自动驾驶3D目标检测研究综述

精确实时地进行目标检测是自动驾驶车辆能够准确感知周围复杂环境的重要功能之一,如何对周围物体的尺寸、距离、位置、姿态等3D信息进行精准判断是自动驾驶3D目标检测的经典难题.服务于自动驾驶的3D目标检测已成为近年来炙手可热的研究领域,鉴于此,对该领域主要研究进展进行综述.首先,介绍自动驾驶感知周围环境各相关传感器的特点;其次,介绍3D目标检测算法并按照传感器获取数据类型将其分为:基于单目/立体图像的算法、基于点云的算法以及图像与点云融合的算法;然后,对每类3D目标检测的经典算法以及改进算法进行详细综述、分析、比较,梳理了当前主流自动驾驶数据集及其3D目标检测算法的评估标准,并对现有文献广泛采用的KITTI和NuScenes数据集实验结果进行对比及分析,归纳了现有算法存在的难点和问题;最后,提出自动驾驶3D目标检测在数据处理、特征提取策略、多传感器融合和数据集分布问题方面可能遇到的机遇及挑战,并对全文进行总结及展望.

3D object detection algorithms in autonomous driving: A review

Accurate and real-time object detection is one of the important functions for autonomous vehicles to accurately perceive the surrounding complex environment. Nevertheless, how to get the accurate size, distance, position, posture and other 3D information of surrounding objects is a classic problem. 3D object detection for autonomous driving has become a popular research field in recent years. Main research progress in this field is reviewed. Firstly, the characteristics of relevant sensors in the surrounding environment of autonomous driving is introduced. Then, the development of object detection from 2D to 3D is introduced and the loss functions is applied for optimization. According to the type of data acquired by the sensor, 3D object detection algorithms is categorized into three types, which are algorithms based on monocular/stereo images, point clouds, image and point cloud fusion. Futhermore, the classic and improved algorithms for each type of 3D object detection are reviewed, analyzed, and compared in detail. Simultaneously, the mainstream autonomous driving datasets and the evaluation criteria of their 3D object detection algorithms are summarized. Extensive experiment results of KITTI and NuScenes datasets are also compared and analyzed, which is widely used inpresent literature, summarizing the difficulties and problems of the existing algorithms. Besides, the opportunities and challenges of 3D object detection in data processing, feature extraction strategy, multi-sensor fusion and data distribution problems are proposed in hope of inspiring more future work.