An Efficient Planar Feature Fitting Method Using Point Cloud Simplification and Threshold-Independent BaySAC

Abstract

Three-dimensional laser scanning can acquire point cloud data with high spatial resolution. However, for practical applications, such as point cloud fitting and 3-D reconstruction, there is usually significant data redundancy, which reduces the operational efficiency. In this letter, we propose a fast point cloud fitting algorithm that uses point cloud simplification to preserve feature boundaries and threshold-independent Bayesian sampling consensus (BaySAC) to fit planar features. We first extract the point features, such as corner points and contour points, using a smoothing analysis of the vicinities of scattered points and an angle analysis of vectors based on search points and their adjacent points. Then, keeping all the feature points, we thin the nonfeature points by constructing a cube grid. Finally, based on the least median squares and the BaySAC algorithm, we propose a robust nonthreshold-dependent method to perform the rapid fitting of planar features in the point cloud after thinning. We used three sets of point cloud data acquired using a 3-D laser scanner to verify the accuracy and efficiency of the planar feature fitting method. The experimental results indicate that the method can extract finer planar features and has significantly better accuracy and computational efficiency than the classical random sample consensus algorithm for the fitting of planar features without using a threshold.