Yan, Z., Sun, L. orcid.org/0000-0002-0393-8665, Krajník, T. et al. (1 more author) (2021) EU long-term dataset with multiple sensors for autonomous driving. In: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). International Conference on Intelligent Robots and Systems (IROS2020), 25 Oct 2020 - 24 Jan 2021, Online conference. IEEE , pp. 10697-10704. ISBN 9781728162133
Abstract
The field of autonomous driving has grown tremendously over the past few years, along with the rapid progress in sensor technology. One of the major purposes of using sensors is to provide environment perception for vehicle understanding, learning and reasoning, and ultimately interacting with the environment. In this paper, we first introduce a multisensor platform allowing vehicle to perceive its surroundings and locate itself in a more efficient and accurate way. The platform integrates eleven heterogeneous sensors including various cameras and lidars, a radar, an IMU (Inertial Measurement Unit), and a GPS-RTK (Global Positioning System / Real-Time Kinematic), while exploits a ROS (Robot Operating System) based software to process the sensory data. Then, we present a new dataset (https://epan-utbm.github.io/utbm_robocar_dataset/) for autonomous driving captured many new research challenges (e.g. highly dynamic environment), and especially for long-term autonomy (e.g. creating and maintaining maps), collected with our instrumented vehicle, publicly available to the community.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 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) |
Funding Information: | Funder Grant number Engineering and Physical Science Research Council EP/R026092/1 The Royal Society RGS\R2\202432 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 09 Mar 2021 10:52 |
Last Modified: | 10 Feb 2022 01:38 |
Published Version: | https://ieeexplore.ieee.org/document/9341406 |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.1109/IROS45743.2020.9341406 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:171956 |