Relative pose estimation and fusion of 2D spectral and 3D Lidar images

Zoltan Kato, Levente Tamas

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

1 Citation (Scopus)

Abstract

This paper presents a unified approach for the relative pose estimation of a spectral camera - 3D Lidar pair without the use of any special calibration pattern or explicit point correspondence. 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. The registration is then traced back to the solution of a non-linear system of equations which directly provides the calibration parameters between the bases of the two sensors. The method has been extended both for perspective and omnidirectional central cameras and was tested on a large set of synthetic lidar-camera image pairs as well as on real data acquired in outdoor environment.

Original languageEnglish
Title of host publicationComputational Color Imaging - 5th International Workshop, CCIW 2015, Proceedings
EditorsAlain Trémeau, Shoji Tominaga, Raimondo Schettini
PublisherSpringer Verlag
Pages33-42
Number of pages10
ISBN (Electronic)9783319159782
DOIs
Publication statusPublished - Jan 1 2015
Event5th International Workshop on Computational Color Imaging, CCIW 2015 - Saint Etienne, France
Duration: Mar 24 2015Mar 26 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9016
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other5th International Workshop on Computational Color Imaging, CCIW 2015
CountryFrance
CitySaint Etienne
Period3/24/153/26/15

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'Relative pose estimation and fusion of 2D spectral and 3D Lidar images'. Together they form a unique fingerprint.

  • Cite this

    Kato, Z., & Tamas, L. (2015). Relative pose estimation and fusion of 2D spectral and 3D Lidar images. In A. Trémeau, S. Tominaga, & R. Schettini (Eds.), Computational Color Imaging - 5th International Workshop, CCIW 2015, Proceedings (pp. 33-42). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9016). Springer Verlag. https://doi.org/10.1007/978-3-319-15979-9_4