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

翻译：我们提出了一种基于逐步非凸性（Graduated Non-Convexity, GNC）的鲁棒位姿图优化新方法。与传统的基于GNC的方法不同，所提方法采用基于B样条的自适应形状函数来优化鲁棒核的形状。这旨在减少GNC迭代次数，在不牺牲精度的情况下提升计算速度。当与开源riSAM算法集成时，该方法在多种数据集上展现出更高的效率。随附的开源代码旨在促进该领域的进一步研究。https://github.com/SNU-DLLAB/AGNC-PGO