An Augmented Reality Interaction Interface for Autonomous Drone

Human drone interaction in autonomous navigation incorporates spatial interaction tasks, including reconstructed 3D map from the drone and human desired target position. Augmented Reality (AR) devices can be powerful interactive tools for handling these spatial interactions. In this work, we build an AR interface that displays the reconstructed 3D map from the drone on physical surfaces in front of the operator. Spatial target positions can be further set on the 3D map by intuitive head gaze and hand gesture. The AR interface is deployed to interact with an autonomous drone to explore an unknown environment. A user study is further conducted to evaluate the overall interaction performance.Comment: 6 pages, 6 figures, IROS 202

Synthetic Data for Deep Learning

Synthetic data is an increasingly popular tool for training deep learning models, especially in computer vision but also in other areas. In this work, we attempt to provide a comprehensive survey of the various directions in the development and application of synthetic data. First, we discuss synthetic datasets for basic computer vision problems, both low-level (e.g., optical flow estimation) and high-level (e.g., semantic segmentation), synthetic environments and datasets for outdoor and urban scenes (autonomous driving), indoor scenes (indoor navigation), aerial navigation, simulation environments for robotics, applications of synthetic data outside computer vision (in neural programming, bioinformatics, NLP, and more); we also survey the work on improving synthetic data development and alternative ways to produce it such as GANs. Second, we discuss in detail the synthetic-to-real domain adaptation problem that inevitably arises in applications of synthetic data, including synthetic-to-real refinement with GAN-based models and domain adaptation at the feature/model level without explicit data transformations. Third, we turn to privacy-related applications of synthetic data and review the work on generating synthetic datasets with differential privacy guarantees. We conclude by highlighting the most promising directions for further work in synthetic data studies.Comment: 156 pages, 24 figures, 719 reference