Estimation of odometer parameters with MMAE and LSE

Extended Kalman filter is used intensively to achieve optimal sensor fusion to estimate the states of plant. In general, parameters of sensor and plant models are inaccurate so biased and random errors are inevitable unless they are calibrated accurately. In this paper, biased parameters of plant are estimated with Multiple-Model-Adaptive-Estimation algorithm (MMAE) and Least Square Estimation (LSE). It is shown that proposed method can learn the parameters of a differential-drive mobile robot odometer e.g. scale factors of left and right wheel radii and distance between wheels, accurately.