大视野、高质量的图像信息对地面移动机器人的遥控操作具有非常重要的意义.提出了一种基于先验信息的自适应图像拼接方法.该方法在图像大致重叠区域中均匀选取待匹配点,利用改进的具有旋转不变性的NCC(Normalized Cross Correlation,归一化互相关)匹配方法进行区域相似性度量,通过RANSAC(Random Sample Consensus,随机采样一致性)算法估计图像射影变换模型,采用线性淡入淡出法进行图像融合.利用GPU强大的并行处理能力对算法进行了并行化实现,使图像拼接效率比单独采用CPU提高了60倍以上,稳定的拼接速度可达21.3fps.%Telepresence with wide field of vision is one of the key technologies for teleoperated unmanned vehicle. To meet the real-time requirement,a fast image mosaic method was proposed, which selects evenly distributed points and calculates their similarity by rotation-invariant NCC (Normalized Cross Correlation) operator, calculates the projective transformation model by RANSAC (Random Sample Consensus) method, and fuses images by linear fade in and fade out Finally,the algorithm was accelerated with a parallelized self-adaptive image matching method using GPU (Graphic Process Unit). Experiment results show that the efficiency of the parallelized image mosaicing method, compared with that of the serial scheme on CPU,is dramatically improved with more than 60 times on frame rate.
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