Speaker: 韩德仁教授，北京航空航天大学数学科学学院

Inviter: 中国科学院数学院学术报告会组委

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

Language: Chinese

Time & Venue: 2024.12.27 10:40-11:50 数学院南楼N204（数学院交流报告会）

Abstract: 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 O(1/ε^2) 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.