A Robust Model Fitting-based Method for Transmission Line Extraction from Airborne LiDAR Point Cloud Data

Airborne Light Detection And Ranging(LiDAR)can provide high-quality three-dimensional information for the safety inspection of electricity corridors.However,the robust extraction of transmission lines from airborne point cloud data is still greatly challenging.Therefore,this paper proposes a robust transmission line extraction method based on model fitting from airborne point cloud data.First,the candidate power line generation method based on height information is used to reduce the computational complexity at the subsequent steps and the false positives in the extracted results.Then,on the basis of the block-and-slice-constraint Euclidean clustering,a linear structure recognition method based on RANdom SAmple Consensus(RANSAC)is proposed to produce the initial individual transmission line components.Finally,a robust nonlinear least square-based fitting method is developed for the individual transmission line to generate the parameters of its mathematical model for further optimizing the extraction.Experiments were performed on LiDAR point cloud data captured from the helicopter and Unmanned Aerial Vehicle(UAV)platform.Results indicate that the proposed method can efficiently extract the different types of transmission lines along electricity corridors,with the average precision of approximately 98.1%,the average recall of approximately 95.9%,and the average quality of approximately 94.2%,respectively.

The applications of robust estimation method BaySAC in indoor point cloud processing

Based on Bayesian theory and RANSAC,this paper applies Bayesian Sampling Consensus(BaySAC)method using convergence evaluation of hypothesis models in indoor point cloud processing.We implement a conditional sampling method,BaySAC,to always select the minimum number of required data with the highest inlier probabilities.Because the primitive parameters calculated by the different inlier sets should be convergent,this paper presents a statistical testing algorithm for a candidate model parameter histogram to compute the prior probability of each data point.Moreover,the probability update is implemented using the simplified Bayes’formula.The performances of the BaySAC algorithm with the proposed strategies of the prior probability determination and the RANSAC framework are compared using real data-sets.The experimental results indicate that the more outliers contain the data points,the higher computational efficiency of our proposed algorithm gains compared with RANSAC.The results also indicate that the proposed statistical testing strategy can determine sound prior inlier probability free of the change of hypothesis models.