A Markov semi-supervised clustering approach and its application in topological map extraction

Ming Liu, Francis Colas, Francois Pomerleau, Roland Siegwart
2012 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems  
In this paper, we present a novel semi-supervised clustering approach based on Markov process. It deals with data which include abundant local constraints. We apply the designed model to a topological region extraction problem, where topological segmentation is constructed based on sparse human inputs (potentially provided by human experts). The model considers human indications as seeds for topological regions, i.e. the partially labeled data. It results in a regional topological segmentation of connected free space.
doi:10.1109/iros.2012.6385683 dblp:conf/iros/LiuCPS12 fatcat:7kucmm2rjffb5mhv37n5em33nm