Fusion Algorithm Of Laser-point Clouds And Optical Images

3D Lidar can collect the 3D point cloud model of the scene,so that the depth and geometric information of the measured object can be obtained quickly and accurately,and the optical camera can collect high-definition 2D image data for the scene.By fusion,the advantages of laser-point cloud and optical images can be combined.It has played an important role in the three-dimensional reconstruction of the scene,environmental interaction and other fields and greatly promote the development of cloud landscape,entertainment interaction,automatic driving and unmanned equipment.Nowadays,the fusion of laser point cloud and optical image has become a hot area of image fusion research.Traditional fusion algorithms of laser-point clouds and images require a lot of interactive operations such as artificial calibration of sensors and setting of control points.This paper proposed an automatic fusion algorithm of laser-point clouds and optical images based on depth and echo intensity information.In this algorithm,the laser-point clouds are converted into a depth-echo intensity images,and the mapping relationship between depth-echo intensity images and optical images will be solved.Then the laser-point clouds and the optical images can be fused.Because of the data source and imaging principle of the depth echo intensity image,the traditional matching algorithm is not good for the recognition and extraction of feature points between the depth echo intensity image and the visible light image.In this paper,by improving the shortcomings of the classical feature matching method,kernel Fisher discriminant analysis is integrated into the SIFT algorithm to improve the process of generating feature descriptors and enhance the robustness of the algorithm.The experimental results show that this method can not only ensure the accuracy,but also realize the automation.