融合相机与激光雷达的目标检测、跟踪与预测
Camera-LiDAR Fusion for Object Detection,Tracking and Prediction
黄远宪 1周剑 1黄琦 2李必军 1王兰兰 1朱佳琳1
作者信息
- 1. 武汉大学测绘遥感信息工程国家重点实验室,湖北 武汉,430079
- 2. 中国舰船研究设计中心,湖北 武汉,430079
- 折叠
摘要
实时、鲁棒的三维动态目标感知系统是自动驾驶技术的关键.提出了一种融合单目相机和激光雷达的三维目标检测流程,首先,在图像上使用卷积神经网络进行二维目标检测,根据几何投影关系生成锥形感兴趣区域(region of interest,ROI),在ROI内对点云进行聚类,并拟合三维外包矩形;然后,基于外观特征和匈牙利算法对三维目标进行帧间匹配,并提出了一种基于四元有限状态机的跟踪器管理模型;最后,设计了 一种利用车道信息的轨迹预测模型,对车辆轨迹进行预测.实验结果表明,在目标检测阶段,所提算法的准确率和召回率分别达到了 92.5％和86.7％.在仿真数据集上对轨迹预测算法进行测试,与现有算法相比,所提算法在直线、弧线和缓和曲线3种类型的车道上均有较小的均方根误差,且算法平均耗时约为25 ms,满足实时性要求.所提算法鲁棒、有效,在不同车道模型下均有较好的结果.
Abstract
Objectives:A real-time and robust 3D dynamic object perception module is a key part of autono-mous driving system.Methods:This paper fuses monocular camera and light detection and ranging(LiDAR)to detect 3D objects.First,we use convolutional neural network to detect 2D bounding boxes and generate 3D frustum region of interest(ROI)according to the geometric projection relation between camera and LiDAR.Then,we cluster the point cloud in the frustum ROI and fit the 3D bounding box of the objects.After detecting 3D objects,we reidentify the objects between adjacent frames by appearance features and Hungarian algorithm,and then propose a tracker management model based on a quad-state machine.Finally,a novel prediction model is proposed,which leverages lane lines to constrain vehicle trajectories.Results:The experimental results show that in the stage of target detection,the accuracy and recall of the proposed algorithm can reach 92.5％and 86.7％,respectively.The root mean square error of the proposed trajectory prediction algorithm is smaller than that of the existing algorithms on the simulation datasets in-cluding straight line,arc and spiral curves.The whole algorithm only takes approximately 25 ms,which meets the real-time requirements.Conclusions:The proposed algorithm is effective and efficient,and has a good performance in different lane lines.
关键词
单目相机/激光雷达/三维目标检测/目标跟踪/轨迹预测Key words
monocular vision/LiDAR/3D detection/object tracking/trajectory prediction引用本文复制引用
基金项目
国家重点研发计划新能源汽车重点专项(2021YFB2501100)
出版年
2024
NETL
NSTL
万方数据