Download or read book Estimation of 3D Object Pose for Packing Problem with a Deep Learning Approach written by Andrés David Rodríguez Torres and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper presents a deep learning approach to the pose estimation of boxes in a packing problem context. We divided the problem into two steps: detection and pose estimation. Each step is performed with a different convolutional neuronal network configured to complete its task without the excessive complexity that would be required to perform them simultaneously. The first neural network detects if a grayscale image of the working environment as captured by a Microsoft Kinect V2 contains a box or not. The second network predicts the two-dimensional position of each vertex of the box in the image plane from an RGB image. With this information, a depth channel of the image and the pinhole camera model we can estimate the position of the center of mass and the orientation of the box. We train and test both networks with synthetic data from a virtual scene of the workstation. For the detection problem, we achieved an accuracy of 99.5%. For the pose estimation problem, a mean error for center of mass distance of 17.78 millimeters and a mean error for orientation of 21.28 degrees were registered. Testing with real-world data remains pending, as well as the use of other network architectures.