Map building via integration of fuzzy systems and clustering algorithms

This paper presents a segment detection and grouping scheme that allows incremental and on-tine learning of indoor environment maps by mobile robots. In this study, the modeling is refined by first dividing the world into discrete regions as local models. The line segments in local models are extracted by clustering algorithm. The local models are grouped together by a hierarchical fuzzy system. Adjusting the membership functions that establish the grouping criteria controls the degree of approximation in such combination. The performance of the algorithm is validated in indoor office environments using a Pioneer H mobile robot.