Abstract: To extend the velocity measurement range for the omni-directional intelligent wheelchair and improve the measurement accuracy and computational efficiency, the traditional optical flow-based velocity estimation method is improved in this paper. First,a TV-L¹ model is introduced to estimate optical flow,the displacement of corresponding pixels between two consecutive frames is efficiently predicted,and the searching area is reduced. Second,a planar surface optical flow model based on random sample consensus( RANSAC) method is presented to remove outlier vectors produced by nonuniform ambient illumination and local motion blur. Finally,the algorithm is accelerated under the framework of compute unified device architecture to improve the real-time performance of the system.Experimental results show that maximum measurable velocity obtained the proposed method is 1. 67 times as fast as original method,and it is more accurate than that of wheel odometry. The proposed method performs robustly in the presence of non-uniform ambient illuminations and local motion blur,and it can improve the maximum measurable velocity of the omni-directional wheelchair and the measurement accuracy.