/Multibeam Data Processing for Underwater Mapping

Multibeam Data Processing for Underwater Mapping

Pedro V. Teixeira, Michael Kaess, Franz S. Hover and John J. Leonard
Conference Paper, IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems, IROS, October, 2018

Download Publication (PDF)

Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author’s copyright. These works may not be reposted without the explicit permission of the copyright holder.


From archaeology to the inspection of subsea structures, underwater mapping has become critical to many applications. Because of the balanced trade-off between range and resolution, multibeam sonars are often used as the primary sensor in underwater mapping platforms. These sonars output an image representing the intensity of the received acoustic echos over space, which must be classified into free and occupied regions before range measurements are determined and spatially registered. Most classifiers found in the underwater mapping literature use local thresholding techniques, which are highly sensitive to noise, outliers, and sonar artifacts typically found in these images. In this paper we present an overview of some of the techniques developed in the scope of our work on sonar-based underwater mapping, with the aim of improving map accuracy through better segmentation performance. We also provide experimental results using data collected with a DIDSON imaging sonar that show that these techniques improve both segmentation accuracy and robustness to outliers.

BibTeX Reference
author = {Pedro V. Teixeira and Michael Kaess and Franz S. Hover and John J. Leonard},
title = {Multibeam Data Processing for Underwater Mapping},
booktitle = {IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems, IROS},
year = {2018},
month = {October},