中国科学院数学与系统科学研究院

科研进展与学术交流报告会

（第93期）

报告人：韩德仁 教授（北京航空航天大学）

题 目：Non-convex Pose Graph Optimization in SLAM via Proximal Linearized Riemannian ADMM

摘 要：Pose graph optimization (PGO) is a well-known technique for solving the pose-based simultaneous localization and mapping (SLAM) problem. In this paper, we represent the rotation and translation by a unit quaternion and a three-dimensional vector, and propose a new PGO model based on the von Mises-Fisher distribution. The constraints derived from the unit quaternions are spherical manifolds, and the projection onto the constraints can be calculated by normalization. Then a proximal linearized Riemannian alternating direction method of multipliers (PieADMM) is developed to solve the proposed model, which not only has low memory requirements, but also can update the poses in parallel. Furthermore, we establish the iteration complexity of of PieADMM for finding an ϵ-stationary solution of our model. The efficiency of our proposed algorithm is demonstrated by numerical experiments on two synthetic and four 3D SLAM benchmark datasets.

时  间：2024.12.27(星期五), 10:40-11:50

地  点：南楼204会议室，腾讯会议地址：534-8268-8299