Guaranteed dynamic localization using constraints propagation techniques on real intervals

In order to get continuous dynamic localization of a mobile robot, dead reckoning and absolute sensors are usually merged. The models used for this fusion are non linear and, therefore, classical tools (such as Kalman filter) cannot guarantee a maximum error estimation. In some applications, integrity is essential and the ability to guaranty the result is a crucial point. There are ensemblist approaches that are insensitive to non linearity. In this context, the random errors are only modeled by their maximum bound. This paper presents a new technique to merge the data of redundant sensors with a guaranteed result based on constraints propagation techniques on intervals. We have thus developed an approach for the fusion of the 4 ABS wheel encoders, a measure of the angle of the driving wheel and a differential GPS receiver. Experimental results show that the precision that one can obtain is very good with a guaranteed result. Moreover, constraints propagation techniques are well adapted to a real time implementation.