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Analytical Formalism for Data Representation and Object Detection with 2D LiDAR: Application in Mobile Robotics.

Leonardo A Fagundes1,2, Alexandre G Caldeira1, Matheus B Quemelli1,2

  • 1Robotics Specialization Center (NERo), Department of Electrical Engineering, Federal University of Viçosa, Viçosa 36570-000, MG, Brazil.

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Summary

This study introduces a unified analytical approach for object identification and localization using 2D LiDAR sensors in mobile robotics. This method standardizes data representation and modeling for enhanced autonomous navigation.

Keywords:
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Area of Science:

  • Robotics
  • Computer Vision
  • Sensor Fusion

Background:

  • Laser scanners (LiDAR) are crucial for mobile robot navigation in diverse environments due to their accuracy.
  • Current research lacks a standardized data representation and modeling strategy for LiDAR sensor data.
  • This heterogeneity hinders the development and integration of new applications.

Purpose of the Study:

  • To develop a formal analytical approach for object identification and localization using 2D LiDAR data.
  • To establish a common formalism for representing and processing LiDAR measurements.
  • To facilitate the design and implementation of advanced robotics applications.

Main Methods:

  • Formal definition of Laser Imaging, Detection, And Ranging (LIDAR) sensor measurements and their representation.
  • Development of an analytical framework for object identification, property extraction, and localization.
  • Experimental validation in semi-structured environments relevant to autonomous navigation.

Main Results:

  • Successful demonstration of multiple object detection and identification based on the proposed analytical representation.
  • Validation of the approach's feasibility in generic, semi-structured environments.
  • Quantification of accuracy and precision in object localization.

Conclusions:

  • The proposed analytical approach provides a unified formalism for 2D LiDAR data processing in mobile robotics.
  • This standardization simplifies object identification, localization, and property analysis.
  • The framework supports the development of diverse LiDAR-based robotic applications.