6DOF pose estimation using 2D-3D sensor fusion

Object pose estimation is a fundamental problem for a robot when manipulating an object. In this paper, we propose a method for estimating the pose of an object using a 2D image and a 3D point cloud. The Speeded Up Robust Feature (SURF) descriptors between the model image and input image were used to match the keypoints. The pose of an object was estimated using the 3D points corresponding to these matches. To produce more accurate results, the outliers were removed from these matches using Random Sample Consensus (RANSAC) and the result was refined using the Iterative Closest Point (ICP) algorithm. The experimental result demonstrated the high efficiency of our method.