Hidden Markov model based graph matching for calibration of localization maps

We investigate the problem of graph matching to translate topological indoor localization to geographical localization, by modeling the building map and the semantic maps as graphs. A matching algorithm based on hidden Markov models is proposed. The matching algorithm is tested on both simulations and real data and accuracies as high as 94% on real data is achieved, while the matching is shown to be robust to noise, scale variance and partial matching via simulations.