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

Tight Junctions01:29

Tight Junctions

7.3K
Tight junctions are molecular seals between cells that prevent the leaking of fluids, ions, and other small solutes across cavities and compartments in multicellular organisms. They are mainly composed of claudin and occludin transmembrane proteins, and other proteins such as tricellulin and JAM (junctional adhesion molecule). All these proteins are 4-pass transmembrane proteins, except JAM, which is a single-pass transmembrane protein belonging to the immunoglobulin superfamily. The...
7.3K
Vision01:24

Vision

60.3K
Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
60.3K
Color Vision01:24

Color Vision

1.5K
Color perception begins in the retina, the light-sensitive layer at the back of the eye. Two main theories explain how colors are seen: the trichromatic theory and the opponent-process theory. The trichromatic theory, proposed by Thomas Young in 1802 and extended by Hermann von Helmholtz in 1852, suggests that color vision is based on three types of cone receptors in the retina. These cones are sensitive to different but overlapping ranges of wavelengths corresponding to red, blue, and green.
1.5K
Spin–Spin Coupling: Two-Bond Coupling (Geminal Coupling)01:20

Spin–Spin Coupling: Two-Bond Coupling (Geminal Coupling)

1.7K
Two NMR-active nuclei bonded to a central atom can be involved in geminal or two-bond coupling. Geminal coupling is commonly seen between diastereotopic protons in chiral molecules and unsymmetrical alkenes, among others.
The central atom need not be NMR-active because its electrons are affected by the electron polarization of the spin-active atoms. However, spin information is transmitted less effectively than in one-bond coupling, and 2J values are usually weaker than 1J values. The energy of...
1.7K
Spin–Spin Coupling: Three-Bond Coupling (Vicinal Coupling)01:22

Spin–Spin Coupling: Three-Bond Coupling (Vicinal Coupling)

1.5K
Vicinal or three-bond coupling is commonly observed between protons attached to adjacent carbons. Here, nuclear spin information is primarily transferred via electron spin interactions between adjacent C‑H bond orbitals. This generally favors the antiparallel arrangement of spins, so 3J values are usually positive.
The extent of coupling depends on the C‑C bond length, the two H‑C‑C angles, any electron-withdrawing substituents, and the dihedral angle between the involved orbitals. The...
1.5K
Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

2.1K
Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
2.1K

You might also read

Related Articles

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

Sort by
Same author

Wearable Multi-Sensor Positioning Prototype for Rowing Technique Evaluation.

Sensors (Basel, Switzerland)·2024
Same author

Combining Multichannel RSSI and Vision with Artificial Neural Networks to Improve BLE Trilateration.

Sensors (Basel, Switzerland)·2022
Same author

Kinematic Zenith Tropospheric Delay Estimation with GNSS PPP in Mountainous Areas.

Sensors (Basel, Switzerland)·2021
Same author

Detecting and Correcting for Human Obstacles in BLE Trilateration Using Artificial Intelligence.

Sensors (Basel, Switzerland)·2020
Same author

Using Step Size and Lower Limb Segment Orientation from Multiple Low-Cost Wearable Inertial/Magnetic Sensors for Pedestrian Navigation.

Sensors (Basel, Switzerland)·2019
Same author

GNSS Code Multipath Mitigation by Cascading Measurement Monitoring Techniques.

Sensors (Basel, Switzerland)·2018

Related Experiment Video

Updated: Feb 11, 2026

A Standardized Obstacle Course for Assessment of Visual Function in Ultra Low Vision and Artificial Vision
09:29

A Standardized Obstacle Course for Assessment of Visual Function in Ultra Low Vision and Artificial Vision

Published on: February 11, 2014

13.5K

Tightly-Coupled GNSS/Vision Using a Sky-Pointing Camera for Vehicle Navigation in Urban Areas.

Paul Verlaine Gakne1, Kyle O'Keefe2

  • 1Position, Location and Navigation (PLAN) Group, Department of Geomatics Engineering, Schulich School of Engineering, University of Calgary, 2500 University Drive, N.W., Calgary, AB T2N 1N4, Canada. pvgakne@ucalgary.ca.

Sensors (Basel, Switzerland)
|April 21, 2018
PubMed
Summary

This study integrates upward-facing camera data with Global Navigation Satellite System (GNSS) for improved vehicle navigation in cities. This fusion enhances positioning accuracy, especially in challenging urban environments with limited satellite visibility.

Keywords:
GNSSclustering algorithmsimage segmentationmotion estimationsatellitestightly-coupled integrationupward-facing cameravehicle navigationvisual odometry

More Related Videos

Design and Construction of an Urban Runoff Research Facility
13:48

Design and Construction of an Urban Runoff Research Facility

Published on: August 8, 2014

13.6K
Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
14:55

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street

Published on: January 20, 2023

4.4K

Related Experiment Videos

Last Updated: Feb 11, 2026

A Standardized Obstacle Course for Assessment of Visual Function in Ultra Low Vision and Artificial Vision
09:29

A Standardized Obstacle Course for Assessment of Visual Function in Ultra Low Vision and Artificial Vision

Published on: February 11, 2014

13.5K
Design and Construction of an Urban Runoff Research Facility
13:48

Design and Construction of an Urban Runoff Research Facility

Published on: August 8, 2014

13.6K
Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
14:55

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street

Published on: January 20, 2023

4.4K

Area of Science:

  • Robotics
  • Navigation Systems
  • Computer Vision

Background:

  • Urban environments pose significant challenges for Global Navigation Satellite System (GNSS) accuracy due to signal obstruction.
  • Traditional forward-facing cameras for ego-motion estimation often suffer from outliers caused by dynamic objects.
  • Integrating complementary sensor data is crucial for robust navigation solutions.

Purpose of the Study:

  • To develop and evaluate a tightly-coupled system fusing upward-facing camera imagery with GNSS signals for enhanced urban navigation.
  • To improve the reliability and accuracy of vehicle ego-motion estimation in GNSS-challenged environments.
  • To mitigate the impact of non-sky obstructions on GNSS signal quality and position computation.

Main Methods:

  • A sky-pointing camera synchronized with a GNSS receiver was mounted on a vehicle.
  • Image segmentation was employed to distinguish sky regions, enabling rejection of GNSS signals blocked by non-sky objects (e.g., buildings, trees).
  • A tightly-coupled Kalman filter integrated GNSS measurements and visual ego-motion estimates for the final position solution.

Main Results:

  • The proposed system achieved satisfactory navigation solutions in deep urban canyons, even with fewer than four visible GNSS satellites.
  • Compared to GNSS-only navigation, the system demonstrated significant accuracy improvements.
  • The tightly-coupled approach outperformed loosely-coupled GNSS/vision systems, showing up to 82% better accuracy in worst-case scenarios.

Conclusions:

  • Fusing upward-facing camera data with GNSS provides a robust and accurate navigation solution for urban environments.
  • The sky-pointing camera effectively filters GNSS signals and aids ego-motion estimation by minimizing dynamic object outliers.
  • This method offers a substantial improvement over traditional GNSS-only and loosely-coupled systems in challenging urban canyons.