  This paper proposes the incorporation of techniques from
stereophotoclinometry (SPC) into a keypoint-based structure-from-motion (SfM)
system to estimate the surface normal and albedo at detected landmarks to
improve autonomous surface and shape characterization of small celestial bodies
from in-situ imagery. In contrast to the current state-of-the-practice method
for small body shape reconstruction, i.e., SPC, which relies on
human-in-the-loop verification and high-fidelity a priori information to
achieve accurate results, we forego the expensive maplet estimation step and
instead leverage dense keypoint measurements and correspondences from an
autonomous keypoint detection and matching method based on deep learning to
provide the necessary photogrammetric constraints. Moreover, we develop a
factor graph-based approach allowing for simultaneous optimization of the
spacecraft's pose, landmark positions, Sun-relative direction, and surface
normals and albedos via fusion of Sun sensor measurements and image keypoint
measurements. The proposed framework is validated on real imagery of the
Cornelia crater on Asteroid 4 Vesta, along with pose estimation and mapping
comparison against an SPC reconstruction, where we demonstrate precise
alignment to the SPC solution without relying on any a priori camera pose and
topography information or humans-in-the-loop
