  This paper presents some optimal real-time and post-processing estimators of
vehicle position using odometer and map-matched GPS measurements. These
estimators were based on a simple statistical error model of the odometer and
the GPS which makes the model generalizable to other applications. Firstly, an
asymptotically minimum variance unbiased estimator and two optimal moving fixed
interval filters which are more flexibles are exposed. Then, the
post-processing case leads to the construction of two moving fixed interval
smoothers. These estimators are tested and compared with the classical Kalman
filter with simulated and real data, and the results show a good accuracy of
each of them.
