Research On Vision Navigation System For Mobile Robot

In recent years, vision attracts a lot of attention for navigating a mobile robot in dynamic environments. Compared with other sensing systems, visual navigation is excellent and effective. With a visual sensing system, wider view of field, rich and intensive data can be obtained for a mobile robot moving in a changing environment. In this study, a visual navigation scheme is proposed for a mobile robot to realize object collection, and it comprises of camera calibration, object recognition, monocular measurement and motion control, as stated in the following.Firstly, the technique of camera calibration is presented on the basis of Zhang's algorithm. Since a PTZ camera is used here, it is controlled to move up and down, from left to right, to extend the view of field. Therefore, calibration in different positions is needed, and a new discrete method is proposed here.Secondly, a modified image segmentation method is used here based on HSI algorithm, with the morphological filtering and multi-channel segmentation techniques. It improves the accuracy of the conventional segmentation methods.Thirdly, a monocular technique is proposed here to measure an object position in 3D space. Visual models are established based on the principle of imaging and spatial geometry constrains. To reduce the measuring errors, a compensating term is added into the method.Finally, these techniques are integrated into a mobile robot platform to implement collection of specified objects. To find an object of long distance, the camera is tilted and panned, the location information is computed from the feedback of wheel motions, and the gripper is control to grasp the specified object.Numerous experiments show that the calibration of camera is precise, and the image segmentation algorithm is effective even under very bad illumination conditions, and the visual navigation scheme works very well for the mobile robot to collect a specified object.