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    Jin, Sangrok Kim, Jongwon and Seo, TaeWon 2015. Optimization of a redundantly actuated 5R symmetrical parallel mechanism based on structural stiffness. Robotica, Vol. 33, Issue. 09, p. 1973.


    Li, Wanli Wang, Jinling Lu, Liangqing and Wu, Wenqi 2013. A Novel Scheme for DVL-Aided SINS In-Motion Alignment Using UKF Techniques. Sensors, Vol. 13, Issue. 1, p. 1046.


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Precise outdoor localization with a GPS–INS integration system

  • Wonkyo Seo (a1), Seoyoung Hwang (a1), Jaehyun Park (a1) and Jang-Myung Lee (a1)
  • DOI: http://dx.doi.org/10.1017/S0263574712000379
  • Published online: 26 July 2012
Abstract
SUMMARY

This paper proposes a precise outdoor localization algorithm with the integration of Global Positioning System (GPS) and Inertial Navigation System (INS). To achieve precise outdoor localization, two schemes are recently proposed, which consist of de-noising the INS signals and fusing the GPS and INS data. To reduce the noise from the internal INS sensors, the discrete wavelet transform and variable threshold method are utilized, and to fuse the GPS and INS data while filtering out the noise caused by the acceleration, deceleration, and unexpected slips, the Unscented Particle Filter (UPF) is adopted. Conventional de-noising methods mainly employ a combination of low-pass and high-pass filters, which results in signal distortion. This newly proposed system also utilizes the vibration information of the actuator according to the fluctuations of the velocity to minimize the signal distortion. The UPF resolves the nonlinearities of the actuator and non-normal distributions of the noise more effectively than the conventional particle filter (PF) or Extended Kalman Filter–PF. The superiority of the proposed algorithm was verified through experiments, and the results are reported.

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Corresponding author
*E-mail: jmlee@pusan.ac.kr
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This list contains references from the content that can be linked to their source. For a full set of references and notes please see the PDF or HTML where available.

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Robotica
  • ISSN: 0263-5747
  • EISSN: 1469-8668
  • URL: /core/journals/robotica
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