Detect in RGB, Optimize in Edge: Accurate 6D Pose Estimation for Texture-less Industrial Parts

In order to solve robotic bin-picking problem in many industrial applications, accurate 6D object pose estimation is one of fundamental technologies. This paper presents a method for accurate 6D pose estimation from a single RGB image for texture-less industrial parts. These objects are common but still challenging to deal with, due to the fact that poor surface texture and brightness makes difficult to compute discriminative local appearance descriptors. The proposed method mainly consists of two stages, which ranges from the detection stage to the optimization stage. Firstly, all known objects in the RGB image are detected with 2D bounding box via a tiny convolutional neural network. Then, the second stage will optimize the 6D pose in the Edge image given several coarse initializations. These coarse initializations are generated from the Edge image via a hypothesis-evaluation scheme. Furthermore, the proposed method is validated by achieving state-of-the-art results of texture-less industrial parts for RGB input. According to practical experiments, the proposed method is accurate and robust enough to be applied on the robotic manipulation platform to complete a simple assembly task.