Underwater localization and 3D mapping of submerged structures with a single-beam scanning sonar

Jinkun Wang, Shi Bai, Brendan Englot

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

24 Scopus citations

Abstract

We present a novel approach to perform underwater simultaneous localization and mapping (SLAM) using a small inspection-class remotely operated vehicle (ROV) equipped with a single-beam scanning sonar, amidst high levels of noise present in the sonar data, and in the absence of inertial/odometry measurements. Features are extracted from hierarchically grouped clusters of sonar returns, data association is performed via the iterative joint compatibility test, and the vehicle's trajectory and map are estimated using incremental smoothing and mapping (iSAM). The resulting point clouds derived from the ROV's sonar are used to produce Gaussian process occupancy maps, which interpolate among gaps in the acoustic range data to produce descriptive 3D maps of submerged structures. The proposed localization and mapping approach is demonstrated using data gathered in two harbor environments in close proximity to piers and seawalls.

Original languageEnglish
Title of host publicationICRA 2017 - IEEE International Conference on Robotics and Automation
Pages4898-4905
Number of pages8
ISBN (Electronic)9781509046331
DOIs
StatePublished - 21 Jul 2017
Event2017 IEEE International Conference on Robotics and Automation, ICRA 2017 - Singapore, Singapore
Duration: 29 May 20173 Jun 2017

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
ISSN (Print)1050-4729

Conference

Conference2017 IEEE International Conference on Robotics and Automation, ICRA 2017
Country/TerritorySingapore
CitySingapore
Period29/05/173/06/17

Fingerprint

Dive into the research topics of 'Underwater localization and 3D mapping of submerged structures with a single-beam scanning sonar'. Together they form a unique fingerprint.

Cite this