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

研究成果: 書貢獻/報告類型會議論文篇章

6 引文 斯高帕斯(Scopus)

摘要

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.

原文英語
主出版物標題2016 IEEE Congress on Evolutionary Computation, CEC 2016
發行者Institute of Electrical and Electronics Engineers Inc.
頁面4522-4527
頁數6
ISBN(電子)9781509006229
DOIs
出版狀態已發佈 - 2016 十一月 14
事件2016 IEEE Congress on Evolutionary Computation, CEC 2016 - Vancouver, 加拿大
持續時間: 2016 七月 242016 七月 29

出版系列

名字2016 IEEE Congress on Evolutionary Computation, CEC 2016

其他

其他2016 IEEE Congress on Evolutionary Computation, CEC 2016
國家/地區加拿大
城市Vancouver
期間2016/07/242016/07/29

ASJC Scopus subject areas

  • 人工智慧
  • 建模與模擬
  • 電腦科學應用
  • 控制和優化

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