Accurate Estimation of Fiducial Marker Positions Using Motion Capture System

In this paper, we present a method for aligning the coordinates of multiple cameras and sensors into a unified coordinate system using a motion capture system. Our simulated convenience store environment includes cameras and sensors with distinct coordinate systems, necessitating coordinate alignment. The motion capture system identifies retroreflective markers, while other cameras detect fiducial markers for position and orientation determination. Three optimization algorithms are experimented with to compute a transformation matrix aligning camera coordinates to motion capture coordinates, with the Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm achieving the best results (average errors of 1.13 centimeters and 3.90 degrees). Comparisons with fiducial marker pose estimation using an open-source Pupil Core software indicate our method is more robust and consistent, with lower repeatability errors. Additionally, we examine the estimation errors in relation to the distances of the fiducial markers from the camera to minimize these errors, enhancing installation accuracy of cameras and sensors in our simulated environment. This approach enables precise determination of positions and orientations across integrated cameras, consistent with the motion capture system. The findings contribute to our ongoing project, which requires an accurate system integration for customer behavior analysis.