Abstract
This thesis presents a learning-based method for recognizing chess pieces from depth information. The proposed method is integrated in a recreational robotic system that is designed to play games of chess against humans. The robot has two arms and an Ensenso N35 Stereo 3D camera. Our goal is to provide the robot visual intelligence so that it can identify the chess pieces on the chessboard using the depth information captured by the 3D camera. We build a convolutional neural network to solve this 3D object recognition problem. While training neural networks for 3D object recognition becomes popular these days, collecting enough training data is still a time-consuming task. We demonstrate that it is much more convenient and effective to generate the required training data from 3D CAD models. The neural network trained using the rendered data performs well on real inputs during testing. More specifically, the experimental results show that using the training data rendered from the CAD models under various conditions enhances the recognition accuracy significantly. When further evaluations are done on real data captured by the 3D camera, our method achieves 90.3% accuracy.