Cloud-Based Improved Monte Carlo Localization Algorithm with Robust Orientation Estimation for Mobile Robots

doi:10.1108/EC-03-2017-0081

淡江大學機構典藏 > 工學院 > 機械與機電工程學系暨研究所 > 期刊論文 > Item 987654321/116051

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题名:	Cloud-Based Improved Monte Carlo Localization Algorithm with Robust Orientation Estimation for Mobile Robots
作者:	Li, I.H.;Wang, W.Y.;Li, C.Y.;Kao, J.Z.;Hsu, C.C.
关键词:	Robotics;Cloud computing;Particle filter;Monte Carlo localization
日期:	2018-09-18
上传时间:	2019-03-23 12:11:00 (UTC+8)
出版者:	Emerald
摘要:	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.
關聯:	Engineering Computation 36(1), p.178-203
DOI:	10.1108/EC-03-2017-0081
显示于类别:	[機械與機電工程學系暨研究所] 期刊論文

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