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Multimedia Tools and Applications

, Volume 78, Issue 21, pp 29747–29763 | Cite as

Motion estimation of indoor robot based on image sequences and improved particle filter

  • Xiaoming Dong
  • Liefu Ai
  • Rong JiangEmail author
Article
  • 57 Downloads

Abstract

Robot motion estimation is fundamental in most robot applications such as robot navigation, which is an indispensable part of future internet of things. Indoor robot motion estimation is difficult to be resolved because GPS (Global Positioning System) is unavailable. Vision sensors can provide larger amount of image sequences information compared with other traditional sensors, but it is subject to the changes of light. In order to improve the robustness of indoor robot motion estimation, an enhanced particle filter framework is constructed: firstly, motion estimation was implemented based on the distinguished indoor feature points; secondly, particle filter method was utilized and the least square curve fitting was inserted into the particle resampling process to solve the problem of particle depletion. The various experiments based on real robots show that the proposed method can reduce the estimation errors greatly and provide an effective resolution for the indoor robot localization and motion estimation.

Keywords

Internet of things Visual odometry Indoor robot Localization Motion estimation 

Notes

Acknowledgments

The paper was supported by National Natural Science Foundation of China (Nos. 61763048,61263022, 61303234), National Social Science Foundation of China (No. 12XTQ012), Science and Technology Foundation of Yunnan Province (Nos. 2017FB095, 201801PF00021), the 18th Yunnan Young and Middle-aged Academic and Technical Leaders Reserve Personnel Training Program (No.2015HB038). It is also supported by the Foundation of University Research and Innovation Platform Team for Intelligent Perception and Computing of Anhui Province, key research project of natural science of Anhui Provincial Education Department (KJ2017A354). Anhui Provincial Natural Science Foundation of China (1608085MF144). The authors would like to thank the anonymous reviewers and the editors for their suggestions.

Author’s contributions

Rong Jiang, Xiaoming Dong designed the experiments and wrote the paper; Rong Jiang, Xiaoming Dong performed the experiments; Liefu Ai analyzed the data; All authors have read and approved the final manuscript.

Compliance with ethical standards

Competing interests

The authors declare that they have no competing interests.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.The University Key Laboratory of Intelligent Perception and Computing of Anhui ProvinceAnqing Normal UniversityAnqingChina
  2. 2.School of InformationYunnan University of Finance and EconomicsKunmingChina

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