In this paper we propose a novel observer to solve the problem of visual simultaneous localization and mapping (SLAM), only using the information from a single monocular camera and an inertial measurement unit (IMU). The system state evolves on the manifold $SE(3)\times \mathbb{R}^{3n}$, on which we design dynamic extensions carefully in order to generate an invariant foliation, such that the problem is reformulated into online \emph{constant parameter} identification. Then, following the recently introduced parameter estimation-based observer (PEBO) and the dynamic regressor extension and mixing (DREM) procedure, we provide a new simple solution. A notable merit is that the proposed observer guarantees almost global asymptotic stability requiring neither persistency of excitation nor uniform complete observability, which, however, are widely adopted in most existing works with guaranteed stability.

翻译：在本文中,我们提出一个新的观察家来解决视觉同步定位和绘图问题(SLAM ), 仅使用单一单镜相机和惯性测量单位(IMU)提供的信息。 系统状态在元值$SE(3)\time \ mathbb{R ⁇ 3n}$(mathb{R ⁇ 3n}$)上演化。 我们仔细设计动态扩展,以产生一种无差异的变换,从而将问题重新改写为在线的\emph{contaant参数识别。 然后,在最近引入的参数估算观察家(PEBO)和动态递增和混合(DREM)程序之后,我们提供了一个新的简单解决方案。 一个显著的优点是,拟议的观察家可以保证几乎全球的静态稳定,既不需要持续的刺激,也不需要统一的完全可观察性,但大多数现有工作都广泛采用这种稳定。