Map Matching Based on Paraconsistent Artificial Neural Networks

This paper presents a new method for matching metric maps generated by mobile robots that act cooperatively. This process of information matching makes it possible to perform global map generation from local maps (possibly partial and nonconsistent) provided by individual robots. The proposed method is based on a paraconsistent artificial neural network model that considers as input data the Euclidean distances between the points from each one of the partial maps. The use of this kind of input information makes the individual maps invariant with respect to relative rotation and translation among the robots in the mapping environment. The neural network then analyzes these distances to determine what are the matching belief relations among the points of the distinct maps. The algorithm implemented for the neural architecture achieved good results with very satisfactory computational performance, and made it possible to determine the certainty and contradiction degress in the map point matching analysis. The results show that the proposed approach is robust for the cases were it was applied. Equally important is the fact that the considered architecture allows for the combination of information from partial maps acquired in execution time during navigation.