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

Modeling and Similitude01:12

Modeling and Similitude

459
Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
459

You might also read

Related Articles

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

Sort by
Same author

Modeling of Ammunition Dynamic Pressure Measurement Chain in Ballistic Tests.

Sensors (Basel, Switzerland)·2023
Same author

Comparative Analysis of Object Digitization Techniques Applied to the Characterization of Deformed Materials in Ballistic Tests.

Sensors (Basel, Switzerland)·2020
Same author

Three-dimensional virtual traveling navigation and three-dimensional printing models of a normal fetal heart using ultrasonography data.

Prenatal diagnosis·2019
Same author

An interactive experiment combining ultrasound, magnetic resonance imaging, and force feedback technology to physically feel the fetus during pregnancy.

European journal of radiology·2019
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

Related Experiment Video

Updated: Nov 25, 2025

Simulating Imaging of Large Scale Radio Arrays on the Lunar Surface
06:14

Simulating Imaging of Large Scale Radio Arrays on the Lunar Surface

Published on: July 30, 2020

5.2K

Development and Validation of LiDAR Sensor Simulators Based on Parallel Raycasting.

Guilherme Ferreira Gusmão1,2, Carlos Roberto Hall Barbosa1, Alberto Barbosa Raposo2

  • 1Postgraduate Programme in Metrology, Pontifical Catholic University of Rio de Janeiro, Rua Marquês de São Vicente, 225, Gávea, Rio de Janeiro, 22451-900 RJ, Brazil.

Sensors (Basel, Switzerland)
|December 18, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a guideline for developing modular Light Detection and Ranging (LiDAR) simulators using parallel raycasting. These simulators generate synthetic point clouds, improving efficiency for large-scale data applications.

Keywords:
LiDARraycastingremote sensingsensor simulatorsynthetic point cloud

More Related Videos

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants
06:28

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants

Published on: August 26, 2018

6.2K
Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research
07:15

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

Published on: December 18, 2020

4.8K

Related Experiment Videos

Last Updated: Nov 25, 2025

Simulating Imaging of Large Scale Radio Arrays on the Lunar Surface
06:14

Simulating Imaging of Large Scale Radio Arrays on the Lunar Surface

Published on: July 30, 2020

5.2K
A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants
06:28

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants

Published on: August 26, 2018

6.2K
Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research
07:15

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

Published on: December 18, 2020

4.8K

Area of Science:

  • Geospatial technology
  • Computer vision
  • Metrology

Background:

  • Three-dimensional (3D) imaging, particularly point cloud generation, is crucial for academia and industry.
  • Acquiring 3D data is often costly and time-consuming, hindering applications like machine learning training and environmental surveys.
  • Synthetic data generation using simulators offers a cost-effective and efficient alternative.

Purpose of the Study:

  • To present a guideline for developing modular Light Detection and Ranging (LiDAR) system simulators.
  • To model LiDAR sensors using metrological parameters and error models.
  • To introduce a sensor calibration procedure by comparing simulated and real-world data.

Main Methods:

  • Development of modular LiDAR simulators based on parallel raycasting algorithms.
  • Modeling of the LiDAR sensor with precise metrological parameters and error models.
  • Implementation of a sensor calibration procedure against a commercial LiDAR sensor.

Main Results:

  • Robust generation of synthetic point clouds across various scenarios using the developed simulator.
  • Successful creation of datasets for concept testing and hybrid real/virtual data applications.
  • Demonstration of the simulator's effectiveness in diverse environmental and industrial contexts.

Conclusions:

  • The developed modular LiDAR simulator provides a viable solution for efficient synthetic point cloud generation.
  • The guideline facilitates the creation of adaptable simulators for various 3D imaging applications.
  • The approach enhances the feasibility of using large datasets for machine learning, environmental monitoring, and subsea surveys.