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Background Point Filtering of Low-Channel Infrastructure-Based LiDAR Data Using a Slice-Based Projection Filtering

Ciyun Lin1, Hui Liu1,2, Dayong Wu3

  • 1Department of Traffic Information and Control Engineering, Jilin University, Changchun 130022, China.

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Summary
This summary is machine-generated.

This study introduces a slice-based projection filtering (SPF) method to efficiently remove background noise from LiDAR point clouds for intelligent transportation systems. The SPF algorithm accurately identifies valuable road user data, outperforming existing methods in speed and accuracy.

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3-D point cloudbackground points filteringinfrastructure-based LiDARslice-based projection

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Area of Science:

  • Intelligent Transportation Systems
  • Computer Vision
  • Sensor Data Processing

Background:

  • Traditional traffic detectors provide limited data for advanced intelligent transportation applications.
  • LiDAR sensors generate rich point cloud data, but require background filtering to isolate relevant information.
  • Efficiently extracting micro-level traffic data necessitates effective removal of non-essential points from LiDAR scans.

Purpose of the Study:

  • To develop and evaluate a novel background point filtering algorithm for LiDAR data.
  • To improve the accuracy and efficiency of extracting traffic flow information.
  • To enhance the adaptability of filtering algorithms to varying road gradients and sensor inclinations.

Main Methods:

  • A slice-based projection filtering (SPF) method is proposed, projecting 3-D LiDAR point clouds to 2-D polar coordinates.
  • Points are classified into Valuable Object Points (VOPs), Worthless Object Points (WOPs), Abnormal Ground Points (AGPs), and Normal Ground Points (NGPs).
  • An Artificial Neuron Network (ANN)-based model is integrated to enhance adaptability to road and sensor inclination.

Main Results:

  • The SPF algorithm successfully filtered background points, extracting valuable data like road users and curbstones.
  • The proposed method demonstrated superior performance compared to Random Sample Consensus (RANSAC) and 3-D Density-Statistic-Filtering (3-D-DSF) algorithms.
  • Key metrics show improved run-time and background filtering accuracy in experimental evaluations.

Conclusions:

  • The slice-based projection filtering (SPF) method offers an efficient and accurate solution for processing LiDAR point clouds in intelligent transportation.
  • The algorithm effectively isolates traffic objects and relevant environmental features, enabling advanced data analysis.
  • The ANN integration improves robustness, making the method suitable for diverse real-world conditions.