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Fast Covariance Recovery in Incremental Nonlinear Least Square Solvers

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F15%3APU116953" target="_blank" >RIV/00216305:26230/15:PU116953 - isvavai.cz</a>

  • Result on the web

    <a href="http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7139841" target="_blank" >http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7139841</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1109/ICRA.2015.7139841" target="_blank" >10.1109/ICRA.2015.7139841</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Fast Covariance Recovery in Incremental Nonlinear Least Square Solvers

  • Original language description

    Many estimation problems in robotics rely on efficiently solving nonlinear least squares (NLS). For example, it is well known that the simultaneous localisation and mapping (SLAM) problem can be formulated as a maximum likelihood estimation (MLE) and solved using NLS, yielding a mean state vector. However, for many applications recovering only the mean vector is not enough. Data association, active decisions, next best view, are only few of the applications that require fast state covariance recovery. The problem is not simple since, in general, the covariance is obtained by inverting the system matrix and the result is dense. The main contribution of this paper is a novel algorithm for fast incremental covariance update, complemented by a highly efficient implementation of the covariance recovery. This combination yields to two orders of magnitude reduction in computation time, compared to the other state of the art solutions. The proposed algorithm is applicable to any NLS solver implementation, and does not depend on incremental strategies described in our previous papers, which are not a subject of this paper.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

    <a href="/en/project/7E13044" target="_blank" >7E13044: IMPART - Intelligent Management Platform for Advanced Real-Time media processes</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Others

  • Publication year

    2015

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    Proceedings of IEEE International Conference on robotics and Automation

  • ISBN

    978-1-4799-6922-7

  • ISSN

  • e-ISSN

  • Number of pages

    8

  • Pages from-to

    1-8

  • Publisher name

    IEEE Computer Society

  • Place of publication

    Seattle

  • Event location

    Seattle

  • Event date

    May 26, 2015

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article

    000370974904084