Abstract
Power lines are extending to complex environments (e.g., lakes and forests), and the distribution of power lines in a tower is becoming complicated (e.g., multi-loop and multi-bundle). Additionally, power line inspection is becoming heavier and more difficult. Advanced LiDAR technology is increasingly being used to solve these difficulties. Based on precise cable inspection robot (CIR) LiDAR data and the distinctive position and orientation system (POS) data, we propose a novel methodology to detect inspection objects surrounding power lines. The proposed method mainly includes four steps: firstly, the original point cloud is divided into single-span data as a processing unit; secondly, the optimal elevation threshold is constructed to remove ground points without the existing filtering algorithm, improving data processing efficiency and extraction accuracy; thirdly, a single power line and its surrounding data can be respectively extracted by a structured partition based on a POS data (SPPD) algorithm from "layer" to "block" according to power line distribution; finally, a partition recognition method is proposed based on the distribution characteristics of inspection objects, highlighting the feature information and improving the recognition effect. The local neighborhood statistics and the 3D region growing method are used to recognize different inspection objects surrounding power lines in a partition. Three datasets were collected by two CIR LIDAR systems in our study. The experimental results demonstrate that an average 90.6% accuracy and average 98.2% precision at the point cloud level can be achieved. The successful extraction indicates that the proposed method is feasible and promising. Our study can be used to obtain precise dimensions of fittings for modeling, as well as automatic detection and location of security risks, so as to improve the intelligence level of power line inspection.