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

Open and closed-loop control systems01:17

Open and closed-loop control systems

1.1K
Control systems are foundational elements in automation and engineering. They are broadly categorized into open-loop and closed-loop systems. These classifications hinge on the presence or absence of feedback mechanisms, significantly influencing the system's performance, complexity, and application.
An open-loop control system operates without feedback from the output. It consists of two primary elements: the controller and the controlled process. The controller receives an input signal...
1.1K
State Space to Transfer Function01:21

State Space to Transfer Function

346
The conversion of state-space representation to a transfer function is a fundamental process in system analysis. It provides a method for transitioning from a time-domain description to a frequency-domain representation, which is crucial for simplifying the analysis and design of control systems.
The transformation process begins with the state-space representation, characterized by the state equation and the output equation. These equations are typically represented as:
346
Linear time-invariant Systems01:23

Linear time-invariant Systems

520
A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be...
520

You might also read

Related Articles

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

Sort by
Same author

PLA vs PE microplastics with cadmium: Time-dependent divergent and microbial disruption of soil carbon and nitrogen cycling in medicinal plant soils.

Journal of hazardous materials·2026
Same author

A win-win recycling strategy for spent lithium-ion batteries: Prioritized selective lithium extraction afterwards triggers intrinsic catalysis.

Journal of hazardous materials·2026
Same author

Quasi-Maximum Exponential Likelihood Estimation of Conditional Quantiles for GARCH Models Based on High-Frequency Augmented Data.

Entropy (Basel, Switzerland)·2026
Same author

Demography and behavioral ecology of the Indian crested porcupine (Hystrix indica) in Punjab.

Scientific reports·2026
Same author

The multilayer coatings on polylactic acid implants spatiotemporally regulates the microenvironment to enhance antibacterial and osseointegration capacity.

Materials today. Bio·2026
Same author

Neutrophil methylmalonic acid promotes microthrombus formation and adverse cardiac remodeling post-myocardial infarction through activating IL-6 signaling pathway-mediated NETosis.

BMC medicine·2026

Related Experiment Video

Updated: Oct 12, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

682

Real-Time Closed-Loop Detection Method of vSLAM Based on a Dynamic Siamese Network.

Quande Yuan1,2, Zhenming Zhang3, Yuzhen Pi2,4

  • 1School of Computer Technology and Engineering, Changchun Institute of Technology, Changchun 130012, China.

Sensors (Basel, Switzerland)
|November 27, 2021
PubMed
Summary

This study introduces a dynamic Siamese network for robust key frame selection in visual simultaneous localization and mapping (vSLAM). The method enhances real-time performance and accuracy in complex, changing environments.

Keywords:
Siamese networkclosed-loop detectiondeep learningelementwise integration strategysimultaneous localization and mapping

More Related Videos

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.3K

Related Experiment Videos

Last Updated: Oct 12, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

682
Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.3K

Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Visual simultaneous localization and mapping (vSLAM) systems struggle with viewpoint and appearance changes, impacting map consistency.
  • Key frame selection robustness and real-time performance are critical limitations in current vSLAM applications.

Purpose of the Study:

  • To propose a novel real-time closed-loop detection method to address the limitations of traditional vSLAM key frame selection.
  • To enhance the robustness and efficiency of vSLAM systems in dynamic and visually complex environments.

Main Methods:

  • Developed a dynamic Siamese network-based fast conversion learning model to mitigate external changes affecting key frame selection.
  • Implemented an elementwise convergence strategy for precise key frame positioning during closed-loop detection.
  • Utilized a joint training strategy for efficient offline, parallel learning of model parameters from tagged video sequences.

Main Results:

  • Experimental validation on three diverse closed-loop detection datasets confirmed the method's effectiveness.
  • The proposed approach demonstrated significant robustness against complex scene interferences.
  • Achieved improved real-time performance in closed-loop detection compared to existing methods.

Conclusions:

  • The dynamic Siamese network approach offers a robust and efficient solution for key frame selection in vSLAM.
  • This method significantly improves the reliability of vSLAM systems in challenging, real-world scenarios.
  • The findings pave the way for more dependable and faster vSLAM applications.