Acoustic Source Localization for Robotics Networks
This chapter presents a technical research contribution in the audio for robotics field where ROS was used for validating the results. More specifically it deals with 2D Audio Localization using only the Directions of Arrival (DOAs) of a fixed acoustic source coming from an audio sensor network and proposes a method for estimating the position of the acoustic source using a Gaussian Probability over DOA approach (GP-DOA). This method was thought for robotics purposes and introduces a new perspective of the audio-video synergy using video sensor localization in the environment for extrinsic audio sensor calibration. Test results using Microsoft Kinects as DOA-sensors mounted on robots within the ROS framework, show that the algorithm is robust and modular and prove that the approach can be easily used for robotics applications. The second part is dedicated to the detailed description of the implemented ROS package.
KeywordsAcoustic Source Localization (ASL) Direction Of Arrival (DOA) Audio-sensor network Robot audition Microsoft kinect Robot Operating System (ROS)
We strongly thank the Université Pierre-et-Marie-Curie (UPMC) and the Institut des Systèmes Intelligents et de Robotique (ISIR) for hosting Ph.D. Student Riccardo Levorato during the first phases of the project. A special thank goes to Prof. Mohamed Chetouani, Post-Doc Salvatore Maria Anzalone and Ph.D. student Stéphane Michelet for their indispensable support and help.
- 1.P. Aarabi, The fusion of distributed microphone arrays for sound localization. EURASIP J. Adv. Sig. Process. 2003(4), 860465 (2003)Google Scholar
- 2.F. Basso, R. Levorato, E. Menegatti, Online calibration for networks of cameras and depth sensors, in OMNIVIS: The 12th Workshop on Non-classical Cameras, Camera Networks and Omnidirectional Vision—2014 IEEE International Conference on Robotics and Automation (ICRA 2014) (2014)Google Scholar
- 3.J.H. Di Biase, H.F. Silverman, M. Brandstein, Robust Localization in Reverberant Rooms, in Microphone Arrays: Signal Processing Techniques and Applications (Springer, New York, 2001)Google Scholar
- 4.D.R. Griffin, Listening in the Dark: The Acoustic Orientation of Bats and Men (Yale University Press, New Haven, 1958)Google Scholar
- 6.R. Levorato, E. Pagello, DOA acoustic source localization in mobile robot sensor networks, in 2015 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), pp. 71–76, Apr 2015Google Scholar
- 7.R. Levorato, E. Pagello, Probabilistic 2D Acoustic Source Localization using Direction of Arrivals in Robot Sensor Networks, in Simulation, Modeling, and Programming for Autonomous Robots, Lecture Notes in Computer Science, ed. by D. Brugali, J.F. Broenink, T. Kroeger, B.A. MacDonald (Springer International Publishing, Switzerland, 2014), pp. 474–485Google Scholar
- 9.M. Omologo, R. De Mori, Acoustic Transduction, in Spoken Dialogue with Computers (Academic Press, New York, 1998)Google Scholar
- 10.P. Pertilä, Acoustic source localization in a room environment and at moderate distances. Ph.D. thesis, Tampere University of Technology, 2009Google Scholar
- 11.K.B. Petersen, M.S. Pedersen, The matrix cookbook, Nov 2012. Version 20121115Google Scholar
- 12.D. Salvati, Acoustic source localization using microphone arrays. Ph.D. thesis, Department of Mathematics and Computer Science, University of Udine, 2012Google Scholar
- 13.G. Valenzise, L. Gerosa, M. Tagliasacchi, E. Antonacci, A. Sarti, Scream and gunshot detection and localization for audio-surveillance systems, in IEEE Conference on Advanced Video and Signal Based Surveillance, 2007. AVSS 2007, Sept 2007, pp. 21–26Google Scholar
- 14.D.B. Ward, E.A. Lehmann, R.C. Williamson, Particle filtering algorithms for tracking an acoustic source in a reverberant environment. IEEE Trans. Speech Audio Process. 11(6):826–836 (2003)Google Scholar