Improving Multi-modal Data Fusion by Anomaly Detection

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F15%3A00223432" target="_blank" >RIV/68407700:21230/15:00223432 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/s10514-015-9431-6" target="_blank" >http://dx.doi.org/10.1007/s10514-015-9431-6</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s10514-015-9431-6" target="_blank" >10.1007/s10514-015-9431-6</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Improving Multi-modal Data Fusion by Anomaly Detection

Popis výsledku v původním jazyce

If we aim for autonomous navigation of a mobile robot, it is crucial and essential to have proper state estimation of its position and orientation. We already designed a multi-modal data fusion algorithm that combines visual, laser-based, inertial, and odometric modalities in order to achieve robust solution to a general localization problem in challenging Urban Search and Rescue environment. Since different sensory modalities are prone to different nature of errors, and their reliability varies vastlyas the environment changes dynamically, we investigated further means of improving the localization. The common practice related to the EKF-based solutions such as ours is a standard statistical test of the observations-or of its corresponding filter residuals-performed to reject anomalous data that deteriorate the filter performance. In this paper we show how important it is to treat well visual and laser anomalous residuals, especially in multi-modal data fusion systems where the frequ

Název v anglickém jazyce

Improving Multi-modal Data Fusion by Anomaly Detection

Popis výsledku anglicky

If we aim for autonomous navigation of a mobile robot, it is crucial and essential to have proper state estimation of its position and orientation. We already designed a multi-modal data fusion algorithm that combines visual, laser-based, inertial, and odometric modalities in order to achieve robust solution to a general localization problem in challenging Urban Search and Rescue environment. Since different sensory modalities are prone to different nature of errors, and their reliability varies vastlyas the environment changes dynamically, we investigated further means of improving the localization. The common practice related to the EKF-based solutions such as ours is a standard statistical test of the observations-or of its corresponding filter residuals-performed to reject anomalous data that deteriorate the filter performance. In this paper we show how important it is to treat well visual and laser anomalous residuals, especially in multi-modal data fusion systems where the frequ

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

JD - Využití počítačů, robotika a její aplikace

OECD FORD obor

—

Projekt

<a href="/cs/project/GA14-13876S" target="_blank" >GA14-13876S: Strojové vnímání pro dlouhodobou autonomii mobilních robotů</a><br>

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2015

Kód důvěrnosti údajů

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

Název periodika

Autonomous Robots

ISSN

0929-5593

e-ISSN

—

Svazek periodika

39

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

16

Strana od-do

139-154

Kód UT WoS článku

000357652400002

EID výsledku v databázi Scopus

2-s2.0-84937513372