Jove
Visualize
Contact Us

Related Experiment Videos

NR-UIO: NLOS-Robust UWB-Inertial Odometry Based on Interacting Multiple Model and NLOS Factor Estimation.

Jieum Hyun1, Hyun Myung2

  • 1School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea.

Sensors (Basel, Switzerland)
|December 10, 2021
PubMed
Summary
This summary is machine-generated.

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

Robotic Sensing and Systems for Smart Cities.

Sensors (Basel, Switzerland)·2021
Same author

A Deep Learning-Based Automatic Mosquito Sensing and Control System for Urban Mosquito Habitats.

Sensors (Basel, Switzerland)·2019
Same author

Light Emitting Marker for Robust Vision-Based On-The-Spot Bacterial Growth Detection.

Sensors (Basel, Switzerland)·2017
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

This study introduces a robust ultra-wideband (UWB) ranging model for improved robot localization indoors. The framework integrates UWB and inertial measurement unit (IMU) sensors to overcome limitations in challenging environments.

Area of Science:

  • Robotics
  • Sensor Fusion
  • Indoor Navigation

Background:

  • Global Navigation Satellite System (GNSS) is unavailable indoors.
  • Existing vision- or LiDAR-based sensing struggles in feature-poor environments.
  • Ultra-wideband (UWB) sensors offer an alternative for indoor robot localization.

Purpose of the Study:

  • To develop a robust robot localization framework for indoor environments.
  • To address limitations of UWB positioning, including orientation acquisition and multipath errors.
  • To improve pose estimation performance using integrated UWB and Inertial Measurement Unit (IMU) sensors.

Main Methods:

  • Proposed a framework integrating an Interacting Multiple Model (IMM) filter with UWB and IMU sensors.
  • Developed a non-line-of-sight (NLOS) robust UWB ranging model to mitigate multipath errors.
Keywords:
IMMIMUNLOSUWBodometry

Related Experiment Videos

  • Validated the framework through experiments in real indoor environments.

    • Main Results:

    • The proposed framework demonstrated improved localization performance compared to existing methods.
    • The NLOS robust UWB ranging model effectively reduced errors in multipath environments.
    • Successful integration of UWB and IMU sensors enabled accurate pose estimation.

    Conclusions:

    • The integrated UWB-IMU framework with a robust ranging model enhances indoor robot localization.
    • This approach overcomes key limitations of UWB-only and other sensor-based methods.
    • The study provides a viable solution for reliable robot navigation in GNSS-denied areas.