Refinement of Asteroid Rotation Parameters via Stereo Intersection Angle Optimization and Masked Feature Matching

Abstract. Determining accurate asteroid rotation parameters is essential for establishing a body-fixed coordinate system during deep-space proximity operations. However, early mission phases often lack prior exterior orientation data. When combined with edge effects from the deep-space background, this deficiency leads to high mismatch rates and ill-conditioned geometries. In this paper, we propose an integrated photogrammetric pipeline to refine these parameters using adaptive feature masking and stereo intersection angle optimization. Rather than using conventional matching, our approach isolates the asteroid target via an adaptive grayscale-threshold mask and morphological refinement, which restricts feature extraction to valid surface textures and significantly reduces outliers. We then introduce a geometric filter to discard stereo pairs with intersection angles below 5°, effectively preventing error propagation along the line of sight. Ultimately, the precise Right Ascension (RA) and Declination (Dec) are determined through an iterative coarse-to-fine grid search that minimizes spatial intersection residuals. Testing our method on 127 images of asteroid (162173) Ryugu from the Hayabusa2 ONC-T camera yielded strong results. The masking strategy successfully cut the number of mismatches in half (from 14,949 to 7,369). Within four iterations, the rotational parameters converged to RA = 96.5° and Dec = -66.4°. These refined results offer a reliable foundation for subsequent 3D reconstruction and high-precision planetary mapping.