Place learning and recognition using hidden Markov models

In this paper, we propose a new method based on hidden Markov models to learn and recognize places in an indoor environment by a mobile robot. Hidden Markov models have been used for a long time in pattern recognition, especially in speech recognition. Their main advantages over other methods (e. g. neural networks) are their capabilities to modelize noisy temporal signals of variable length. We show in this paper that this approach is well adapted for learning and recognition of places by a mobile robot. Results of experiments on a real robot with five distinctive places are given.

Authors

• Olivier Aycard
• François Charpillet
• Dominique Fohr
• Jean-François Mari

Keywords

• Hidden Markov models
• Mobile robots
• Tactile sensors
• Indoor environments
• Infrared sensors
• Neural networks
• Stochastic processes
• Speech
• US Department of Transportation
• Pattern recognition
• Spatial Memory
• Neural Network
• Hidden Markov Model
• Speech Recognition
• Mobile Robot
• Distinct Places
• Place Recognition
• Infrared Imaging
• Transition Probabilities
• Stochastic Model
• Line Segment
• Second-order Model
• Open Door
• Tactile Sensor
• Sequence Of States
• Part Of Line
• Probability Of Sequence
• Position Of The Robot
• Ultrasonic Sensors
• Recognition Phase
• Recursive Estimation