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arXiv · 2308.11444

Adaptive Graduated Non-Convexity for Pose Graph Optimization

Seungwon Choi·Wonseok Kang·Jiseong Chung·Jaehyun Kim·Tae-wan Kim
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Abstract

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

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Seungwon Choi, Wonseok Kang, Jiseong Chung, Jaehyun Kim, Tae-wan Kim. 2023-08-22. Adaptive Graduated Non-Convexity for Pose Graph Optimization. https://arxiv.org/abs/2308.11444

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