Robust underwater SLAM using autonomous relocalisation

Jonatan Scharff Willners*, Yaniel Carreno, Shida Xu, Tomasz Luczynski, Sean Katagiri, Joshua Roe, Èric Pairet, Yvan Petillot, Sen Wang

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

6 Citations (Scopus)
128 Downloads (Pure)


This paper presents a robust underwater simultaneous localisation and mapping (SLAM) framework using autonomous relocalisation. The proposed approach strives to maintain a single consistent map during operation and updates its current plan when the SLAM loses feature tracking. The updated plan transverses viewpoints that are likely to aid in merging the current map into the global map. We present the sub-systems of the framework: the SLAM, viewpoint generation, and high level planning. In-water experiments show the advantage of our approach used on an autonomous underwater vehicle (AUV) performing inspections.

Original languageEnglish
Pages (from-to)273-280
Number of pages8
Issue number16
Early online date2 Nov 2021
Publication statusPublished - 2021
Event13th IFAC Conference on Control Applications in Marine Systems, Robotics, and Vehicles 2021 - Oldenburg, Germany
Duration: 22 Sept 202124 Sept 2021


  • Autonomy
  • AUV
  • Inspection
  • Path planning
  • SLAM
  • Stereo vision
  • Temporal planning

ASJC Scopus subject areas

  • Control and Systems Engineering


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