Automatic Dataset Generation From CAD for Vision-Based Grasping

Recent developments in robotics and deep learning enable the training of models for a wide variety of tasks, from large amounts of collected data. Visual and robotic tasks, such as pose estimation or grasping, are trained from image data (RGB-D) or point clouds that need to be representative for the actual objects, to acquire accurate and robust results. This implies either generalized object models or large datasets that include all object and environment variability, for training. However, data collection is often a bottleneck in the fast development of learning-based models. In fact, data collection might be impossible or even undesirable, as physical objects are unavailable or the physical recording of data is too time-consuming and expensive. For example, when building a data recording setup with cameras and robotic hardware. CAD tools, in combination with robot simulation, offer a solution for the generation of training data that can be easily automated and that can be just as realistic as real world data. In this work, we propose a data generation pipeline that takes as input a CAD model of an object and automatically generates the required training data for object pose estimation and object grasp detection. The object data generated are: RGB and depth image, object binary mask, class label and ground truth pose in camera- and world frame. We demonstrate the dataset generation of several sets of industrial object assemblies and evaluate the trained models on state of the art pose estimation and grasp detection approaches. Code and video are available at: https://github.com/KulunuOS/gazebo_dataset_generation.