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

Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

152
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...
152
Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

199
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...
199
Uncertainty: Overview00:59

Uncertainty: Overview

1.1K
In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
1.1K
Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

1.1K
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.
1.1K
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

999
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
999
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

1.2K
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
1.2K

You might also read

Related Articles

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

Sort by
Same author

High-performance electrochemical sensing of 2-aminophenol enabled by a boron-doped diamond electrode.

RSC advances·2026
Same author

From Single Atom to Five-Atom Cluster Catalysts on Boron-Doped Diamond: Interface Engineering and Dynamic Active Sites Exploration for Acidic OER.

The journal of physical chemistry letters·2026
Same author

Research progress of high-entropy catalysts in electrochemical oxidation of organic small molecules.

Chemical communications (Cambridge, England)·2026
Same author

d-Orbital modulation of high-entropy sulfides with amorphous/crystalline heterostructures for simultaneous hydrogen production and sulfur recovery.

Chemical science·2026
Same author

Electronic Structure Modulation in High-Entropy@Cu<sub><i>x</i></sub>S<sub><i>y</i></sub> Heterostructured Nanorods via Interface Engineering for Enhanced Multifunctional Electrocatalysis.

Inorganic chemistry·2026
Same author

A relative methylation ordering biomarker of lactylation-related genes predicts prognosis and therapeutic response in cutaneous melanoma.

Epigenetics·2026
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

Related Experiment Video

Updated: Oct 12, 2025

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

16.8K

Uncertainty Estimation of Dense Optical Flow for Robust Visual Navigation.

Yonhon Ng1, Hongdong Li1, Jonghyuk Kim2

  • 1College of Engineering & Computer Science, The Australian National University, Canberra, ACT 2601, Australia.

Sensors (Basel, Switzerland)
|November 27, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a new dense optical-flow algorithm for robot navigation, improving simultaneous localization and mapping (SLAM) by fully utilizing optical flow uncertainty for enhanced accuracy and collision avoidance.

Keywords:
dense optical flowepipolar constraintsmonocular visual navigationuncertainty estimation

More Related Videos

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
07:05

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Published on: October 27, 2016

9.3K
Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques
10:53

Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques

Published on: March 12, 2019

7.2K

Related Experiment Videos

Last Updated: Oct 12, 2025

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

16.8K
Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
07:05

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Published on: October 27, 2016

9.3K
Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques
10:53

Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques

Published on: March 12, 2019

7.2K

Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Monocular Simultaneous Localization and Mapping (SLAM) is crucial for robot navigation.
  • Existing methods often underutilize optical flow uncertainty, limiting performance.
  • Dense optical flow aids ego-motion estimation and obstacle avoidance.

Purpose of the Study:

  • To develop a novel dense optical-flow algorithm for monocular SLAM.
  • To improve robot localization and mapping accuracy and robustness.
  • To enhance collision avoidance capabilities in autonomous systems.

Main Methods:

  • Estimating full uncertainty of dense optical flow.
  • Proposing a new eight-point algorithm using Mahalanobis distance.
  • Integrating with pose-graph optimization for SLAM.

Main Results:

  • Demonstrated enhanced robustness and accuracy in SLAM.
  • Validated performance on the KITTI autonomous car dataset.
  • Achieved superior results on aerial monocular datasets.

Conclusions:

  • The proposed dense optical-flow algorithm significantly advances monocular SLAM.
  • Full uncertainty estimation in optical flow is key for improved robot perception.
  • The method offers a more reliable solution for autonomous navigation.