3D pose estimation with one plane correspondence using kinect and IMU

Hyungi Cho, Suyong Yeon, Hyunga Choi, Nakju Doh

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

6 Citations (Scopus)

Abstract

In indoor spaces, plane features are general geometric primitives that are robust to noise. To estimate the relative displacement and orientation between two consecutive 3D poses, at least three-pair correspondences of planes with different normals are needed at both poses. Otherwise, the estimated state falls into degeneracy, which is usually caused by the narrow field-of-view (FoV) of sensors as well as environmental structures. In the case where there are changes in degeneracy, with the exception of rotation, the state can be resolved using existing methods. To compensate for rotation in degeneracy for one-pair correspondences, the main approach is to project the initial prediction of an inertial measurement unit's (IMU) orientation to the normal of the corresponding plane such that it satisfies the shortest arc length. We also propose an algorithm that detects various degeneracy cases through which a seamless implementation becomes feasible, regardless of the number of plane pairs. We show the usefulness of the proposed method by performing a 3D mapping experiment where several degenerate cases arise due to limited FoV.

Original languageEnglish
Title of host publicationIEEE International Conference on Intelligent Robots and Systems
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1970-1975
Number of pages6
Volume2015-December
ISBN (Print)9781479999941
DOIs
Publication statusPublished - 2015 Dec 11
EventIEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2015 - Hamburg, Germany
Duration: 2015 Sept 282015 Oct 2

Other

OtherIEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2015
Country/TerritoryGermany
CityHamburg
Period15/9/2815/10/2

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Computer Vision and Pattern Recognition
  • Computer Science Applications

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