Robust point cloud registration for map-based autonomous robot navigation

Amit Efraim, Joseph M. Francos

Research output: Contribution to journalArticlepeer-review


Autonomous navigation in large-scale and complex environments in the absence of a GPS signal is a fundamental challenge encountered in a variety of applications. Since 3-D scans provide inherent robustness to ambient illumination changes and the type of the surface texture, we present Point Cloud Map-based Navigation (PCMN), a robust robot navigation system, based exclusively on 3-D point cloud registration between an acquired observation and a stored reference map. It provides a drift-free navigation solution, equipped with a failed registration detection capability. The backbone of the navigation system is a robust point cloud registration method, of the acquired observation to the stored reference map. The proposed registration algorithm follows a hypotheses generation and evaluation paradigm, where multiple statistically independent hypotheses are generated from local neighborhoods of putative matching points. Then, hypotheses are evaluated using a multiple consensus analysis that integrates evaluation of the point cloud feature correlation and a consensus test on the Special Euclidean Group SE(3) based on independent hypothesized estimates. The proposed PCMN is shown to achieve significantly better performance than state-of-the-art methods, both in terms of place recognition recall and localization accuracy, achieving submesh resolution accuracy, both for indoor and outdoor settings.

Original languageEnglish
Article number57
JournalEurasip Journal on Advances in Signal Processing
Issue number1
StatePublished - 1 Dec 2024


  • Navigation
  • Place recognition
  • Point clouds
  • Registration

ASJC Scopus subject areas

  • Signal Processing
  • Hardware and Architecture
  • Electrical and Electronic Engineering


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