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FILNet: Fast Image-Based Indoor Localization Using an Anchor Control Network.

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  • 1School of Communication Engineering, Hangzhou Dianzi University, Hangzhou 310018, China.

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Summary
This summary is machine-generated.

This study introduces FILNet, a fast indoor localization method using an anchor control network. It significantly enhances feature matching recall and speed, improving localization accuracy for real-world applications.

Keywords:
affine invariance enhancementanchor control networkfast spatial indexingfeature matchingindoor localization

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Area of Science:

  • Computer Vision
  • Robotics
  • Indoor Localization

Background:

  • Accurate indoor localization is crucial for various applications.
  • Existing image-based methods face challenges in feature matching speed and accuracy.
  • Perspective transformations and feature matching efficiency hinder performance.

Purpose of the Study:

  • To design a fast image-based indoor localization method (FILNet).
  • To improve localization accuracy and reduce feature matching duration.
  • To address challenges in feature matching and image perspective transformation.

Main Methods:

  • Developed a two-stage FILNet algorithm: offline database construction and online localization.
  • Utilized an anchor control network and visual-inertial odometry (VIO) for feature fingerprinting.
  • Implemented an affine invariance enhancement algorithm and a fast spatial indexing approach.
  • Employed a homography matrix filter model for feature matching verification.

Main Results:

  • FILNet improved feature matching recall from 26% to 57% (angular deviation < 60 degrees).
  • Reduced average feature matching time from 30.3 ms to 12.7 ms compared to K-D tree.
  • Increased the percentage of images with localization error < 0.1m from 31.61% to 55.89%.

Conclusions:

  • FILNet offers a significant improvement in image-based indoor localization.
  • The method enhances affine invariance, feature matching speed, and localization accuracy.
  • FILNet demonstrates robust performance in real-world scenarios.