Shape Dependency of ICP Pose Uncertainties in the Context of Pose Estimation Systems

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The iterative closest point (ICP) algorithm is used to fine tune the alignment of two point clouds in many pose estimation algorithms. The uncertainty in these pose estimation algorithms is thus mainly dependent on the pose uncertainty in ICP. This paper investigates the uncertainties in the ICP algorithm by the use of Monte Carlo simulation. A new descriptor based on object shape and a pose error descriptor are introduced. Results show that it is reasonable to approximate the pose errors by multivariate Gaussian distributions, and that there is a linear relationship between the parameters of the Gaussian distributions and the shape descriptor. As a consequence the shape descriptor potentially provides a computationally cheap way to approximate pose uncertainties.

Original languageEnglish
Title of host publicationComputer Vision Systems : 10th International Conference, ICVS 2015, Copenhagen, Denmark, July 6-9, 2015, Proceedings
EditorsAntonios Gasteratos, Lazaros Nalpantidis, Volker Kruger, Jan-Olof Eklundh
Publication date2015
ISBN (Print)978-3-319-20903-6
ISBN (Electronic)978-3-319-20904-3
Publication statusPublished - 2015
Event10th International Conference on Computer Vision Systems - København, Denmark
Duration: 6. Jul 20159. Jul 2015


Conference10th International Conference on Computer Vision Systems
SeriesLecture Notes in Computer Science


  • ICP
  • Pose estimation
  • Robot vision
  • Uncertainty modeling


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