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

You might also read

Related Articles

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

Sort by

Same author

Periodic Molecular-Level Asymmetric Channels for Synergistical Purification of Iodide Wastewater and Osmotic Power Generation.

Journal of the American Chemical Society·2026

Same author

Spin-state regulation of high-entropy Ruddlesden-Popper perovskite oxides for efficient seawater electrolysis.

Nature communications·2026

Same author

W<sub>2</sub>N<sub>3</sub> nanodot/2D C<sub>3</sub>N<sub>4</sub> heterostructures with interfacial covalent bonding toward Pt-free photocatalytic hydrogen evolution.

Nanoscale·2026

Same author

Iodine-Based Electrolyte Chemistry Enabling Reversible Ca Metal Anodes.

JACS Au·2026

Same author

Stabilization of Single Metal Atoms on Graphitic Carbon Nitride: Synthetic Strategies and Emerging Applications.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026

Same author

Sequential-chain coupling over hierarchical click-sites enables highly selective urea electrosynthesis.

Nature communications·2026

Search research articles

Related Experiment Video

Updated: Jun 13, 2025

Operation of the Collaborative Composite Manufacturing CCM System

Operation of the Collaborative Composite Manufacturing CCM System

Published on: October 1, 2019

Multi-Robot Collaborative Mapping with Integrated Point-Line Features for Visual SLAM.

Yu Xia¹, Xiao Wu², Tao Ma²

¹School of Information Engineering, Yangzhou University, Yangzhou 225127, China.

Sensors (Basel, Switzerland)

|September 14, 2024

Summary

This summary is machine-generated.

This study introduces a multi-robot collaborative mapping method using point-line fusion for improved Simultaneous Localization and Mapping (SLAM) in challenging indoor environments. The approach enhances accuracy and efficiency in weak-texture areas, outperforming traditional methods.

Keywords:

map fusion multi-robot mapping point and line features visual SLAM

More Related Videos

Author Spotlight: Insights into the Analysis of Human Interaction with 3D Virtual Objects

Author Spotlight: Insights into the Analysis of Human Interaction with 3D Virtual Objects

Published on: October 18, 2024

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

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

Published on: April 18, 2025

Related Experiment Videos

Last Updated: Jun 13, 2025

Operation of the Collaborative Composite Manufacturing CCM System

Operation of the Collaborative Composite Manufacturing CCM System

Published on: October 1, 2019

Author Spotlight: Insights into the Analysis of Human Interaction with 3D Virtual Objects

Author Spotlight: Insights into the Analysis of Human Interaction with 3D Virtual Objects

Published on: October 18, 2024

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

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

Published on: April 18, 2025

Area of Science:

Robotics
Computer Vision
Artificial Intelligence

Background:

Visual Simultaneous Localization and Mapping (SLAM) faces challenges in large-scale, weak-texture indoor environments.
Reliance on single robots and point features limits mapping efficiency and accuracy in such scenarios.

Purpose of the Study:

To propose a multi-robot collaborative mapping method using point-line fusion for enhanced SLAM in weak-texture indoor environments.
To improve mapping efficiency and accuracy in large-scale indoor scenes.

Main Methods:

A feature-extraction algorithm combining point and line features for robust visual odometry.
A scene-recognition-based map-fusion method utilizing visual bag of words and photogrammetry-based keyframe extraction.
Integration of Perspective-3-Point (P3P) and Bundle Adjustment (BA) algorithms for multi-robot pose estimation and map fusion.

Main Results:

The proposed algorithm demonstrates higher robustness and mapping accuracy compared to existing methods.
Effective performance in weak-texture and structured indoor environments.
Successful small-scale map fusion achieved.

Conclusions:

The multi-robot collaborative mapping method effectively addresses the limitations of traditional SLAM in challenging indoor environments.
The point-line fusion approach significantly enhances localization and mapping accuracy and efficiency.