Detection of Unsorted Metal Components for Robot Bin Picking Using an Inexpensive RGB-D Sensor

This work investigates the problem of 6D pose estimation and robot bin picking of non-Lambertian reflecting objects based on a low-cost commercial 3D sensor. In particular, we address the task of estimating the pose of small metal hydraulic components of the same type, randomly placed in a bin. The system consists of a robot arm and an RGB-D sensor in eye-in-hand configuration. The proposed method works in two main phases. In the first phase a Convolutional Neural Network (CNN) extracts the bounding boxes of the objects contained in the bin from a single RGB image of the environment. In the second phase the 6D pose of the objects is estimated using a dense 3D reconstruction of the scene and by applying a template matching algorithm from multiple virtual views of the object CAD model. Experimental results have been carried out on a dataset containing both RGB and depth images. Preliminary experiments are also reported in the real setup.