Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

One-Degree-of-Freedom System

One-Degree-of-Freedom System

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...

Relative Motion Analysis using Rotating Axes

Relative Motion Analysis using Rotating Axes

Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame.
However, to express the relative position of point B relative to point A, an additional frame of reference, denoted as x'y', is necessary. This additional frame not only translates but also rotates relative to the fixed frame, making it...

Relative Motion Analysis using Rotating Axes-Problem Solving

Relative Motion Analysis using Rotating Axes-Problem Solving

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...

Multi-input and Multi-variable systems

Multi-input and Multi-variable systems

Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...

Three-Dimensional Force System

Three-Dimensional Force System

In mechanical engineering, a three-dimensional force system is a system of forces acting in three dimensions, with forces applied along the x, y, and z coordinate axes. The three-dimensional force system is an important concept in mechanical engineering, as it allows engineers to understand and analyze the behavior of objects and structures in three dimensions. By understanding the forces acting on a system, engineers can design more efficient and effective mechanical systems that can withstand...

Three-Dimensional Force System:Problem Solving

Three-Dimensional Force System:Problem Solving

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...

You might also read

Related Articles

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

Sort by

Same author

A Multi-Physics Modeling Framework for Optimizing Spreading and Sintering Parameters in Powder Bed Fusion.

Polymers·2026

Same author

Material Evaluation and Dynamic Powder Deposition Modeling of PEEK/CF Composite for Laser Powder Bed Fusion Process.

Polymers·2023

Same author

Commentary: The Dynamic Features of Lip Corners in Genuine and Posed Smiles.

Frontiers in psychology·2018

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 10, 2025

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

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

Published on: December 15, 2023

Improved Multi-Sensor Fusion Dynamic Odometry Based on Neural Networks.

Lishu Luo¹, Fulun Peng¹, Longhui Dong¹

¹Xi'an Institute of Applied Optics, Xi'an 710065, China.

Sensors (Basel, Switzerland)

|October 16, 2024

Summary

This study introduces a novel dynamic odometry method for robust robot navigation. FAST-LIVO enhances simultaneous localization and mapping (SLAM) in complex environments by removing dynamic elements from sensor data.

Keywords:

dynamic elimination multi-sensor fusion simultaneous localization and mapping (SLAM)

More Related Videos

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

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

Published on: October 27, 2016

Functional Near Infrared Spectroscopy of the Sensory and Motor Brain Regions with Simultaneous Kinematic and EMG Monitoring During Motor Tasks

Functional Near Infrared Spectroscopy of the Sensory and Motor Brain Regions with Simultaneous Kinematic and EMG Monitoring During Motor Tasks

Published on: December 5, 2014

Related Experiment Videos

Last Updated: Jun 10, 2025

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

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

Published on: December 15, 2023

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

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

Published on: October 27, 2016

Functional Near Infrared Spectroscopy of the Sensory and Motor Brain Regions with Simultaneous Kinematic and EMG Monitoring During Motor Tasks

Functional Near Infrared Spectroscopy of the Sensory and Motor Brain Regions with Simultaneous Kinematic and EMG Monitoring During Motor Tasks

Published on: December 5, 2014

Area of Science:

Robotics
Computer Vision
Sensor Fusion

Background:

Autonomous systems require precise localization and mapping (SLAM) in dynamic environments.
Existing SLAM methods struggle with the challenges posed by moving objects and changing scenery.
Reliable navigation for robots, self-driving cars, and drones is critical.

Purpose of the Study:

To propose a dynamic odometry method for high-precision simultaneous localization and mapping (SLAM).
To enhance the robustness and reliability of odometry in real-world dynamic environments.
To integrate multiple sensor modalities for improved environmental perception.

Main Methods:

Developed a dynamic odometry method based on the FAST-LIVO system.
Integrated laser, camera, and inertial measurement unit (IMU) data.
Utilized a lightweight neural network to remove dynamic elements from visual data.
Applied dynamic clustering to LiDAR data to filter out moving objects.
Constructed parallel visual-inertial and LiDAR-inertial odometry subsystems.

Main Results:

The proposed method achieves high-precision pose estimation in dynamic environments.
Demonstrated high continuity and reliability of the odometry system.
Validated the dynamic robustness of the multi-sensor fusion approach.
Successfully filtered dynamic elements from both visual and LiDAR data streams.

Conclusions:

The multi-sensor fusion dynamic odometry method significantly improves SLAM performance in complex, dynamic settings.
FAST-LIVO provides a robust foundation for reliable autonomous navigation.
The integration of neural networks and dynamic clustering enhances environmental data reliability.