and bushings with respect to their unique physical shape and the topological relationships among them; and a novel method for simultaneous identification, modeling, and registration-refinement of poles with circular and regular polygonal cross sectional shapes. The proposed methods in this study work quite robustly despite the challenges introduced by non-uniform point sampling, registration error, occlusion, attached objects, gap, dense configuration of neighboring objects, and outliers. Five datasets with quite different volume and configuration were employed in this work. The first three datasets contain point clouds of two different electrical substations. The fourth and fifth datasets contain point clouds of an urban roadway and a pole-like monument with a regular dodecagonal cross section, respectively. The obtained results indicate that 367 out of 382 objects of interest (96.1%) in the first dataset; 354 out of 382 objects of interest (92.7%) in the second dataset; and 255 out of 264 objects of interest (96.6%) in the third dataset were successfully recognized. At point cloud level, it achieved greater than 99%, 96%, and 97% average recognition precision and accuracy in the first, second, and third dataset, respectively. Furthermore, the poles in the fourth and fifth datasets were successfully identified and the registration-refined version and as-built model of poles in all five datasets were automatically generated. The center and size standard deviation of the constructed models was less than 3 mm and the rotation angle standard deviation was less than 0.3° for all identified poles.
Arastounia, M. (2017). Automated Recognition of Electrical Substation Components from 3D LiDAR Point Clouds (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from doi:10.11575/PRISM/25077