Based On Omni-directional Vision Object Tracking Algorithm Research

Omni-directional vision navigation for AGVs appears definite significant since its advantage of panoramic sight with a single compact visual scene. This unique guidance technique involves target recognition, vision tracking, object positioning, path programming. The mapping between image coordinates and physical space parameters of the targets can be obtained by means of the imaging principle on the fisheye lens. The localization of the robot can be achieved by geometric computation.Dynamic localization employs a beacon tracker to follow the landmarks in real time during the arbitrary movement of the vehicle. The coordinate transformation is devised for path programming based on time sequence images analysis. The beacon recognition and tracking are a key procedure for an omni-vision guided mobile unit. The conventional image processing such as shape decomposition, description, matching and other usually employed technique are not directly applicable in omni-vision. Mean Shift and Kalman filter has been shown to be successful for several nonlinear estimation problems. A beacon tracker based on Kalman Filter which offers a probabilistic framework for dynamic state estimation in visual tracking has been developed. Mean Shift and Kalman Filters are independently used to track landmarks but a composite algorithm on multiple objects tracking conducts. The tracking and localization system has been implemented and the relevant of the algorithm has been demonstrated.