Predicting Alignment Risk to Prevent Localization Failure

Simona Nobili, Georgi Tinchev, Maurice Fallon

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

Abstract / Description of output

During localization and mapping the success of point cloud registration can be compromised when there is an absence of geometric features or constraints in corridors or across doorways, or when the volumes scanned only partly overlap, due to occlusions or constrictions between subsequent observations. This work proposes a strategy to predict and prevent laser-based localization failure. Our solution relies on explicit analysis of the point cloud content prior to registration. A model predicting the risk of a failed alignment is learned by analysing the degree of spatial overlap between two input point clouds and the geometric constraints available within the region of overlap. We define a novel measure of alignability for these constraints. The method is evaluated against three real-world datasets and compared to baseline approaches. The experiments demonstrate how our approach can help improve the reliability of laser-based localization during exploration of unknown and cluttered man-made environments.
Original languageEnglish
Title of host publication2018 IEEE International Conference on Robotics and Automation (ICRA)
Place of PublicationBrisbane, QLD, Australia
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages8
ISBN (Electronic)978-1-5386-3081-5, 978-1-5386-3080-8
ISBN (Print)978-1-5386-3082-2
Publication statusPublished - 13 Sept 2018
Event2018 IEEE International Conference on Robotics and Automation - The Brisbane Convention & Exhibition Venue, Brisbane, Australia
Duration: 21 May 201825 May 2018

Publication series

Name2018 IEEE International Conference on Robotics and Automation (ICRA)
ISSN (Electronic)2577-087X


Conference2018 IEEE International Conference on Robotics and Automation
Abbreviated titleICRA2018
Internet address

Keywords / Materials (for Non-textual outputs)

  • feature extraction
  • image registration
  • SLAM (robots)
  • cluttered man-made environments
  • geometric constraints
  • spatial overlap
  • failed alignment
  • point cloud content
  • laser-based localization failure
  • geometric features
  • point cloud registration
  • alignment risk
  • Three-dimensional displays
  • Cloud computing
  • Robot sensing systems
  • Measurement
  • Iterative closest point algorithm
  • Octrees


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