6D Gripper Pose Estimation from RGB-D Image

This paper proposes an end-to-end system to directly estimate the 6D pose of gripper given RGB and depth images of an object. A dataset containing RGB-D images and 6D poses of 20 kinds, 10 for known objects and 10 for unknown ones, is developed in the first place. With all coordinates information gained from successful grasp, the separation between object properties and grasping strategies could be avoided. To improve the usability and uniformity of raw data, distinctive data preprocessing approach is illustrated immediately after the creation of the dataset. Entire convolutional neural network frame is given subsequently and the training with unique loss function adjusts the model to desired accuracy. Testing on both known and unknown objects verifies our system when it comes to grasping precision.