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

Abstract

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.