应用单层小波包近似压缩感知的道路理解

Road Understanding Using Single Level Wavelet Package Approximately Compressed Sensing

针对室外自主移动机器人道路理解中遇到的阴影、裂纹等奇异信号造成的视觉算法不鲁棒问题,以及导航图像实时处理时遇到的大数据难题,提出了单层小波包近似压缩感知(SLWPCS)概念,并给出了其实现方法,与基于自适应遗传算法的图像分割法相结合,构建出一套实时道路理解算法系统.通过粗测各级小波包分解后的近似道路图像,确定出不影响"路-非路"二分类的最佳尺度空间;在最佳尺度空间中采用sym8小波对道路图像进行小波包分解,采用压缩感知矩阵对除斜线方向外的高频系数进行降维处理,并采用OMP算法重构高频系数,再与低频系数一起重构原图像;用灰度类间最大方差和类内最小方差构造适应度函数,对各帧道路图像进行最佳阈值自适应分割,确定出道路边界.采用轮式自主移动机器人作为研究平台,在实际道路和CMU提供的机器人道路视频中进行算法实验,结果表明,文中方法能够在具有阴影、裂纹、光照度变化的条件下鲁棒分割出道路边界,满足系统实时性要求.

In process of outdoor autonomous mobile robot visual based road understanding ,the vision algorithms are not so robust ,and a big data is encountered in the real time process of navigation images ,which are all led to by shadows ,causing cracks and other singular signals encountered .To resolve these difficulties ,the Single Level Wavelet Package Compressed Sensing （SLWPCS） algorithm is proposed . Combined with the Adaptive Genetic Algorithms （AGA ） based image segmentation algorithm ,a real time road understanding algorithm system is established .Through coarse measured approximation road image by wavelet packet decomposition of multi‐levels ,the best scale space not affecting the“road‐non‐road” second classification is determined .Road image is decomposed by using sym8 wavelet in the best scale space . A compressed sensing matrix is used to realize dimension reduction of the high‐frequency coefficients in addition to the diagonal direction coefficients . OM P algorithm is used to reconstruct high‐frequency coefficients . And together with the low‐frequency coefficients ,the reconstruction of the original image is finished .The fitness function is constructed by the gray value largest variance between classes and the minimum variance within a class for each image frame .T he optimal adaptive threshold segmentation is then realized ,and the road boundaries can be found .A w heeled autonomous land vehicle is selected as the research platform ,and the algorithm is＆amp;nbsp;tested by the actual roads and the outdoor path driving video of the mobile robot provided by CM U . T he experimental results show that this method can detect the boundaries robustly under varying road conditions including shadow ,crack ,and illumination change .The real‐time performance can be well satisfied .