Research On Visual Technologies Of Autonomous Navigation System In Non-structure Environment

In this dissertation, the visual image segmentation methods, color features extraction methods, and visual target locating methods for visual navigation system are researched. On the bases of those researches, a visual navigation system guided by the path boundary is established in non-structure environments.For visual image segmentation, a method based on sub-region growing for segmentation of visual image is presented. In this method, seeded process and image segmentation are combined by the growing of sub-regions which are coherent, instead of making certain seeded pixels before segmentation in traditional automatic seeded region growing method, and improves the efficiency of segmentation. Estimation of coherence of region is important for sub-region growing. Root-mean square error is usually used as standard of coherence of region, but its sensitiveness for image noise is discovered in our research. Based on the analysis for the reason of sensitiveness, the mean distance of region pixels is defined, and is used as standard of coherence of region. Theoretic analysis and experiment results show that mean distance of region pixels is better than root-mean square error on the capability of noise suppressed and region coherence distinguished.On color feature extraction of objects, a color features extraction method based on adaptive quantification and combination of components of HSI color model is presented. This method makes use of the description stability of HSI color model for colors, combining with adaptiveness of quantification based on multithresholding classification of histogram, and extracts color features of image. Experiments show that the presented method for color feature extraction is effective. In multithresholding classification of histogram method, aimed at the excessive classification problem of color components caused by local maximums of color components histogram, a method which is used to eliminate local maximums of the color components histogram is proposed. Distributing characteristic of color components histogram is analyzed quantificationally, and then the local maximums of color components histogram are confirmed and amended. The results of experiments indicate the validity of the method to eliminate local maximums of the histogram data.Using visual locating methods based on planar homography matrix estimation, the locating precision relates to the position of targets. In the application of targets locating on the movement plane of mobile robots, for each pitch degree of camera, one corresponding planar homography matrix is necessary. Aimed at these problem, a visual locating method based on planar homography estimation using RBF network is presented, this method uses the pitch degree of camera as one of the input of RBF network, makes use of the non-linear approach of RBF network fits the non-linear transformation relationship from the image plane to the movement plane, and positions the visual targets on the movement plane by planar homography estimation using RBF network. Setting different pitch degrees, the results of experiments express this method is effective in the application of target locating on the movement plane. The comparison of results of experiments shows that, under the same condition, the locating precision of this method is higher than the locating precision of the visual locating methods based on planar homography matrix estimation.On the bases of the researches on key vision technologies such as image segmentation, features extraction, and visual target locating, a way of navigation based on the guidance of path boundary is proposed. The information of the path boundary of mobile robot is attained by using visual process techniques in this method, the control parameters of navigation and obstacles avoidance are extracted from it. This navigation manner is effective for guiding mobile robot to explore the non-structure environment autonomously.