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IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

880
IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...
880

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Research on a Visual/Ultra-Wideband Tightly Coupled Fusion Localization Algorithm.

Pin Jiang1, Chen Hu1, Tingting Wang1

  • 1College of Mechanical and Electrical Engineering, Hunan Agricultural University, Changsha 410128, China.

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|March 13, 2024
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Summary
This summary is machine-generated.

This study introduces a visual SLAM/UWB fusion method for robot localization, improving accuracy in challenging environments by identifying and correcting Ultra-Wideband errors using visual odometry displacement. The combined system enhances positioning accuracy and stability.

Keywords:
extended Kalman filteringmulti-sensor fusionultra-wideband positioningvisual SLAM

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Area of Science:

  • Robotics
  • Computer Vision
  • Navigation Systems

Background:

  • Precise mobile robot localization is essential for autonomous navigation.
  • Satellite positioning systems struggle in environments with weak signals or complex terrain, like forests.
  • Existing methods often lack the accuracy and stability required for demanding autonomous tasks.

Purpose of the Study:

  • To develop a robust and accurate localization method for mobile robots in challenging environments.
  • To propose a tightly coupled visual SLAM/Ultra-Wideband (UWB) localization approach.
  • To design a UWB non-line-of-sight (NLOS) error identification method.

Main Methods:

  • A tightly coupled localization method integrating visual SLAM and UWB ranging.
  • A novel UWB NLOS error identification technique using visual odometry displacement.
  • Extended Kalman filtering for data fusion of visual odometry and UWB measurements.
  • Experimental validation using a dedicated platform in outdoor environments.

Main Results:

  • The proposed vision SLAM/UWB tightly coupled method significantly outperforms individual UWB or loosely coupled systems.
  • The system effectively identifies and mitigates UWB non-line-of-sight errors.
  • Experimental results demonstrate improved positioning accuracy and enhanced system stability.

Conclusions:

  • The vision SLAM/UWB tightly coupled localization method offers superior performance for mobile robots.
  • The developed UWB error identification enhances the reliability of combined positioning systems.
  • This approach provides a viable solution for accurate robot navigation in GPS-denied or complex environments.