Jove
Visualize
Contact Us

Related Concept Videos

Relative Motion Analysis using Rotating Axes - Acceleration01:22

Relative Motion Analysis using Rotating Axes - Acceleration

367
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. The absolute velocity of point B is determined by adding the absolute velocity of point A, the relative velocity of point B in the rotating frame, and the effects caused by the angular velocity within the rotating frame.
Time differentiation is...
367
Relative Motion Analysis - Acceleration01:10

Relative Motion Analysis - Acceleration

388
A slider-crank mechanism converts rotational motion from the crank into linear motion of the slider or vice versa. This mechanism consists of three main parts: the crank, the connecting rod, and the slider. The movement of the slider-crank is an example of general plane motion as the fluctuating angle between the crank and the connecting rod. Consider a segment AB where point A is at the end of the slider and point B is on the diametrically opposite end to point A, on a crack. The variance in...
388
Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

432
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...
432
Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

498
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...
498
Area Computation by the Alternative Coordinate Method01:24

Area Computation by the Alternative Coordinate Method

123
The alternative coordinate method, also known as the Shoelace Formula, is a technique for determining the area of a traverse using Cartesian coordinates. This method relies on the sequential arrangement of x and y coordinates for each point of the shape, ensuring accuracy and ease of application.In this approach, each corner's x and y coordinates are listed as fractions, with the x-coordinate as the numerator and the y-coordinate as the denominator. These coordinates are arranged sequentially...
123
Design Example: Measuring Distance Between Two Points with Obstructions01:10

Design Example: Measuring Distance Between Two Points with Obstructions

79
When measuring distances in areas with physical obstructions, such as a lake in a field, surveyors must employ techniques to calculate accurate lengths without direct line measurements. One effective method is the offset technique, which allows for precise distance estimation over inaccessible stretches.In this scenario, a surveyor must measure a side of an area that crosses a lake. Since the measuring tape cannot span the lake, the surveyor begins by establishing a baseline that aligns with...
79

You might also read

Related Articles

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

Sort by
Same author

Parallax-Tolerant Weakly-Supervised Pixel-Wise Deep Color Correction for Image Stitching of Pinhole Camera Arrays.

Sensors (Basel, Switzerland)·2025
Same author

Semantic Segmentation of Remote Sensing Images Depicting Environmental Hazards in High-Speed Rail Network Based on Large-Model Pre-Classification.

Sensors (Basel, Switzerland)·2024
Same author

Compressed Imaging Reconstruction Based on Block Compressed Sensing with Conjugate Gradient Smoothed <i>l</i><sub>0</sub> Norm.

Sensors (Basel, Switzerland)·2023
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: Aug 8, 2025

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging
09:19

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

Published on: April 18, 2025

706

A Structure-Based Iterative Closest Point Using Anderson Acceleration for Point Clouds with Low Overlap.

Chao Zeng1,2, Xiaomei Chen1,2, Yongtian Zhang1,2

  • 1School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China.

Sensors (Basel, Switzerland)
|February 28, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel point-cloud registration method using high-level structural features, enabling accurate scans with minimal overlap. The enhanced Iterative Closest Point (ICP) algorithm improves efficiency and robustness in laser datasets.

Keywords:
Anderson accelerationoverlappoint cloudsregistration

More Related Videos

Measuring 3D In-vivo Shoulder Kinematics using Biplanar Videoradiography
06:09

Measuring 3D In-vivo Shoulder Kinematics using Biplanar Videoradiography

Published on: March 12, 2021

3.2K
Sample Drift Correction Following 4D Confocal Time-lapse Imaging
10:04

Sample Drift Correction Following 4D Confocal Time-lapse Imaging

Published on: April 12, 2014

16.5K

Related Experiment Videos

Last Updated: Aug 8, 2025

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging
09:19

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

Published on: April 18, 2025

706
Measuring 3D In-vivo Shoulder Kinematics using Biplanar Videoradiography
06:09

Measuring 3D In-vivo Shoulder Kinematics using Biplanar Videoradiography

Published on: March 12, 2021

3.2K
Sample Drift Correction Following 4D Confocal Time-lapse Imaging
10:04

Sample Drift Correction Following 4D Confocal Time-lapse Imaging

Published on: April 12, 2014

16.5K

Area of Science:

  • Computer Vision
  • Robotics
  • Geometric Processing

Background:

  • Traditional point-cloud registration demands significant overlap between scans, limiting data acquisition flexibility.
  • Manual scanner repositioning is often required to ensure sufficient overlap for accurate registration.

Purpose of the Study:

  • To develop a robust point-cloud registration method that overcomes the limitations of large overlap requirements.
  • To enhance the accuracy and efficiency of registration for laser datasets with low overlap.

Main Methods:

  • Feature extraction based on high-level structural information to establish correspondences.
  • Formulation of an optimization problem solved as a fixed-point problem using Lie algebra for transform matrix parameterization.
  • Integration of Anderson acceleration with heuristics to expedite convergence.

Main Results:

  • The proposed Iterative Closest Point (ICP) method demonstrates robustness and high registration accuracy on point clouds with low overlap.
  • The method effectively utilizes structural features within the overlap region, rather than point-to-point correspondences.
  • Achieved competitive computational time compared to existing prevalent registration methods.

Conclusions:

  • The novel ICP approach significantly advances point-cloud registration by enabling accurate results with reduced overlap constraints.
  • This method offers a more flexible and efficient solution for 3D data acquisition and processing in various applications.