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The use of global positioning system (GPS) in outdoor localization is quite a common solution in large environments where no other reference is available and there are not so demanding positioning requirements. Of course, fine motion without the use of an expensive differential device is not an easy task, even now that available precision has been greatly improved as the military encoding has been removed. In this paper we present a localization algorithm based on Kalman filtering that tries to fuse information coming from an inexpensive single GPS with inertial data and map-based data. The algorithm is able to produce an estimated configuration for the robot that can be successfully fed back in a navigation system. Some experiments show difficulties and possible solutions of this sensor fusion problem.
Panzieri et al. (Sat,) studied this question.
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