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Multi-Floor Indoor Localization Based on Multi-Modal Sensors.

Guangbing Zhou1,2,3, Shugong Xu1, Shunqing Zhang1

  • 1School of Information and Communication Engineering, Shanghai University, Shanghai 200444, China.

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Summary
This summary is machine-generated.

This study introduces a novel high-precision indoor localization system combining lidar, WiFi, and visual data. The integrated approach significantly enhances accuracy and reliability for multi-floor environments.

Keywords:
WiFi signalindoor localizationlidar SLAMmulti-floorvisual-based localization

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Area of Science:

  • Robotics
  • Computer Vision
  • Wireless Communication

Background:

  • High-precision indoor localization is crucial, especially in multi-floor environments.
  • Existing single-sensor methods (wireless, visual, lidar) have limitations.
  • Combining multiple sensors offers potential for improved accuracy and reliability.

Purpose of the Study:

  • To propose a high-precision multi-floor indoor positioning method integrating vision, wireless signals, and lidar.
  • To leverage advancements in Simultaneous Localization and Mapping (SLAM) for hybrid approaches.
  • To explore the synergistic interactions between different sensor modalities.

Main Methods:

  • Utilizing lidar SLAM output as a reference for visual data.
  • Employing WiFi signals for probabilistic rough area estimation.
  • Fine-tuning floor estimation and location using visual image analysis.
  • Developing a joint localization scheme integrating these three modalities.

Main Results:

  • Achieved an average 3D localization accuracy of 0.62 meters.
  • Obtained a Mean Squared Error (MSE) of 1.24 meters for 2D tracking trajectories.
  • Reached an 89.22% accuracy for floor estimation.
  • Localization process completed in under 0.25 seconds.

Conclusions:

  • The proposed joint localization scheme demonstrates superior performance in multi-floor indoor environments.
  • The integration of lidar, WiFi, and visual data significantly enhances localization accuracy and efficiency.
  • The method's speed and precision are suitable for practical implementation in real-world applications.