Segmentation and outlier removal in 3D line identification based on fuzzy clustering

Abstract

In this paper, we present a novel method based on clustering for identifying 3D line from point clouds, called “self-organizing fuzzy k-means algorithm”. The algorithm automatically finds the optimal number of cluster and self organizes the clusters based on inter/intra-cluster distances and cluster's performance evaluation. The self-organizing fuzzy k-means is applied in 3D line identification from point clouds. We use the point clouds provided by Stereo camera and 2D images. The 3D point clouds of each line is clustered by clustering algorithm, then we perform eigen-analysis on clusters and estimate the final 3D lines; the 3D lines can be cut off into several segments. In addition, to increase the accuracy of detection, the error evaluation is invoked to analyze the error of the 3D candidate lines. Our algorithm was evaluated on the real test scenes, which content noisy point clouds, and shows the high performance and robust results.