Missile Pose Measurement Based on ArUco Markers

This paper presents a monocular vision-based system for high-precision missile pose measurement using ArUco markers and Perspective-n-Point (PnP) algorithms. By deploying 6 × 6 ArUco markers on a cylindrical missile mock-up, the system establishes 3D-2D correspondences between structured-light-scanned models and camera images to solve the PnP problem. The proposed approach integrates optimized ArUco marker recognition — leveraging adaptive thresholding, contour simplification, and grid-based validation — with the Efficient PnP (EPnP) algorithm to achieve real-time pose estimation. Experimental validation demonstrates angular accuracy of ± 0.3° in roll/pitch/yaw and positional accuracy of ± 2 mm within a 2 m range under controlled conditions. The system exhibits robustness against partial occlusions and motion blur, with degraded performance (± 1.2°, ± 5 mm) in extreme scenarios. Key innovations include a streamlined marker detection pipeline and adaptive pose refinement using Levenberg-Marquardt optimization. This work provides a cost-effective, non-contact solution for flight tests, with potential applications in weapon separation testing.