Pipe pose estimation based on machine vision

To realize the automatic assembly and connection of pipelines, one of the core tasks is target object identification and pose estimation. The problem is challenging due to low precision and efficiency caused by the pipes being textureless and self-occluding. In this work, we introduce a machine vision-based method for 6D pipe pose estimation. First, the pipe's initial pose is estimated by a template matching algorithm. Second, a 3D-2D projection mapping relationship is established, and the distance between the edge pixel points and the edge of the projection model is optimized using the least squares method to obtain a more accurate pipe pose. Experiments demonstrate that the proposed method is able to robustly estimate pose in real environments, while achieving position and pitch accuracies of 0.0732 mm and 0.5°, respectively. Furthermore, the whole pose estimation process lasted 2–3 s, which meets the requirements of industrial applications.