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

I. hsum Li, Wei Yen Wang, Chung Ying Li, Jia Zwei Kao, Chen Chien Hsu

Research output: Contribution to journalArticle

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.

Original languageEnglish
Pages (from-to)178-203
Number of pages26
JournalEngineering Computations (Swansea, Wales)
Volume36
Issue number1
DOIs
Publication statusPublished - 2019 Feb 11

Fingerprint

Mobile robots
Robots
Servers
Printed circuit boards
Data storage equipment

Keywords

  • Cloud computing
  • Monte Carlo localization
  • Particle filter
  • Robotics

ASJC Scopus subject areas

  • Software
  • Engineering(all)
  • Computer Science Applications
  • Computational Theory and Mathematics

Cite this

Cloud-based improved Monte Carlo localization algorithm with robust orientation estimation for mobile robots. / Li, I. hsum; Wang, Wei Yen; Li, Chung Ying; Kao, Jia Zwei; Hsu, Chen Chien.

In: Engineering Computations (Swansea, Wales), Vol. 36, No. 1, 11.02.2019, p. 178-203.

Research output: Contribution to journalArticle

@article{5535d9fde1894c8c8d995ea3018ed483,
title = "Cloud-based improved Monte Carlo localization algorithm with robust orientation estimation for mobile robots",
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 = "Cloud computing, Monte Carlo localization, Particle filter, Robotics",
author = "Li, {I. hsum} and Wang, {Wei Yen} and Li, {Chung Ying} and Kao, {Jia Zwei} and Hsu, {Chen Chien}",
year = "2019",
month = "2",
day = "11",
doi = "10.1108/EC-03-2017-0081",
language = "English",
volume = "36",
pages = "178--203",
journal = "Engineering Computations",
issn = "0264-4401",
publisher = "Emerald Group Publishing Ltd.",
number = "1",

}

TY - JOUR

T1 - Cloud-based improved Monte Carlo localization algorithm with robust orientation estimation for mobile robots

AU - Li, I. hsum

AU - Wang, Wei Yen

AU - Li, Chung Ying

AU - Kao, Jia Zwei

AU - Hsu, Chen Chien

PY - 2019/2/11

Y1 - 2019/2/11

N2 - 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.

AB - 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.

KW - Cloud computing

KW - Monte Carlo localization

KW - Particle filter

KW - Robotics

UR - http://www.scopus.com/inward/record.url?scp=85058642795&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85058642795&partnerID=8YFLogxK

U2 - 10.1108/EC-03-2017-0081

DO - 10.1108/EC-03-2017-0081

M3 - Article

AN - SCOPUS:85058642795

VL - 36

SP - 178

EP - 203

JO - Engineering Computations

JF - Engineering Computations

SN - 0264-4401

IS - 1

ER -