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標題:Cloud-Based Improved Monte Carlo Localization Algorithm with Robust Orientation Estimation for Mobile Robots
學年107
學期1
出版(發表)日期2018/09/18
作品名稱Cloud-Based Improved Monte Carlo Localization Algorithm with Robust Orientation Estimation for Mobile Robots
作品名稱(其他語言)
著者I.H. Li; W.Y. Wang; C.Y. Li; J.Z. Kao; C.C. Hsu
單位
出版者
著錄名稱、卷期、頁數Engineering Computation 36(1), p.178-203
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.
關鍵字Robotics;Cloud computing;Particle filter;Monte Carlo localization
語言英文
ISSN
期刊性質國外
收錄於SCI;
產學合作
通訊作者
審稿制度
國別英國
公開徵稿
出版型式,電子版
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