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Multisensor data fusion for automated guided vehicles

The paper addresses multi-sensor data fusion for the navigation of a 4 wheel vehicle with two driven wheels. The main advantage of such a configuration is its flexibility concerning free motion and navigation, this advantage is paid for, however, with an increased complexity concerning the dynamic model of the vehicle. The basic sensors of the vehicle comprise a fiber optical gyro, continuously delivering angular orientation information (namely the angular velocity), a landmark sensor, delivering global position information at those instants where a landmark is available and within the reach of the sensor. Eventually an undriven and therefore not subject to slippage measuring wheel can be added. The control inputs to the vehicle are taken to be noisy but nominally known but subject to errors due to measuring errors and unknown influences. The approach taken in the paper essentially uses Kalman filtering ideas namely extended Kalman filtering to implement multi model filtering. The Kalman filter incorporates the different noisy measurements in order to `fuse' them to one precise position and orientation estimate, copes with the only temporary available global information, and automatically realizes dead-reckoning where no global information is available. The paper covers the state space formulation of the problem, discuses the different models needed to describe the different motions. Based on a realistic state space model the corresponding Kalman filter is designed and tested with simulated measurement data delivered by a truth model simulator.