Low-cost Stereo Vision System For Distance Measurement

With the rapid development in the robotic area,many computer vision systems have been developed.Stereo vision is a computer vision technique that uses two aligned cameras to extract depth information from a scene.A depth map can be estimated by comparing the shift of the correspondent pixels from the two cameras.However,in real time stereo vision the images have to be computed at a reasonable frame rate.Real time stereo visions allows the mobile robot platform to autonomous navigate and interact with the environment by providing the depth of the scene.Currently there are many solutions to solve the stereo correspondence problem.However,a few solutions were developed to work in real time mobile robots platforms which the computation power and hardware are very limited.In this thesis,we propose a low cost stereo vision system able to extract depth from moving object at frame rate of 30 Hz.The proposed method is based in a local dense stereo correspondence algorithm that uses a prior foreground extraction to simplify the disparity.Additional this thesis propose a method based in weight mean to improve the stability of the disparity image.The experiments show the robustness and good time performance of the proposed approaches.