Department of Electrical and Computer Engineering, James Worth Bagley College of Engineering, Mississippi State University, Starkville, MS 39762, USA.
Sensors (Basel). 2022 Dec 7;22(24):9577. doi: 10.3390/s22249577.
LiDAR is a commonly used sensor for autonomous driving to make accurate, robust, and fast decision-making when driving. The sensor is used in the perception system, especially object detection, to understand the driving environment. Although 2D object detection has succeeded during the deep-learning era, the lack of depth information limits understanding of the driving environment and object location. Three-dimensional sensors, such as LiDAR, give 3D information about the surrounding environment, which is essential for a 3D perception system. Despite the attention of the computer vision community to 3D object detection due to multiple applications in robotics and autonomous driving, there are challenges, such as scale change, sparsity, uneven distribution of LiDAR data, and occlusions. Different representations of LiDAR data and methods to minimize the effect of the sparsity of LiDAR data have been proposed. This survey presents the LiDAR-based 3D object detection and feature-extraction techniques for LiDAR data. The 3D coordinate systems differ in camera and LiDAR-based datasets and methods. Therefore, the commonly used 3D coordinate systems are summarized. Then, state-of-the-art LiDAR-based 3D object-detection methods are reviewed with a selected comparison among methods.
激光雷达是自动驾驶中常用的传感器，可在驾驶时做出准确、稳健和快速的决策。该传感器用于感知系统，特别是物体检测，以了解驾驶环境。尽管在深度学习时代 2D 物体检测已经取得了成功，但缺乏深度信息限制了对驾驶环境和物体位置的理解。三维传感器，如激光雷达，提供了周围环境的 3D 信息，这对 3D 感知系统至关重要。尽管计算机视觉社区由于机器人和自动驾驶等多个应用而关注 3D 物体检测，但仍存在挑战，例如尺度变化、稀疏性、激光雷达数据分布不均和遮挡。已经提出了不同的激光雷达数据表示形式和方法来最小化激光雷达数据稀疏性的影响。本调查介绍了基于激光雷达的 3D 物体检测和激光雷达数据特征提取技术。基于相机和激光雷达的数据集和方法的 3D 坐标系不同。因此，总结了常用的 3D 坐标系。然后，回顾了基于激光雷达的 3D 物体检测方法的最新进展，并对方法进行了选择比较。
