Robust Monte Carlo localization based on vector model

Bing Gang Jhong; Mei Yung Chen

doi:10.1109/ICSSE.2016.7551616

Robust Monte Carlo localization based on vector model

Bing Gang Jhong^*, Mei Yung Chen

^*Corresponding author for this work

Department of Mechatronic Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

This paper proposed an enhanced Monte Carlo localization algorithm, which is more effective, stable and robust than traditional localization algorithm by using many strengthening mechanisms, such as vector model, re-initialization and reverse convergence. The vector model redefines the pattern of environment map, so that the localization result is not limited by the resolution of map. Re-initialization gives second chance when the algorithm is missing the right location and can't jump out the local solution. Reverse convergence, the most important in this paper, can let the algorithm spread particle swarm moderately. It is simple but very useful, especially for the case within noise or sensing distance limitations in the sensors. The simulation results also show the excellent performance of proposed algorithm.

Original language	English
Title of host publication	2016 IEEE International Conference on System Science and Engineering, ICSSE 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781467389662
DOIs	https://doi.org/10.1109/ICSSE.2016.7551616
Publication status	Published - 2016 Aug 24
Event	2016 IEEE International Conference on System Science and Engineering, ICSSE 2016 - Puli, Taiwan Duration: 2016 Jul 7 → 2016 Jul 9

Publication series

Name	2016 IEEE International Conference on System Science and Engineering, ICSSE 2016

Other

Other	2016 IEEE International Conference on System Science and Engineering, ICSSE 2016
Country/Territory	Taiwan
City	Puli
Period	2016/07/07 → 2016/07/09

ASJC Scopus subject areas

Computer Networks and Communications
Artificial Intelligence
Control and Optimization
Control and Systems Engineering

Access to Document

10.1109/ICSSE.2016.7551616

Cite this

Jhong, B. G., & Chen, M. Y. (2016). Robust Monte Carlo localization based on vector model. In 2016 IEEE International Conference on System Science and Engineering, ICSSE 2016 Article 7551616 (2016 IEEE International Conference on System Science and Engineering, ICSSE 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICSSE.2016.7551616

Robust Monte Carlo localization based on vector model. / Jhong, Bing Gang; Chen, Mei Yung.
2016 IEEE International Conference on System Science and Engineering, ICSSE 2016. Institute of Electrical and Electronics Engineers Inc., 2016. 7551616 (2016 IEEE International Conference on System Science and Engineering, ICSSE 2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Jhong, BG & Chen, MY 2016, Robust Monte Carlo localization based on vector model. in 2016 IEEE International Conference on System Science and Engineering, ICSSE 2016., 7551616, 2016 IEEE International Conference on System Science and Engineering, ICSSE 2016, Institute of Electrical and Electronics Engineers Inc., 2016 IEEE International Conference on System Science and Engineering, ICSSE 2016, Puli, Taiwan, 2016/07/07. https://doi.org/10.1109/ICSSE.2016.7551616

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title = "Robust Monte Carlo localization based on vector model",

abstract = "This paper proposed an enhanced Monte Carlo localization algorithm, which is more effective, stable and robust than traditional localization algorithm by using many strengthening mechanisms, such as vector model, re-initialization and reverse convergence. The vector model redefines the pattern of environment map, so that the localization result is not limited by the resolution of map. Re-initialization gives second chance when the algorithm is missing the right location and can't jump out the local solution. Reverse convergence, the most important in this paper, can let the algorithm spread particle swarm moderately. It is simple but very useful, especially for the case within noise or sensing distance limitations in the sensors. The simulation results also show the excellent performance of proposed algorithm.",

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AB - This paper proposed an enhanced Monte Carlo localization algorithm, which is more effective, stable and robust than traditional localization algorithm by using many strengthening mechanisms, such as vector model, re-initialization and reverse convergence. The vector model redefines the pattern of environment map, so that the localization result is not limited by the resolution of map. Re-initialization gives second chance when the algorithm is missing the right location and can't jump out the local solution. Reverse convergence, the most important in this paper, can let the algorithm spread particle swarm moderately. It is simple but very useful, especially for the case within noise or sensing distance limitations in the sensors. The simulation results also show the excellent performance of proposed algorithm.

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Robust Monte Carlo localization based on vector model

Abstract

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