Improving Multi-modal Data Fusion by Anomaly Detection
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Improving Multi-modal Data Fusion by Anomaly Detection
Original language description
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
Czech name
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Czech description
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Classification
Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
JD - Use of computers, robotics and its application
OECD FORD branch
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Result continuities
Project
<a href="/en/project/GA14-13876S" target="_blank" >GA14-13876S: Perception methods for long-term autonomy of mobile robots</a><br>
Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2015
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Autonomous Robots
ISSN
0929-5593
e-ISSN
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Volume of the periodical
39
Issue of the periodical within the volume
2
Country of publishing house
US - UNITED STATES
Number of pages
16
Pages from-to
139-154
UT code for WoS article
000357652400002
EID of the result in the Scopus database
2-s2.0-84937513372