PEBO-SLAM: Observer Design for Visual Inertial SLAM With Convergence Guarantees

• 成果类型: Article
• 署名作者: Yi, Bowen; Jin, Chi; Wang, Lei; Shi, Guodong; Ila, Viorela; Manchester, Ian R.
• 署名单位: Universite de Montreal; Polytechnique Montreal; Universite de Montreal; Zhejiang University; University of Sydney
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2024.3466874
• 发表日期: 2025
• 页码: 1714-1729
• 关键词: Simultaneous localization and mapping; observers; Smoothing methods; robots; Visualization; trajectory; optimization; Least Squares; nonlinear observer; parameter estimation-based observer (PEBO); simultaneous localization and mapping (SLAM)

摘要

In this article, we introduce a new parameterization for the problem of visual inertial simultaneous localization and mapping (VI-SLAM), i.e., for a robot only equipped with a single monocular camera and an inertial measurement unit. In this problem, the system state evolves on the nonlinear manifold SE(3)xR(3n), on which we design dynamic extensions such that the deterministic VI-SLAM problem can be reformulated-without any approximation-into online constant parameter identification, expressed as a linear regression. This demonstrates that deterministic VI-SLAM can be translated into a linear least squares problem globally and exactly. Based on this observation, we propose a novel SLAM observer, following the recently established parameter estimation-based observer methodology. A notable merit of the proposed observer is its almost global asymptotic stability. Unlike most existing methods, its convergence does not rely on persistency of excitation or uniform complete observability-assumptions commonly used in stability proofs that can be challenging to satisfy in real-world applications.