Fast plane segmentation with line primitives for RGB-D sensor

This article presents a fast and robust plane segmentation approach for RGB-D type sensor, which detects plane candidates by line segments extracted from 2-D scanline projected from row or column points. It neither requires high computation to calculate local normals for the entire point cloud as most of approaches do nor randomly chooses plane candidates such as RANSAC-like approaches. First, a line extraction algorithm is utilized to extract line segments. Second, the plane candidates are detected by estimating local normal of points lying on line segments. Finally, the plane having most inlier is recursively extracted from the plane candidates as the result plane. Experiments were conducted with different data sets and the segmentation performances were evaluated quantitatively and qualitatively. We demonstrated the efficiency and robustness of our proposed approach, especially in the none plane scenario, the approach needs little computational cost.