Lollypop: Camera-to-Motion-Capture Calibration Verification with a Reference Target

2026-10-03

AI Summary

This study addresses the susceptibility to post-deployment drift and the difficulty of independent verification in joint motion capture and camera calibration. To this end, it proposes Lollypop, a hybrid reference target that physically couples ArUco visual markers with motion capture reflective markers. By precisely aligning the visual center with the motion capture centroid, this approach establishes a unified cross-modal reference frame. Integrating ArUco detection, motion capture tracking, and reprojection error computation, the proposed target provides a quantifiable means for independently verifying extrinsic parameter stability. Experimental results demonstrate that the system achieves sub-pixel reprojection errors, effectively detects extrinsic perturbations, and accurately quantifies calibration degradation over time.

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.

Problem

• camera-to-mocap calibration
• calibration verification
• calibration drift
• motion capture
• fiducial marker

Innovation

• Camera-to-Motion-Capture Calibration
• Calibration Verification
• Fiducial Reference Target
• ArUco Marker
• Sub-pixel Accuracy