Online simultaneous localization and mapping of multiple sound sources and asynchronous microphone arrays

Abstract

This paper presents an online method of simultaneous localization and mapping (SLAM) for estimating the positions of multiple moving sound sources and stationary robots and synchronizing microphone arrays attached to those robots. Since each robot with a microphone array can solely estimate the directions of sound sources, the two-dimensional source positions can be estimated from the source directions estimated by multiple robots using a triangulation method. In addition, sound mixtures can be separated accurately by regarding distributed microphone arrays as one big array. To perform these tasks, some methods have been proposed for localizing and synchronizing microphone arrays. These methods, however, can be used only if a single sound source exists because the time differences of arrival (TDOAs) between microphones are assumed to be directly observed. To overcome this limitation, we propose a unified state-space model that encodes the source and robot positions and the time offsets between microphone arrays in a latent space. Given the TDOAs and directions of arrival (DOAs) estimated by separating observed mixture sounds into source sounds, the latent variables are estimated jointly in an online manner using a FastSLAM2.0 algorithm that can deal with an unknown time-varying number of moving sound sources.