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Accurate 3D Localization Using RGB-TOF Camera and IMU for Industrial Mobile Robots

Published online by Cambridge University Press:  22 February 2021

Majid Yekkehfallah ,
Ming Yang Open the ORCID record for Ming Yang [Opens in a new window] ,
Zhiao Cai ,
Liang Li  and
Chuanxiang Wang
Majid Yekkehfallah
Affiliation:
Department of Automation, Shanghai Jiao Tong University, Shanghai200240, China E-mails: [email protected], [email protected], [email protected], [email protected] Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai200240, China
Ming Yang*
Affiliation:
Department of Automation, Shanghai Jiao Tong University, Shanghai200240, China E-mails: [email protected], [email protected], [email protected], [email protected] Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai200240, China
Zhiao Cai
Affiliation:
Department of Automation, Shanghai Jiao Tong University, Shanghai200240, China E-mails: [email protected], [email protected], [email protected], [email protected] Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai200240, China
Liang Li
Affiliation:
Department of Automation, Shanghai Jiao Tong University, Shanghai200240, China E-mails: [email protected], [email protected], [email protected], [email protected]
Chuanxiang Wang
Affiliation:
Department of Automation, Shanghai Jiao Tong University, Shanghai200240, China E-mails: [email protected], [email protected], [email protected], [email protected] Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai200240, China
*
*Corresponding author. E-mail: [email protected]
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Summary

Localization based on visual natural landmarks is one of the state-of-the-art localization methods for automated vehicles that is, however, limited in fast motion and low-texture environments, which can lead to failure. This paper proposes an approach to solve these limitations with an extended Kalman filter (EKF) based on a state estimation algorithm that fuses information from a low-cost MEMS Inertial Measurement Unit and a Time-of-Flight camera. We demonstrate our results in an indoor environment. We show that the proposed approach does not require any global reflective landmark for localization and is fast, accurate, and easy to use with mobile robots.

Keywords

RGB-TOF cameraIndustrial mobile robotsLocalizationSLAMSensor fusionExtended Kalman filter
Type
Article
Information
Robotica , Volume 39 , Issue 10 , October 2021 , pp. 1816 - 1833
Copyright
© The Author(s), 2021. Published by Cambridge University Press

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