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A 3-Component Inverse Depth Parameterization for Particle Filter SLAM

Evren Imre Marie-Odile Berger 1 
1 MAGRIT - Visual Augmentation of Complex Environments
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : The non-Gaussianity of the depth estimate uncertainty degrades the performance of monocular extended Kalman filter SLAM (EKF-SLAM) systems employing a 3-component Cartesian landmark parameterization, especially in low-parallax configurations. Even particle filter SLAM (PF-SLAM) approaches are affected, as they utilize EKF for estimating the map. The inverse depth parameterization (IDP) alleviates this problem through a redundant representation, but at the price of increased computational complexity. The authors show that such a redundancy does not exist in PF-SLAM, hence the performance advantage of the IDP comes almost without an increase in the computational cost.
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Submitted on : Monday, November 2, 2009 - 3:23:47 PM
Last modification on : Thursday, January 20, 2022 - 5:30:24 PM

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Evren Imre, Marie-Odile Berger. A 3-Component Inverse Depth Parameterization for Particle Filter SLAM. 31st annual pattern recognition symposium of the German Association for Pattern Recognition - DAGM 2009, Sep 2009, Jena, Germany. pp.1--10, ⟨10.1007/978-3-642-03798-6_1⟩. ⟨inria-00429327⟩



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