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In this paper we propose a novel observer to solve the problem of visualsimultaneous localization and mapping (SLAM), only using the information from asingle monocular camera and an inertial measurement unit (IMU). The systemstate evolves on the manifold $SE(3)\times \mathbb{R}^{3n}$, on which we designdynamic extensions carefully in order to generate an invariant foliation, suchthat the problem is reformulated into online \emph{constant parameter}identification. Then, following the recently introduced parameterestimation-based observer (PEBO) and the dynamic regressor extension and mixing(DREM) procedure, we provide a new simple solution. A notable merit is that theproposed observer guarantees almost global asymptotic stability requiringneither persistency of excitation nor uniform complete observability, which,however, are widely adopted in most existing works with guaranteed stability.

An almost globally convergent observer for visual SLAM without persistent excitation