KFU. Card publication. Virtual Experiments on ArUco and AprilTag Systems Comparison for Fiducial Marker Rotation Resistance under Noisy Sensory Data

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VIRTUAL EXPERIMENTS ON ARUCO AND APRILTAG SYSTEMS COMPARISON FOR FIDUCIAL MARKER ROTATION RESISTANCE UNDER NOISY SENSORY DATA

Form of presentation

Conference proceedings in international journals and collections

Year of publication

2020

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Authors, employees of KFU

Zakiev Aufar Azatovich, author

Magid Evgeniy Arkadevich, author

Coy Tatyana Grigorevna, author

Shabalina Kseniya Sergeevna, author

Bibliographic description in the original language

Zakiev A., Tsoy T., Shabalina K., Magid E., Saha S.K. Virtual Experiments on ArUco and AprilTag Systems Comparison for Fiducial Marker Rotation Resistance under Noisy Sensory Data // Proceedings of the International Joint Conference on Neural Networks (IJCNN) (Glasgow, Scotland; 19-24 July 2020) - p. 1-6.

Annotation

Proceedings of the International Joint Conference on Neural Networks

Keywords

experimental, comparison, fiducial marker system, Gazebo, recognition algorithms, robotics, ROS

The name of the journal

Proceedings of the International Joint Conference on Neural Networks

URL

https://ieeexplore.ieee.org/document/9207701

Please use this ID to quote from or refer to the card

https://repository.kpfu.ru/eng/?p_id=248648&p_lang=2

Resource files

File name	Size (MB)	Format
F_2020_Virtual_Experiments_on_ArUco_and_AprilTag_Systems_Comparison_for_Fiducial_Marker_Rotation.pdf	4,12	pdf	show / download

Full metadata record

Field DC	Value	Language
dc.contributor.author	Zakiev Aufar Azatovich	ru_RU
dc.contributor.author	Magid Evgeniy Arkadevich	ru_RU
dc.contributor.author	Coy Tatyana Grigorevna	ru_RU
dc.contributor.author	Shabalina Kseniya Sergeevna	ru_RU
dc.date.accessioned	2020-01-01T00:00:00Z	ru_RU
dc.date.available	2020-01-01T00:00:00Z	ru_RU
dc.date.issued	2020	ru_RU
dc.identifier.citation	Zakiev A., Tsoy T., Shabalina K., Magid E., Saha S.K. Virtual Experiments on ArUco and AprilTag Systems Comparison for Fiducial Marker Rotation Resistance under Noisy Sensory Data // Proceedings of the International Joint Conference on Neural Networks (IJCNN) (Glasgow, Scotland; 19-24 July 2020) - p. 1-6.	ru_RU
dc.identifier.uri	https://repository.kpfu.ru/eng/?p_id=248648&p_lang=2	ru_RU
dc.description.abstract	Proceedings of the International Joint Conference on Neural Networks	ru_RU
dc.description.abstract	Modern robotic researches propose various machine vision methods for accomplishing robotic tasks. The recognition quality in these tasks is very important for successful performance. A large number of them use fiducial marker systems as a main element of algorithms. However, only a few researches are comparing standard marker systems. This paper is dedicated to the comparison of AprilTag and ArUco markers resistance to rotations in the presence of synthetic noise. Experiments were conducted in ROS/Gazebo virtual environment in order to provide a fair comparison of marker detection and recognition algorithms while eliminating external environment conditions that influence the algorithms' performance. The presented virtual environments allow collecting a significant amount of data by experiment process automation. Different levels of additive white Gaussian noise were applied to input sensory data in order to simulate the imperfection of real digital cameras. The main contribution of the paper is the systematic comparison of AprilTag and ArUco markers for rotation resistance in the presence of optical sensor noise.	ru_RU
dc.language.iso	ru	ru_RU
dc.subject	experimental	ru_RU
dc.subject	comparison	ru_RU
dc.subject	fiducial marker system	ru_RU
dc.subject	Gazebo	ru_RU
dc.subject	recognition algorithms	ru_RU
dc.subject	robotics	ru_RU
dc.subject	ROS	ru_RU
dc.title	Virtual Experiments on ArUco and AprilTag Systems Comparison for Fiducial Marker Rotation Resistance under Noisy Sensory Data	ru_RU
dc.type	Conference proceedings in international journals and collections	ru_RU