Reconstruction of Power Pylons From LiDAR Point Clouds Based on Structural Segmentation and Parameter Estimation

The reconstruction of 3-D models of power pylons from light detection and ranging (LiDAR) data plays an important role in power transmission safety. However, accurate reconstruction of power pylon models still faces challenges, e.g., complex structures, missing data, and occlusion. In this letter, a novel four-component segmentation method is proposed for reconstructing power pylon models. In the proposed method, the pylon components of the pylon head, pylon body, cross-arms, and pedestal are first defined in terms of the common features and functionality of each component. Then, these four components are each segmented and identified based on their position and shape features from the raw point cloud. An improved approach based on Metropolis-Hastings sampling and a simulated annealing algorithm is proposed to estimate the model parameters. Based on the estimated parameters, the 3-D shapes of the individual components are reconstructed and stacked to form the whole pylon model. Experimental results show that our methods are able to reconstruct pylons with complex-shaped heads and multiple cross-arms, with an average reconstruction error of less than 0.3 m. Compared with the standard Metropolis-Hastings algorithm with annealing, the parameter estimation process in our strategy improves the computational efficiency by 7.54%.