MIS-SLAM: Real-Time Large-Scale Dense Deformable SLAM System in Minimal Invasive Surgery Based on Heterogeneous Computing

Song, J; Wang, J; Zhao, L; Huang, S; Dissanayake, G

MIS-SLAM: Real-Time Large-Scale Dense Deformable SLAM System in Minimal Invasive Surgery Based on Heterogeneous Computing

Song, J

Wang, J Zhao, L

Huang, S

Dissanayake, G

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Publication Type:: Journal Article
Citation:: IEEE Robotics and Automation Letters, 2018, 3 (4), pp. 4068 - 4075
Issue Date:: 2018-10-01

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Field	Value	Language
dc.contributor.author	Song, J https://orcid.org/0000-0003-4438-4627	en_US
dc.contributor.author	Wang, J	en_US
dc.contributor.author	Zhao, L https://orcid.org/0000-0003-4063-8183	en_US
dc.contributor.author	Huang, S https://orcid.org/0000-0002-6124-4178	en_US
dc.contributor.author	Dissanayake, G https://orcid.org/0000-0002-7992-0680	en_US
dc.date.available	2020-10-01T19:01:27Z
dc.date.issued	2018-10-01	en_US
dc.identifier.citation	IEEE Robotics and Automation Letters, 2018, 3 (4), pp. 4068 - 4075	en_US
dc.identifier.uri	http://hdl.handle.net/10453/130107
dc.description.abstract	© 2016 IEEE. Real-time simultaneous localization and dense mapping is very helpful for providing virtual reality and augmented reality for surgeons or even surgical robots. In this letter, we propose MIS-SLAM: A complete real-time large-scale dense deformable SLAM system with stereoscope in minimal invasive surgery (MIS) based on heterogeneous computing by making full use of CPU and GPU. Idled CPU is used to perform ORB-SLAM for providing robust global pose. Strategies are taken to integrate modules from CPU and GPU. We solved the key problem raised in the previous work, that is, fast movement of scope and blurry images make the scope tracking fail. Benefiting from improved localization, MIS-SLAM can achieve large-scale scope localizing and dense mapping in real time. It transforms and deforms current model and incrementally fuses new observation while keeping vivid texture. In-vivo experiments conducted on publicly available datasets presented in the form of videos demonstrate the feasibility and practicality of MIS-SLAM for potential clinical purpose.	en_US
dc.relation.ispartof	IEEE Robotics and Automation Letters	en_US
dc.relation.isbasedon	10.1109/LRA.2018.2856519	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.title	MIS-SLAM: Real-Time Large-Scale Dense Deformable SLAM System in Minimal Invasive Surgery Based on Heterogeneous Computing	en_US
dc.type	Journal Article
utslib.citation.volume	4	en_US
utslib.citation.volume	3	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0913 Mechanical Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CAS - Centre for Autonomous Systems
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	open_access	*
pubs.issue	4	en_US
pubs.publication-status	Published	en_US
pubs.volume	3	en_US

Abstract:

© 2016 IEEE. Real-time simultaneous localization and dense mapping is very helpful for providing virtual reality and augmented reality for surgeons or even surgical robots. In this letter, we propose MIS-SLAM: A complete real-time large-scale dense deformable SLAM system with stereoscope in minimal invasive surgery (MIS) based on heterogeneous computing by making full use of CPU and GPU. Idled CPU is used to perform ORB-SLAM for providing robust global pose. Strategies are taken to integrate modules from CPU and GPU. We solved the key problem raised in the previous work, that is, fast movement of scope and blurry images make the scope tracking fail. Benefiting from improved localization, MIS-SLAM can achieve large-scale scope localizing and dense mapping in real time. It transforms and deforms current model and incrementally fuses new observation while keeping vivid texture. In-vivo experiments conducted on publicly available datasets presented in the form of videos demonstrate the feasibility and practicality of MIS-SLAM for potential clinical purpose.

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http://hdl.handle.net/10453/130107