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

doi:10.1016/j.ifacol.2021.10.104

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 journal › Conference article › peer-review

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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 language	English
Pages (from-to)	273-280
Number of pages	8
Journal	IFAC-PapersOnLine
Volume	54
Issue number	16
Early online date	2 Nov 2021
DOIs	https://doi.org/10.1016/j.ifacol.2021.10.104
Publication status	Published - 2021
Event	13th IFAC Conference on Control Applications in Marine Systems, Robotics, and Vehicles 2021 - Oldenburg, Germany Duration: 22 Sept 2021 → 24 Sept 2021

Keywords

Autonomy
AUV
Inspection
Path planning
SLAM
Stereo vision
Temporal planning

ASJC Scopus subject areas

Control and Systems Engineering

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title = "Robust underwater SLAM using autonomous relocalisation",

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.",

keywords = "Autonomy, AUV, Inspection, Path planning, SLAM, Stereo vision, Temporal planning",

author = "Willners, {Jonatan Scharff} and Yaniel Carreno and Shida Xu and Tomasz Luczynski and Sean Katagiri and Joshua Roe and {\`E}ric Pairet and Yvan Petillot and Sen Wang",

note = "Funding Information: This work was supported by the EPSRC funded ORCA HUB (EP/R026173/1). Publisher Copyright: {\textcopyright} 2021 The Authors.; 13th IFAC Conference on Control Applications in Marine Systems, Robotics, and Vehicles 2021, CAMS 2021 ; Conference date: 22-09-2021 Through 24-09-2021",

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doi = "10.1016/j.ifacol.2021.10.104",

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AU - Willners, Jonatan Scharff

AU - Carreno, Yaniel

AU - Xu, Shida

AU - Luczynski, Tomasz

AU - Katagiri, Sean

AU - Roe, Joshua

AU - Pairet, Èric

AU - Petillot, Yvan

AU - Wang, Sen

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

KW - Autonomy

KW - AUV

KW - Inspection

KW - Path planning

KW - SLAM

KW - Stereo vision

KW - Temporal planning

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SN - 2405-8963

VL - 54

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JO - IFAC-PapersOnLine

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Y2 - 22 September 2021 through 24 September 2021

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Robust underwater SLAM using autonomous relocalisation

Abstract

Keywords

ASJC Scopus subject areas

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