KFU. Card publication. Evaluation of visual slam methods in usar applications using ros/gazebo simulation

KAZAN
FEDERAL UNIVERSITY

- RUS
- 中文
- ES

EVALUATION OF VISUAL SLAM METHODS IN USAR APPLICATIONS USING ROS/GAZEBO SIMULATION

Form of presentation

Articles in international journals and collections

Year of publication

2021

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Authors, employees of KFU

Lavrenov Roman Olegovich, author

Safin Ramil Nabiullovich, author

Bibliographic description in the original language

Safin R, Lavrenov R, Martinez-Garcia E.A., Evaluation of visual slam methods in usar applications using ros/gazebo simulation//Smart Innovation, Systems and Technologies. - 2021. - Vol.187, Is.. - P.371-382.

Annotation

The problem of determining the position of a robot and at the same time building the map of the environment is referred to as SLAM. A SLAM system generally outputs the estimated trajectory (a sequence of poses) and the map. In practice, it is hard to obtain ground-truth for the map; hence, only trajectory ground-truth is considered. There are various works that provide datasets to evaluate SLAM algorithms in different scenarios including sensor configurations, robots, and environments. Dataset collection in a real-world environment is a complicated task, which requires an elaborate sensor and robot configuration. Different SLAM systems demand various sensors resulting in the problem of finding an appropriate dataset for their evaluation. Thus, in this paper, a solution that is based on ROS/Gazebo simulations is proposed. Two indoor environments with flat and uneven terrain to evaluate laser range and visual SLAM systems are created. Changing the sensor configuration and the environment does not require an elaborate setup. The results of the evaluation for two popular SLAM methods—ORB-SLAM2 and RTAB-Map—are presented.

Keywords

SLAM, ROS, Gazebo, simulation

The name of the journal

Smart Innovation, Systems and Technologies

URL

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091145716&doi=10.1007%2f978-981-15-5580-0_30&partnerID=40&md5=7070300ff0fef3289914f7a5b2562518

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

https://repository.kpfu.ru/eng/?p_id=240254&p_lang=2

Resource files

File name	Size (MB)	Format
Chapter_ZR_2020___Safin.pdf	1,70	pdf	show / download

Full metadata record

Field DC	Value	Language
dc.contributor.author	Lavrenov Roman Olegovich	ru_RU
dc.contributor.author	Safin Ramil Nabiullovich	ru_RU
dc.date.accessioned	2021-01-01T00:00:00Z	ru_RU
dc.date.available	2021-01-01T00:00:00Z	ru_RU
dc.date.issued	2021	ru_RU
dc.identifier.citation	Safin R, Lavrenov R, Martinez-Garcia E.A., Evaluation of visual slam methods in usar applications using ros/gazebo simulation//Smart Innovation, Systems and Technologies. - 2021. - Vol.187, Is.. - P.371-382.	ru_RU
dc.identifier.uri	https://repository.kpfu.ru/eng/?p_id=240254&p_lang=2	ru_RU
dc.description.abstract	Smart Innovation, Systems and Technologies	ru_RU
dc.description.abstract	The problem of determining the position of a robot and at the same time building the map of the environment is referred to as SLAM. A SLAM system generally outputs the estimated trajectory (a sequence of poses) and the map. In practice, it is hard to obtain ground-truth for the map; hence, only trajectory ground-truth is considered. There are various works that provide datasets to evaluate SLAM algorithms in different scenarios including sensor configurations, robots, and environments. Dataset collection in a real-world environment is a complicated task, which requires an elaborate sensor and robot configuration. Different SLAM systems demand various sensors resulting in the problem of finding an appropriate dataset for their evaluation. Thus, in this paper, a solution that is based on ROS/Gazebo simulations is proposed. Two indoor environments with flat and uneven terrain to evaluate laser range and visual SLAM systems are created. Changing the sensor configuration and the environment does not require an elaborate setup. The results of the evaluation for two popular SLAM methods—ORB-SLAM2 and RTAB-Map—are presented.	ru_RU
dc.language.iso	ru	ru_RU
dc.subject	SLAM	ru_RU
dc.subject	ROS	ru_RU
dc.subject	Gazebo	ru_RU
dc.subject	simulation	ru_RU
dc.title	Evaluation of visual slam methods in usar applications using ros/gazebo simulation	ru_RU
dc.type	Articles in international journals and collections	ru_RU