Adnan, M.; Slavic, G.; Martin Gomez, D.; Marcenaro, L.; Regazzoni, C. Systematic and Comprehensive Review of Clustering and Multi-Target Tracking Techniques for LiDAR Point Clouds in Autonomous Driving Applications. Sensors2023, 23, 6119.
Adnan, M.; Slavic, G.; Martin Gomez, D.; Marcenaro, L.; Regazzoni, C. Systematic and Comprehensive Review of Clustering and Multi-Target Tracking Techniques for LiDAR Point Clouds in Autonomous Driving Applications. Sensors 2023, 23, 6119.
Adnan, M.; Slavic, G.; Martin Gomez, D.; Marcenaro, L.; Regazzoni, C. Systematic and Comprehensive Review of Clustering and Multi-Target Tracking Techniques for LiDAR Point Clouds in Autonomous Driving Applications. Sensors2023, 23, 6119.
Adnan, M.; Slavic, G.; Martin Gomez, D.; Marcenaro, L.; Regazzoni, C. Systematic and Comprehensive Review of Clustering and Multi-Target Tracking Techniques for LiDAR Point Clouds in Autonomous Driving Applications. Sensors 2023, 23, 6119.
Abstract
The safe and reliable operation of autonomous vehicles in complex and dynamic environments is heavily dependent upon exteroceptive and proprioceptive sensors. A LiDAR system generates accurate 3D point clouds, which are crucial for the detection, classification, and tracking of multiple targets. LiDAR data, however, presents significant challenges due to its density, noise, and varying sampling rates. In this study, various clustering and MTT techniques for LiDAR point clouds will be identified and classified within the context of autonomous driving to assess the state-of-the-art methods’ key challenges and their performances. We have categorized clustering and MTT methods used in AV applications, identified research gaps and challenges and analyzed existing algorithms and their challenges in detail. Researchers and practitioners in the field of autonomous driving will find this review to be a valuable resource.
Keywords
Autonomous Vehicles (AVs); LiDAR (Light Detection and Ranging); Point Clouds; Clustering Algorithms; Multi-Target Tracking (MTT); Object Detection; Sensor Fusion; Deep Learning; 3D Point Cloud Segmentation
Subject
Engineering, Control and Systems Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
