车载LiDAR点云中建筑物的自动识别与立面几何重建
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摘要
激光雷达(Light Detection And Ranging, LiDAR)或激光扫描系统是一种非接触主动式的对地观测系统。LiDAR技术在三维空间信息的实时获取方面产生了重大突破,为高时空分辨率地球空间数据的获取提供了一种全新的技术手段。随着传感器、电子、光学、计算机等技术的发展,以车辆为搭载平台综合利用GPS、IMU、激光扫描仪、CCD相机,在多传感器同步集成与控制的基础上构建的车载LiDAR测量系统,已成为一种快速的空间数据获取手段,广泛运用于基础测绘、城市规划、交通、环保等领域。车载LiDAR系统能够在高速移动状态下获取道路以及道路两侧建筑物、树木等地物的表面数据。一方面而言,它具有数据获取速度快、点云密集、场景目标丰富的特点；另一方面,其获取的数据具有海量特性(激光扫描仪每秒可获取上万个点),且带有噪声并存在遮挡,这给车载LiDAR点云数据的处理方法带来了巨大的挑战。
目前车载LiDAR点云数据处理的主要问题在于：场景复杂、目标丰富,不同目标的自动分类与识别智能化程度低；建筑物面片结构复杂,立面细节特征丰富,造成建筑物立面几何三维重构的自动化程度低。
针对以上问题,本文重点研究了车载LiDAR点云数据中不同目标的快速有效自动分类与识别方法以及具有窗户细节特征的建筑物点云立面几何三维重建方法。具体研究内容如下：
1、介绍了LiDAR技术的发展以及车载LiDAR系统在三维街景环境数据快速获取中的重大作用,针对现有车载LiDAR点云数据处理方面的不足和难点,确定了本文的研究目标。
2、针对当前的车载LiDAR测量系统,详细介绍了其传感器组成与工作原理,并分析了当前国内外知名的商用车载LiDAR系统的组成、传感器搭载模式及相关技术指标。从数据量、数据排列方式、噪声、强度信息、多次回波信息、遮挡情况、点云密度、点云空间分布、扫描方式、场景复杂性、立面结构以及透射与孔洞等方面,详细说明了车载LiDAR点云数据与传统的机载LiDAR点云数据之间的异同。对目前国内外车载LiDAR点云目标的分类与识别、点云建筑物立面的几何重建两方面的相关研究进行了综述,归纳总结了目前车载LiDAR点云数据目标提取方面的不足及其发展趋势。
3、分析了常用的辅助图像在车载LiDAR点云目标分类与识别中应用的不足,提出了一种区别于传统的距离图像、强度图像和CCD图像,且能够反映车载LiDAR点云数据中不同目标几何属性的地理参考点云特征图像生成方法。通过车载LiDAR点云数据生成扫描区域的地理参考点云特征图像,并对生成的图像的参数设置进行了详细分析,讨论了格网采样间隔与权值系数对生成的地理参考点云特征图像的影响。
4、针对车载LiDAR点云目标分类与识别的迫切需求,提出了一种基于地理参考点云特征图像的车载LiDAR点云中建筑物目标的自动识别方法。采用由粗到细的策略,通过对地理参考点云特征图像进行阈值分割、目标轮廓边界提取,实现了原始车载LiDAR散乱点云中地面目标与非地面目标的分离。在点云空间中通过分析目标点云的空间分布特征,采用剖面分析与特征值分析相结合的方法,实现了非地面目标中建筑物目标点云的自动识别。通过实例数据验证了本文提出的建筑物目标自动识别方法,并对其识别准确率以及其与相关算法进行了分析比较。
5、针对车载LiDAR点云数据中提取的建筑物点云,从立面几何位置边界、立面细节几何特征以及面片拓扑结构等三个方面入手,重点介绍了建筑物点云面片分割、立面细节的几何特征与语义描述以及面片之间的拓扑关系,提出了立面几何位置边界的自动提取方法,通过窗户、门洞等细节的几何特征与语义描述自动识别了建筑物点云中的不同几何结构面片,并提出了基于立面栅格图像和立面三角网的矩形窗户特征提取方法,通过构建建筑物面片之间的几何拓扑关系恢复了建筑物面片结构,实现了建筑物点云立面几何重建。
Laser scanning or light detection and ranging (LiDAR) provides an efficient solution for capturing spatial data in a fast, efficient, and highly reproducible way. It has been widely used in many fields, such as cultural heritage documentation, reverse engineering, three-dimensional (3D) object reconstruction, and digital elevation model (DEM) generation, as it can directly obtain the3D coordinates of objects. LiDAR can be divided into three categories, namely, airborne LiDAR, terrestrial LiDAR, and mobile LiDAR. Airborne LiDAR has been successfully used for digital elevation model (DEM) generation and reconstruction of building roofs. However, it has difficulties for capturing points of the facades of buildings. As mobile mapping technology has made a great progress, mobile LiDAR allows the rapid and cost-effective capturing of3D data from large street scenes including the dense points of building facades.
In recent years, the processing of mobile LiDAR data has been focused mainly on objects extraction and facades reconstruction. To extract street-scene objects or detailed features of building facades, mobile LiDAR point clouds need to be classified into different categories (e.g., buildings, trees), which is a key step for accurate identification and3D reconstruction of street-scene objects. Moreover, facades reconstruction requires the detailed features of building facades like windows and facade footprints to be automatically recognized.
On the one hand, mobile LiDAR systems capture high-accuracy, high-density points both at accuracies and resolutions, which beyonds those availablities through aerial Photogrammetry, and when using the terrestrial LiDAR is impractical. However, compared with advances in mobile LiDAR systems, automated algorithms and software tools for efficiently extracting3D street-scene objects of interest from mobile LiDAR point clouds rather fall behind, due to huge data volumes and complexity of urban street scenes, as well as the presence of occlusion. On the other hand, different from the approaches for airborne LiDAR data processing, methods for processing mobile LiDAR data have to deal with fully3D point clouds. Due to the non-unique correspondence between (X, Y) coordinates and Z coordinate, the algorithms for filtering and classifying airborne LiDAR data, such as triangulated irregular network (TIN)-based filtering method, are difficult in handling with the mobile LiDAR data because of data dimensionalities.
This dissertation aims to fulfill two tasks, namely, the automated classification and buildings recognition from mobile LiDAR point clouds, the reconstruction of building facades with detailed features like facade footprints and windows from the point clouds of buildings.
This dissertation proposes a novel method to generate the georeferenced feature image of mobile LiDAR data, which represents the spatial distribution of scanning points and preserves the local geometric features of street-scene objects. Then the approach based on the georeferenced feature image was proposed for automated extraction of buildings from mobile LiDAR data. The proposed approach consists of two steps:coarse classification of buildings in image space using image segmentation and contour extraction, accurate identification in3D space adopting profile analysis and eigenvalues analysis.
Secondly, this dissertation focuses on facade footprints extraction, windows extraction from building facades, and geometric reconstruction of wire-frame building models. A coarse-to-fine approach using RANSAC planar segmentation was firstly proposed to automatically extract the facade footprints of building point clouds. The geometric features and semantic description of different planar patches of buildings were elaborated to distinguish facade walls from segmented planar patches. To extract detailed features from building facades, this dissertation presents a method combining facade raster images and facade TIN models to automatically extract rectangle windows in facade walls. Finally, the topology between different planar patches like facade walls and roofs were built to reconstruct the geometric wire-frame models of buildings.
The proposed method of the georeferenced feature image generation transforms the extraction of street-scene objects like buildings from3D mobile LiDAR point clouds into the geometric features extraction from2D imagery space, thus simplifies the automated building extraction process. Four datasets captured by Optech's Lynx Mobile Mapper system were selected for assessing the performance of the proposed methods of buildings recognition and facades reconstruction. Experimental results demonstrate that the proposed methods provide promising solutions for automatically extracting street-scene buildings and building facade details like windows and footprints from mobile LiDAR point clouds.
目前车载LiDAR点云数据处理的主要问题在于：场景复杂、目标丰富,不同目标的自动分类与识别智能化程度低；建筑物面片结构复杂,立面细节特征丰富,造成建筑物立面几何三维重构的自动化程度低。
针对以上问题,本文重点研究了车载LiDAR点云数据中不同目标的快速有效自动分类与识别方法以及具有窗户细节特征的建筑物点云立面几何三维重建方法。具体研究内容如下：
1、介绍了LiDAR技术的发展以及车载LiDAR系统在三维街景环境数据快速获取中的重大作用,针对现有车载LiDAR点云数据处理方面的不足和难点,确定了本文的研究目标。
2、针对当前的车载LiDAR测量系统,详细介绍了其传感器组成与工作原理,并分析了当前国内外知名的商用车载LiDAR系统的组成、传感器搭载模式及相关技术指标。从数据量、数据排列方式、噪声、强度信息、多次回波信息、遮挡情况、点云密度、点云空间分布、扫描方式、场景复杂性、立面结构以及透射与孔洞等方面,详细说明了车载LiDAR点云数据与传统的机载LiDAR点云数据之间的异同。对目前国内外车载LiDAR点云目标的分类与识别、点云建筑物立面的几何重建两方面的相关研究进行了综述,归纳总结了目前车载LiDAR点云数据目标提取方面的不足及其发展趋势。
3、分析了常用的辅助图像在车载LiDAR点云目标分类与识别中应用的不足,提出了一种区别于传统的距离图像、强度图像和CCD图像,且能够反映车载LiDAR点云数据中不同目标几何属性的地理参考点云特征图像生成方法。通过车载LiDAR点云数据生成扫描区域的地理参考点云特征图像,并对生成的图像的参数设置进行了详细分析,讨论了格网采样间隔与权值系数对生成的地理参考点云特征图像的影响。
4、针对车载LiDAR点云目标分类与识别的迫切需求,提出了一种基于地理参考点云特征图像的车载LiDAR点云中建筑物目标的自动识别方法。采用由粗到细的策略,通过对地理参考点云特征图像进行阈值分割、目标轮廓边界提取,实现了原始车载LiDAR散乱点云中地面目标与非地面目标的分离。在点云空间中通过分析目标点云的空间分布特征,采用剖面分析与特征值分析相结合的方法,实现了非地面目标中建筑物目标点云的自动识别。通过实例数据验证了本文提出的建筑物目标自动识别方法,并对其识别准确率以及其与相关算法进行了分析比较。
5、针对车载LiDAR点云数据中提取的建筑物点云,从立面几何位置边界、立面细节几何特征以及面片拓扑结构等三个方面入手,重点介绍了建筑物点云面片分割、立面细节的几何特征与语义描述以及面片之间的拓扑关系,提出了立面几何位置边界的自动提取方法,通过窗户、门洞等细节的几何特征与语义描述自动识别了建筑物点云中的不同几何结构面片,并提出了基于立面栅格图像和立面三角网的矩形窗户特征提取方法,通过构建建筑物面片之间的几何拓扑关系恢复了建筑物面片结构,实现了建筑物点云立面几何重建。
Laser scanning or light detection and ranging (LiDAR) provides an efficient solution for capturing spatial data in a fast, efficient, and highly reproducible way. It has been widely used in many fields, such as cultural heritage documentation, reverse engineering, three-dimensional (3D) object reconstruction, and digital elevation model (DEM) generation, as it can directly obtain the3D coordinates of objects. LiDAR can be divided into three categories, namely, airborne LiDAR, terrestrial LiDAR, and mobile LiDAR. Airborne LiDAR has been successfully used for digital elevation model (DEM) generation and reconstruction of building roofs. However, it has difficulties for capturing points of the facades of buildings. As mobile mapping technology has made a great progress, mobile LiDAR allows the rapid and cost-effective capturing of3D data from large street scenes including the dense points of building facades.
In recent years, the processing of mobile LiDAR data has been focused mainly on objects extraction and facades reconstruction. To extract street-scene objects or detailed features of building facades, mobile LiDAR point clouds need to be classified into different categories (e.g., buildings, trees), which is a key step for accurate identification and3D reconstruction of street-scene objects. Moreover, facades reconstruction requires the detailed features of building facades like windows and facade footprints to be automatically recognized.
On the one hand, mobile LiDAR systems capture high-accuracy, high-density points both at accuracies and resolutions, which beyonds those availablities through aerial Photogrammetry, and when using the terrestrial LiDAR is impractical. However, compared with advances in mobile LiDAR systems, automated algorithms and software tools for efficiently extracting3D street-scene objects of interest from mobile LiDAR point clouds rather fall behind, due to huge data volumes and complexity of urban street scenes, as well as the presence of occlusion. On the other hand, different from the approaches for airborne LiDAR data processing, methods for processing mobile LiDAR data have to deal with fully3D point clouds. Due to the non-unique correspondence between (X, Y) coordinates and Z coordinate, the algorithms for filtering and classifying airborne LiDAR data, such as triangulated irregular network (TIN)-based filtering method, are difficult in handling with the mobile LiDAR data because of data dimensionalities.
This dissertation aims to fulfill two tasks, namely, the automated classification and buildings recognition from mobile LiDAR point clouds, the reconstruction of building facades with detailed features like facade footprints and windows from the point clouds of buildings.
This dissertation proposes a novel method to generate the georeferenced feature image of mobile LiDAR data, which represents the spatial distribution of scanning points and preserves the local geometric features of street-scene objects. Then the approach based on the georeferenced feature image was proposed for automated extraction of buildings from mobile LiDAR data. The proposed approach consists of two steps:coarse classification of buildings in image space using image segmentation and contour extraction, accurate identification in3D space adopting profile analysis and eigenvalues analysis.
Secondly, this dissertation focuses on facade footprints extraction, windows extraction from building facades, and geometric reconstruction of wire-frame building models. A coarse-to-fine approach using RANSAC planar segmentation was firstly proposed to automatically extract the facade footprints of building point clouds. The geometric features and semantic description of different planar patches of buildings were elaborated to distinguish facade walls from segmented planar patches. To extract detailed features from building facades, this dissertation presents a method combining facade raster images and facade TIN models to automatically extract rectangle windows in facade walls. Finally, the topology between different planar patches like facade walls and roofs were built to reconstruct the geometric wire-frame models of buildings.
The proposed method of the georeferenced feature image generation transforms the extraction of street-scene objects like buildings from3D mobile LiDAR point clouds into the geometric features extraction from2D imagery space, thus simplifies the automated building extraction process. Four datasets captured by Optech's Lynx Mobile Mapper system were selected for assessing the performance of the proposed methods of buildings recognition and facades reconstruction. Experimental results demonstrate that the proposed methods provide promising solutions for automatically extracting street-scene buildings and building facade details like windows and footprints from mobile LiDAR point clouds.
引文
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