Multi-sensor Data Fusion for Wheelchair Position Estimation with Unscented Kalman Filter

This paper investigates the problem of estimation of the wheelchair position in indoor environments with noisy measurements. The measuring system is based on two odometers placed on the axis of the wheels combined with a magnetic compass to determine the position and orientation. Determination of displacements is implemented by an accelerometer. Data coming from sensors are combined and used as inputs to unscented Kalman filter (UKF). Two data fusion architectures: measurement fusion (MF) and state vector fusion (SVF) are proposed to merge the available measurements. Comparative studies of these two architectures show that the MF architecture provides states estimation with relatively less uncertainty compared to SVF. However, odometers measurements determine the position with relatively high uncertainty followed by the accelerometer measurements. Therefore, fusion in the navigation system is needed. The obtained simulation results show the effectiveness of proposed architectures.

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