Dynamic Vehicle Localization using Constraints Propagation Techniques on Intervals A comparison with Kalman Filtering

Abstract

In order to implement a continuous and robust dynamic localization of a mobile robot, the fusion of dead reckoning and absolute sensors is often used. Depending on the objectives of precision or integrity, the choice of an algorithm could be crucial. For example, if the models used for the fusion are non linear, classical tools (such as a Kalman filter) cannot guarantee maximum error estimation. There are bounded error approaches that are insensitive to non linearity. In this context, the random errors are only modeled by their maximum bound. This paper compares a technique based on constraints propagation on intervals, with the usual Extended Kalman Filter for the data fusion of redundant sensors. We have thus developed both techniques and we consider the fusion of wheel encoders, a gyro and a differential GPS receiver. Experimental results show that the precision of a constraints propagation technique can be very good with guaranteed estimations. Moreover, such an approach is well adapted to a real time implementation.