Object Motion Estimation Based on Hybrid Vision for Soccer Robots in 3D Space

Effective object motion estimation is significant to improve the performance of soccer robots in RoboCup Middle Size League. In this paper, a hybrid vision system is constructed by combining omnidirectional vision and stereo vision for ball recognition and motion estimation in three-dimensional (3D) space. When the ball is located on the ground field, a novel algorithm based on RANSAC and Kalman filter is proposed to estimate the ball velocity using the omnidirectional vision. When the ball is kicked up into the air, an iterative and coarse-to-fine method is proposed to fit the moving trace of the ball with paraboic curve and predict the touchdown-point in 3D space using the stereo vision. Experimental results show that the robot can effectively estimate ball motion in 3D space using the hybrid vision system and the proposed algorithms, furthermore, the advantages of the 360◦ field of view of the omnidirectional vision and the high object localization accuracy of the stereo vision in 3D space can be combined.