Probabilistic 2D Acoustic Source Localization Using Direction of Arrivals in Robot Sensor Networks

Transcription

1 Probabilistic 2D Acoustic Source Localization Using Direction of Arrivals in Robot Sensor Networks Riccardo Levorato and Enrico Pagello Department of Information Engineering, University of Padova, Italy Bergamo, 23 October 2014 INTELLIGENT AUTONOMOUS SYSTEMS LAB

2 Introduction Motivations: Focus on Direction of arrival (DOA) sensor fusion algorithms instead of focusing on DOA algorithms for single DOA estimation - use of HARK1 software Provide a general algorithm for heterogeneous DOA sensor networks for Audio Source Localization (ASL) Ad hoc approach for Autonomous Robotics: Audio-Video synergy using Video Sensor Localization in the environment for extrinsic Audio Sensor Calibration MATLAB implementation for simulation tests C++ implementation using ROS2 for real tests

3 DOA Localization Problem a DOA (Direction of Arrival) sensor consists of an array of at least two microphones

4 Previous Works (1) WLS-DOA Approach3 minimizes the sum of the distances of the estimated point to the estimated DOAs 3 Hawkes, M., Nehorai, A.: Wideband source localization using a distributed acoustic vector-sensor array. IEEE Trans. Sig. Proc. 51, (2003)

5 Previous Works (2) Robust WLS-DOA Approach4 search for the group of sensors that minimizes the sum of the distances of the estimated point to the estimated DOAs 4 Pertilä, P.: Acoustic Source Localization in a Room Environment and at Moderate a Distances - [PhD Thesis]. Tampere University of Technology (2009)

6 Previous Works (2) Robust WLS-DOA Approach4 search for the group of sensors that minimizes the sum of the distances of the estimated point to the estimated DOAs PROBLEM: Considering only a line without a precise direction 4 Pertilä, P.: Acoustic Source Localization in a Room Environment and at Moderate a Distances - [PhD Thesis]. Tampere University of Technology (2009)

7 Assumptions For simplicity, are considered the following assumptions: all sensors are connected together within a network and can share data; all DOA estimations are synchronized in time; there is only one fixed (not moving) acoustic source at time; the precision of all αk estimations depends only on the accuracy of each DOA sensor and on the DOA estimation methods used by HARK; echoes and sound reflection effects are not considered.

8 Why ROS? Simplifies sensors signal connections in a network; very easy data sharing; data synchronization in time; abstracts the concept of device and avoids driver implementation; Modular approach using logical nodes. for large community development and reuse

9 Gaussian Probability over DOA Approach (1) Gaussian Probability error Model in the angle domain. Spatial 2D grid

10 Gaussian Probability over DOA Approach (2) DOA estimation respect to q

11 Gaussian Probability over DOA Approach (3) Gaussian Probability error of sk respect to the Grid G

12 Gaussian Probability over DOA Approach (4) Gaussian Probability error of all sk respect to the Grid G

13 Gaussian Probability over DOA Approach (4) Gaussian Probability error of all sk respect to the Grid G

14 Validation and Test Comparisons with WLS approach Simulation Simulated error over the angle of arrival using MATLAB Real Test Camera Calibration using multisensor_calibration ROS package implemented in C++ for ROS Hydro/Indigo5 5 Basso, F., Levorato, R.: Online calibration for networks of cameras and depth sensors. OMNIVIS: The 12th Workshop on Non-classical Cameras, Camera Networks and Omnidirectional Vision IEEE International Conference on Robotics and Automation (ICRA 2014) (2014)

15 Microsoft Kinect Sensor DOA estimation using HARK within ROS

16 Results - Simulation

17 Results - Simulation Constant difference error between WLS-DOA and G-DOA!

18 Results - Real Test (1)

19 Results - Real Test (2)

20 Conclusions In this paper we proposed a new method for Audio Source Localization (ASL) using a Gaussian probability over DOA (G-DOA). Video calibration for microphones arrays calibration is also a new ad hoc approach for calibrating microphones arrays in a robotics environment. We tested it both in simulation (MATLAB) and real (ROS) environments and showed that G-DOA performs better than WLS-DOA.

21 Future Work Speed up the G-DOA algorithm that takes the most of the time [O(n2)] in the product formula, in order to expand it to the 3D space. Test the algorithm with mobile robots using Vision (SLAM) for Audio Calibration Test the approach with mobile and multiple sound sources

22 Thanks Thank you for your attention!

23 Any Question?