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This work presents a practical method for estimating the full kinematic state of a vehicle, along with sensor errorparameters, through the integration of inertial and GPS measurements. This kind of system for determining attitudeand position of vehicles and craft (either manned or unmanned) is essential for real time, guidance and navigationtasks, as well as for mobile robot applications.
The architecture of the system is based in an Extended Kalman filtering approach in direct configuration. In this case,the filter is explicitly derived from the kinematic model, as well as from the models of sensors error. The architecturehas been designed in a manner that it permits to be easily modified, in order to be applied to vehicles with diversedynamical behaviors.
The estimated variables and parameters are: i) Attitude and bias-compensated rotational speed of the vehicle, ii)Position, velocity and bias-compensated acceleration of the vehicle and iii) bias of gyroscopes and accelerometers.Experimental results with real data show that the proposed method is enough robust for its use along with low-costsensors.