Robust template feature matching method using motion-constrained DCF designed for visual navigation in asteroid landing

Abstract

A robust and efficient feature matching method is necessary for visual navigation in asteroid-landing missions. Based on the visual navigation framework and motion characteristics of asteroids, a robust and efficient template feature matching method is proposed to adapt to feature distortion and scale change cases for visual navigation of asteroids. The proposed method is primarily based on a motion-constrained discriminative correlation filter (DCF). The prior information provided by the motion constraints between sequence images is used to provide a predicted search region for template feature matching. Additionally, some specific template feature samples are generated using the motion constraints for correlation filter learning, which is beneficial for training a scale and feature distortion adaptive correlation filter for accurate feature matching. Moreover, average peak-to-correlation energy (APCE) and jointly consistent measurements (JCMs) were used to eliminate false matching. Images captured by the Touch And Go Camera System (TAGCAMS) of the Bennu asteroid were used to evaluate the performance of the proposed method. In particular, both the robustness and accuracy of region matching and template center matching are evaluated. The qualitative and quantitative results illustrate the advancement of the proposed method in adapting to feature distortions and large-scale changes during spacecraft landing.