  We present a novel approach to robust pose graph optimization based on
Graduated Non-Convexity (GNC). Unlike traditional GNC-based methods, the
proposed approach employs an adaptive shape function using B-spline to optimize
the shape of the robust kernel. This aims to reduce GNC iterations, boosting
computational speed without compromising accuracy. When integrated with the
open-source riSAM algorithm, the method demonstrates enhanced efficiency across
diverse datasets. Accompanying open-source code aims to encourage further
research in this area. https://github.com/SNU-DLLAB/AGNC-PGO
