Visual-inertial curve SLAM

Kevin Meier, Soon Jo Chung, Seth Hutchinson

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


We present a simultaneous localization and mapping (SLAM) algorithm that uses Bézier curves as static landmark primitives rather than sparse feature points. Our approach allows us to estimate the full 6-DOF pose of a robot while providing a structured map which can be used to assist a robot in motion planning and control. We demonstrate how to reconstruct the 3-D location of curve landmarks from a stereo pair without searching for point-based stereo correspondences and how to compare the 3-D shape of curve landmarks between chronologically sequential stereo frames to solve the data association problem. We present a method to combine curve landmarks for mapping purposes, resulting in a map with a continuous set of curves that contain fewer landmark states than conventional sparse point-based SLAM algorithms. Note, to combine curves, we assume the curved landmarks are fixed to a larger curved object naturally occurring in the scene. While our algorithm is less accurate than point-based SLAM algorithms, we are able to create maps with considerably less landmark states and our algorithm can operate in settings lacking texture.

Original languageEnglish (US)
Title of host publicationIROS 2016 - 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages8
ISBN (Electronic)9781509037629
StatePublished - Nov 28 2016
Event2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2016 - Daejeon, Korea, Republic of
Duration: Oct 9 2016Oct 14 2016

Publication series

NameIEEE International Conference on Intelligent Robots and Systems
ISSN (Print)2153-0858
ISSN (Electronic)2153-0866


Other2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2016
Country/TerritoryKorea, Republic of

ASJC Scopus subject areas

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


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