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Multimodal sensory fusion for soccer robot self-localization based on long short-term memory recurrent neural network

  • Wenhuan Lu
  • Ju Zhang
  • Xinli Zhao
  • Jianrong Wang
  • Jianwu Dang
Original Research
  • 328 Downloads

Abstract

Self-localization is a fundamental requirement for autonomous mobile robots. With the rapid development in sensor technology, the sensor suites of robot provide multimodal information that naturally ensures perception robustness, multimodal sensory fusion are able to provide a better solution for enhance the capability of self-localization. This paper proposes a multimodal sensory fusion method based on Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) for RoboCup 3D Simulation league. This approach fuses Inertia Navigation System (INS) and vision perceptor information from different sensors at feature level instead of raw data. The experiment results demonstrate that the proposed approach makes an improvement in predictive accuracy and efficiency compared with the standard Extended Kalman Filter (EKF) and the static Particle Filter (PF) methods.

Keywords

Robot self-localization Multimodal sensory fusion Long short-term memory Recurrent neural network 

Notes

Acknowledgements

The research is supported by part of the National Natural Science Foundation (Surface Project No. 61304250).

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Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Wenhuan Lu
    • 1
  • Ju Zhang
    • 2
  • Xinli Zhao
    • 1
  • Jianrong Wang
    • 2
  • Jianwu Dang
    • 2
  1. 1.School of Computer SoftwareTianjin UniversityTianjinChina
  2. 2.School of Computer Science and TechnologyTianjin UniversityTianjinChina

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