This thesis focuses on the realization of vision based mobile robot localization in indoor environment and the method to improve the precision of the localization. Firstly, computer vision and robot vision system are analyzed. The main research content and development directions of the localization for mobile robots are summarized. The background and the structures of this thesis are also introduced. Secondly, this paper build color model and frame model of target objects, and are analyzed and the color space that is most suitable for this application is chosen. Then, the color model of the object is built using the statistical learning method, which is used together with the frame model of the object to realize the target objects quickly and exactly. Thirdly, the process of camera calibration using the Direct Linear Transformation (DLT) method is given in this paper, and the results of the camera calibration is analyzed. To reduce the influence of unevenly placed platform and the low control precision of the platform, this paper adopts a method of numerous calibrations to get the relationship between the camera posture and the angle of the platform movement. Utilizing this relationship to correct the result of localization can reduce the influence greatly. Fourthly, This thesis introduces a vision based localization algorithm, which only uses two points on the same level, and analyzes the robustness of this algorithm. At the same time, a method to abstract the characteristic points exactly is prompted. To solve the problem of low precision of the robot localization, which may not meet the requirement for precisely positional information in certain tasks, this paper presents a method based on off-line learning to improve the precision of robot localization. Finally, the obtained research results are summarized and future work is addressed.
修改评论