SLAM with consistent mapping in an hybrid model

This paper presents a methodology for improving consistency of the simultaneous localization and mapping (SLAM) in large scale cyclic environments. The SLAM problem is embedded in a reactive sensor-based navigation approach and exploits data provided by a rotative laser range finder. The model of the unknown indoor environment is structured as an hybrid representation, both topological and metric, which is incrementally built during the exploration task. A global likelihood function is modeled from the geometric elastic relationships between different places of the environment, constrained by a rigid metric model inside each place. The inconsistencies in the final resulted map are minimized by deforming the hybrid model with the optimization of the global likelihood of the system by applying a relaxation methodology. Results are presented which shows the minimization of inconsistencies related to an autonomous constructed hybrid model by the application of the proposed methodology