Map learning and clustering in autonomous systems

Building autonomous systems, self-learning while moving in an unknown environment, finds a variety of challenging applications. This paper presents a new approach, called clustering by discovery, for identification of clusters in a map which is being learned by exploration. The concomitance of exploration and clustering, we argue, is a mandatory feature for an autonomous system, hence the clustering technique we propose is an incremental process performed while the system is learning the map. Clusters supply an abstract description of the environment and can be used to decrease the complexity of the navigational tasks. The environment is viewed as a map of distinctive places which we assume to be sensed and recognized by the system. The presence of distinctive places and the environment scale are the only facts which we assume known apriori to the system. Clustering by discovery is based on a heuristic indicator called scattering, whose increment is minimized at each exploration step compatibly with a connectivity constraint imposed on clusters. Scattering is defined according to a number of functional and structural requirements. Two variants are presented, and their performance is discussed on a sample of maps including a real urban map and some randomly generated ones. In particular, one of the variants shows robust behaviour in terms of independence of the exploration strategy adopted