面向无人机自主导航的立体匹配算法

A new stereo matching algorithm for UAV autonomous navigation

无人机自主导航是无人机发展的必然趋势,立体视觉技术作为一种优秀的环境信息测量技术能够为无人机自主导航提供关键信息.但是,导航图像存在幅度失真,现有立体匹配方法的匹配精度较低,针对Census变换舍弃了图像像素色彩信息而造成的误匹配问题,本文提出了一种Census变换和图像色彩信息相结合的联合匹配算法,并经过理论分析提出了正交积分的方法以提高算法的实时性.首先,将Census变换和图像色彩信息联合,构造初始匹配代价;然后,采用改进的自适应窗口作为代价累积窗口,并使用正交积分思想提高累积速度;最后,经过视差提精,获得最终的视差图.实验结果表明:本文算法对幅度失真图像的匹配误差比单独使用Census变换提高了40%～50%,算法的运算时间提速了3～12倍,与Census变换和图像灰度单独作为匹配代价时相比,该方法具有更高的匹配精度,对幅度失真有很强的鲁棒性,能够较好地应用于无人机自主导航场景中.

Autonomous navigation is of great significance for information access and landing of unmanned aerial vehicle （UAV）. It is the inevitable trend of UAV development. As an excellent image navigation technology, stereo vision can provide critical information for UAV autonomous navigation. The existing stereo matching algorithms reach a low matching accuracy in amplitude distortion images. For the mismatch problem caused by Census transform, a joint matching algorithm consisting of Census transforms and image color information is proposed in this paper. At the same time, the orthogonal integration method is used to raise the speed according to our theory analysis. First, the initial matching cost is constructed by combining the Census transform and color information. Second, the matching cost is aggregated in an improved adaptive window and we use the orthogonal integration method to raise the speed. Finally, after the optimization and refinement, the disparity map can be acquired. The experimental results demonstrate that our method is of higher accuracy by 40%-50% compared with the Census transform method or the image gray information method. The calculation speed has risen by 3 to 12 times. It is robust to the amplitude distortion image and the method can be used in the UAV autonomous navigation very well.