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Multiple microphone arrays have many applications in robot audition,including sound source localization, audio scene perception and analysis, etc.However, accurate calibration of multiple microphone arrays remains a challengebecause there are many unknown parameters to be identified, including the Eulerangles, geometry, asynchronous factors between the microphone arrays. Thispaper is concerned with joint calibration of multiple microphone arrays andsound source localization using graph simultaneous localization and mapping(SLAM). By using a Fisher information matrix (FIM) approach, we focus on theobservability analysis of the graph SLAM framework for the above-mentionedcalibration problem. We thoroughly investigate the identifiability of theunknown parameters, including the Euler angles, geometry, asynchronous effectsbetween the microphone arrays, and the sound source locations. We establishnecessary/sufficient conditions under which the FIM and the Jacobian matrixhave full column rank, which implies the identifiability of the unknownparameters. These conditions are closely related to the variation in the motionof the sound source and the configuration of microphone arrays, and haveintuitive and physical interpretations. We also discover several scenarioswhere the unknown parameters are not uniquely identifiable. All theoreticalfindings are demonstrated using simulation data.

Observability Analysis of Graph SLAM-Based Joint Calibration of Multiple Microphone Arrays and Sound Source Localization