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

Yuan, Xin; Martínez Ortega, José Fernán; Eckert, Martina y López Santidrián, M. Lourdes (2016). An Improved Otsu Threshold Segmentation Method for Underwater Simultaneous Localization and Mapping-Based Navigation. "Sensors", v. 16 (n. 7); ISSN 1424-8220. https://doi.org/10.3390/s16071148.

Descripción

Título: An Improved Otsu Threshold Segmentation Method for Underwater Simultaneous Localization and Mapping-Based Navigation
Autor/es:
  • Yuan, Xin
  • Martínez Ortega, José Fernán
  • Eckert, Martina
  • López Santidrián, M. Lourdes
Tipo de Documento: Artículo
Título de Revista/Publicación: Sensors
Fecha: 22 Julio 2016
Volumen: 16
Materias:
Escuela: Centro de Investigación en Tecnologías Software y Sistemas Multimedia para la Sostenibilidad (CITSEM) (UPM)
Departamento: Otro
Licencias Creative Commons: Ninguna

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Resumen

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.

Proyectos asociados

TipoCódigoAcrónimoResponsableTítulo
Horizonte 2020662107SWARMsJosé Fernán Martínez OrtegaSmart and Networking Underwater Robots in Cooperation Meshes
Gobierno de EspañaPCIN-2014-022-C02-02Sin especificarJosé Fernán Martínez OrtegaRedes de cooperación inteligente de vehículos submarinos

Más información

ID de Registro: 43044
Identificador DC: http://oa.upm.es/43044/
Identificador OAI: oai:oa.upm.es:43044
Identificador DOI: 10.3390/s16071148
URL Oficial: http://www.mdpi.com/1424-8220/16/7/1148
Depositado por: Prof. José-Fernán Martínez
Depositado el: 28 Jul 2016 05:45
Ultima Modificación: 16 Nov 2016 10:00
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