Motion estimation of non-holonomic ground vehicles from a single feature correspondence measured over n views

Kun Huang, Yifu Wang, Laurent Kneip

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    14 Citations (Scopus)

    Abstract

    The planar motion of ground vehicles is often non-holonomic, which enables a solution of the two-view relative pose problem from a single point feature correspondence. Man-made environments such as underground parking lots are however dominated by line features. Inspired by the planar tri-focal tensor and its ability to handle lines, we establish an n-linear constraint on the locally circular motion of non-holonomic vehicles able to handle an arbitrarily large and dense window of views. We prove that this stays a uni-variate problem under the assumption of locally constant vehicle speed, and it can transparently handle both point and vertical line correspondences. In particular, we prove that an application of Viète's formulas for extrapolating trigonometric functions of angle multiples and the Weierstrass substitution casts the problem as one that merely seeks the roots of a uni-variate polynomial. We present the complete theory of this novel solver, and test it on both simulated and real data. Our results prove that it successfully handles a variety of relevant scenarios, eventually outperforming the 1-point two-view solver.

    Original languageEnglish
    Title of host publicationProceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019
    PublisherIEEE Computer Society
    Pages12698-12707
    Number of pages10
    ISBN (Electronic)9781728132938
    DOIs
    Publication statusPublished - Jun 2019
    Event32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019 - Long Beach, United States
    Duration: 16 Jun 201920 Jun 2019

    Publication series

    NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
    Volume2019-June
    ISSN (Print)1063-6919

    Conference

    Conference32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019
    Country/TerritoryUnited States
    CityLong Beach
    Period16/06/1920/06/19

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