We present a novel sensor calibration methodology that is suited to an evidence theoretic Unmanned Ground Vehicle (UGV) localization system. The proposed procedure for sensor calibration employs a series of designed experiments with the objective of creating parametric calibration models and forming a mass assignment table for a Dempster-Shafer belief system. Sensors calibrated include custom built magnetic encoders positioned at the rear wheels of the UGV, an accelerometer, a solid-state rate-gyro, a digital compass, and a Global Positioning System (GPS). The estimated parameters together with a mass assignment table are presented. This table is created for the GPS unit based on the factors that significantly impact the accuracy of the readings using an experimental procedure. We conclude with a brief summary of the main results.
In this paper, we present a novel evidence theoretic fusion filter, and its application to the Unmanned Ground Vehicle (UGV) localization problem. The various components of the sensor fusion framework such as the adaptive pre-processing unit, the evidence extraction and combination unit, and the extended Kalman filter are described in detail. The crux of this architecture is the evidence extraction and combination unit that employs a twopronged approach, one to switch between parametric models, and another to adaptively vary the measurement noise covariance matrix. The process of evidence extraction using fuzzy-type or rule-based techniques, and their subsequent combination using the Dempster's rule for combination are detailed. An experiment is conducted to demonstrate the merits of this UGV localization approach. Finally, we conclude with a brief summary of the results.
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