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 (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.

In this paper, we present a new method to localize a mobile robot in dynamic environments. This method is based on places recognition, and a match between places recognized and the sequence of places that the mobile robot is able to see during a run from an initial place to an ending place. Our method gives a coarse idea of the robot's position and orientation. Moreover, we can determine the actual state of places (i.e open doors, closed doors).

Abstract: In this paper, we propose a new method based on Hidden Markov Models to interpret temporal sequences of sensor data from mobile robots to automatically detect features. Hidden Markov Models have been used for a long time in pattern recognition, especially in speech recognition. Their main advantages over other methods (such as neural networks) are their ability to model noisy temporal signals of variable length. We show in this paper that this approach is well suited for interpretation of temporal sequences of mobile-robot sensor data. We present two distinct experiments and results: the first one in an indoor environment where a mobile robot learns to detect features like open doors or Tintersections, the second one in an outdoor environment where a different mobile robot has to identify situations like climbing a hill or crossing a rock.

: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : xiii 1 Introduction : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 1 1.1 The Problem: Too Many Parameters : : : : : : : : : : : : : : : : : : : : : : 3 1.2 Proposed Solution: It Is Time to Share More! : : : : : : : : : : : : : : : : : 4 1.3 Thesis Summary and Outline : : : : : : : : : : : : : : : : : : : : : : : : : : 6 2 Review of Acoustic Modeling Using Hidden Markov Model : : : : : : : 9 2.1 Speech Characteristics : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 9 2.2 Selection of Input Speech Space and Speech Model : : : : : : : : : : : : : : 10 2.2.1 Cepstral Input : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 10 2.2.2 Hidden Markov Model : : : : : : : : : : : : : : : : : : : : : : : : : : 11 2.2.3 Our Choice of HMM for Acoustic Modeling : : : : : : : : : : : : : : 14 2.3 Speech Unit to Model : : : : : : : : : : : : : : : : : : : : : : : : : : ...

First order hidden Markov models have been used for a long time in image processing, especially in image segmentation. In this paper, we propose a technique for the unsupervised segmentation of images, based on high-order hidden Markov chains. We also show that it is possible to relax the classical hypothesis regarding the state observation probability density, which allows to take into account some particular correlated noise. Model parameter estimation is performed from an extension of the general Iterative Conditional Estimation (ICE) method that takes into account the order of the chain. A comparative study conducted on a simulated image is carried out according to the order of the chain. Experimental results on Synthetic Aperture Radar (SAR) images show that the new approach can provide a more homogeneous segmentation than the classical one, implying higher complexity algorithm and computation time.

: Stochastic modeling is a flexible method for handling the large variability in speech for recognition applications. In contrast to dynamic time warping where heuristic training methods for estimating word templates are used, stochastic modeling allows a probabilistic and automatic training for estimating models. This paper deals with the improvement of stochastic techniques, especially for a better representation of time varying phenomena. Key-words: Speech recognition, HMM, stochastic trajectory modeling (R'esum'e : tsvp) chapter in the book "Progress and Prospects of Speech Research and Technology", H. Nieman, R. De Mori and G. Hanrieder, editors, INFIX, Sankt Augustin, 1994 Unite de recherche INRIA Lorraine Technopole de Nancy-Brabois, Campus scientifique, 615 rue de Jardin Botanique, BP 101, 54600 VILLERS LE S NANCY (France) Telephone : (33) 83 59 30 30 -- Telecopie : (33) 83 27 83 19 Antenne de Metz, technopole de Metz 2000, 4 rue Marconi, 55070 METZ Telephone : (33) 87 20 35 0...

I, the undersigned, hereby declare that the work contained in this dissertation is my own original work and that I have not previously in its entirety or in part submitted it at any university for a degree. Signature: Date: ii Currently, first-order hidden Markov models (HMMs) form the backbone around which most automatic speech processing applications are built. Their higher-order extensions are known to be more powerful, but, due to their complexity and computational demands, they are seldomly used. It is the purpose of this work to advance their application In this work we unify HMMs of all orders by deriving and proving the ORder rEDucing (ORED) algorithm. This algorithm will reduce any higher-order HMM (also mixed-order) to an equivalent first-order representation. This makes it possible to process any higher-order HMM using known first-order algorithms, thereby

In this work, we propose to improve the neighboring relationship ability of the Hidden Markov Chain (HMC) model, by extending the memory lengthes of both the Markov chain process and the data-driven densities arising in the model. The new model is able to learn more complex noise structures, with respect to the configuration of several previous states and observations. Model parameters estimation is performed from an extension of the general Iterative Conditional Estimation (ICE) method to take into account memories, which makes the classification algorithm unsupervised. The higher-order HMC model is then evaluated in the image segmentation context. A comparative study conducted on a simulated image is carried out according to the order of the chain. Experimental results on a Synthetic Aperture Radar (SAR) image show that higher-order model can provide more homogeneous segmentations than the classical model, but to the cost of higher memory and computing time requirements.

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 . . . ) are their capabilities to model 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 places recognition. Results of experiments on a real robot with distinctive places are given.

A planetary rover must be able to identify states where it should stop or change its plan. With limited and infrequent communication from ground, the rover must recognize states accurately. However, the sensor data is inherently noisy, so identifying the temporal patterns of data that correspond to interesting or important states becomes a complex problem. In this paper, we present an approach to state identification using second-order Hidden Markov Models. Models are trained automatically on a set of labeled training data; the rover uses those models to identify its state from the observed data. The approach is demonstrated on data from a planetary rover platform. 1 Introduction An autonomous mobile robot exploring or operating in an unknown environment needs to correctly identify fault states and environmental states in order to react to them appropriately. In the case of limited and delayed communication, such as for planetary rovers, human interaction is restricted, so these stat...