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DeepVIO: Self-supervised Deep Learning of Monocular Visual Inertial Odometry using 3D Geometric Cons...

DeepVIO: Self-supervised Deep Learning of Monocular Visual Inertial Odometry using 3D Geometric Cons...

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2248800872

DeepVIO: Self-supervised Deep Learning of Monocular Visual Inertial Odometry using 3D Geometric Constraints

About this item

Full title

DeepVIO: Self-supervised Deep Learning of Monocular Visual Inertial Odometry using 3D Geometric Constraints

Publisher

Ithaca: Cornell University Library, arXiv.org

Journal title

arXiv.org, 2019-06

Language

English

Formats

Publication information

Publisher

Ithaca: Cornell University Library, arXiv.org

More information

Scope and Contents

Contents

This paper presents an self-supervised deep learning network for monocular visual inertial odometry (named DeepVIO). DeepVIO provides absolute trajectory estimation by directly merging 2D optical flow feature (OFF) and Inertial Measurement Unit (IMU) data. Specifically, it firstly estimates the depth and dense 3D point cloud of each scene by using...

Alternative Titles

Full title

DeepVIO: Self-supervised Deep Learning of Monocular Visual Inertial Odometry using 3D Geometric Constraints

Authors, Artists and Contributors

Identifiers

Primary Identifiers

Record Identifier

TN_cdi_proquest_journals_2248800872

Permalink

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2248800872

Other Identifiers

E-ISSN

2331-8422

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