Relative pose estimation and fusion of omnidirectional and Lidar cameras

Levente Tamas, Robert Frohlich, Z. Kato

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

This paper presents a novel approach for the extrinsic parameter estimation of omnidirectional cameras with respect to a 3D Lidar coordinate frame. The method works without specific setup and calibration targets, using only a pair of 2D-3D data. Pose estimation is formulated as a 2D-3D nonlinear shape registration task which is solved without point correspondences or complex similarity metrics. It relies on a set of corresponding regions, and pose parameters are obtained by solving a small system of nonlinear equations. The efficiency and robustness of the proposed method was confirmed on both synthetic and real data in urban environment.

Original languageEnglish
Title of host publicationComputer Vision - ECCV 2014 Workshops, Proceedings
PublisherSpringer Verlag
Pages640-651
Number of pages12
Volume8926
ISBN (Print)9783319161808
DOIs
Publication statusPublished - 2015
Event13th European Conference on Computer Vision, ECCV 2014 - Zurich, Switzerland
Duration: Sep 6 2014Sep 12 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8926
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other13th European Conference on Computer Vision, ECCV 2014
CountrySwitzerland
CityZurich
Period9/6/149/12/14

Fingerprint

Pose Estimation
Lidar
Optical radar
Nonlinear equations
Parameter estimation
Fusion
Fusion reactions
Camera
Cameras
Calibration
System of Nonlinear Equations
Registration
Parameter Estimation
Correspondence
Robustness
Metric
Target
Similarity

Keywords

  • Fusion
  • Lidar
  • Omnidirectional camera
  • Pose estimation

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Tamas, L., Frohlich, R., & Kato, Z. (2015). Relative pose estimation and fusion of omnidirectional and Lidar cameras. In Computer Vision - ECCV 2014 Workshops, Proceedings (Vol. 8926, pp. 640-651). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8926). Springer Verlag. https://doi.org/10.1007/978-3-319-16181-5_49

Relative pose estimation and fusion of omnidirectional and Lidar cameras. / Tamas, Levente; Frohlich, Robert; Kato, Z.

Computer Vision - ECCV 2014 Workshops, Proceedings. Vol. 8926 Springer Verlag, 2015. p. 640-651 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8926).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Tamas, L, Frohlich, R & Kato, Z 2015, Relative pose estimation and fusion of omnidirectional and Lidar cameras. in Computer Vision - ECCV 2014 Workshops, Proceedings. vol. 8926, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8926, Springer Verlag, pp. 640-651, 13th European Conference on Computer Vision, ECCV 2014, Zurich, Switzerland, 9/6/14. https://doi.org/10.1007/978-3-319-16181-5_49
Tamas L, Frohlich R, Kato Z. Relative pose estimation and fusion of omnidirectional and Lidar cameras. In Computer Vision - ECCV 2014 Workshops, Proceedings. Vol. 8926. Springer Verlag. 2015. p. 640-651. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-16181-5_49
Tamas, Levente ; Frohlich, Robert ; Kato, Z. / Relative pose estimation and fusion of omnidirectional and Lidar cameras. Computer Vision - ECCV 2014 Workshops, Proceedings. Vol. 8926 Springer Verlag, 2015. pp. 640-651 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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