Next Article in Journal
An Omnidirectional Polarization Detector Based on a Metamaterial Absorber
Next Article in Special Issue
DeepFruits: A Fruit Detection System Using Deep Neural Networks
Previous Article in Journal
Modeling and Assessment of GPS/BDS Combined Precise Point Positioning
Previous Article in Special Issue
Filtering Based Adaptive Visual Odometry Sensor Framework Robust to Blurred Images
Article Menu

Export Article

Open AccessArticle
Sensors 2016, 16(7), 1148; doi:10.3390/s16071148

An Improved Otsu Threshold Segmentation Method for Underwater Simultaneous Localization and Mapping-Based Navigation

Centro de Investigación en Tecnologías Software y Sistemas Multimedia para la Sostenibilidad (CITSEM), Campus Sur, Universidad Politécnica de Madrid (UPM), Madrid 28031, Spain
*
Author to whom correspondence should be addressed.
Academic Editors: Gabriel Oliver-Codina, Nuno Gracias and Antonio M. López
Received: 25 April 2016 / Revised: 7 July 2016 / Accepted: 19 July 2016 / Published: 22 July 2016
(This article belongs to the Special Issue Vision-Based Sensors in Field Robotics)
View Full-Text   |   Download PDF [4456 KB, uploaded 25 July 2016]   |  

Abstract

The main focus of this paper is on extracting features with SOund Navigation And Ranging (SONAR) sensing for further underwater landmark-based Simultaneous Localization and Mapping (SLAM). According to the characteristics of sonar images, in this paper, an improved Otsu threshold segmentation method (TSM) has been developed for feature detection. In combination with a contour detection algorithm, the foreground objects, although presenting different feature shapes, are separated much faster and more precisely than by other segmentation methods. Tests have been made with side-scan sonar (SSS) and forward-looking sonar (FLS) images in comparison with other four TSMs, namely the traditional Otsu method, the local TSM, the iterative TSM and the maximum entropy TSM. For all the sonar images presented in this work, the computational time of the improved Otsu TSM is much lower than that of the maximum entropy TSM, which achieves the highest segmentation precision among the four above mentioned TSMs. As a result of the segmentations, the centroids of the main extracted regions have been computed to represent point landmarks which can be used for navigation, e.g., with the help of an Augmented Extended Kalman Filter (AEKF)-based SLAM algorithm. The AEKF-SLAM approach is a recursive and iterative estimation-update process, which besides a prediction and an update stage (as in classical Extended Kalman Filter (EKF)), includes an augmentation stage. During navigation, the robot localizes the centroids of different segments of features in sonar images, which are detected by our improved Otsu TSM, as point landmarks. Using them with the AEKF achieves more accurate and robust estimations of the robot pose and the landmark positions, than with those detected by the maximum entropy TSM. Together with the landmarks identified by the proposed segmentation algorithm, the AEKF-SLAM has achieved reliable detection of cycles in the map and consistent map update on loop closure, which is shown in simulated experiments. View Full-Text
Keywords: threshold segmentation; underwater object detection; simultaneous localization and mapping (SLAM); augmented extended Kalman filter (AEKF) threshold segmentation; underwater object detection; simultaneous localization and mapping (SLAM); augmented extended Kalman filter (AEKF)
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Yuan, X.; Martínez, J.-F.; Eckert, M.; López-Santidrián, L. An Improved Otsu Threshold Segmentation Method for Underwater Simultaneous Localization and Mapping-Based Navigation. Sensors 2016, 16, 1148.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top