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Research on PF-SLAM Indoor Pedestrian Localization Algorithm Based on Feature Point Map.

Jingjing Shi1,2,3, Mingrong Ren4,5,6, Pu Wang7,8,9

  • 1College of Automation, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China. shijingjing123@emails.bjut.edu.cn.

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|November 15, 2018
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Summary
This summary is machine-generated.

This study introduces a novel indoor pedestrian positioning algorithm using particle filter-SLAM and inertial measurement units. It improves accuracy by creating feature point maps and correcting heading errors without prior map knowledge.

Keywords:
feature point matchingindoor localizationinertial navigation system (INS)particle filteringsimultaneous localization and map building (SLAM) algorithm

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Area of Science:

  • Indoor positioning
  • Pedestrian navigation
  • Simultaneous Localization and Mapping (SLAM)

Background:

  • Existing indoor positioning methods often rely on pre-existing electronic maps.
  • Micro-electromechanical systems (MEMS) inertial devices are popular for indoor pedestrian positioning but face challenges with map dependency and accuracy.

Purpose of the Study:

  • To develop an indoor pedestrian location algorithm that does not require prior map knowledge.
  • To enhance indoor pedestrian positioning accuracy using a feature point map and SLAM.
  • To improve heading angle estimation and correct position errors in pedestrian dead reckoning.

Main Methods:

  • Proposed a particle filter-SLAM (PF-SLAM) algorithm for indoor pedestrian localization based on a feature point map.
  • Implemented a turn-straight-state threshold detection method to correct heading angle errors.
  • Developed a feature point matching algorithm to correct divergent particle positions at corners.
  • Integrated SLAM with an inertial navigation system (INS) for real-time map updates and positioning.

Main Results:

  • Achieved accurate indoor pedestrian positioning without relying on existing maps.
  • Successfully suppressed heading angle error accumulation through improved detection methods.
  • Corrected pedestrian position errors effectively, especially at corners.
  • Enabled long-time, high-precision, and low-cost indoor positioning.

Conclusions:

  • The proposed PF-SLAM algorithm offers a robust solution for indoor pedestrian positioning independent of prior map information.
  • The integration of map building and localization enhances positioning accuracy and reliability.
  • The method provides a practical approach for cost-effective and precise indoor navigation systems.