Real-time 3D pose estimation of small ring-shaped bin-picking objects using deep learning and ICP algorithm

Bin picking is an important task in smart manufacturing and intelligent robotics. For a robot to pick or grip an object with a human-like gripping action, it needs to know the accurate 3D pose of the object. In this paper, we propose a method for estimating the 3D pose of a small ring-shaped object using infrared and depth images generated by a depth camera. The proposed method consists of two algorithm modules, the first to recognize an object in a 2D infrared image and the second to estimate the 3D pose by applying the ICP (iterative closest point) algorithm to 3D depth data. In the first module, we propose a method to generate a three-channel integrated image with features from the depth and infrared images. Next, we introduce a method for training an object detector based on deep-learning. Because the bin-picking test object in this paper is small and ring-shaped, it is difficult to detect and find 3D poses of individual objects when many such objects are piled up. We solved this problem with a depth-based filtering method. Using the filtered image, each object region is separated by the deep learning approach. In the second module, the ICP algorithm is employed to estimate the 3D pose of the ring object. We match a 3D reference model of the object and the real object using the point-to-point ICP algorithm. Performance of the proposed method is evaluated by using two different types of depth camera in the experiments.