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

You might also read

Related Articles

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

Sort by
Same author

Engineering Rhodotorula toruloides as a platform organism for de novo synthesis of fatty-acid esters.

Nature communications·2026
Same author

Mn/V Co-Doping Enables Multielectron Transfer and Above-Theoretical Capacity in Na<sub>4</sub>Fe<sub>3</sub>(PO<sub>4</sub>)<sub>2</sub>P<sub>2</sub>O<sub>7</sub> Cathode.

Small methods·2026
Same author

Study on the canopy structure and light distribution of <i>Hippophae rhamnoides</i> at different ages.

Frontiers in plant science·2026
Same author

Up-regulation of SLC26A3 enhances oxaliplatin sensitivity in colorectal cancer.

Discover oncology·2026
Same author

Pan-genomic insights into resistance, virulence, and stress adaptation in <i>Clostridium perfringens</i> from the Tibetan Plateau.

mSphere·2026
Same author

Licochalcone A from Ma-Xing-Shi-Gan decoction to prevent Asthma through the inhibition of ferroptosis CD4+ T Cell by GART/HSP90α signaling pathway.

International immunopharmacology·2026

Related Experiment Video

Updated: Nov 20, 2025

Haptic/Graphic Rehabilitation: Integrating a Robot into a Virtual Environment Library and Applying it to Stroke Therapy
13:44

Haptic/Graphic Rehabilitation: Integrating a Robot into a Virtual Environment Library and Applying it to Stroke Therapy

Published on: August 8, 2011

14.3K

Visual SLAM for robot navigation in healthcare facility.

Baofu Fang1,2,3, Gaofei Mei1, Xiaohui Yuan4

  • 1School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, 230009, China.

Pattern Recognition
|January 26, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a new Simultaneous Localization and Mapping (SLAM) technology using semantic descriptors and knowledge graphs to enhance robot navigation in dynamic hospital environments, improving efficiency and reducing infection risks during pandemics.

Keywords:
COVID-19 pandemicDynamic scenesKnowledge graphSemantic descriptorsVisual SLAM

More Related Videos

Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery
05:12

Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery

Published on: August 12, 2021

2.3K
Dynamic Navigation for Dental Implant Placement
05:42

Dynamic Navigation for Dental Implant Placement

Published on: September 13, 2022

4.2K

Related Experiment Videos

Last Updated: Nov 20, 2025

Haptic/Graphic Rehabilitation: Integrating a Robot into a Virtual Environment Library and Applying it to Stroke Therapy
13:44

Haptic/Graphic Rehabilitation: Integrating a Robot into a Virtual Environment Library and Applying it to Stroke Therapy

Published on: August 8, 2011

14.3K
Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery
05:12

Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery

Published on: August 12, 2021

2.3K
Dynamic Navigation for Dental Implant Placement
05:42

Dynamic Navigation for Dental Implant Placement

Published on: September 13, 2022

4.2K

Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • The COVID-19 pandemic highlighted the need for efficient and safe hospital operations.
  • Existing Simultaneous Localization and Mapping (SLAM) technologies struggle with dynamic environments common in hospitals.

Purpose of the Study:

  • To develop a novel SLAM technology for dynamic environments like hospitals.
  • To improve robot navigation accuracy, efficiency, and safety in healthcare settings.
  • To reduce the risk of cross-infection and aid in pandemic control.

Main Methods:

  • Utilized RGB and depth images for SLAM.
  • Developed a method incorporating knowledge graphs to handle dynamic objects.
  • Constructed rotation-invariant and illumination-robust semantic descriptors based on a knowledge graph.
  • Integrated semantic descriptors to eliminate dynamic objects and improve tracking.

Main Results:

  • Demonstrated significant improvements in accuracy and robustness compared to state-of-the-art methods in dynamic environments.
  • Successfully established semantic maps for robot-assisted medical services in healthcare facilities.
  • Achieved competitive computational efficiency.

Conclusions:

  • The proposed knowledge graph-based semantic SLAM method effectively addresses challenges in dynamic environments.
  • This technology enhances robot positioning and tracking accuracy, crucial for hospital applications.
  • The approach offers a promising solution for improving healthcare operations and safety, particularly during health crises.