The Research On Autonomous Run Of An Intelligent Vehicle Based On Visual Navigation

The application of vision-based systems to an intelligent vehicle is a very challenging task. The challenge arises from both the methodologies to handle the multiple problems caused by the structure of the vision system and the computational requirement due to the huge information supported by the vision systems compared to poor sensors like range finder. Furthermore, detection and tracking of the road boundaries is a major problem to achieve autonomy of vehicles that work in hazardous environment. This dissertation adopted a method based on lane marker recognition navigation with more merits than other visual navigation methods, such as the setting up and changing of mark line is relatively easy, low technical cost and so on. But the primary problems are the image distortion rectifying and border detection. This dissertation, taking car-like of two driving wheels as the research object, proposes a simple algorithm—calibration, to rectify the lane marker image. The field experiment result shows that the image-rectifying algorithm is efficient and provides the correct information for the recognition of white lane marker. A method about image processing is put forward with simple principle including the improved image segmentation of optimal threshold, the little data-processing filter based on mathematical morphology and border detection based on two-stage derivative. Meanwhile, the way to get the relative position of the body is improved. Fuzzy-optimal controller is introduced in the simulation. The results of simulation show that the control system can make the intelligent vehicle get good performance on control effects. In order to testify the correctness of the above image processing algorithm and tracking controller and the working effect of the intelligent vehicle, the main procedure (including dynamic image processing, controller and data communication) on the upper-stage computer and motor-controlled procedure on the lower-stage chip are compiled and the tracking path experiment is implemented on an intelligent vehicle experimental platform with visual navigation. The experiment results show that the procedure based on the above algorithm can perform the real-time image-processing task, and the intelligent vehicle has the strong capability against noise disturbance and can carry out the path tracking accurately and reliably.