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GCNv2: Efficient Correspondence Prediction for Real-Time SLAM
KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.
KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.ORCID iD: 0000-0002-7796-1438
KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.ORCID iD: 0000-0002-7796-1438
KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.
2019 (English)In: IEEE Robotics and Automation Letters, ISSN 2377-3766, E-ISSN 1949-3045, Vol. 4, no 4, p. 3505-3512Article in journal (Refereed) Published
Abstract [en]

In this letter, we present a deep learning-based network, GCNv2, for generation of keypoints and descriptors. GCNv2 is built on our previous method, GCN, a network trained for 3D projective geometry. GCNv2 is designed with a binary descriptor vector as the ORB feature so that it can easily replace ORB in systems such as ORB-SLAM2. GCNv2 significantly improves the computational efficiency over GCN that was only able to run on desktop hardware. We show how a modified version of ORBSLAM2 using GCNv2 features runs on a Jetson TX2, an embedded low-power platform. Experimental results show that GCNv2 retains comparable accuracy as GCN and that it is robust enough to use for control of a flying drone. Source code is available at: https://github.com/jiexiong2016/GCNv2_SLAM.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2019. Vol. 4, no 4, p. 3505-3512
National Category
Robotics
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-257883DOI: 10.1109/LRA.2019.2927954ISI: 000477983400013Scopus ID: 2-s2.0-85069905338OAI: oai:DiVA.org:kth-257883DiVA, id: diva2:1349146
Funder
Wallenberg AI, Autonomous Systems and Software Program (WASP)Swedish Foundation for Strategic Research , FactSwedish Research Council
Note

QC 20190909

Available from: 2019-09-06 Created: 2019-09-06 Last updated: 2019-10-10Bibliographically approved

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Publisher's full textScopushttps://doi.org/10.1109/LRA.2019.2927954

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Tang, JiexiongEricson, Ludvig

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