GPS/IMU Data Fusion using multisensor Kalman filtering: Introduction of contextual aspects

Francois Caron , Emmanuel Duflos (1) , Denis Pomorski (2) , Philippe Vanheeghe (3)

1 LAGIS-SI
2 LAGIS - Laboratoire d'Automatique, Génie Informatique et Signal
3 CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189

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.

Domaines: Automatique / Robotique; Sciences de l'ingénieur [physics]

Francois Caron, Emmanuel Duflos, Denis Pomorski, Philippe Vanheeghe. GPS/IMU Data Fusion using multisensor Kalman filtering: Introduction of contextual aspects. Information Fusion, 2006, 7, pp.221-230. ⟨10.1016/j.inffus.2004.07.002⟩. ⟨hal-01509438⟩