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A Method of Multiple Dynamic Objects Identification and Localization Based on Laser and RFID.

Wenpeng Fu1, Ran Liu1,2, Heng Wang1

  • 1School of Information Engineering, Southwest University of Science and Technology, Mianyang 621010, China.

Sensors (Basel, Switzerland)
|July 26, 2020
PubMed
Summary
This summary is machine-generated.

This study fuses Radio Frequency Identification (RFID) and laser range finder data for accurate indoor object identification and localization. The novel approach achieves a 90.18% matching rate and 0.33m localization accuracy, even with obstacles.

Keywords:
Pearson correlation coefficientdynamic objects identification and localizationlaser clusterparticle filterradial velocity similarity

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Area of Science:

  • Robotics
  • Sensor Fusion
  • Indoor Localization

Background:

  • Accurate object identification and localization are crucial for human-object interaction in indoor environments.
  • Existing methods using visual or laser sensors are computationally intensive and struggle with obstacles.
  • Radio Frequency Identification (RFID) offers unique IDs but lacks precise localization capabilities.

Purpose of the Study:

  • To develop a robust method for identifying and localizing multiple dynamic objects in indoor environments.
  • To overcome the limitations of individual RFID and laser-based sensing technologies.
  • To improve the accuracy and reliability of object tracking in complex, obstacle-ridden settings.

Main Methods:

  • Fusing RFID phase data with laser range finder measurements to estimate object radial velocities.
  • Employing a sliding time window to identify clusters with high RFID tag similarity.
  • Utilizing the Pearson correlation coefficient (PCC) within a particle filter (PF) update stage for enhanced trajectory estimation.

Main Results:

  • Achieved a 90.18% matching rate for object identification.
  • Reached a localization accuracy of 0.33m in environments with obstacles.
  • Demonstrated superior performance compared to Bray-Curtis similarity and standard particle filter approaches.

Conclusions:

  • The proposed RFID and laser range finder fusion method significantly enhances multi-object identification and localization in indoor environments.
  • The integration of PCC in the particle filter improves tracking accuracy amidst environmental complexities.
  • This approach offers a more effective solution for dynamic object tracking than existing methods.