Second order hidden Markov models for place recognition: new results

Second order hidden Markov models have been used for a long time in pattern recognition, especially in speech recognition. Their main advantages over other methods (neural networks etc.) are their capabilities of modeling noisy temporal signals of variable length. In a previous work, we proposed a new method based on second order hidden Markov models to learn and recognize places in an indoor environment by a mobile robot, and showed that this approach is well suited for learning and recognizing places. In this paper, we propose major modifications to increase the global rate of place recognition. Results of experiments on a real robot with distinctive places are given.