Global consistency mapping with a hybrid representation

This paper presents a methodology of error correction applied to autonomously mapping large scale cyclic environment by mobile robots based on laser scan measurements. The model of the unknown indoor environment is structured as a hybrid representation, both topological and geometrical, which is incrementally built during the exploration task. The correction methodology is based on the minimization of geometric elastic relations between different places of the environment, constrained by rigid geometrical model inside each place. Results are presented which shows the minimization of inconsistencies related to an autonomous constructed hybrid model by the application of the proposed methodology.