基于主动式全景视觉的管道形变检测及重构技术的研究

研究了一种采用主动式全景视觉传感器(ASODVS)的 管道内表面全方位检测方法。基于主动式全景视觉检测原理, 提出了一种能够快速、全方位获取管道内壁三维点云信息,实现视觉检测管道形变的全景激 光视觉系统； 利用携带有ASODVS的爬行机器人进入管道内部,采集激光横断扫描全 景图像；然后根据提 取的管道内壁三维点云数据计算最小直径、横断面面积等几何特征判断变形率；最后根据三 维点云圆周分 布的特征,采用三角格网模型进行三维重构。实验结果表明:基于主动式全景视觉的管道内 壁全方位检测 系统能够实时完成对管道凹凸形变的检测和识别,并恢复管道内壁的三维形貌,具有较高的 检测精度。

In-pipe deformation detection and reconstruction based on active stereo omni-directional vision sensor

Underground pipe inner surface defect detection is constrained by space accessib ility.In this work we propose an in-pipe defect detection method based on active stereo omni-directional vision.An omni-directional laser visual sys tem is designed to detect the geometric information of the pipe.The system can obtain the three-dimensional point cloud data in real time.In our system,a crawling robot equipped with the active stereo omni -directional vision sensor (ASODVS) traverses along the pipeline and takes pano ramic image sequences using laser streaks on the inner surface of the pipe.Afterwards,the 3D point cloud data are extracted and the 3D coordinates are calculated.The 3D point cloud data are used to calculate the geometrical feature s,such as minimum diameter,cross sectional area.Based on the geometrical information,the de formation rate of the pipe can be estimated.Since 3D point cloud data are circularly distributed,a triangu lar grid model is used for 3D reconstruction.Experimental results show that our deformation detection and the 3D topography recovery technique are accurate.