中文摘要：

为扩大全向智能轮椅的速度测量范围并改善其测量精度及计算效率,对传统基于光流的测速方法进行了改进.首先,采用TV-L1模型求解光流,并有效地预测帧间像点位移量,缩小像点的搜索区域.然后,针对由光照不均、局部运动模糊等因素产生的异质光流矢量,提出了基于平面片光流模型的随机抽样一致算法（random sampleconsensus,RANSAC）,以实现光流场的排异.最后,在统一计算设备架构（compute unified device architecture,CUDA）的体系框架下实现光流计算的并行加速,提高了系统的实时性.实验结果表明：改进方法使轮椅的最大可测量速度提升了1.67倍,且测速精度优于轮式里程计,该方法在光照不均、局部运动模糊情况下也具有较好的鲁棒性,能够提升全向轮椅的最大可测量速度及测量精度.

英文摘要：

To extend the velocity measurement range for the omni-directional intelligent wheelchair andimprove 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 non-uniform 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 methodis 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 canimprove the maximum measurable velocity of the omni-directional wheelchair and the measurement accuracy.