In this paper, we address the problem of concurrently computing the transformation between multiple, gravity-aligned maps given common point feature observations. In particular, we formulate the problem as a minimization of the distances between shared features expressed with respect to different maps. We show that by marginalizing the maps’ relative positions, the KKT conditions of the resulting minimization problem correspond to a system of multivariate polynomial equations (in the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sin $$\end{document} and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\cos $$\end{document} of the relative yaw angles) which can be solved analytically for the case of a few maps. Furthermore, and to improve the efficiency when considering numerous maps, we present a fast, iterative process for determining the unknown relative map orientations.


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    Title :

    Efficient Alignment of Visual-Inertial Maps


    Additional title:

    Springer Proceedings in Advanced Robotics


    Contributors:

    Conference:

    International Symposium on Experimental Robotics ; 2018 ; Buenos Aires, Argentina November 05, 2018 - November 08, 2018



    Publication date :

    2020-01-23


    Size :

    13 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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