Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • 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
Sparse2Dense: From Direct Sparse Odometry to Dense 3-D Reconstruction
KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, perception and learning, RPL.
KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, perception and learning, RPL.ORCID iD: 0000-0002-7796-1438
KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, perception and learning, RPL.ORCID iD: 0000-0002-1170-7162
2019 (English)In: IEEE Robotics and Automation Letters, ISSN 2377-3766, E-ISSN 1949-3045, Vol. 4, no 2, p. 530-537Article in journal (Refereed) Published
Abstract [en]

In this letter, we proposed a new deep learning based dense monocular simultaneous localization and mapping (SLAM) method. Compared to existing methods, the proposed framework constructs a dense three-dimensional (3-D) model via a sparse to dense mapping using learned surface normals. With single view learned depth estimation as prior for monocular visual odometry, we obtain both accurate positioning and high-quality depth reconstruction. The depth and normal are predicted by a single network trained in a tightly coupled manner. Experimental results show that our method significantly improves the performance of visual tracking and depth prediction in comparison to the state-of-the-art in deep monocular dense SLAM.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC , 2019. Vol. 4, no 2, p. 530-537
Keywords [en]
Visual-based navigation, SLAM, deep learning in robotics and automation
National Category
Robotics
Identifiers
URN: urn:nbn:se:kth:diva-243927DOI: 10.1109/LRA.2019.2891433ISI: 000456673300007OAI: oai:DiVA.org:kth-243927DiVA, id: diva2:1296056
Available from: 2019-03-13 Created: 2019-03-13 Last updated: 2019-03-13Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full text

Authority records BETA

Tang, JiexiongFolkesson, JohnJensfelt, Patric

Search in DiVA

By author/editor
Tang, JiexiongFolkesson, JohnJensfelt, Patric
By organisation
Robotics, perception and learning, RPL
In the same journal
IEEE Robotics and Automation Letters
Robotics

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 171 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • 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