SLAM-based Joint Calibration of Multiple Asynchronous Microphone Arrays and Sound Source Localization

TL;DR

This paper presents a novel SLAM-based framework for the joint calibration of multiple asynchronous microphone arrays and sound source localization, addressing challenges in parameter identifiability and improving accuracy and convergence speed.

Contribution

It introduces an observability analysis using Fisher information matrix and a new initialization framework to enhance calibration accuracy and convergence in multi-array microphone systems.

Findings

The proposed method achieves higher calibration accuracy.

It converges faster than existing methods.

It effectively handles asynchronous and unknown parameters.

Abstract

Robot audition systems with multiple microphone arrays have many applications in practice. However, accurate calibration of multiple microphone arrays remains challenging because there are many unknown parameters to be identified, including the relative transforms (i.e., orientation, translation) and asynchronous factors (i.e., initial time offset and sampling clock difference) between microphone arrays. To tackle these challenges, in this paper, we adopt batch simultaneous localization and mapping (SLAM) for joint calibration of multiple asynchronous microphone arrays and sound source localization. Using the Fisher information matrix (FIM) approach, we first conduct the observability analysis (i.e., parameter identifiability) of the above-mentioned calibration problem and establish necessary/sufficient conditions under which the FIM and the Jacobian matrix have full column rank, which implies the identifiability of the unknown parameters. We also discover several scenarios where the unknown parameters are not uniquely identifiable. Subsequently, we propose an effective framework to initialize the unknown parameters, which is used as the initial guess in batch SLAM for multiple microphone arrays calibration, aiming to further enhance optimization accuracy and convergence. Extensive numerical simulations and real experiments have been conducted to verify the performance of the proposed method. The experiment results show that the proposed pipeline achieves higher accuracy with fast convergence in comparison to methods that use the noise-corrupted ground truth of the unknown parameters as the initial guess in the optimization and other existing frameworks.