Deep Learning for Object Detection: Training Data Generation using Parametric CAD Modelling and Gazebo Simulation

Abstract

Deep learning-based object detection and pose estimation methods need a large number of synthetic data for application in robotic assembly tasks. The acquisition of such data from real objects tends to be arduous, erroneous, and time-consuming. Alternatively, synthetic data can be generated autonomously from 3D models efficiently and relatively quickly in a simulated environment. These 3D models can be generated by utilizing either conventional or parametric approaches. Conventional approaches generate free-form mesh models that are generally unalterable when repetitive changes are required in the models, which is an important aspect in parts customization in an industrial context. This challenge is addressed by implementing a script-based parametric modelling approach to automate the generation of 3D models of an industrial part via parameters. Then, the 3D models of the dataset are loaded in the simulation environment for synthetic data generation to train and evaluate a state-of-the-art model-based pose estimation network for 6DoF object pose estimation. This thesis comprehensively illustrates the implementation of automated parametric modelling of an industrial part to create a dataset of CAD models, generate synthetic data for deep learning-based object detection methods, and compute the 6DoF poses of the dataset objects in a cluttered scene using a state-of-the-art pose estimation method. The results of the computation speed for generating and rendering the models are analysed. Finally, the study analyses the results of the benchmark 6DoF pose estimation network evaluated for 6DoF poses of the custom dataset objects.