KFU. Card publication. Comparative Analysis of Monocular SLAM Algorithms Using TUM & EuRoC Benchmarks

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COMPARATIVE ANALYSIS OF MONOCULAR SLAM ALGORITHMS USING TUM & EUROC BENCHMARKS

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

Authors, students KFU

Mingachev Eldar Rinatovich, author

Bibliographic description in the original language

Mingachev E. Comparative Analysis of Monocular SLAM Algorithms Using TUM & EuRoC Benchmarks / Lavrenov R., Magid E., Svinin M. // Smart Innovation, Systems and Technologies. - 2020. - �187. - p. 343-356.

Annotation

Stable and robust path planning and movement in ground mobile robots require a combination of accuracy and low latency in their state estimation. However, state estimation algorithms must provide these qualities under the computational and power constraints of embedded hardware. Simultaneous localization and mapping (SLAM) algorithms are the best choices for state estimation in these scenarios, in addition to their ability to operate without external localization from motion capture or global positioning systems. Moreover, a single-camera setup is the most common solution for robotic platforms, which reduces our domain of interest to the specific SLAM algorithms type—monocular SLAM. Yet, it is still not clear from the existing literature, which monocular SLAM algorithms perform well under the accuracy, latency, and computational constraints of a ground mobile robot with onboard state estimation. This paper evaluates an array of the most recent publicly available monocular SLAM methods: ORB-SLAM2, DSO, and LDSO. The evaluation considers the pose estimation accuracy (alignment error, absolute trajectory error, and relative pose error) while processing the TUMMono and EuRoC datasets on the specific hardware platform with a balanced amount of computational resources and power consumption. We present our complete results as a benchmark for the research community.

Keywords

SLAM algoritms, visual SLAM, comparation

The name of the journal

Smart Innovation, Systems and Technologies

URL

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091177868&doi=10.1007%2f978-981-15-5580-0_28&partnerID=40&md5=407289d9fc461b8aac254051eb39b79d

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

https://repository.kpfu.ru/eng/?p_id=240253&p_lang=2

Resource files

File name	Size (MB)	Format
Chapter_ZR_2020___Mingachev.pdf	0,52	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	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. Comparative Analysis of Monocular SLAM Algorithms Using TUM & EuRoC Benchmarks / Lavrenov R., Magid E., Svinin M. // Smart Innovation, Systems and Technologies. - 2020. - �187. - p. 343-356.	ru_RU
dc.identifier.uri	https://repository.kpfu.ru/eng/?p_id=240253&p_lang=2	ru_RU
dc.description.abstract	Smart Innovation, Systems and Technologies	ru_RU
dc.description.abstract	Stable and robust path planning and movement in ground mobile robots require a combination of accuracy and low latency in their state estimation. However, state estimation algorithms must provide these qualities under the computational and power constraints of embedded hardware. Simultaneous localization and mapping (SLAM) algorithms are the best choices for state estimation in these scenarios, in addition to their ability to operate without external localization from motion capture or global positioning systems. Moreover, a single-camera setup is the most common solution for robotic platforms, which reduces our domain of interest to the specific SLAM algorithms type—monocular SLAM. Yet, it is still not clear from the existing literature, which monocular SLAM algorithms perform well under the accuracy, latency, and computational constraints of a ground mobile robot with onboard state estimation. This paper evaluates an array of the most recent publicly available monocular SLAM methods: ORB-SLAM2, DSO, and LDSO. The evaluation considers the pose estimation accuracy (alignment error, absolute trajectory error, and relative pose error) while processing the TUMMono and EuRoC datasets on the specific hardware platform with a balanced amount of computational resources and power consumption. We present our complete results as a benchmark for the research community.	ru_RU
dc.language.iso	ru	ru_RU
dc.subject	SLAM algoritms	ru_RU
dc.subject	visual SLAM	ru_RU
dc.subject	comparation	ru_RU
dc.title	Comparative Analysis of Monocular SLAM Algorithms Using TUM & EuRoC Benchmarks	ru_RU
dc.type	Articles in international journals and collections	ru_RU