GPS/IMU data fusion using multisensor Kalman filtering: introduction of contextual aspects

  • Authors:

  • Francois Caron;Emmanuel Duflos;Denis Pomorski;Philippe Vanheeghe

  • Affiliations:

  • LAGIS UMR 8146, Ecole Centrale de Lille Cite Scientifique, BP 48, F59651 Villeneuve d'Ascq Cedex, France;LAGIS UMR 8146, Ecole Centrale de Lille Cite Scientifique, BP 48, F59651 Villeneuve d'Ascq Cedex, France;LAGIS UMR 8146, Bat. P2, Universite Lille I, F59655 Villeneuve d'Ascq Cedex, France;LAGIS UMR 8146, Ecole Centrale de Lille Cite Scientifique, BP 48, F59651 Villeneuve d'Ascq Cedex, France

  • Venue:
  • Information Fusion
  • Year:
  • 2006

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Abstract

The aim of this article is to develop a GPS/IMU multisensor fusion algorithm, taking context into consideration. Contextual variables are introduced to define fuzzy validity domains of each sensor. The algorithm increases the reliability of the position information. A simulation of this algorithm is then made by fusing GPS and IMU data coming from real tests on a land vehicle. Bad data delivered by GPS sensor are detected and rejected using contextual information thus increasing reliability. Moreover, because of a lack of credibility of GPS signal in some cases and because of the drift of the INS, GPS/INS association is not satisfactory at the moment. In order to avoid this problem, the authors propose to feed the fusion process based on a multisensor Kalman filter directly with the acceleration provided by the IMU. Moreover, the filter developed here gives the possibility to easily add other sensors in order to achieve performances required.