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

Research output: Contribution to journalConference articlepeer-review

1 Citation (Scopus)
3 Downloads (Pure)

Abstract

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
JournalIFAC-PapersOnLine
Volume54
Issue number16
Early online date2 Nov 2021
DOIs
Publication statusPublished - 2021
Event13th IFAC Conference on Control Applications in Marine Systems, Robotics, and Vehicles 2021 - Oldenburg, Germany
Duration: 22 Sep 202124 Sep 2021

Keywords

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

ASJC Scopus subject areas

  • Control and Systems Engineering

Fingerprint

Dive into the research topics of 'Robust underwater SLAM using autonomous relocalisation'. Together they form a unique fingerprint.

Cite this