Point Cloud Derived from Binocular Vision Based on P-KLT Algorithm

The method of sparse point cloud derived from binocular vision based on P-KLT (Pyramid - Kanade - Lucas Tomasi) algorithm is proposed. Firstly, this paper introduces the basic mathematical model of binocular vision and feature point PKLT tracking model. Secondly, the process and steps of sparse point cloud derived from binocular vision are introduced; this paper expounds target area segmentation of the left camera image and the Harris feature point detection. Finally, P-KLT algorithm is used to track the feature points of the left gray image in the right gray image and the matching points are used to solve the 3D point cloud data. In simulation experiments Zhang’ s calibration is used to obtain the parameters of binocular vision, the P-KLT algorithm is used to track Harris feature point of left gray image and the target of 3D point clouds are obtained to verify the object size and height. Simulation results show that the method can efficiently obtain the target of sparse three-dimensional (3D) point cloud and the feasibility of the method is validated.