Sushrutha Raghavan, V, Kanoulas, D, Zhou, C orcid.org/0000-0002-6677-0855 et al. (2 more authors) (2019) A Study on Low-Drift State Estimation for Humanoid Locomotion, Using LiDAR and Kinematic-Inertial Data Fusion. In: IEEE-RAS International Conference on Humanoid Robots (Humanoids). 2018 IEEE-RAS, 06-09 Nov 2018, Beijing, China. IEEE , pp. 524-531. ISBN 978-1-5386-7283-9
Abstract
Several humanoid robots will require to navigate in unsafe and unstructured environments, such as those after a disaster, for human assistance and support. To achieve this, humanoids require to construct in real-time, accurate maps of the environment and localize in it by estimating their base/pelvis state without any drift, using computationally efficient mapping and state estimation algorithms. While a multitude of Simultaneous Localization and Mapping (SLAM) algorithms exist, their localization relies on the existence of repeatable landmarks, which might not always be available in unstructured environments. Several studies also use stop-and-map procedures to map the environment before traversal, but this is not ideal for scenarios where the robot needs to be continuously moving to keep for instance the task completion time short. In this paper, we present a novel combination of the state-of-the-art odometry and mapping based on LiDAR data and state estimation based on the kinematics-inertial data of the humanoid. We present experimental evaluation of the introduced state estimation on the full-size humanoid robot WALK-MAN while performing locomotion tasks. Through this combination, we prove that it is possible to obtain low-error, high frequency estimates of the state of the robot, while moving and mapping the environment on the go.
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 uses, in any current or future media, 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 component of this work in other works. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mechanical Engineering (Leeds) > Institute of Engineering Systems and Design (iESD) (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 04 Apr 2019 10:15 |
Last Modified: | 04 Apr 2019 10:15 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/HUMANOIDS.2018.8624953 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:144457 |