Sun, L. orcid.org/0000-0002-0393-8665, Yan, Z., Mellado, S.M. et al. (2 more authors) (2018) 3DOF pedestrian trajectory prediction learned from long-term autonomous mobile robot deployment data. In: 2018 IEEE International Conference on Robotics and Automation (ICRA). 2018 IEEE International Conference on Robotics and Automation (ICRA), 21-25 May 2018, Brisbane, QLD, Australia. IEEE ISBN 9781538630822
Abstract
This paper presents a novel 3DOF pedestrian trajectory prediction approach for autonomous mobile service robots. While most previously reported methods are based on learning of 2D positions in monocular camera images, our approach uses range-finder sensors to learn and predict 3DOF pose trajectories (i.e. 2D position plus 1D rotation within the world coordinate system). Our approach, T-Pose-LSTM (Temporal 3DOF-Pose Long-Short-Term Memory), is trained using long-term data from real-world robot deployments and aims to learn context-dependent (environment- and time-specific) human activities. Our approach incorporates long-term temporal information (i.e. date and time) with short-term pose observations as input. A sequence-to-sequence LSTM encoder-decoder is trained, which encodes observations into LSTM and then decodes the resulting predictions. On deployment, the approach can perform on-the-fly prediction in real-time. Instead of using manually annotated data, we rely on a robust human detection, tracking and SLAM system, providing us with examples in a global coordinate system. We validate the approach using more than 15 km of pedestrian trajectories recorded in a care home environment over a period of three months. The experiments show that the proposed T-Pose-LSTM model outperforms the state-of-the-art 2D-based method for human trajectory prediction in long-term mobile robot deployments.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 18 May 2021 12:59 |
Last Modified: | 18 May 2021 12:59 |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.1109/ICRA.2018.8461228 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:161365 |