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Indoor Localization Based on VIO System and Three-Dimensional Map Matching.

Jitong Zhang1,2,3, Mingrong Ren1,2,3, Pu Wang1,2,3

  • 1College of Automation, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China.

Sensors (Basel, Switzerland)
|May 20, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a new indoor localization method using visual inertial odometry (VIO) and 3D map matching. The enhanced algorithm improves VIO positioning accuracy in challenging environments like poor lighting and featureless areas.

Keywords:
conditional random fieldindoor localizationmap matchingthree-dimensionalvisual inertial odometry

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Area of Science:

  • Robotics
  • Computer Vision
  • Geomatics

Background:

  • High-precision indoor localization is crucial for many applications.
  • Visual Inertial Odometry (VIO) systems offer advanced indoor localization capabilities.
  • VIO systems struggle in environments with poor lighting or lack of distinct features.

Purpose of the Study:

  • To develop an improved indoor localization algorithm addressing VIO limitations.
  • To enhance VIO system accuracy in challenging indoor environments.
  • To create a more universally applicable indoor localization solution.

Main Methods:

  • Proposed an indoor localization algorithm integrating VIO with 3D map matching.
  • Incorporated height matching into traditional 2D map matching for broader applicability.
  • Utilized a conditional random field (CRF) model with a 3D digital map as prior information.
  • Employed VIO-derived pose and position data as observational input for the CRF.

Main Results:

  • The proposed algorithm effectively corrects VIO trajectories using optimal state sequences.
  • Demonstrated significant improvements in positioning accuracy for VIO systems.
  • Validated performance in indoor areas characterized by poor lighting and featureless conditions.

Conclusions:

  • The novel VIO-based 3D map matching algorithm enhances indoor localization precision.
  • The method offers a robust solution for VIO systems operating in difficult environments.
  • This approach broadens the applicability of VIO for reliable indoor navigation.