Stereo matching is an important research area in stereovision and stereo matching of curved surface is especially crucial A novel correspondence algorithm is presented and its matching uncertainty is computed robustly...Stereo matching is an important research area in stereovision and stereo matching of curved surface is especially crucial A novel correspondence algorithm is presented and its matching uncertainty is computed robustly for feature points of curved surface. The comers are matched by using homography constraint besides epipolar constraint to solve the occlusion problem. The uncertainty sources are analyzed. A cost function is established and acts as an optimal rule to compute the matching uncertainty. An adaptive scheme Gauss weights are put forward to make the matching results robust to noises. It makes the practical application of comer matching possible. From the experimental results of an image pair of curved surface it is shown that computing uncertainty robustly can restrain the affection caused by noises to the matching precision.展开更多
An obstacle perception system for intelligent vehicle is proposed.The proposed system combines the stereo version technique and the deep learning network model,and is applied to obstacle perception tasks in complex en...An obstacle perception system for intelligent vehicle is proposed.The proposed system combines the stereo version technique and the deep learning network model,and is applied to obstacle perception tasks in complex environment.In this paper,we provide a complete system design project,which includes the hardware parameters,software framework,algorithm principle,and optimization method.In addition,special experiments are designed to demonstrate that the performance of the proposed system meets the requirements of actual application.The experiment results show that the proposed system is valid to both standard obstacles and non-standard obstacles,and suitable for different weather and lighting conditions in complex environment.It announces that the proposed system is flexible and robust to the intelligent vehicle.展开更多
基金This project was supported by the National Natural Science Foundation of China (60275042) and"Shuguang"Project ofShanghai Municipal Education Committee
文摘Stereo matching is an important research area in stereovision and stereo matching of curved surface is especially crucial A novel correspondence algorithm is presented and its matching uncertainty is computed robustly for feature points of curved surface. The comers are matched by using homography constraint besides epipolar constraint to solve the occlusion problem. The uncertainty sources are analyzed. A cost function is established and acts as an optimal rule to compute the matching uncertainty. An adaptive scheme Gauss weights are put forward to make the matching results robust to noises. It makes the practical application of comer matching possible. From the experimental results of an image pair of curved surface it is shown that computing uncertainty robustly can restrain the affection caused by noises to the matching precision.
基金supported by the National Natural Science Foundation of China(61673381)the National Key R&D Program of China(2018AAA0103103)the Science and Technology Development Fund(0024/2018/A1)。
文摘An obstacle perception system for intelligent vehicle is proposed.The proposed system combines the stereo version technique and the deep learning network model,and is applied to obstacle perception tasks in complex environment.In this paper,we provide a complete system design project,which includes the hardware parameters,software framework,algorithm principle,and optimization method.In addition,special experiments are designed to demonstrate that the performance of the proposed system meets the requirements of actual application.The experiment results show that the proposed system is valid to both standard obstacles and non-standard obstacles,and suitable for different weather and lighting conditions in complex environment.It announces that the proposed system is flexible and robust to the intelligent vehicle.
