Jove
Visualize
Contact Us

Related Concept Videos

Gauss's Law01:07

Gauss's Law

8.1K
If a closed surface does not have any charge inside where an electric field line can terminate, then the electric field line entering the surface at one point must necessarily exit at some other point of the surface. Therefore, if a closed surface does not have any charges inside the enclosed volume, then the electric flux through the surface is zero. What happens to the electric flux if there are some charges inside the enclosed volume? Gauss's law gives a quantitative answer to this question.
8.1K
Magnetostatic Boundary Conditions01:28

Magnetostatic Boundary Conditions

1.1K
An electric field suffers a discontinuity at a surface charge. Similarly, a magnetic field is discontinuous at a surface current. The perpendicular component of a magnetic field is continuous across the interface of two magnetic mediums. In contrast, its parallel component, perpendicular to the current, is discontinuous by the amount equal to the product of the vacuum permeability and the surface current. Like the scalar potential in electrostatics, the vector potential is also continuous...
1.1K
Magnetic Damping01:17

Magnetic Damping

583
Eddy currents can produce significant drag on motion, called magnetic damping. For instance, when a metallic pendulum bob swings between the poles of a strong magnet, significant drag acts on the bob as it enters and leaves the field, quickly damping the motion.
If, however, the bob is a slotted metal plate, the magnet produces a much smaller effect. When a slotted metal plate enters the field, an emf is induced by the change in flux; however, it is less effective because the slots limit the...
583
Potential Due to a Magnetized Object01:24

Potential Due to a Magnetized Object

365
Magnetic dipoles in magnetic materials are aligned when placed under an external magnetic field. For paramagnets and ferromagnets, dipole alignment occurs in the direction of the magnetic field. However, the dipoles align opposite to the field in the case of diamagnets. This state of magnetic polarization due to the external field is called magnetization. Magnetization is defined as the dipole moment per unit volume. It plays a similar role to polarization in electrostatics.
The vector...
365
Magnetic Vector Potential01:15

Magnetic Vector Potential

838
In electrostatics, the electric field can be written as the negative gradient of the potential. In magnetostatics, the zero divergence of the magnetic field ensures that the magnetic field can be expressed as the curl of a vector potential. This potential is known as the magnetic vector potential.
Consider an ideal solenoid with n turns per unit length and radius R. If I is the current through the solenoid, the magnetic field inside the solenoid is expressed as the product of vacuum...
838
Magnetic Flux01:18

Magnetic Flux

3.7K
The magnetic flux measures the number of magnetic field lines passing through a given surface area. The SI unit for magnetic flux is the weber (Wb). Magnetic flux is a scalar quantity. It depends on three factors: the strength of the magnetic field B, the area through which the field lines pass, and the relative orientation of the field with the surface area.
Suppose a surface is divided into elements of area dA. For each element, the component of the magnetic field that is normal to the...
3.7K

You might also read

Related Articles

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

Sort by
Same author

A Tightly Coupled Multibody Dynamics and Multi-Sensor Fusion Algorithm for Simultaneous Kinematics and Kinetics Estimation.

Sensors (Basel, Switzerland)·2026
Same author

Pin-on-Disc Modelling with Mesh Deformation Using Discrete Element Method.

Materials (Basel, Switzerland)·2022
Same author

Inertial Sensors-Applications and Challenges in a Nutshell.

Sensors (Basel, Switzerland)·2020
Same author

Robust Plug-and-Play Joint Axis Estimation Using Inertial Sensors.

Sensors (Basel, Switzerland)·2020
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
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: Sep 26, 2025

Frequency Mixing Magnetic Detection Scanner for Imaging Magnetic Particles in Planar Samples
07:01

Frequency Mixing Magnetic Detection Scanner for Imaging Magnetic Particles in Planar Samples

Published on: June 9, 2016

9.7K

An Extended Kalman Filter for Magnetic Field SLAM Using Gaussian Process Regression.

Frida Viset1, Rudy Helmons2, Manon Kok1

  • 1Delft Center for Systems and Control, Delft University of Technology, 2628 CD Delft, The Netherlands.

Sensors (Basel, Switzerland)
|April 23, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a faster algorithm for simultaneous localization and mapping (SLAM) using magnetic fields. The new method improves position accuracy by efficiently compensating for dead-reckoning drift, comparable to existing approaches.

Keywords:
Kalman filteringlocalizationmagnetic fieldsimultaneous localization and mapping

More Related Videos

Detecting Pre-Stimulus Source-Level Effects on Object Perception with Magnetoencephalography
09:25

Detecting Pre-Stimulus Source-Level Effects on Object Perception with Magnetoencephalography

Published on: July 26, 2019

7.0K
A Method for Tracking the Time Evolution of Steady-State Evoked Potentials
12:03

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials

Published on: May 25, 2019

8.6K

Related Experiment Videos

Last Updated: Sep 26, 2025

Frequency Mixing Magnetic Detection Scanner for Imaging Magnetic Particles in Planar Samples
07:01

Frequency Mixing Magnetic Detection Scanner for Imaging Magnetic Particles in Planar Samples

Published on: June 9, 2016

9.7K
Detecting Pre-Stimulus Source-Level Effects on Object Perception with Magnetoencephalography
09:25

Detecting Pre-Stimulus Source-Level Effects on Object Perception with Magnetoencephalography

Published on: July 26, 2019

7.0K
A Method for Tracking the Time Evolution of Steady-State Evoked Potentials
12:03

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials

Published on: May 25, 2019

8.6K

Area of Science:

  • Robotics
  • Geophysics
  • Computer Science

Background:

  • Odometry systems suffer from position drift, impacting localization accuracy.
  • Magnetic field variations offer a potential source for accurate mapping and localization.
  • Existing magnetic field SLAM methods using Rao-Blackwellized particle filters (RBPF) are computationally intensive.

Purpose of the Study:

  • To develop a computationally efficient algorithm for simultaneous localization and mapping (SLAM) using ambient magnetic field variations.
  • To reduce the computational complexity of magnetic field-based SLAM.
  • To improve position accuracy by compensating for dead-reckoning drift.

Main Methods:

  • Representing the magnetic field map with a reduced-rank Gaussian process (GP) using Laplace basis functions.
  • Deriving analytic expressions for the gradient of the learned magnetic field.
  • Applying an extended Kalman filter (EKF) utilizing the magnetic field gradients for SLAM.

Main Results:

  • The proposed EKF-based algorithm achieves a computational complexity of O(Nm2) per time step, significantly lower than the O(NpNm2) of RBPF methods.
  • The algorithm effectively compensates for position drift in integrated odometry measurements.
  • Performance in drift compensation is comparable to existing RBPF-based magnetic field SLAM techniques.

Conclusions:

  • The EKF-based approach offers a computationally efficient alternative for magnetic field SLAM.
  • This method enables accurate localization by leveraging magnetic field gradients.
  • The algorithm demonstrates practical applicability in real-world scenarios, validated on diverse datasets.