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An Entropy Analysis-Based Window Size Optimization Scheme for Merging LiDAR Data Frames.

Taesik Kim1, Jinman Jung2, Hong Min3

  • 1Department of Civil Engineering, Hongik University, Seoul 04066, Republic of Korea.

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|December 11, 2022
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Summary
This summary is machine-generated.

This study introduces a novel merging scheme for low-cost LiDAR data frames to enhance location estimation accuracy for moving objects. By analyzing entropy, the method optimizes data merging, significantly improving linear structure extraction.

Keywords:
LiDARentropy analysislinear structure extractionmerging point cloud data frameswindow size optimization

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Area of Science:

  • Robotics
  • Computer Vision
  • Geospatial Technology

Background:

  • LiDAR (Light Detection and Ranging) is crucial for environmental point cloud data acquisition.
  • Estimating object location using LiDAR, especially with cost-efficient systems, faces accuracy challenges with linear structures.

Purpose of the Study:

  • To improve the accuracy of linear structure extraction for location estimation using a cost-efficient LiDAR system on a moving object.
  • To develop a data merging scheme that overcomes the limitations of low-cost LiDAR in stationary states.

Main Methods:

  • A novel merging scheme for LiDAR data frames is proposed, leveraging the motion of the object.
  • Entropy analysis is employed to determine the optimal window size for data merging.
  • A unique indicator is developed to quantify path accuracy across different window sizes.

Main Results:

  • The proposed merging scheme significantly enhances the accuracy of extracting linear structures from LiDAR data.
  • Entropy analysis effectively identifies the optimal window size for improved data integration.
  • The developed indicator provides a single-value metric for assessing overall path accuracy.

Conclusions:

  • The merging scheme effectively addresses the low accuracy of linear structure extraction from low-cost LiDAR in stationary conditions.
  • The method offers a practical solution for improving location estimation accuracy in mobile LiDAR applications.
  • This approach enhances the utility of cost-efficient LiDAR systems for various applications.