Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Personalized Use of an Adjustable Movement-Controlled Video Game in Obstetric Brachial Plexus Palsy during Physiotherapy Sessions at School: A Case Report.

Healthcare (Basel, Switzerland)·2023
Same author

Serious Game for the Screening of Central Auditory Processing Disorder in School-Age Children: Development and Validation Study.

JMIR serious games·2023
Same author

Use of Virtual Reality and Videogames in the Physiotherapy Treatment of Stroke Patients: A Pilot Randomized Controlled Trial.

International journal of environmental research and public health·2023
Same author

Finding Effective Adjustment Levels for Upper Limb Exergames: Focus Group Study With Children With Physical Disabilities.

JMIR serious games·2023
Same author

A New Architecture for Customizable Exergames: User Evaluation for Different Neuromuscular Disorders.

Healthcare (Basel, Switzerland)·2022
Same author

The role of circular RNA hsa_circ_0001789 as a diagnostic biomarker in gastric carcinoma.

Scandinavian journal of gastroenterology·2022

Related Experiment Video

Updated: Mar 17, 2026

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging
09:19

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

Published on: April 18, 2025

1.6K

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

Xin Yuan1, José-Fernán Martínez2, Martina Eckert3

  • 1Centro de Investigación en Tecnologías Software y Sistemas Multimedia para la Sostenibilidad (CITSEM), Campus Sur, Universidad Politécnica de Madrid (UPM), Madrid 28031, Spain. xin.yuan@upm.es.

Sensors (Basel, Switzerland)
|July 26, 2016
PubMed
Summary

This study introduces an improved Otsu threshold segmentation method for sonar images, enhancing underwater Simultaneous Localization and Mapping (SLAM). The new method offers faster, more precise feature detection and landmark extraction for robust robot navigation.

Keywords:
augmented extended Kalman filter (AEKF)simultaneous localization and mapping (SLAM)threshold segmentationunderwater object detection

More Related Videos

Development of New Methods for Quantifying Fish Density Using Underwater Stereo-video Tools
09:32

Development of New Methods for Quantifying Fish Density Using Underwater Stereo-video Tools

Published on: November 20, 2017

9.9K
Reefshape: A System for the Efficient Collection and Automated Processing of Time-Series Underwater Photogrammetry Data for Benthic Habitat Monitoring
13:35

Reefshape: A System for the Efficient Collection and Automated Processing of Time-Series Underwater Photogrammetry Data for Benthic Habitat Monitoring

Published on: June 13, 2025

1.7K

Related Experiment Videos

Last Updated: Mar 17, 2026

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging
09:19

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

Published on: April 18, 2025

1.6K
Development of New Methods for Quantifying Fish Density Using Underwater Stereo-video Tools
09:32

Development of New Methods for Quantifying Fish Density Using Underwater Stereo-video Tools

Published on: November 20, 2017

9.9K
Reefshape: A System for the Efficient Collection and Automated Processing of Time-Series Underwater Photogrammetry Data for Benthic Habitat Monitoring
13:35

Reefshape: A System for the Efficient Collection and Automated Processing of Time-Series Underwater Photogrammetry Data for Benthic Habitat Monitoring

Published on: June 13, 2025

1.7K

Area of Science:

  • Robotics and Autonomous Systems
  • Computer Vision
  • Signal Processing

Background:

  • Underwater exploration relies on accurate localization and mapping (SLAM).
  • Sonar sensing is crucial for underwater navigation but faces challenges in feature extraction and segmentation.
  • Existing threshold segmentation methods for sonar images can be computationally intensive and lack precision.

Purpose of the Study:

  • To develop an improved Otsu threshold segmentation method (TSM) for enhanced feature extraction from sonar images.
  • To enable precise underwater landmark detection for Simultaneous Localization and Mapping (SLAM).
  • To improve the accuracy and robustness of robot pose and landmark position estimation in underwater environments.

Main Methods:

  • An improved Otsu threshold segmentation method (TSM) was developed for sonar image feature detection.
  • A contour detection algorithm was integrated with the TSM for precise object separation.
  • The proposed method was tested on side-scan sonar (SSS) and forward-looking sonar (FLS) images, comparing performance against traditional Otsu, local, iterative, and maximum entropy TSMs.
  • Centroids of segmented regions were computed as point landmarks for an Augmented Extended Kalman Filter (AEKF)-based SLAM algorithm.

Main Results:

  • The improved Otsu TSM demonstrated significantly lower computational time compared to the maximum entropy TSM.
  • The proposed method achieved higher segmentation precision than traditional Otsu, local, and iterative TSMs.
  • Using the improved Otsu TSM with AEKF-SLAM resulted in more accurate and robust estimations of robot pose and landmark positions compared to using the maximum entropy TSM.
  • Reliable detection of map cycles and consistent map updates on loop closure were achieved in simulated experiments.

Conclusions:

  • The improved Otsu TSM offers an efficient and precise solution for feature extraction in sonar images for underwater SLAM.
  • The integration of this segmentation method with AEKF-SLAM enhances robot localization and mapping capabilities.
  • This approach provides a foundation for more reliable underwater navigation and exploration systems.