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

Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

435
Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
Here, in order to determine the magnitude of velocity and acceleration for point...
435
Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

107
To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
107

You might also read

Related Articles

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

Sort by
Same journal

RETRACTED: Ndaguba et al. Operability of Smart Spaces in Urban Environments: A Systematic Review on Enhancing Functionality and User Experience. <i>Sensors</i> 2023, <i>23</i>, 6938.

Sensors (Basel, Switzerland)·2026
Same journal

Correction: Ma et al. A Lightweight, Low-Frequency, Broadband Underwater Acoustic Transducer with Ternary Symmetric Excitation: Integrating KNN and Terfenol-D for Enhanced Performance. <i>2026</i>, <i>26</i>, 3645.

Sensors (Basel, Switzerland)·2026
Same journal

Correction: He et al. An Edge-Computing-Based Emotion-Aware Adaptive Lighting System for Intelligent Cockpits. <i>Sensors</i> 2026, <i>26</i>, 3489.

Sensors (Basel, Switzerland)·2026
Same journal

Correction: Tu et al. Lower Limb Motion Recognition with Improved SVM Based on Surface Electromyography. <i>Sensors</i> 2024, <i>24</i>, 3097.

Sensors (Basel, Switzerland)·2026
Same journal

Real-Time Detection System for Road Roughness Based on Ultrasonic Technology.

Sensors (Basel, Switzerland)·2026
Same journal

FedHSFV: Federated Learning for Finger Vein Recognition via Hierarchical Decoupling and Subspace Metric.

Sensors (Basel, Switzerland)·2026

Related Experiment Video

Updated: Aug 15, 2025

Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss
07:12

Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss

Published on: April 11, 2025

498

Mutual-Aided INS/Vision Navigation System Analysis and Optimization Using Sequential Filtering with State

Ayham Shahoud1, Dmitriy Shashev1, Stanislav Shidlovskiy1

  • 1Faculty of Innovative Technology, Tomsk State University, 36 Lenin Ave, 634050 Tomsk, Russia.

Sensors (Basel, Switzerland)
|January 8, 2023
PubMed
Summary

This study introduces a new aerial vehicle navigation system using sensor fusion to overcome image processing delays. The system achieves high accuracy and independence, with root-mean-square position errors under 3.5 meters.

Keywords:
GPSIMUKd-Treeasynchronized measurementsclusteringdelayfilter recalculationintegrated navigationmutual aidingsequential filtering

More Related Videos

Quantification of Oculomotor Responses and Accommodation Through Instrumentation and Analysis Toolboxes
08:27

Quantification of Oculomotor Responses and Accommodation Through Instrumentation and Analysis Toolboxes

Published on: March 3, 2023

1.0K
Integrating Visual Psychophysical Assays within a Y-Maze to Isolate the Role that Visual Features Play in Navigational Decisions
07:09

Integrating Visual Psychophysical Assays within a Y-Maze to Isolate the Role that Visual Features Play in Navigational Decisions

Published on: May 2, 2019

6.2K

Related Experiment Videos

Last Updated: Aug 15, 2025

Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss
07:12

Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss

Published on: April 11, 2025

498
Quantification of Oculomotor Responses and Accommodation Through Instrumentation and Analysis Toolboxes
08:27

Quantification of Oculomotor Responses and Accommodation Through Instrumentation and Analysis Toolboxes

Published on: March 3, 2023

1.0K
Integrating Visual Psychophysical Assays within a Y-Maze to Isolate the Role that Visual Features Play in Navigational Decisions
07:09

Integrating Visual Psychophysical Assays within a Y-Maze to Isolate the Role that Visual Features Play in Navigational Decisions

Published on: May 2, 2019

6.2K

Area of Science:

  • Robotics and Autonomous Systems
  • Navigation and Control
  • Computer Vision

Background:

  • Effective navigation for aerial vehicles relies on integrating diverse sensor data.
  • Image processing in navigation systems can introduce significant time delays.
  • Traditional sensor fusion methods often struggle to adequately address these timing issues.

Purpose of the Study:

  • To develop a mutual-aided navigation system for aerial vehicles that mitigates image processing delays.
  • To enhance navigation accuracy and system independence by compensating for inertial system drift.
  • To improve the efficiency of visual feature matching in real-time navigation.

Main Methods:

  • Implementation of a sequential Kalman Filter for fusing data from an inertial measurement unit (IMU), camera, barometer, and magnetometer.
  • State estimation recalculation during sensor data delay periods.
  • Offline map processing with key point clustering to reduce online feature recalculation.
  • Utilizing sensor data to bound visual feature search space and reprojecting features onto images.

Main Results:

  • The developed system demonstrated efficient navigation with high independence for an aerial vehicle.
  • Root-mean-square (RMS) position error was reduced to less than 3.5 meters.
  • The mutual-aided approach effectively compensated for inertial system drift, improving overall accuracy.

Conclusions:

  • The proposed mutual-aided navigation system effectively addresses time delays in image processing for aerial vehicles.
  • The system achieves robust and accurate navigation, suitable for real-world applications.
  • Sensor fusion combined with state estimation recalculation offers a promising approach for enhanced aerial navigation.