Template Matching Used for Small Body Optical Navigation with Poorly Detailed Objects

Abstract

Object and template matching becomes difficult when an image lacks detail. This is particularly worrisome when typical matching techniques, cross-correlation, log-polar mapping, and key point matching fail. Work herein describes a formulation that identifies objects of interest, estimates the affine transformation between a template object and scene using Principal Component Analysis (PCA), and provides a fit value for the objects and template incorporating Hu's Moments. The algorithm presented is tested on synthetic images and images obtained from the OSIRIS-REx mission while the spacecraft was approaching its target, Bennu. Results for the current formulation show that, with the presence of large-scale variations and rotation, the fitting scheme performs well when compared with other techniques