About: Sensor fusion for prediction of orientation and position from obstacle using multiple IR sensors

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About: Sensor fusion for prediction of orientation and position from obstacle using multiple IR sensors - an approach based on Kalman Filter Goto Sponge NotDistinct Permalink

An Entity of Type : http://linked.opendata.cz/ontology/domain/vavai/Vysledek, within Data Space : linked.opendata.cz associated with source document(s)

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rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
Description	Kalman filters have gained immense research attention in robotics, throughout the last decades. Among the applications, localization of robots through Kalman filters proved promising results. This paper presents an application of sensor fusion for prediction of orientation and depth to wall/obstacle by fusing the inputs from three IR range finders. The experimental result demonstrates the capability of Kalman filter to predict the parameters precisely, from noisy sensor inputs. The technique finds application in determining the position and orientation from the wall which will be helpful in obstacle avoidance decision making, automatic parking of automobiles etc. Kalman filters have gained immense research attention in robotics, throughout the last decades. Among the applications, localization of robots through Kalman filters proved promising results. This paper presents an application of sensor fusion for prediction of orientation and depth to wall/obstacle by fusing the inputs from three IR range finders. The experimental result demonstrates the capability of Kalman filter to predict the parameters precisely, from noisy sensor inputs. The technique finds application in determining the position and orientation from the wall which will be helpful in obstacle avoidance decision making, automatic parking of automobiles etc. (en)
Title	Sensor fusion for prediction of orientation and position from obstacle using multiple IR sensors - an approach based on Kalman Filter Sensor fusion for prediction of orientation and position from obstacle using multiple IR sensors - an approach based on Kalman Filter (en)
skos:prefLabel	Sensor fusion for prediction of orientation and position from obstacle using multiple IR sensors - an approach based on Kalman Filter Sensor fusion for prediction of orientation and position from obstacle using multiple IR sensors - an approach based on Kalman Filter (en)
skos:notation	RIV/00216275:25530/14:39899025!RIV15-MSM-25530___
http://linked.open...avai/riv/aktivita	S
http://linked.open...avai/riv/aktivity	S
http://linked.open...vai/riv/dodaniDat	2015
http://linked.open...aciTvurceVysledku	Dušek, František Honc, Daniel Sharma, Rahul
http://linked.open.../riv/druhVysledku	D - Článek ve sborníku
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	Univerzita Pardubice / Fakulta elektrotechniky a informatiky
http://linked.open...dnocenehoVysledku	44470
http://linked.open...ai/riv/idVysledku	RIV/00216275:25530/14:39899025
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	prediction; obstacle avoidance; Kalman filter; localization (en)
http://linked.open.../riv/klicoveSlovo	obstacle avoidance Kalman filter prediction localization
http://linked.open...ontrolniKodProRIV	[CFAD7BD6669D]
http://linked.open...v/mistoKonaniAkce	Plzeň
http://linked.open...i/riv/mistoVydani	Plzeň
http://linked.open...i/riv/nazevZdroje	Proceedings of 19th International Conference on Applied Electronics 2014
http://linked.open...in/vavai/riv/obor	BC
http://linked.open...ichTvurcuVysledku	3 (xsd:int)
http://linked.open...cetTvurcuVysledku	3 (xsd:int)
http://linked.open...UplatneniVysledku	2014
http://linked.open...iv/tvurceVysledku	Dušek, František Honc, Daniel Sharma, Rahul
http://linked.open...vavai/riv/typAkce	EUR - Evropská
http://linked.open.../riv/zahajeniAkce	2014-09-09 (xsd:date)
number of pages	4 (xsd:int)
http://bibframe.org/vocab/doi	10.1109/AE.2014.7011716
http://purl.org/ne...btex#hasPublisher	Západočeská univerzita v Plzni
https://schema.org/isbn	978-80-261-0276-2
http://localhost/t...ganizacniJednotka	25530

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