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Register a new userAn almost globally convergent observer for visual SLAM without persistent excitation
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Added by
Bowen Yi
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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In this paper we propose a novel observer to solve the problem of visual simultaneous localization and mapping, using the information of only the bearing vectors of landmarks observed from a single monocular camera and body-fixed velocities. 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 parameter identification. Then, following the recently introduced parameter estimation-based observer, we provide a novel and simple solution to address the problem. A notable merit is that the proposed observer guarantees almost global asymptotic stability requiring neither persistent excitation nor uniform complete observability, which, however, are widely adopted in the existing works.
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State estimation problems without absolute position measurements routinely arise in navigation of unmanned aerial vehicles, autonomous ground vehicles, etc., whose proper operation relies on accurate state estimates and reliable covariances. Unaware of absolute positions, these problems have immanent unobservable directions. Traditional causal estimators, however, usually gain spurious information on the unobservable directions, leading to over-confident covariance inconsistent with actual estimator errors. The consistency problem of fixed-lag smoothers (FLSs) has only been attacked by the first estimate Jacobian (FEJ) technique because of the complexity to analyze their observability property. But the FEJ has several drawbacks hampering its wide adoption. To ensure the consistency of a FLS, this paper introduces the right invariant error formulation into the FLS framework. To our knowledge, we are the first to analyze the observability of a FLS with the right invariant error. Our main contributions are twofold. As the first novelty, to bypass the complexity of analysis with the classic observability matrix, we show that observability analysis of FLSs can be done equivalently on the linearized system. Second, we prove that the inconsistency issue in the traditional FLS can be elegantly solved by the right invariant error formulation without artificially correcting Jacobians. By applying the proposed FLS to the monocular visual inertial simultaneous localization and mapping (SLAM) problem, we confirm that the method consistently estimates covariance similarly to a batch smoother in simulation and that our method achieved comparable accuracy as traditional FLSs on real data.
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