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 Experiment Video

Updated: Jan 15, 2026

Determining 3D Flow Fields via Multi-camera Light Field Imaging
14:25

Determining 3D Flow Fields via Multi-camera Light Field Imaging

Published on: March 6, 2013

17.1K

Monocular Visual/IMU/GNSS Integration System Using Deep Learning-Based Optical Flow for Intelligent Vehicle

Jeongmin Kang1

  • 1School of Information Technology, Halmstad University, 30118 Halmstad, Sweden.

Sensors (Basel, Switzerland)
|October 16, 2025
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

525
Uniform depth channel flow keeps fluid depth consistent along channels such as irrigation canals. In natural channels, such as rivers, approximate uniform flow is often assumed. This condition occurs when the channel’s bottom slope matches the energy slope, balancing potential energy lost from gravity with head loss due to shear stress. This balance prevents depth changes along the channel length, resulting in a steady, uniform flow.Uniform flow in open channels with a constant cross-section...
525
Field Application of Global Positioning System01:28

Field Application of Global Positioning System

315
The Global Positioning System (GPS) has become an indispensable tool in fieldwork, offering unparalleled precision and efficiency for surveying, navigation, and infrastructure development. By harnessing signals from a constellation of satellites, GPS receivers determine the location of objects with remarkable speed and accuracy, often completing calculations within a second.Advantages of Modern GPS TechnologyContemporary GPS receivers are designed to meet the practical demands of field...
315
Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

426
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...
426
Introduction to Global Positioning System01:30

Introduction to Global Positioning System

462
The Global Positioning System (GPS) revolutionized positioning on Earth, providing precise location data through satellite ranging. The GPS system was developed in 1978 by the U.S. Department of Defense  for military use, and it became available for civilian applications in 1983, transforming fields including navigation, fleet management, and time synchronization for telecommunications systems.GPS consists of satellites in medium Earth orbit, about 20,200 kilometers above the surface,...
462
Applications of Integration to Find Blood Flow01:27

Applications of Integration to Find Blood Flow

3
Blood flow through a cylindrical blood vessel can be mathematically described using the principles of laminar flow, a regime in which fluid moves smoothly in parallel layers. In this model, the velocity of the blood is not uniform across the cross-section of the vessel; rather, it varies with the radial distance from the center. The maximum velocity occurs along the central axis, decreasing progressively toward the vessel walls, where it reaches zero due to viscous drag.Approximating Blood...
3
Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device01:30

Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device

378
Surveyors use Global Positioning System (GPS) technology to measure the precise location and elevation of points on Earth. In a recent survey, GPS receivers were used to determine the coordinates and elevations of two park monuments. The process involved careful mission planning, data collection, and correction to ensure accuracy. The survey began with mission planning to identify optimal satellite visibility and minimize Position Dilution of Precision (PDOP). A geodetic control point...
378

You might also read

Related Articles

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

Sort by
Same author

Effects of Dust and Moisture Surface Contaminants on Automotive Radar Sensor Frequencies.

Sensors (Basel, Switzerland)·2025
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

This study introduces a robust multi-sensor fusion system for autonomous driving localization. By combining visual-inertial odometry (VIO) with enhanced deep learning optical flow and Global Navigation Satellite System (GNSS) data, it achieves accurate, drift-free navigation in challenging outdoor environments.

Area of Science:

  • Robotics and Autonomous Systems
  • Computer Vision
  • Sensor Fusion

Background:

  • Vehicle localization is critical for autonomous driving, but traditional visual-inertial odometry (VIO) struggles with sparse features and illumination changes in outdoor settings.
  • Existing deep learning optical flow methods lack robustness in low-texture or ambiguous areas.
  • Global Navigation Satellite System (GNSS) performance degrades in urban canyons due to multipath interference.

Purpose of the Study:

  • To develop a robust and drift-free multi-sensor fusion system for autonomous vehicle localization.
  • To enhance the reliability of visual odometry using deep learning optical flow with improved consistency constraints.
  • To integrate Global Navigation Satellite System (GNSS) measurements for global localization stability.

Main Methods:

Keywords:
Kalman filterglobal navigation satellite system (GNSS)localizationmulti-sensor fusionoptical flowvisual-inertial odometry

More Related Videos

Deep Vascular Imaging in the Eye with Flow-Enhanced Ultrasound
07:29

Deep Vascular Imaging in the Eye with Flow-Enhanced Ultrasound

Published on: October 4, 2021

2.8K
Using Eye-tracking to Assess the Relative Importance of Visual and Vestibular Input to Subcortical Motion Processing in the Roll Plane
07:24

Using Eye-tracking to Assess the Relative Importance of Visual and Vestibular Input to Subcortical Motion Processing in the Roll Plane

Published on: August 22, 2025

475

Related Experiment Videos

Last Updated: Jan 15, 2026

Determining 3D Flow Fields via Multi-camera Light Field Imaging
14:25

Determining 3D Flow Fields via Multi-camera Light Field Imaging

Published on: March 6, 2013

17.1K
Deep Vascular Imaging in the Eye with Flow-Enhanced Ultrasound
07:29

Deep Vascular Imaging in the Eye with Flow-Enhanced Ultrasound

Published on: October 4, 2021

2.8K
Using Eye-tracking to Assess the Relative Importance of Visual and Vestibular Input to Subcortical Motion Processing in the Roll Plane
07:24

Using Eye-tracking to Assess the Relative Importance of Visual and Vestibular Input to Subcortical Motion Processing in the Roll Plane

Published on: August 22, 2025

475
  • A hybrid visual-inertial odometry (VIO) framework integrating monocular VIO with GNSS measurements.
  • Utilizing a deep learning-based optical flow network with an enhanced consistency constraint incorporating local structure and motion coherence.
  • Fusing refined optical flow with inertial measurements and GNSS updates for improved localization accuracy and drift mitigation.

Main Results:

  • The proposed multi-sensor fusion system demonstrated superior localization performance on the KITTI dataset compared to existing methods.
  • Enhanced optical flow extraction using the novel consistency constraint improved robustness in challenging visual conditions.
  • The integration of GNSS data effectively mitigated long-term drift, ensuring global localization stability.

Conclusions:

  • The developed filter-based multi-sensor fusion framework provides accurate and reliable vehicle localization in large-scale outdoor environments.
  • The enhanced optical flow consistency constraint is key to robust visual measurements for autonomous driving.
  • This approach offers a significant advancement for dependable autonomous navigation systems.