3D imaging for automated manufacturing assembly applications

Object pose estimation is an important application in 3D recognition. A 3D object pose estimating method is developed for an automated manufacturing assembly application. The target parts are extracted from the original range images using the traditional edge detection and segmentation methods. The center position is then computed through the circle Hough transform algorithm. For the 3D orientation estimation, a 3D geometrical feature descriptor, Angle Distance Map (ADM), is proposed to describe the 3D local surface feature. A triangular mesh model of 3D object is used for reducing the computational complexity. The principal component analysis (PCA) method is applied on the ADM descriptions for efficient comparison. The orientation information is computed according to the extracted 3D feature points. The proposed method is tested in an application for flexible robot assembly. The experimental results show that accurate 3D pose estimation can be obtained.