SLAM-based online calibration of asynchronous microphone array for robot audition

This paper addresses the online calibration of an asynchronous microphone array for robots. Conventional microphone array technologies require a lot of measurements of transfer functions to calibrate microphone locations, and a multi-channel A/D converter for inter-microphone synchronization. We solve these two problems using a framework combining Simultaneous Localization and Mapping (SLAM) and beamforming in an online manner. To do this, we assume that estimations of microphone locations, a sound source location, and microphone clock difference correspond to mapping, self-localization, observation errors in SLAM, respectively. In our framework, the SLAM process calibrates locations and clock differences of microphones every time a microphone array observes a sound like a human's clapping, and a beamforming process works as a cost function to decide the convergence of calibration by localizing the sound with the estimated locations and clock differences. After calibration, beamforming is used for sound source localization. We implemented a prototype system using Extended Kalman Filter (EKF) based SLAM and Delay-and-Sum Beamforming (DS-BF). The experimental results showed that microphone locations and clock differences were estimated properly with 10–15 sound events (handclaps), and the error of sound source localization with the estimated information was less than the grid size of beamforming, that is, the lowest error was theoretically attained.