3,050 research outputs found
A Global Asymptotic Convergent Observer for SLAM
This paper examines the global convergence problem of SLAM algorithms, an
issue that faces topological obstructions. This is because the state-space of
attitude dynamics is defined on a non-contractible manifold: the special
orthogonal group of order three SO(3). Therefore, this paper presents a novel,
gradient-based hybrid observer to overcome these topological obstacles. The
Lyapunov stability theorem is used to prove the globally asymptotic convergence
of the proposed algorithm. Finally, comparative analyses of two simulations
were conducted to evaluate the performance of the proposed scheme and to
demonstrate the superiority of the proposed hybrid observer to a smooth
observer.Comment: 7 pages, 8 figures, conferenc
PEBO-SLAM: Observer design for visual inertial SLAM with convergence guarantees
This paper introduces a new linear parameterization to the problem of visual
inertial simultaneous localization and mapping (VI-SLAM) -- without any
approximation -- for the case only using information from a single monocular
camera and an inertial measurement unit. In this problem set, the system state
evolves on the nonlinear manifold , on which we
design dynamic extensions carefully to generate invariant foliations, such that
the problem can be reformulated into online \emph{constant parameter}
identification, then interestingly with linear regression models obtained. It
demonstrates that VI-SLAM can be translated into a linear least squares
problem, in the deterministic sense, \emph{globally} and \emph{exactly}. Based
on this observation, we propose a novel SLAM observer, following the recently
established parameter estimation-based observer (PEBO) methodology. A notable
merit is that the proposed observer enjoys almost global asymptotic stability,
requiring neither persistency of excitation nor uniform complete observability,
which, however, are widely adopted in most existing works with provable
stability but can hardly be assured in many practical scenarios
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