Fusing the RGBD SLAM with Wheel Odometry
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F19%3APU133890" target="_blank" >RIV/00216305:26220/19:PU133890 - isvavai.cz</a>
Výsledek na webu
<a href="https://www.sciencedirect.com/science/article/pii/S2405896319326734" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2405896319326734</a>
DOI - Digital Object Identifier
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Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Fusing the RGBD SLAM with Wheel Odometry
Popis výsledku v původním jazyce
This paper deals with data fusion of existing visual SLAM algorithm and wheel odometry. The result of this connection is the possibility of suppressing measurement error of each position estimation method and creating more accurate 3D model of the examined environment. We have made a brief review of existing visual SLAM projects that are available in open-source domain and as the best option we have choose the Elastic Fusion project and we enriched the existing localization pipeline with another data source that is realized by our high-precision odometry vehicle frame. The modified technique has been validated on three different scenarios. The result is the more robust SLAM algorithm and better precision 3D model of sensed environment compared to original one.
Název v anglickém jazyce
Fusing the RGBD SLAM with Wheel Odometry
Popis výsledku anglicky
This paper deals with data fusion of existing visual SLAM algorithm and wheel odometry. The result of this connection is the possibility of suppressing measurement error of each position estimation method and creating more accurate 3D model of the examined environment. We have made a brief review of existing visual SLAM projects that are available in open-source domain and as the best option we have choose the Elastic Fusion project and we enriched the existing localization pipeline with another data source that is realized by our high-precision odometry vehicle frame. The modified technique has been validated on three different scenarios. The result is the more robust SLAM algorithm and better precision 3D model of sensed environment compared to original one.
Klasifikace
Druh
O - Ostatní výsledky
CEP obor
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OECD FORD obor
20204 - Robotics and automatic control
Návaznosti výsledku
Projekt
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Návaznosti
S - Specificky vyzkum na vysokych skolach
Ostatní
Rok uplatnění
2019
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů