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Improved Extrinsic Calibration of Acoustic Cameras via Batch Optimization

Zhi Li, Jiang Wang, Xiaoyang Li, He Kong

TL;DR

This work tackles the extrinsic calibration problem between a microphone array and a camera in acoustic cameras, addressing limitations of prior methods that require known array geometry or rely on grid-search optimization. It introduces a batch nonlinear least squares framework that identifies each microphone position in the camera frame using a moving calibration board with both visual and acoustic markers, and solves the problem with Gauss-Newton iterations. The approach leverages TDOA measurements and multiple board poses to form a unified optimization, demonstrating improved accuracy and robustness in both numerical simulations and real-world experiments, outperforming a grid-search baseline. The method is validated across varying noise levels and data quantities, and the authors open-source all code and data to facilitate community use and replication.

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.

Improved Extrinsic Calibration of Acoustic Cameras via Batch Optimization

TL;DR

This work tackles the extrinsic calibration problem between a microphone array and a camera in acoustic cameras, addressing limitations of prior methods that require known array geometry or rely on grid-search optimization. It introduces a batch nonlinear least squares framework that identifies each microphone position in the camera frame using a moving calibration board with both visual and acoustic markers, and solves the problem with Gauss-Newton iterations. The approach leverages TDOA measurements and multiple board poses to form a unified optimization, demonstrating improved accuracy and robustness in both numerical simulations and real-world experiments, outperforming a grid-search baseline. The method is validated across varying noise levels and data quantities, and the authors open-source all code and data to facilitate community use and replication.

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.

Paper Structure

This paper contains 9 sections, 10 equations, 4 figures, 3 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. The Proposed Method
  3. Problem Formulation
  4. Batch Optimization
  5. Numerical Simulations and Results
  6. Experiments and Results
  7. Experimental Setup
  8. Results and Discussion
  9. Conclusions

Figures (4)

  • Figure 1: Extrinsic calibration of an acoustic camera using a moving calibration board with both visual and acoustic marks
  • Figure 2: Real-world 3D acoustic camera calibration environment setup. (a) Microphone and camera. (b) Typical calibration scenario.
  • Figure 3: Experiment results in the real-world experiment: (a) Box plot of microphone position estimation errors. (b) Calibration results and the true values of the acoustic camera.
  • Figure 4: Calibration results across different size of datasets.