Estimating OpenCV ArUco performance from linear camera and scene parameters

An efficient means to determine a camera’s location in a volume is to incorporate fiducials at known locations in the volume and triangulate their found locations. ArUco markers are an efficient means to use in this model because there are many pre-defined means to locate ArUco markers through open source software like OpenCV. The algorithms that are used to determine if an ArUco marker is found is not always well characterized, yet there will always be an output from the algorithm that states in a definitive that either an ArUco target was found or not and if it is found it was found at this location. There are many parameters that affect the results of an accurate detection and calculated pose estimation, including system blur, image entropy, input illumination, additional camera attributes, the size of marker, its orientation, and its distance. Because each of these variables impacts the detection algorithm, each variable space must be tested to determine the operating bounds for a given set of ArUco markers. This correspondence demonstrates a method to quantify the ArUco detection performance based upon a simulation that separates each of the previously defined variables. Using virtually constructed imagery that simulates these effects, it is possible to create a sufficiently large data set that can give a definitive performance for ArUco target detection as a function of the OpenCV algorithm.