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Geometric Correspondence Network for Camera Motion Estimation
KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.ORCID iD: 0000-0002-7796-1438
KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.ORCID iD: 0000-0002-1170-7162
2018 (English)In: IEEE Robotics and Automation Letters, ISSN 2377-3766, E-ISSN 1949-3045, Vol. 3, no 2, p. 1010-1017Article in journal (Refereed) Published
Abstract [en]

In this paper, we propose a new learning scheme for generating geometric correspondences to be used for visual odometry. A convolutional neural network (CNN) combined with a recurrent neural network (RNN) are trained together to detect the location of keypoints as well as to generate corresponding descriptors in one unified structure. The network is optimized by warping points from source frame to reference frame, with a rigid body transform. Essentially, learning from warping. The overall training is focused on movements of the camera rather than movements within the image, which leads to better consistency in the matching and ultimately better motion estimation. Experimental results show that the proposed method achieves better results than both related deep learning and hand crafted methods. Furthermore, as a demonstration of the promise of our method we use a naive SLAM implementation based on these keypoints and get a performance on par with ORB-SLAM.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC , 2018. Vol. 3, no 2, p. 1010-1017
Keywords [en]
Visual-based navigation, SLAM, deep learning in robotics and automation
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-223775DOI: 10.1109/LRA.2018.2794624ISI: 000424646100022OAI: oai:DiVA.org:kth-223775DiVA, id: diva2:1188320
Note

QC 20180307

Available from: 2018-03-07 Created: 2018-03-07 Last updated: 2018-03-07Bibliographically approved

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Folkesson, JohnJensfelt, Patric

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