Improved Monte Carlo localization with robust orientation estimation based on cloud computing

Chung Ying Li, I. Hsum Li, Yi Hsing Chien, Wei Yen Wang, Chen Chien Hsu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

Abstract

Robot localization plays an important role in the field of robot navigation. One of the most commonly used localization algorithms is Monte Carlo Localization algorithm (MCL). Unfortunately, the traditional MCL is not reliable all the time in both pose tracking and global localization. Many modified MCL algorithms have been proposed to improve the efficiency and performance, such as improved Monte Carlo Localization with robust orientation estimation algorithm (IMCLROE) proposed by the authors. However, the IMCLROE requires a lot of storage space and intensive computation, especially in a highly complicated environment. In recent years, cloud computing has been widely used because of ubiquitous network. As an attempt to solve the above problems based on cloud computing, we propose a cloud-based improved Monte Carlo Localization algorithm with robust orientation estimation with a distributed orientation estimation technique in calculating important factor of each particle. With the use of cloud computing, real-time paradox between accuracy and efficiency in a high-resolution grid map can be addressed. Experimental results confirm that the proposed cloud-based architecture can efficiently establish a map database and reduce the computational load for robot localization.

Original languageEnglish
Title of host publication2016 IEEE Congress on Evolutionary Computation, CEC 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4522-4527
Number of pages6
ISBN (Electronic)9781509006229
DOIs
Publication statusPublished - 2016 Nov 14
Event2016 IEEE Congress on Evolutionary Computation, CEC 2016 - Vancouver, Canada
Duration: 2016 Jul 242016 Jul 29

Publication series

Name2016 IEEE Congress on Evolutionary Computation, CEC 2016

Other

Other2016 IEEE Congress on Evolutionary Computation, CEC 2016
CountryCanada
CityVancouver
Period16/7/2416/7/29

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Keywords

  • Cloud computing
  • Monte Carlo Localization
  • Particle filter
  • Robot localization

ASJC Scopus subject areas

  • Artificial Intelligence
  • Modelling and Simulation
  • Computer Science Applications
  • Control and Optimization

Cite this

Li, C. Y., Li, I. H., Chien, Y. H., Wang, W. Y., & Hsu, C. C. (2016). Improved Monte Carlo localization with robust orientation estimation based on cloud computing. In 2016 IEEE Congress on Evolutionary Computation, CEC 2016 (pp. 4522-4527). [7744365] (2016 IEEE Congress on Evolutionary Computation, CEC 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CEC.2016.7744365