Lollypop verifies camera and motion capture alignment with high precision
Lollypop: Camera-to-Motion-Capture Calibration Verification with a Reference Target
Computer Vision and Pattern Recognition
Summary
When using motion capture cameras and regular cameras together, it's important to make sure they stay perfectly aligned. The authors created Lollypop, a special marker that can be seen both by motion capture sensors and regular cameras at the same physical point. This helps check whether the two systems are still properly calibrated by comparing where the motion capture system says the point is versus where the camera sees it. Their tests show very small errors and can detect when the systems become misaligned over time.
What this means in practice
- •For robotics developers: Check and maintain accurate alignment between motion capture and camera systems to ensure reliable ground truth data.
- •For augmented reality engineers: Verify ongoing calibration between cameras and motion capture setups to support accurate AR tracking during device use.
Authors
Tianyi Liu, Kevin Harris, Mihika Dave, Kun He
Abstract
Camera-to-motion-capture (mocap) calibration is essential for using mocap as ground truth in robotics, AR/VR, and other computer vision tasks. However, the calibration can drift after deployment, while calibration residuals and visual inspection provide limited independent verification. We present Lollypop, a fiducial-mocap reference target for independent calibration verification. The target couples an ArUco fiducial with a mocap marker constellation so the visual center and tracked centroid represent the same physical point. Given a candidate calibration, verification projects the mocap point into the image and measures its disagreement with the detected fiducial center. Experiments show sub-pixel nominal error, sensitivity to controlled extrinsic perturbations, and increasing error during an illustrative mixed-handling sequence.