Aluminum AprilTag Calibration Board with Active Lighting and 95% Light Source Uniformity for AprilGrid Calibration Target

The AprilTag / AprilGrid QR Code Calibration Board with Light Source is a visual benchmark calibration tool that integrates an active lighting function, designed on the AprilTag visual benchmark system. Its core feature is a built-in light source that provides a stable and uniform illumination environment, ensuring that QR code patterns can be clearly and accurately recognized and captured under a wide range of lighting conditions.

Compared with traditional checkerboard patterns, each AprilTag on the AprilGrid calibration board carries a unique ID, allowing the system to accurately identify corner positions even in partially occluded situations. This greatly improves the robustness and accuracy of the calibration process.

• Active lighting with anti-interference capability
• Light source uniformity of 95%
• High precision and stability
• Fast identification and dynamic calibration
• Environmental adaptability and traceability
• Integrated design and portability
• Controllable performance parameters
• Customizable size and frequency band
• Customizable for underwater use requirements

• Robot grasping and positioning visual calibration
• Multi-camera system global calibration
• Autonomous driving vehicle vision system calibration
• AR/VR device spatial positioning
• Unmanned aerial vehicle autonomous navigation visual calibration
• Mobile robot SLAM system calibration
• Camera and LiDAR joint calibration
• Industrial automation dynamic scene calibration

The QR code calibration board with light source uses AprilGrid — multiple AprilTag arrays arranged in a grid — as the physical coordinate reference, with a built-in active light source providing uniform illumination. The algorithm identifies the unique ID of each tag and its precise corner position, and can accurately complete orientation recognition and coordinate system establishment even if the calibration board is partially obstructed.

After the camera captures the image, the algorithm extracts the sub-pixel coordinates of all tag corners and solves for the distortion coefficients involved in the camera. Through multi-tag collaboration, it can achieve global coordinate unification and stitching calibration of multi-camera systems.