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Systematic and Comprehensive Review of Clustering and Multi-Target Tracking Techniques for LiDAR Point Clouds in
Muhammad Adnan1,2, Giulia Slavic1,2, David Martin Gomez2
1Department of Electrical, Electronic, Telecommunications Engineering and Naval Architecture (DITEN), University of Genova, Via Opera Pia 11a, I-16145 Genoa, Italy.
Sensors (Basel, Switzerland)
|July 14, 2023
Summary
This study reviews clustering and Multi-Target Tracking (MTT) techniques for autonomous vehicles (AVs) using Light Detection and Ranging (LiDAR) data. It identifies challenges and advancements in processing 3D point clouds for improved AV perception.
Area of Science:
- Robotics and Artificial Intelligence
- Computer Vision and Sensor Fusion
Background:
- Autonomous vehicles (AVs) require robust perception systems for safe navigation in complex environments.
- Light Detection and Ranging (LiDAR) provides crucial 3D point cloud data for object detection, classification, and tracking.
- Challenges in LiDAR data include varying density, noise, and sampling rates, necessitating advanced processing techniques.
Conclusions:
- Provides a comprehensive overview of clustering and MTT techniques for LiDAR point clouds in autonomous driving.
- Offers valuable insights for researchers and practitioners developing AV perception systems.
- Emphasizes the need for transparent and reproducible research in this domain.
