Cartographie d'un environnement sonore par un robot mobile.Mapping of a sound environment by a mobile robot

Robot audition provides a hearing capability for robot and helps it to explore and understand a sound environment. In this thesis, we focus on the task of sound source localization using a mobile robot equipped with a microphone array. We consider source localization for a single or multiple, intermittent, and possibly moving sources in a reverberant environment and exploiting robot motion to improve the source localization. We first propose a Bayesian filtering framework to localize the position of a single, intermittent and possibly moving sound source. This framework jointly estimates the source location and its activity over time and can apply to any microphone array geometry. Thanks to the movement of the robot, it can estimate the distance to the source and also can solve the frontback ambiguity which appears in the case of a linear microphone array. We conduct a number of experiments to show the robustness of the extended mixture Kalman filter (MKF) framework to false measurements of the angle of arrival (AoA) or the source activity detection (SAD) when localizing an intermittent, moving source. Experiments and statistical results also show that our proposed MKF method outperforms the method that does not consider the source activity in the model. In addition, we propose a particle filtering technique for jointly estimating the source location and its activity and comparing with the performance of the extended MKF in term of localization performance, and computational time. After the contribution on a single source, we extend it to the context of multiple intermittent, possibly moving sources. In the context of multiple sound sources, the uncertainty is not only increased when the sources are inactive but also when they are active because we have no information to tell an AoA measurement is originated from which target. The peaks of the multiple signal classification (MUSIC) spectrum may be occluded and false alarms may occur. By implementing an extended MKF with joint probabilistic data association filter (JPDAF), we can jointly estimate the two source locations and their activities over time. The experimental evaluation shows the effectiveness of the proposed method in localizing and tracking two target sources in reverberant environment. Lastly, we make a contribution on long-term robot motion planning to optimally reduce the uncertainty on estimating the location of a single, intermittent, and moving source. We define a cost function for long-term planning with two alternative criteria: the Shannon entropy or the standard deviation of the estimation belief on the source location. These entropies or standard deviations are integrated over time with a discount factor. We represent the belief about the source location at each time step by a mixture of Gaussians, and propagate this belief by the proposed extended MKF framework. Then, we adapt the Monte Carlo tree search (MCTS) method for efficiently finding the optimal robot motion that will minimize the above cost function. Experiments show that the proposed method outperforms other robot motion planning methods for robot audition in the long run, especially compared to a greedy planning method. The results of the MCTS with standard deviation highlight the effect of the discount factor on the performance of MCTS over time. Moreover, the analysis of all the results show a coherent estimation error result when optimizing the standard deviation of the estimated belief instead of the entropy.