Creating computer vision datasets requires careful planning and lots of time and effort. In robotics research, we often have to use standardized objects, such as the YCB object set, for tasks such as object tracking, pose estimation, grasping and manipulation, as there are datasets and pre-learned methods available for these objects. This limits the impact of our research since learning-based computer vision methods can only be used in scenarios that are supported by existing datasets. In this work, we present a full object keypoint tracking toolkit, encompassing the entire process from data collection, labeling, model learning and evaluation. We present a semi-automatic way of collecting and labeling datasets using a wrist mounted camera on a standard robotic arm. Using our toolkit and method, we are able to obtain a working 3D object keypoint detector and go through the whole process of data collection, annotation and learning in just a couple hours of active time.
@article{arxiv.2201.07665,
title = {Semi-automatic 3D Object Keypoint Annotation and Detection for the Masses},
author = {Kenneth Blomqvist and Jen Jen Chung and Lionel Ott and Roland Siegwart},
journal= {arXiv preprint arXiv:2201.07665},
year = {2022}
}