Research On The Key Technology Of Auto Navigation For Vision Mobile Robots

With the research and development of mobile robots, they are widely used, but more and more intelligence and autonomy are required to fit complex environment, so better auto navigation system is very important. As simulating human eyes, vision sensor technologies have more intelligence and advantages than other sensors, so more and more importance is added to vision mobile robots. Vision mobile robots have good development and future, especially in military. Auto navigation for vision mobile robots in complex and unknown environment has been focused on this dissertation. The key technologies of localization, obstacle detection and road detection based on vision technology for vision mobile robots have been researched to improve mobile robot's intelligence, and a vision mobile robot system including hardware and software has also been established.In vision localization, image feature(corners) detection and matching method have been presented. As to image feature detection, a hierarchical fast corner detection algorithm by coarse-to-fine based on SUSAN algorithm has been proposed to improve runtime. According to the gray similarity around pixels and corner property in image, firstly the theory of lifting wavelet transform and coarse-to-fine hierarchical strategy have been used to find the coarse positions of corners, then SUSAN algorithm has been used to locate the corners accurately. As to feature matching, a corner matching method based on moment invariants of RSTC(Rotation, Scale, Translation and Contrast) has been proposed to improve precision and reliability. A new moment invariant of RSTC has been constructed to describe the corner features and to measure the similarity of corner matching, and a guided matching with improved RANSAC robust estimation and epipolar line constraint has been performed. The long lines caused by wrong feature matching have been eliminated, and better matching results has been got by this method than the gray matching method.In obstacle detection based on vision technology, a method of dense disparity map estimation using PSO algorithm with adaptive hierarchical images has been proposed to solve the stereo correspondence problem. In this method, firstly image features have been extracted and matched by SIFT algorithm, and the disparity range has been got easily and accurately. Then, according to restriction of the image size and the disparity range, the coarse to fine adaptive hierarchical image pyramids have been built to search fast and reduce wrong matching. With a regulation parameter varying with matching window used to give different power for grayness and smoothness data in optimization function while the matching window has been different in dissimilar supporting areas, an improved particle swarm optimization algorithm with variation operation for integer has been used to find the fittest solution from a set of potential disparity maps avoiding Genetic algorithm's blind searching and easy getting in local best solutions. Experimental results on synthetic and real images have demonstrated that the proposed approach performs dense disparity estimation accurately and quickly.In road detection based on vision technology, road detecting using normalized mutual information of SCT color model has been presented to fit for complex images after comparing and analyzing different color models. The SCT color model is not only closer to HVS(Human Vision System), but also has the characters of strong noise-rejection and good real-time performance. Normalized mutual information has the advantage of good robustness and accuracy. They have been integrated in the proposed algorithm, and image processing time has been improved by using the relationship of pixels around. So safe areas and dubious obstacles information has been provided fast for vision system by the proposed algorithm.The hardware and software of vision mobile robot have been designed and implemented in this dissertation. Firstly, based on "Hummer" overland model car, two servo motors with planet type gearbox have been fixed on the left and right side for driving. Camera fixing bracket and rotary mechanism have been designed, and new hardware has also been designed for controlling. Moreover, with digital image acquisition card and the wireless video & audio transmitting system, the vision mobile robot platform has been built. Based on the platform, the software of vision system has been developed, and auto navigation by vision system has been implemented.