车载MMS激光点云与序列全景影像自动配准方法

陈驰; 杨必胜; 田茂; 李健平; 邹响红; 吴唯同; 宋易恒

doi:10.11947/j.AGCS.2018.20170520

测绘学报 >

2018 , Vol. 47 >Issue 2: 215 - 224

DOI: https://doi.org/10.11947/j.AGCS.2018.20170520

车载MMS激光点云与序列全景影像自动配准方法

陈驰 ,
杨必胜 ,
田茂 ,
李健平 ,
邹响红 ,
吴唯同 ,
宋易恒

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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. 武汉大学时空数据智能获取技术与应用教育部工程研究中心, 湖北武汉 430079

陈驰(1989-),男,博士,助理研究员,研究方向为无人机、车载、机器人移动测量测绘数据融合。E-mail:chenchi_liesmars@foxmail.com

收稿日期: 2017-09-14

修回日期: 2017-12-04

网络出版日期: 2018-03-02

基金资助

国家自然科学基金（41701530；41725005；41531177）；国家重点研发计划（2016YFF0103501）；中国博士后科学基金（2016M600614）

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Automatic Registration of Vehicle-borne Mobile Mapping Laser Point Cloud and Sequent Panoramas

CHEN Chi ,
YANG Bisheng ,
TIAN Mao ,
LI Jianping ,
ZOU Xianghong ,
WU Weitong ,
SONG Yiheng

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1. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China;
2. Engineering Research Center for Spatio-temporal Data Smart Acquisition and Application, Ministry of Education of China, Wuhan University, Wuhan 430079, China

Received date: 2017-09-14

Revised date: 2017-12-04

Online published: 2018-03-02

Supported by

The National Natural Science Foundation of China (Nos. 41701530;41725005;41531177);The National Key Research and Development Program of China (No. 2016YFF0103501);The China Postdoctoral Science Foundation (No. 2016M600614)

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摘要

提出一种车载移动测量系统（MMS）激光点云与序列全景影像自动配准方法。首先采用层次化城市场景目标提取方法自激光点云提取天际线矢量，在全景影像中经虚拟成像与分割角点提取算法生成天际线矢量。然后，将提取结果作为几何配准基元，构建配准基元图，通过最小化配准基元图编辑距离进行匹配，组成共轭配准基元对，解算2D-3D粗配准模型，获得全景影像与LiDAR点云参考坐标系之间的初始转换关系。最后，为消除几何配准基元提取与匹配误差对配准结果的影响，自序列全景影像虚拟成像影像生成多视立体密集匹配点云，继而使用变种ICP算法优化其与激光点云数据间3D-3D配准参数，间接优化全景影像与激光点云间的配准参数，精化配准结果。试验结果表明，本文提出的自动配准方法可以实现车载MMS激光点云与序列全景影像的1.5像素级自动配准，配准成果可应用于真彩色点云生成等点云/影像数据融合应用。

关键词： 激光点云; 全景影像; 配准; 特征提取; 车载移动测量系统

本文引用格式

陈驰 , 杨必胜 , 田茂 , 李健平 , 邹响红 , 吴唯同 , 宋易恒 . 车载MMS激光点云与序列全景影像自动配准方法[J]. 测绘学报, 2018 , 47(2) : 215 -224 . DOI: 10.11947/j.AGCS.2018.20170520

Abstract

An automatic registration method of mobile mapping system laser point cloud and sequence panoramic image is proposed in this paper.Firstly,hierarchical object extraction method is applied on LiDAR data to extract the building façade and outline polygons are generated to construct the skyline vectors.A virtual imaging method is proposed to solve the distortion on panoramas and corners on skylines are further detected on the virtual images combining segmentation and corner detection results.Secondly,the detected skyline vectors are taken as the registration primitives.Registration graphs are built according to the extracted skyline vector and further matched under graph edit distance minimization criteria.The matched conjugate primitives are utilized to solve the 2D-3D rough registration model to obtain the initial transformation between the sequence panoramic image coordinate system and the LiDAR point cloud coordinate system.Finally,to reduce the impact of registration primitives extraction and matching error on the registration results,the optimal transformation between the multi view stereo matching dens point cloud generated from the virtual imaging of the sequent panoramas and the LiDAR point cloud are solved by a 3D-3D ICP registration algorithm variant,thus,refine the exterior orientation parameters of panoramas indirectly.Experiments are undertaken to validate the proposed method and the results show that 1.5 pixel level registration results are achieved on the experiment dataset.The registration results can be applied to point cloud and panoramas fusion applications such as true color point cloud generation.

Key words： LiDAR point cloud; panoramas; registration; feature extraction; vehicle-borne mobile mapping system

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