| Форма представления | Статьи в зарубежных журналах и сборниках |
| Год публикации | 2020 |
| Язык | английский |
|
Лавренов Роман Олегович, автор
Магид Евгений Аркадьевич, автор
Цой Татьяна Григорьевна, автор
|
|
Мингачев Эльдар Ринатович, автор
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| Библиографическое описание на языке оригинала |
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. |
| Аннотация |
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. |
| Ключевые слова |
Simultaneous localization and mapping, visual SLAM, monocular SLAM, visual odometry, state estimation, path planning, benchmark testing, robot sensing systems |
| Название журнала |
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 |
| Пожалуйста, используйте этот идентификатор, чтобы цитировать или ссылаться на эту карточку |
https://repository.kpfu.ru/?p_id=240257 |
| Файлы ресурса | |
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Полная запись метаданных  |
| Поле DC |
Значение |
Язык |
| dc.contributor.author |
Лавренов Роман Олегович |
ru_RU |
| dc.contributor.author |
Магид Евгений Аркадьевич |
ru_RU |
| dc.contributor.author |
Цой Татьяна Григорьевна |
ru_RU |
| dc.contributor.author |
Мингачев Эльдар Ринатович |
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/?p_id=240257 |
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 |
Статьи в зарубежных журналах и сборниках |
ru_RU |
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