Improved Extrinsic Calibration of Acoustic Cameras via Batch Optimization

TL;DR

This paper introduces an automatic, batch-optimization-based extrinsic calibration method for acoustic cameras that enhances accuracy and robustness, using a calibration board with visual and acoustic markers, validated through simulations and real-world tests.

Contribution

It presents a novel nonlinear least squares formulation and batch optimization approach for acoustic camera calibration, overcoming prior limitations of prior methods.

Findings

Improved calibration accuracy over existing methods

Enhanced robustness in diverse conditions

Validated effectiveness through simulations and real-world experiments

Abstract

Acoustic cameras have found many applications in practice. Accurate and reliable extrinsic calibration of the microphone array and visual sensors within acoustic cameras is crucial for fusing visual and auditory measurements. Existing calibration methods either require prior knowledge of the microphone array geometry or rely on grid search which suffers from slow iteration speed or poor convergence. To overcome these limitations, in this paper, we propose an automatic calibration technique using a calibration board with both visual and acoustic markers to identify each microphone position in the camera frame. We formulate the extrinsic calibration problem (between microphones and the visual sensor) as a nonlinear least squares problem and employ a batch optimization strategy to solve the associated problem. Extensive numerical simulations and realworld experiments show that the proposed method improves both the accuracy and robustness of extrinsic parameter calibration for acoustic cameras, in comparison to existing methods. To benefit the community, we open-source all the codes and data at https://github.com/AISLAB-sustech/AcousticCamera.