A Global Calibration Method for Widely Distributed Cameras Based on Vanishing Features.

Area of Science:

• Computer Vision
• Robotics
• Sensor Networks

Background:

• Accurate calibration of distributed vision sensors is crucial for many applications.
• Existing methods often struggle with accumulated errors in large-scale or complex configurations.
• Ring-topologies present unique challenges for global calibration.

Purpose of the Study:

• To develop a robust and accurate global calibration method for vision sensors arranged in ring-topologies.
• To address and mitigate accumulated errors in coordinate transformations within distributed sensor networks.
• To provide a simple and versatile calibration solution applicable to various camera setups.

Main Methods:

• Utilizes planar targets with orthogonal line groups for initial camera-target pose estimation via vanishing points.
• Employs an auxiliary camera to determine relative poses between adjacent calibration targets.
• Applies a chain of transformations for initial target-to-reference pose, followed by nonlinear optimization leveraging ring-topology constraints.

Main Results:

• Demonstrated reliability and accuracy using synthetic data, simulations, and real-world experiments.
• Achieved high precision, with real-world experiments showing an accuracy of approximately 0.465 mm.
• Effectively adjusted accumulated errors from multiple coordinate transformations.

Conclusions:

• The proposed global calibration method is effective for vision sensors in ring-topologies.
• The method offers a simple, accurate, and reliable solution for distributed sensor calibration.
• It successfully minimizes accumulated errors, enhancing overall system precision.