Quan V. Nguyen will defend his thesis on Friday, November 3 at 13:30 in room C005 at LORIA. The thesis is entitled “Mapping of a sound environment by a mobile robot”.
Robot audition provides hearing capability for robots and helps them explore and understand their sound environment. In this thesis, we focus on the task of sound source localization for a single or multiple, intermittent, possibly moving sources using a mobile robot and exploiting robot motion to improve the source localization.
We propose a Bayesian filtering framework to localize the position of a single, intermittent, possibly moving sound source. This framework jointly estimates the source location and its activity over time and is applicable to any microphone array geometry. Thanks to the movement of the robot, it can estimate the distance to the source and solve the front-back ambiguity which appears in the case of a linear microphone array. We propose two implementations of this framework based on an extended mixture Kalman filter (MKF) and on a particle filter, that we compare in terms of performance and computation time.
We then extend our model to the context of multiple, intermittent, possibly moving sources. By implementing an extended MKF with joint probabilistic data association filter (JPDAF), we can jointly estimate the locations of two sources and their activities over time.
Lastly, we make a contribution on long-term robot motion planning to optimally reduce the uncertainty in the source location. We define a cost function with two alternative criteria: the Shannon entropy or the standard deviation of the estimated belief. These entropies or standard deviations are integrated over time with a discount factor. 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.