Print Email Facebook Twitter Self-supervised Monocular Multi-robot Relative Localization with Efficient Deep Neural Networks Title Self-supervised Monocular Multi-robot Relative Localization with Efficient Deep Neural Networks Author Li, S. (TU Delft Control & Simulation) de Wagter, C. (TU Delft Control & Simulation) de Croon, G.C.H.E. (TU Delft Control & Simulation) Contributor Pappas, George J. (editor) Kumar, Vijay (editor) Date 2022 Abstract Relative localization is an important ability for multiple robots to perform cooperative tasks in GPS-denied environments. This paper presents a novel autonomous positioning framework for monocular relative localization of multiple tiny flying robots. This approach does not require any groundtruth data from external systems or manual labeling. Instead, the proposed framework is able to label real-world images with 3D relative positions between robots based on another onboard relative estimation technology, using ultra-wideband (UWB). After training in this self-supervised manner, the proposed deep neural network (DNN) can predict relative positions of peer robots by purely using a monocular camera. This deep learning-based visual relative localization is scalable, distributed, and autonomous. We also built an open-source and lightweight simulation pipeline by using Blender for 3D rendering, which allows synthetic image generation of other robots, and generalized training of the neural network. The proposed localization framework is tested on two real-world Crazyflie2 quadrotors by running the DNN on the onboard AIdeck (a tiny AI chip and monocular camera). All results demonstrate the effectiveness of the self-supervised multi-robot localization method. Video: https://youtu.be/7arkaIblPps To reference this document use: http://resolver.tudelft.nl/uuid:fe24f256-8c0e-4422-b820-9ee42cc708e3 DOI https://doi.org/10.1109/ICRA46639.2022.9812150 Embargo date 2023-07-01 ISBN 978-1-7281-9681-7 Source 2022 IEEE International Conference on Robotics and Automation, ICRA 2022 Event 2022 International Conference on Robotics and Automation (ICRA), 2022-05-23 → 2022-05-27, Philadelphia, United States Series Proceedings - IEEE International Conference on Robotics and Automation, 1050-4729 Bibliographical note Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public. Part of collection Institutional Repository Document type conference paper Rights © 2022 S. Li, C. de Wagter, G.C.H.E. de Croon Files PDF Self_supervised_Monocular ... tworks.pdf 5.15 MB Close viewer /islandora/object/uuid:fe24f256-8c0e-4422-b820-9ee42cc708e3/datastream/OBJ/view