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Dynamic Multi-LiDAR Based Multiple Object Detection and Tracking.

Muhammad Sualeh1, Gon-Woo Kim2

  • 1Intelligent Robotics Laboratory, Department of Control and Robot Engineering, Chungbuk National University, Chungdae-ro 1, Seowon-Gu, Cheongju, Chungbuk 28644, Korea. er.sualeh@gmail.com.

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Summary
This summary is machine-generated.

This study presents a robust Multiple Object Detection and Tracking (MODT) algorithm for autonomous driving using 3D LiDAR. The framework achieves real-time performance on embedded systems, offering a feasible solution for complex urban environments.

Keywords:
data associationground classificationmultiple object detectionmultiple object tracking

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Area of Science:

  • Robotics and Artificial Intelligence
  • Computer Vision for Autonomous Systems

Background:

  • Environmental perception is critical for autonomous driving, especially in cluttered urban settings.
  • Existing methods face challenges with non-stationary bases and complex environments.

Purpose of the Study:

  • To develop a robust Multiple Object Detection and Tracking (MODT) algorithm for non-stationary platforms using multiple 3D LiDARs.
  • To ensure real-time performance within vehicle-embedded computing constraints.

Main Methods:

  • A novel MODT framework merging 3D LiDAR data.
  • Grid-based ground classification for non-planar terrain.
  • 3D grid-based clustering for detecting objects under elevated structures.
  • Interactive Multiple Model-Unscented Kalman Filter-Joint Probabilistic Data Association Filter (IMM-UKF-JPDAF) for robust tracking.

Main Results:

  • The algorithm demonstrated real-time performance on embedded computers.
  • Achieved effective object detection under elevated structures and on non-planar ground.
  • Successfully tracked objects in cluttered dynamic environments.

Conclusions:

  • The proposed MODT framework is a feasible and robust solution for autonomous driving perception.
  • The algorithm offers a significant advancement for real-time environmental perception in complex scenarios.