  In this paper, we propose an approach to the distributed storage and fusion
of data for collective perception in resource-limited robot swarms. We
demonstrate our approach in a distributed semantic classification scenario. We
consider a team of mobile robots, in which each robot runs a pre-trained
classifier of known accuracy to annotate objects in the environment. We provide
two main contributions: (i) a decentralized, shared data structure for
efficient storage and retrieval of the semantic annotations, specifically
designed for low-resource mobile robots; and (ii) a voting-based, decentralized
algorithm to reduce the variance of the calculated annotations in presence of
imperfect classification. We discuss theory and implementation of both
contributions, and perform an extensive set of realistic simulated experiments
to evaluate the performance of our approach.
