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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Deep Learning for LiDAR Point Clouds in Autonomous Driving: A Review
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Deep Learning for LiDAR Point Clouds in Autonomous Driving: A Review

机译：自治驾驶中LIDAR点云的深入学习：综述

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摘要

Recently, the advancement of deep learning (DL) in discriminative feature learning from 3-D LiDAR data has led to rapid development in the field of autonomous driving. However, automated processing uneven, unstructured, noisy, and massive 3-D point clouds are a challenging and tedious task. In this article, we provide a systematic review of existing compelling DL architectures applied in LiDAR point clouds, detailing for specific tasks in autonomous driving, such as segmentation, detection, and classification. Although several published research articles focus on specific topics in computer vision for autonomous vehicles, to date, no general survey on DL applied in LiDAR point clouds for autonomous vehicles exists. Thus, the goal of this article is to narrow the gap in this topic. More than 140 key contributions in the recent five years are summarized in this survey, including the milestone 3-D deep architectures, the remarkable DL applications in 3-D semantic segmentation, object detection, and classification; specific data sets, evaluation metrics, and the state-of-the-art performance. Finally, we conclude the remaining challenges and future researches.
机译：最近，深度学习（DL）在3-D LIDAR数据中学习的歧视特征的进步导致了自主驾驶领域的快速发展。然而，自动化处理不均匀，非结构化，嘈杂，并庞大的3-D点云是一个挑战性和繁琐的任务。在本文中，我们提供了对LIDAR点云中的现有引人注目的DL架构的系统审查，详细描述了自动驾驶中的特定任务，例如分段，检测和分类。迄今为止，迄今为止，迄今为止，几项公布的研究文章专注于计算机愿景的特定主题，迄今为止，存在于自治车辆激光乐队点云中应用的DL一般性调查。因此，本文的目标是缩小本主题中的差距。这项调查中总结了近五年来的140多个主要贡献，包括里程碑3-D深度架构，三维语义分割，物体检测和分类中的非凡的DL应用;具体数据集，评估度量和最先进的性能。最后，我们得出了剩余的挑战和未来的研究。

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