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Monocular Depth Estimation Using Deep Convolutional Neural Networks
Linköping University, Department of Electrical Engineering, Computer Vision.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

For a long time stereo-cameras have been deployed in visual Simultaneous Localization And Mapping (SLAM) systems to gain 3D information. Even though stereo-cameras show good performance, the main disadvantage is the complex and expensive hardware setup it requires, which limits the use of the system. A simpler and cheaper alternative are monocular cameras, however monocular images lack the important depth information. Recent works have shown that having access to depth maps in monocular SLAM system is beneficial since they can be used to improve the 3D reconstruction. This work proposes a deep neural network that predicts dense high-resolution depth maps from monocular RGB images by casting the problem as a supervised regression task. The network architecture follows an encoder-decoder structure in which multi-scale information is captured and skip-connections are used to recover details. The network is trained and evaluated on the KITTI dataset achieving results comparable to state-of-the-art methods. With further development, this network shows good potential to be incorporated in a monocular SLAM system to improve the 3D reconstruction.

Place, publisher, year, edition, pages
2019. , p. 62
Keywords [en]
Depth estimation, depth maps, monocular SLAM, mono-SLAM, pixelwise depth prediction, encoder-decoder network
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:liu:diva-159981ISRN: LiTH-ISY-EX--19/5234--SEOAI: oai:DiVA.org:liu-159981DiVA, id: diva2:1347284
External cooperation
Saab Dynamics
Subject / course
Computer Vision Laboratory
Supervisors
Examiners
Available from: 2019-09-02 Created: 2019-08-30 Last updated: 2019-09-02Bibliographically approved

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf