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Guaranteed Performance Nonlinear Observer for Simultaneous Localization and Mapping

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Document pages: 7 pages

Abstract: A geometric nonlinear observer algorithm for Simultaneous Localization andMapping (SLAM) developed on the Lie group of mathbb{SLAM} {n} left(3 right) isproposed. The presented novel solution estimates the vehicle s pose (i.e.attitude and position) with respect to landmarks simultaneously positioning thereference features in the global frame. The proposed estimator on manifold ischaracterized by predefined measures of transient and steady-state performance.Dynamically reducing boundaries guide the error function of the system toreduce asymptotically to the origin from its starting position within a largegiven set. The proposed observer has the ability to use the available velocityand feature measurements directly. Also, it compensates for unknown constantbias attached to velocity measurements. Unit-qauternion of the proposedobserver is presented. Numerical results reveal effectiveness of the proposedobserver. Keywords: Nonlinear filter algorithm, Nonlinear observer forSimultaneous Localization and Mapping, Nonlinear estimator, nonlinear SLAMobserver on manifold, nonlinear SLAM filter on matrix Lie Group, observerdesign, asymptotic stability, systematic convergence, Prescribed performancefunction, pose estimation, attitude filter, position filter, feature filter,landmark filter, gradient based SLAM observer, gradient based observer forSLAM, adaptive estimate, SLAM observer, observer SLAM framework, equivariantobserver, inertial vision unit, visual, SLAM filter, SE(3), SO(3).

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