Affiliations: [a]
Anhui University of Technology, Maanshan, China
| [b]
Maanshan University, Maanshan, China
Correspondence:
[*]
Corresponding author. Yuxiu Wu, Anhui University of Technology, Maanshan, China. E-mail: yuxiuwu@ahut.edu.cn.
Abstract: If robot uses 2D lidar or binocular camera to locate obstacles, there will be some problems such as missing obstacle information or inaccurate obstacle locating, which will affect the normal work of the robot. In order to obtain accurate 3D obstacle information, this paper proposes an algorithm for fusing 2D lidar and binocular vision to complete the obstacle location. In this paper, the depth value of the 2D lidar point cloud is used as a benchmark. By fitting the error equation of the binocular camera point cloud depth value, the depth value of the camera point cloud is modified to obtain an accurate 3D camera point cloud, thereby obtaining an accurate 3D obstacle information. Many experiments have proved that the fusion algorithm of 2D lidar and binocular vision can obtain accurate 3D obstacle information. The method of fusion 2D lidar and binocular vision can approximately achieve the measurement effect of 3D lidar, and the point cloud of obstacles is relatively dense, so the accurate 3D obstacle information can be obtained. This method can reduce the influence of single sensor on the robot locating obstacles, thus completing the accurate locating of obstacles, which is of certain significance to robot navigation.
