Mapping sound emitting structures in 3D

This paper presents a framework for creating a 3D map of an environment that contains the probability of a geometric feature to emit a sound. The goal is to provide an automated tool for condition monitoring of plants. The map is created by a mobile platform equipped with a microphone array and laser range sensors. The microphone array is used to estimate the sound power received from different directions whereas the laser range sensors are used for estimating the platform pose in the environment. During navigation, a ray casting method projects the audio measurements made onboard the mobile platform to the map of the environment. Experimental results show that the created map is an efficient tool for sound source localization.

[1]  Kiyohiro Shikano,et al.  An improved permutation solver for blind signal separation based front-ends in robot audition , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[2]  Eric Martinson,et al.  Auditory Evidence Grids , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[3]  Eric Martinson,et al.  Robotic Discovery of the Auditory Scene , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[4]  Wolfram Burgard,et al.  OctoMap: an efficient probabilistic 3D mapping framework based on octrees , 2013, Autonomous Robots.

[5]  Paul J. Besl,et al.  A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Darren B. Ward,et al.  Particle filtering algorithms for tracking an acoustic source in a reverberant environment , 2003, IEEE Trans. Speech Audio Process..

[7]  Michael S. Brandstein,et al.  A robust method for speech signal time-delay estimation in reverberant rooms , 1997, 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[8]  Norihiro Hagita,et al.  Probabilistic approach for building auditory maps with a mobile microphone array , 2013, 2013 IEEE International Conference on Robotics and Automation.

[9]  François Michaud,et al.  Evaluating real-time audio localization algorithms for artificial audition in robotics , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[10]  Patrick Danès,et al.  Broadband variations of the MUSIC high-resolution method for Sound Source Localization in Robotics , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[11]  Hiroshi G. Okuno,et al.  An open source software system for robot audition HARK and its evaluation , 2008, Humanoids 2008 - 8th IEEE-RAS International Conference on Humanoid Robots.

[12]  Michael S. Brandstein,et al.  Microphone Arrays - Signal Processing Techniques and Applications , 2001, Microphone Arrays.

[13]  Joachim Hertzberg,et al.  The Efficient Extension of Globally Consistent Scan Matching to 6 DoF , 2008 .

[14]  Tetsuya Ogata,et al.  Exploiting known sound source signals to improve ICA-based robot audition in speech separation and recognition , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[15]  Satoshi Kagami,et al.  Map-generation and identification of multiple sound sources from robot in motion , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[16]  Daming Lin,et al.  A review on machinery diagnostics and prognostics implementing condition-based maintenance , 2006 .

[17]  Wolfram Burgard,et al.  Probabilistic Robotics (Intelligent Robotics and Autonomous Agents) , 2005 .