Systematic and Comprehensive Review of Clustering and Multi-Target Tracking Techniques for LiDAR Point Clouds in Autonomous Driving Applications

被引:4
|
作者
Adnan, Muhammad [1 ,2 ]
Slavic, Giulia [1 ,2 ]
Gomez, David Martin [2 ]
Marcenaro, Lucio [1 ]
Regazzoni, Carlo [1 ]
机构
[1] Univ Genoa, Dept Elect Elect Telecommun Engn & Naval Architect, Via Opera Pia 11a, I-16145 Genoa, Italy
[2] Univ Carlos III Madrid, Dept Ingn Sistemas & Automat, Butarque 15, Leganes 28911, Madrid, Spain
关键词
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; MULTIOBJECT TRACKING; OBJECT DETECTION; ALGORITHMS; PERCEPTION; CHALLENGES; TAXONOMY;
D O I
10.3390/s23136119
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Autonomous vehicles (AVs) rely on advanced sensory systems, such as Light Detection and Ranging (LiDAR), to function seamlessly in intricate and dynamic environments. LiDAR produces highly accurate 3D point clouds, which are vital for the detection, classification, and tracking of multiple targets. A systematic review and classification of various clustering and Multi-Target Tracking (MTT) techniques are necessary due to the inherent challenges posed by LiDAR data, such as density, noise, and varying sampling rates. As part of this study, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology was employed to examine the challenges and advancements in MTT techniques and clustering for LiDAR point clouds within the context of autonomous driving. Searches were conducted in major databases such as IEEE Xplore, ScienceDirect, SpringerLink, ACM Digital Library, and Google Scholar, utilizing customized search strategies. We identified and critically reviewed 76 relevant studies based on rigorous screening and evaluation processes, assessing their methodological quality, data handling adequacy, and reporting compliance. As a result of this comprehensive review and classification, we were able to provide a detailed overview of current challenges, research gaps, and advancements in clustering and MTT techniques for LiDAR point clouds, thus contributing to the field of autonomous driving. Researchers and practitioners working in the field of autonomous driving will benefit from this study, which was characterized by transparency and reproducibility on a systematic basis.
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页数:34
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