Credibilist multi-sensors fusion for the mapping of dynamic environment

In this article, we present how, starting from an credibilist multi-object association algorithm we can carry out a multi-sensor fusion algorithm. The tracking algorithm makes a data association between predicted information and observations. These information are imperfect. The algorithm takes into account the inaccuracy and the uncertainty of the data and the reliability of the sensors. Association is realized with the belief theory. This method can be applied to the fusion of several homogeneous data sources. The problem arises when information are heterogeneous. Here, we answer this problem by using a decentralized architecture which breaks up into two stages. The first consists in having at first a local processor to each sensor. This local processing makes it possible to obtain a set of homogeneous data. The second stage uses these homogeneous data to carry out global fusion. This fusion gives a representation and a global view of a dynamic environment around a reference vehicle the most faithful and most reliable by using all available information. Moreover, this very general approach shows the polyvalence of this algorithm which con be in any case-used for multi-object matching, local tracking, multi-sensors fusion and global tracking.