KFU. Card publication. Comparison of ROS-Based Monocular Visual SLAM Methods: DSO, LDSO, ORB-SLAM2 and DynaSLAM

KAZAN
FEDERAL UNIVERSITY

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- 中文
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COMPARISON OF ROS-BASED MONOCULAR VISUAL SLAM METHODS: DSO, LDSO, ORB-SLAM2 AND DYNASLAM

Form of presentation

Articles in international journals and collections

Year of publication

2020

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Authors, employees of KFU

Lavrenov Roman Olegovich, author

Magid Evgeniy Arkadevich, author

Coy Tatyana Grigorevna, author

Authors, students KFU

Mingachev Eldar Rinatovich, author

Bibliographic description in the original language

Mingachev E. Comparison of ROS-based monocular visual SLAM methods: DSO, LDSO, ORB-SLAM2 & DynaSLAM / Lavrenov R., Tsoy T., Matsuno F., Svinin M., Suthakorn J., Magid E. // Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). - 2020. - �12336. - p. 222-233.

Annotation

Stable and robust path planning of a ground mobile robot requires a combination of accuracy and low latency in its state estimation. Yet, state estimation algorithms should provide these under computational and power constraints of a robot embedded hardware. The presented study offers a comparative analysis of four cutting edge publicly available within robot operating system (ROS) monocular simultaneous localization and mapping methods: DSO, LDSO, ORB-SLAM2, and DynaSLAM. The analysis considers pose estimation accuracy (alignment, absolute trajectory, and relative pose root mean square error) and trajectory precision of the four methods at TUM-Mono and EuRoC datasets.

Keywords

Simultaneous localization and mapping, visual SLAM, monocular SLAM, visual odometry, state estimation, path planning, benchmark testing, robot sensing systems

The name of the journal

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

URL

https://link.springer.com/chapter/10.1007/978-3-030-60337-3_22

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

https://repository.kpfu.ru/eng/?p_id=240257&p_lang=2

Resource files

File name	Size (MB)	Format
F_ICR_2020_Mingachev.pdf	1,08	pdf	show / download

Full metadata record

Field DC	Value	Language
dc.contributor.author	Lavrenov Roman Olegovich	ru_RU
dc.contributor.author	Magid Evgeniy Arkadevich	ru_RU
dc.contributor.author	Coy Tatyana Grigorevna	ru_RU
dc.contributor.author	Mingachev Eldar Rinatovich	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	Mingachev E. Comparison of ROS-based monocular visual SLAM methods: DSO, LDSO, ORB-SLAM2 & DynaSLAM / Lavrenov R., Tsoy T., Matsuno F., Svinin M., Suthakorn J., Magid E. // Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). - 2020. - �12336. - p. 222-233.	ru_RU
dc.identifier.uri	https://repository.kpfu.ru/eng/?p_id=240257&p_lang=2	ru_RU
dc.description.abstract	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	ru_RU
dc.description.abstract	Stable and robust path planning of a ground mobile robot requires a combination of accuracy and low latency in its state estimation. Yet, state estimation algorithms should provide these under computational and power constraints of a robot embedded hardware. The presented study offers a comparative analysis of four cutting edge publicly available within robot operating system (ROS) monocular simultaneous localization and mapping methods: DSO, LDSO, ORB-SLAM2, and DynaSLAM. The analysis considers pose estimation accuracy (alignment, absolute trajectory, and relative pose root mean square error) and trajectory precision of the four methods at TUM-Mono and EuRoC datasets.	ru_RU
dc.language.iso	ru	ru_RU
dc.subject	Simultaneous localization and mapping	ru_RU
dc.subject	visual SLAM	ru_RU
dc.subject	monocular SLAM	ru_RU
dc.subject	visual odometry	ru_RU
dc.subject	state estimation	ru_RU
dc.subject	path planning	ru_RU
dc.subject	benchmark testing	ru_RU
dc.subject	robot sensing systems	ru_RU
dc.title	Comparison of ROS-Based Monocular Visual SLAM Methods: DSO, LDSO, ORB-SLAM2 and DynaSLAM	ru_RU
dc.type	Articles in international journals and collections	ru_RU