Fast Covariance Recovery in Incremental Nonlinear Least Square Solvers

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Fast Covariance Recovery in Incremental Nonlinear Least Square Solvers

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Fast Covariance Recovery in Incremental Nonlinear Least Square Solvers

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

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

Projekt

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

Návaznosti

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

Rok uplatnění

2015

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of IEEE International Conference on robotics and Automation

ISBN

978-1-4799-6922-7

ISSN

—

e-ISSN

—

Počet stran výsledku

8

Strana od-do

1-8

Název nakladatele

IEEE Computer Society

Místo vydání

Seattle

Místo konání akce

Seattle

Datum konání akce

26. 5. 2015

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000370974904084