基于机器视觉的爬管机器人 路径信息识别算法

目前管道尤其是高空架设管道的检测主要是通过人工手持设备进行，而且测试条件十分受限，易造成检测效率低和安全风险问题。为了提高管道检测效率，保护检测人员的生命安全，本文提出一种基于机器视觉的管道外爬行机器人路径信息识别算法。首先，对获取的图像进行感兴趣区域(region of interest , ROI)设置和透视变换，并对RGB和HLS色彩空间的二值图像进行融合处理；然后，通过轮廓提取的方法不仅实现对障碍物及破损的提取，并且将管道上的非管道像素进行填充补偿，提高了多项式拟合的稳定性和准确性。最后，对融合后的二值图像使用滑动窗格法提取管道像素，采用多项式拟合出管道中心线。实验表明，该算法能在复杂的环境光下准确识别中心线、障碍物以及破损，抗干扰能力强，满足爬管机器人自主沿管线行进的需要。

Path Information Recognition algorithm of tube climbing robot based on machine vision

At present, the detection of pipeline, especially high altitude pipeline, is mainly carried out by manual hand-held equipment, and the testing conditions are very limited, which is easy to cause low detection efficiency and safety risks. In order to improve the efficiency of pipeline inspection and protect the life safety of inspectors, this paper proposes a path information recognition algorithm based on machine vision for pipeline crawling robot. Firstly, region of interest (ROI) setting and perspective transformation are performed on the obtained images, and binary images of RGB and HLS color space are fused. Then, the contour extraction method not only realizes the extraction of obstacles and damages, but also compensates the non-pipeline pixels, which improves the stability and accuracy of polynomial fitting. Finally, the sliding pane method is used to extract the pixel of the fused binary image, and the centerline of the pipeline is fitted by polynomial. Experiments show that the algorithm presented can identify the centerline, obstacles and damage in complex ambient light accurately, and has strong anti-interference ability, which can meet the needs of pipe climbing robot to travel along the pipeline independently.