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Cloud-based improved Monte Carlo localization algorithm with robust orientation estimation for mobile robots

I-hsum Li (Department of Electrical Engineering, Chinese Culture University, Taipei, Taiwan)
Wei-Yen Wang (Department of Electrical Engineering, National Taiwan Normal University, Taipei, Taiwan)
Chung-Ying Li (School of Computer Science, University of Birmingham, Birmingham, UK)
Jia-Zwei Kao (MSI Corporation, Taipei, Taiwan)
Chen-Chien Hsu (Department of Electrical Engineering, National Taiwan Normal University, Taipei, Taiwan)

Engineering Computations

ISSN: 0264-4401

Article publication date: 14 December 2018

Issue publication date: 8 February 2019

130

Abstract

Purpose

This paper aims to demonstrate a cloud-based version of the improved Monte Carlo localization algorithm with robust orientation estimation (IMCLROE). The purpose of this system is to increase the accuracy and efficiency of indoor robot localization.

Design/methodology/approach

The cloud-based IMCLROE is constructed with a cloud–client architecture that distributes computation between servers and a client robot. The system operates in two phases: in the offline phase, two maps are built under the MapReduce framework. This framework allows parallel and even distribution of map information to a cloud database in pre-described formats. In the online phase, an Apache HBase is adopted to calculate a pose in-memory and promptly send the result to the client robot. To demonstrate the efficiency of the cloud-based IMCLROE, a two-step experiment is conducted: first, a mobile robot implemented with a non-cloud IMCLROE and a UDOO single-board computer is tested for its efficiency on pose-estimation accuracy. Then, a cloud-based IMCLROE is implemented on a cloud–client architecture to demonstrate its efficiency on both pose-estimation accuracy and computation ability.

Findings

For indoor localization, the cloud-based IMCLROE is much more effective in acquiring pose-estimation accuracy and relieving computation burden than the non-cloud system.

Originality/value

The cloud-based IMCLROE achieves efficiency of indoor localization by using three innovative strategies: firstly, with the help of orientation estimation and weight calculation (OEWC), the system can sort out the best orientation. Secondly, the system reduces computation burden with map pre-caching. Thirdly, the cloud–client architecture distributes computation between the servers and client robot. Finally, the similar energy region (SER) technique provides a high-possibility region to the system, allowing the client robot to locate itself in a short time.

Keywords

Acknowledgements

This research is partially supported by the “Aim for the Top University Project 103J1A12” and “Center of Learning Technology for Chinese” of National Taiwan Normal University (NTNU), sponsored by the Ministry of Education, Taiwan, R.O.C., and the “International Research-Intensive Center of Excellence Program” of NTNU and Ministry of Science and Technology, Taiwan, R.O.C., under Grants No. MOST 106-2221-E-003-007-MY2, MOST 106-2221-E-003-008-MY2, MOST 107-2634-F-003-002, MOST 107-2634-F-003-001 and MOST 106-2221-E-034 -022 -MY2.

Citation

Li, I.-h., Wang, W.-Y., Li, C.-Y., Kao, J.-Z. and Hsu, C.-C. (2019), "Cloud-based improved Monte Carlo localization algorithm with robust orientation estimation for mobile robots", Engineering Computations, Vol. 36 No. 1, pp. 178-203. https://doi.org/10.1108/EC-03-2017-0081

Publisher

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Emerald Publishing Limited

Copyright © 2018, Emerald Publishing Limited

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