World-Base Calibration by Global Polynomial Optimization

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F15%3A00226740" target="_blank" >RIV/68407700:21230/15:00226740 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/3DV.2014.78" target="_blank" >http://dx.doi.org/10.1109/3DV.2014.78</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/3DV.2014.78" target="_blank" >10.1109/3DV.2014.78</a>

Jazyk výsledku

angličtina

Název v původním jazyce

World-Base Calibration by Global Polynomial Optimization

Popis výsledku v původním jazyce

This paper presents a novel solution to the world-base calibration problem. It is applicable in situations where a known calibration target is observed by a camera attached to the end effector of a robotic manipulator. The presented method works by minimizing geometrically meaningful error function based on image projections. Our formulation leads to a non-convex multivariate polynomial optimization problem of a constant size. However, we show how such a problem can be relaxed using linear matrix inequality (LMI) relaxations and effectively solved using Semi definite Programming. Although the technique of LMI relaxations guaranties only a lower bound on the global minimum of the original problem, it can provide a certificate of optimality in cases whenthe global minimum is reached. Indeed, we reached the global minimum for all calibration tasks in our experiments with both synthetic and real data. The experiments also show that the presented method is fast and noise resistant.

Název v anglickém jazyce

World-Base Calibration by Global Polynomial Optimization

Popis výsledku anglicky

This paper presents a novel solution to the world-base calibration problem. It is applicable in situations where a known calibration target is observed by a camera attached to the end effector of a robotic manipulator. The presented method works by minimizing geometrically meaningful error function based on image projections. Our formulation leads to a non-convex multivariate polynomial optimization problem of a constant size. However, we show how such a problem can be relaxed using linear matrix inequality (LMI) relaxations and effectively solved using Semi definite Programming. Although the technique of LMI relaxations guaranties only a lower bound on the global minimum of the original problem, it can provide a certificate of optimality in cases whenthe global minimum is reached. Indeed, we reached the global minimum for all calibration tasks in our experiments with both synthetic and real data. The experiments also show that the presented method is fast and noise resistant.

Druh

D - Stať ve sborníku

CEP obor

JD - Využití počítačů, robotika a její aplikace

OECD FORD obor

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Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

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

3DV 2014: International Conference on 3D Vision

ISBN

978-1-4799-7001-8

ISSN

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e-ISSN

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Počet stran výsledku

8

Strana od-do

593-600

Název nakladatele

IEEE Computer Society Press

Místo vydání

Los Alamitos

Místo konání akce

Tokyo

Datum konání akce

8. 12. 2014

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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