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

Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device

Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device

Surveyors use Global Positioning System (GPS) technology to measure the precise location and elevation of points on Earth. In a recent survey, GPS receivers were used to determine the coordinates and elevations of two park monuments. The process involved careful mission planning, data collection, and correction to ensure accuracy. The survey began with mission planning to identify optimal satellite visibility and minimize Position Dilution of Precision (PDOP). A geodetic control point...

You might also read

Related Articles

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

Sort by

Same author

ResNet-SE-CBAM Siamese Networks for Few-Shot and Imbalanced PCB Defect Classification.

Sensors (Basel, Switzerland)·2025

Same author

Person Re-Identification with Improved Performance by Incorporating Focal Tversky Loss in AGW Baseline.

Sensors (Basel, Switzerland)·2022

Same author

Vision-Based Learning from Demonstration System for Robot Arms.

Sensors (Basel, Switzerland)·2022

Same author

Efficient Spin-Orbit Torque Generation in Semiconducting WTe<sub>2</sub> with Hopping Transport.

ACS applied materials & interfaces·2021

Same author

Deep Learning-Based Real-Time Multiple-Person Action Recognition System.

Sensors (Basel, Switzerland)·2020

Same author

Iterative Pose Refinement for Object Pose Estimation Based on RGBD Data.

Sensors (Basel, Switzerland)·2020

Search research articles

Related Experiment Video

Updated: Nov 29, 2025

Author Spotlight: Integrating Ultrasound Imaging with Biochemical Markers for Thyroid Disease Diagnosis

Author Spotlight: Integrating Ultrasound Imaging with Biochemical Markers for Thyroid Disease Diagnosis

Published on: February 9, 2024

Cost Effective Mobile Mapping System for Color Point Cloud Reconstruction.

Cheng-Wei Peng¹, Chen-Chien Hsu¹, Wei-Yen Wang¹

¹Department of Electrical Engineering, National Taiwan Normal University, Taipei 106, Taiwan.

Sensors (Basel, Switzerland)

|November 19, 2020

Summary

This summary is machine-generated.

This study introduces a cost-effective mobile mapping system (MMS) for autonomous vehicles, generating accurate 3D color point clouds. The system achieves centimeter-level accuracy, making high-definition map creation affordable for self-driving applications.

Keywords:

HD map autonomous driving color point cloud mobile mapping system (MMS)

More Related Videos

Photorealistic Learned Landscapes for Augmented Reality

Photorealistic Learned Landscapes for Augmented Reality

Published on: June 27, 2025

High-Throughput Total Internal Reflection Fluorescence and Direct Stochastic Optical Reconstruction Microscopy Using a Photonic Chip

High-Throughput Total Internal Reflection Fluorescence and Direct Stochastic Optical Reconstruction Microscopy Using a Photonic Chip

Published on: November 16, 2019

Related Experiment Videos

Last Updated: Nov 29, 2025

Author Spotlight: Integrating Ultrasound Imaging with Biochemical Markers for Thyroid Disease Diagnosis

Author Spotlight: Integrating Ultrasound Imaging with Biochemical Markers for Thyroid Disease Diagnosis

Published on: February 9, 2024

Photorealistic Learned Landscapes for Augmented Reality

Photorealistic Learned Landscapes for Augmented Reality

Published on: June 27, 2025

High-Throughput Total Internal Reflection Fluorescence and Direct Stochastic Optical Reconstruction Microscopy Using a Photonic Chip

High-Throughput Total Internal Reflection Fluorescence and Direct Stochastic Optical Reconstruction Microscopy Using a Photonic Chip

Published on: November 16, 2019

Area of Science:

Robotics and Autonomous Systems
Geomatics Engineering
Computer Vision

Background:

Survey-grade Lidar-based mobile mapping systems (MMSs) offer high accuracy but are prohibitively expensive for widespread autonomous driving adoption.
Existing systems face challenges in cost-effectiveness and precise 3D color point cloud generation for autonomous vehicle applications.

Purpose of the Study:

To develop a cost-effective MMS capable of generating accurate 3D color point clouds for autonomous driving.
To address the limitations of high-cost, survey-grade systems in practical autonomous vehicle implementation.

Main Methods:

Sensor timestamp synchronization and camera-Lidar calibration for integrating color attributes into Lidar points.
Utilizing control points for fine-tuning absolute point cloud positioning.
Employing Normal Distribution Transform (NDT) localization to refine trajectories and mitigate multi-scan dispersion issues, overcoming Global Navigation Satellite System/Inertial Measurement Unit (GNSS/IMU) limitations.

Main Results:

The proposed MMS successfully generates 3D color point clouds with centimeter-level position accuracy.
The system demonstrates the ability to meet the stringent accuracy requirements for high-definition (HD) maps essential for autonomous driving.

Conclusions:

The developed cost-effective MMS provides a viable solution for creating accurate 3D color point clouds for autonomous vehicles.
This advancement facilitates the affordable creation of HD maps, a critical component for the advancement of self-driving technology.