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JRM Vol.29 No.1 pp. 94-104
doi: 10.20965/jrm.2017.p0094
(2017)

Paper:

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Probabilistic 3D Sound Source Mapping System Based on Monte Carlo Localization Using Microphone Array and LIDAR

Ryo Tanabe*,**, Yoko Sasaki**, and Hiroshi Takemura*,**

*Department of Mechanical Engineering, Tokyo University of Science
2641 Yamazaki, Noda-shi, Chiba 278-8510, Japan

**National Institute of Advanced Industrial Science and Technology (AIST)
2-3-26 Aomi, Kouto-ku, Tokyo 135-0064, Japan

Received:
July 23, 2016
Accepted:
November 1, 2016
Published:
February 20, 2017
Creative Commons License
Keywords:
sound source mapping, microphone array, 3D LIDAR, robot audition
Abstract
The study proposes a probabilistic 3D sound source mapping system for a moving sensor unit. A microphone array is used for sound source localization and tracking based on the multiple signal classification (MUSIC) algorithm and a multiple-target tracking algorithm. Laser imaging detection and ranging (LIDAR) is used to generate a 3D geometric map and estimate the location of its six-degrees-of-freedom (6 DoF) using the state-of-the-art gyro-integrated iterative closest point simultaneous localization and mapping (G-ICP SLAM) method. Combining these modules provides sound detection in 3D global space for a moving robot. The sound position is then estimated using Monte Carlo localization from the time series of a tracked sound stream. The results of experiments using the hand-held sensor unit indicate that the method is effective for arbitrary motions of the sensor unit in environments with multiple sound sources.
3D sound source environmental map

3D sound source environmental map

Cite this article as:
R. Tanabe, Y. Sasaki, and H. Takemura, “Probabilistic 3D Sound Source Mapping System Based on Monte Carlo Localization Using Microphone Array and LIDAR,” J. Robot. Mechatron., Vol.29 No.1, pp. 94-104, 2017.
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References
  1. [1] Y. Sasaki, S. Kagami, and H. Mizoguchi, “Map-Generation and Identification of Multiple Sound Sources from Robot in Motion,” Proc. of 2010 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp. 437-443, October 2010.
  2. [2] S. Kagami, S. Thompson, Y. Sasaki, H. Mizoguchi, and T. Enomoto, “2D sound source mapping from mobile robot using beamforming and particle filtering,” Proc. of 2009 IEEE Int. Conf. on Acoustics, Speech and Signal Processing, pp. 3689-3692, April 2009.
  3. [3] K. Takami, T. Furukawa, M. Kumon, and G. Dissanayake, “Non-field-of-view acoustic target estimation in complex indoor environment,” Springer Tracts in Advanced Robotics, Vol.113, pp. 577-592, 2015.
  4. [4] E. Martinson and A. C. Schultz, “Discovery of sound sources by an autonomous mobile robot,” Autonomous Robots, Vol.27, No.3, pp. 221-237, 2009.
  5. [5] T. Iyama, O. Sugiyama, T. Otsuka, K. Itoyama, and H. G. Okuno, “Visualization of auditory awareness based on sound source positions estimated by depth sensor and microphone array,” Proc. of 2014 IEEE/RSJ Int. Conf. on Intelligent Robot and Systems, pp. 1908-1913, September 2014.
  6. [6] J. Even, Y. Morales, N. Kallakuri, J. Furrer, C. T. Ishi, and N. Hagita, “Mapping sound emitting structures in 3D,” Proc. of IEEE-RAS Int. Conf. on Robots and Automation, pp. 677-682, May 2014.
  7. [7] R. Schmidt, “Multiple emitter location and signal parameter estimation,” IEEE Trans. on antennas and propagation, Vol.34, No.3, pp. 276-280, 1986.
  8. [8] Y. Sasaki, S. Kagami, and H. Mizoguchi, “Multiple sound source mapping for a mobile robot by self-motion triangulation,” IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp. 380-385, October 2006.
  9. [9] D. Reid, “An algorithm for tracking multiple targets,” IEEE Trans. on Automatic Control, Vol.24, No.6, pp. 843-854, 1979.
  10. [10] R. Kuramachi, A. Ohsato, Y. Sasaki, and H. Mizoguchi, “G-ICP SLAM: An odometry-free 3D mapping system with robust 6DoF pose estimation,” Proc. of 2015 IEEE Int. Conf. on Robotics and Biomimetics, pp. 176-181, December 2015.
  11. [11] Y. Sasaki, T. Fujihara, S. Kagami, H. Mizoguchi, and K. Oro, “32-channel omni-directional microphone array design and implementation,” J. of Robotics and Mechatronics, Vol.23, No.3, pp. 378-385, 2011.
  12. [12] K. Nakadai, T. Takahashi, H. G. Okuno, H. Nakajima, Y. Hasegawa, and H. Tsujino, “Design and Implementation of Robot Audition System ‘HARK’ – Open Source Software for Listening to Three Simultaneous Speakers,” Advanced Robotics, Vol.24, No.5-6, pp. 739-761, 2010.
  13. [13] G. Grisetti, C. Stachniss, and W. Burgard, “Improving grid-based slam with rao-blackwellized particle filters by adaptive proposals and selective resampling,” Proc. of the 2005 IEEE Int. Conf. on Robotics and Automation, pp. 2432-2437, April 2005.
  14. [14] Y. Chen and G. Medioni, “Object modelling by registration of multiple range images,” Image and vision computing, Vol.10, No.3, pp. 145-155, 1992.
  15. [15] F. Pomerleau, F. Colas, R. Siegwart, and S. Magnenat, “Comparing ICP variants on real-world data sets,” Autonomous Robots, Vol.34, No.3, pp. 133-148, 2013.
  16. [16] F. Moosmann and C. Stiller, “Velodyne slam,” Proc. of Intelligent Vehicles Symposium (IV), pp. 393-398, June 2011.

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