DISTRIBUTED SPATIAL REASONING FOR MULTISENSORY IMAGE INTERPRETATION

A hierarchical distributed system is presented, which interprets 3D sc enes through fusion of multisensory images. The recognition problem is partitioned into a set of less complex subproblems by associating wit h each representation level expert processing units that filter out un reliable solutions and focus attention on promising ones. In this way, the search space for possible solutions is limited in a distributed w ay, as a priori knowledge about observations and constraints is used a t multiple levels. Different instances of the same inference mechanism are applied at each level. As a consequence, each processing unit is able to search autonomously for a local solution in order to contribut e to obtaining a globally consistent solution. An important characteri stic of the system is to be easy to maintain and extend. The results r eported have been obtained by using multisensory images of real scenes considered in the context of an autonomous-driving application. Two e xamples of interpretation of 3D road scenes are given. and the distrib ution of computational load is discussed.