Research On Camera Motion Estimation And 3-Dimensional Reconstruction Algorithms Based On Monocular Vision

Vision-based navigation and 3-D reconstruction, which have great value in research and application, are the key areas of the computer vision research.3D reconstruction based on stereo vision uses Parallax information to recover the depth information from the 2D images captured in a scene by two or more cameras. The results of stereo vision and 3-D reconstruction are more accurate because of the precise equipment; however, there are some limitations like higher precise image registration, larger computational complexity and higher cost and so on. And this paper has proposed a method which can estimate motion of the camera and complete 3-D reconstruction based on feature points matching of sequence images captured from monocular. The principle of the algorithm is to recover the motion parameter of the camera by the parallax-images, then get the depth information of the feature points in the images. The algorithm has some advantages, for examples, little computational complexity, lower cost and flexible.The paper has discussed as follows:1. Introduced the theories of homography and its function in vision navigation and 3-D reconstruction.2. Studied the feature extracting and feature matching. We improve on the algorithm of Lucas-Kanade tracking used in road images.3. Studied the classical methods and robust methods for solving homography like RANSAC and proposed some amelioration on RANSAC. As a result, the accuracy of homography has been improved.4. Recovered the real motion parameters of the camera includes rotation matrix R and translation vector T and resumed the 3-D coordinate of all the feature points in the image.In the end, the paper provided the analysis of the experimental results based on 3-D reconstruction of monocular, showed some problems should be noticed. All the algorithms and methods referred in the paper came from the simulation or real data.