基于多源点云与图像数据的车辆定位与再识别方法  

Vehicle Location and Reidentification Using Multisource Point Clouds and Images

作　　者：汪威[1] 吕斌 杨轶睿 胡新宇[1] 黄玉春[2] 叶仲韬 王鸣辉 Wang Wei;LüBin;Yang Yirui;Hu Xinyu;Huang Yuchun;Ye Zhongtao;Wang Minghui(School of Mechanical Engineering,Hubei University of Technology,Wuhan 430068,Hubei,China;School of Remote Sensing and Information Engineering,Wuhan University,Wuhan 430072,Hubei,China;China Railway Bridge Science Research Institute,Ltd.,Wuhan 430034,Hubei,China;State Key Laboratory for Health and Safety of Bridge Structures,Wuhan 430034,Hubei,China)

机构地区：[1]湖北工业大学机械工程学院,湖北武汉430068 [2]武汉大学遥感信息工程学院,湖北武汉430072 [3]中铁大桥科学研究院有限公司,湖北武汉430034 [4]桥梁结构健康与安全国家重点实验室,湖北武汉430034

出　　处：《激光与光电子学进展》2023年第10期375-384,共10页Laser & Optoelectronics Progress

基　　金：国家自然科学基金(61976083)。

摘　　要：针对大型桥梁车辆移动荷载监测场合中车辆的重心估计能力不足和车辆再识别困难等问题,提出一种基于点云灰度图的彩色图像与点云快速融合方法,以提高对车辆的空间定位能力和对目标的辨识能力。首先利用立体标定靶对不同视角相机和点云采集装置的位姿进行标定,获取它们彼此间的相对位置和姿态;然后利用标定的结果对不同视角采集到的点云进行拼接,得到完整的车辆点云;再将完整点云转换至彩色相机坐标系并投影,提取点云灰度图,实现彩色图像与点云灰度图的配准,将其姿态调整至与车辆实体在彩色相机坐标系内的位置和姿态一致。进一步建立彩色像素点与三维点云间的映射关系,并将颜色信息与点云相关联,从而实现彩色图像与点云的融合。利用融合后的彩色点云和相机成像模型,可以得到车辆在彩色相机坐标系中的虚拟图像,为车辆的再识别提供依据。结果显示,相比于采样一致性算法,所提配准算法缩短了约74.1%的耗时。实验表明,所提算法实现数据融合后生成的彩色点云具有较高的还原度,证明了所提算法的可行性,为解决类似的问题提供了新的思路和方法。To solve the problems of insufficient estimation ability for the center of gravity of a vehicle and difficulty in vehicle reidentification in large bridge vehicle moving load monitoring,a fast fusion method of color image and point cloud based on point cloud grayscale image is proposed to improve the spatial positioning ability for vehicles and recognition ability for targets.The stereo calibration target was used to calibrate the position and attitude of cameras and point cloud collection devices with different viewing angles to obtain their relative positions and attitudes.Then,the calibration results to splice the point clouds collected from different perspectives were used to obtain a complete vehicle point cloud.The complete point cloud was converted to the color camera coordinate system and projected,extracting the point cloud grayscale image and realizing the registration of the color image and point cloud grayscale image.We adjusted the attitude to be consistent with the position and attitude of the vehicle entity in the color camera coordinate system.The mapping relationship between color pixels and 3D point cloud was established.The color information was associated with the point cloud to fuse the color images and point clouds.Using the fused color point cloud and camera imaging model,the virtual image of the vehicle in the color camera coordinate system can be obtained,providing a basis for vehicle recognition.The results demonstrate that compared with the sampling consistency algorithm,the proposed registration algorithm requires approximately 74.1%less time.Experiments reveal that the color point cloud generated by the proposed algorithm after data fusion has a high degree of restoration,proving the feasibility of the proposed algorithm.The proposed algorithm provides new ideas and methods for solving similar problems.

关 键 词：激光点云 多源数据融合 立体标定靶 点云灰度图 虚拟图像 点云着色 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术] TH741[自动化与计算机技术—计算机科学与技术]