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

One-Degree-of-Freedom System01:24

One-Degree-of-Freedom System

568
In mechanical engineering, one-degree-of-freedom systems form the basis of a wide range of electrical and mechanical components. Using these models, engineers can predict the behavior of various parts in a larger system, which gives them insight into how different forces interact with each other.
A one-degree-of-freedom system is defined by an independent variable that determines its state and behavior. One example of a one-degree-of-freedom system is a simple harmonic oscillator, such as a...
568
Three-Dimensional Force System:Problem Solving01:30

Three-Dimensional Force System:Problem Solving

948
A three-dimensional force system refers to a scenario in which three forces act simultaneously in three different directions. This type of problem is commonly encountered in physics and engineering, where it is necessary to calculate the resultant force on the system, which can then be used to predict or analyze the behavior of the object or structure under consideration.
To solve a three-dimensional force system, first resolve each force into its respective scalar components. Do this using...
948
Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

470
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...
470

You might also read

Related Articles

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

Sort by
Same author

Structural Insights into AgBi(SC<sub>12</sub>H<sub>25</sub>)<sub>4</sub> Mixed-Metal <i>n</i>-Alkanethiolate: Heterometallic Thiolate Bridging and Metallophilic Interaction-Directed Self-Assembly.

Inorganic chemistry·2026
Same author

Exploring the correlates of COVID-19 vaccination inequity: a global analysis using machine learning from a health economic lens.

Frontiers in health services·2026
Same author

Predicting and identifying correlates of inequalities in breast cancer screening uptake using national level data from India.

Frontiers in artificial intelligence·2026
Same author

Classifying complex multimorbidity using latent class analysis and machine learning to generate insights into clustering of mental and cardiometabolic conditions.

PloS one·2025
Same author

Machine learning to predict the role of CHWs in shifting birth preferences away from homebirth in India.

Scientific reports·2025
Same author

LncRNA CASC19 promotes pancreatic cancer progression by increasing PSPC1 protein stability and facilitating the oncogenic PSPC1/ β-Catenin pathway.

Molecular medicine (Cambridge, Mass.)·2025

Related Experiment Video

Updated: Oct 8, 2025

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.9K

A Decentralized Sensor Fusion Scheme for Multi Sensorial Fault Resilient Pose Estimation.

Moumita Mukherjee1, Avijit Banerjee1, Andreas Papadimitriou1

  • 1Robotics and AI Group, Department of Computer, Electrical and Space Engineering, Luleå University of Technology, SE-97187 Luleå, Sweden.

Sensors (Basel, Switzerland)
|December 28, 2021
PubMed
Summary

This study introduces a resilient pose estimation scheme using a novel decentralized, two-layered fusion architecture. It enhances accuracy and reliability, even with sensor failures, for micro aerial vehicles.

Keywords:
decentralized fusionfault resilient optimal information fusionlinear minimum variancemaximum likelihood functionmulti sensor fusionoptimal information filter

More Related Videos

Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring
06:32

Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring

Published on: July 14, 2023

1.5K
Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
09:41

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Published on: April 21, 2023

1.8K

Related Experiment Videos

Last Updated: Oct 8, 2025

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.9K
Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring
06:32

Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring

Published on: July 14, 2023

1.5K
Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
09:41

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Published on: April 21, 2023

1.8K

Area of Science:

  • Robotics and Autonomous Systems
  • Sensor Fusion
  • Navigation and Localization

Background:

  • Accurate pose estimation is critical for autonomous systems, but traditional methods struggle with sensor noise and failures.
  • Multi-sensor fusion offers improved robustness, yet centralized approaches can be vulnerable to single points of failure.

Purpose of the Study:

  • To propose a novel decentralized, two-layered, multi-sensorial fusion architecture for resilient pose estimation.
  • To introduce a Fault Resilient Optimal Information Fusion (FR-OIF) paradigm for enhanced accuracy and self-resiliency.
  • To validate the proposed scheme's effectiveness and superiority against centralized methods.

Main Methods:

  • A two-layered fusion architecture with distributed nodes in the first layer using extended Kalman filters.
  • Integration of pose information from diverse sensors (3D lidar, camera, UWB, IMU).
  • Implementation of the FR-OIF paradigm in the second layer, employing maximum likelihood fusion and fault isolation.

Main Results:

  • The proposed architecture successfully achieved resilient pose estimation for a micro aerial vehicle.
  • Experimental results demonstrated high accuracy and robustness in the presence of sensor failures and erroneous measurements.
  • The decentralized FR-OIF approach outperformed the classical centralized multi-sensorial fusion method.

Conclusions:

  • The novel decentralized two-layered fusion architecture provides a highly resilient and accurate pose estimation scheme.
  • The FR-OIF paradigm effectively handles sensor inaccuracies and failures, ensuring reliable localization.
  • This approach offers a significant advancement for robust navigation in autonomous systems.