激光扫描仪检校及车载激光点云的分类与矢量化研究
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摘要
激光扫描技术是近几年刚刚发展起来的新型测绘技术,采用这项技术可以在短时间内获取大面积,高精度和高密度的三维空间数据,因此被认为是测绘领域的一场新的革命。车载激光数据采集又是激光扫描技术一个新的发展方向,目前对其研究尚处于起步阶段。车载激光数据采集系统集成了GPS, IMU, LS和CCD等多种传感器,在车辆行进过程中获取道路两侧的点云和影像信息。由于集成的传感器数量多,采集的点云信息量大,因此在数据采集和处理过程中有许多关键问题需要研究。
论文在激光扫描仪的外方位检校,车载激光点云的分类和矢量化方面做了深入研究,创新性工作包括：
1、提出并实现了基于同名线段对应的激光扫描仪外方位间接检校的数学模型,为实现该数学模型,设计了检校场,并设计制作了相关检校标志。对该算法的检校结果进行分析并给出相关改进方案。
2、提出并实现了基于多分辨率的渐进式车载激光点云分类算法。由于车载激光点云建筑物立面信息多样,街景两侧成分复杂,点云采集过程中干扰信息多,直接将点云中的地物信息提取出来难度很大。根据高低分辨率图像各自的优势,各种物体信息在点云中不同,论文提出了多分辨率渐进式分类算法。
3、提出并实现了基于点线面模式的车载激光点云矢量化算法。与逆向工程等领域的点云处理方法不同,在测绘领域中,点云处理的一个重要的考虑因素是大量干扰点的剔除。相对于固定站点和机载激光点云,车载激光点云表现的地物更复杂,干扰点更多。论文利用基于扫描线矢量化的思想,对于车载散乱激光点云进行二维排序后,在每一条扫描线中进行线段拟合,在不同扫描线间将线段进行平面拟合,从而完成了基于点线面模式的车载激光点云矢量化算法。
Laser scanning, which is rapidly developing in recent years, is seen as a new technique revolution in surveying & mapping field. It can get density and definition point cloud of a large scene in a short time. Vehicle-borne laser data collection is a new technique in laser scanning, and the researchers begin to study it. Vehicle-borne laser data collection system integrates many sensors such as GPS, IMU, LS and CCD. It can get point cloud and image of the scene on the two sides of the street. Because the system integrates many sensors, and the point cloud has a large amount of information, there are many key techniques need study for data collection and data processing.
The main work in this paper is laser scanner calibration, the classification and the vectorization of the point cloud got by vehicle-borne laser scanner. The innovation works include:
1. Propose and realize an algorithm to calibrate a laser scanner and a camera. The algorithm is based on the homologous lines in the same plane. Design the calibration scene and made the calibration models. Analyze the algorithm result and give some advice to get better result.
2. Propose and realize an algorithm to classify the point cloud got by vehicle-borne laser scanner. The algorithm is based on multi-resolution and step-classification. Because there are various objects and disturbing information in the point cloud, it is difficult to extract the objects directly. In the algorithm, the advantages of high resolution image and low resolution image are used, and the objects are extracted in several steps.
3. Propose an algorithm to realize the vectorization of the point cloud got by vehicle-borne laser scanner based on point-line-face mode. The point cloud processing method in surveying & mapping field is different from that in reverse engineering field. The point cloud in surveying & mapping field has a lot of disturbing points, and the methods must remove them. The point cloud got by vehicle-borne laser scanner has more complex object structure and more disturbing points than the point cloud got by air-borne laser scanner or fixed station scanner. The vectorization in this paper is based on scan line. The algorithm of point-line-face vectorization includes the following steps. Firstly the points are sorted in two directions and the scan lines are formed. Then line segments are fitted in each scan line. Finally faces are fitted by the line segments.
论文在激光扫描仪的外方位检校,车载激光点云的分类和矢量化方面做了深入研究,创新性工作包括：
1、提出并实现了基于同名线段对应的激光扫描仪外方位间接检校的数学模型,为实现该数学模型,设计了检校场,并设计制作了相关检校标志。对该算法的检校结果进行分析并给出相关改进方案。
2、提出并实现了基于多分辨率的渐进式车载激光点云分类算法。由于车载激光点云建筑物立面信息多样,街景两侧成分复杂,点云采集过程中干扰信息多,直接将点云中的地物信息提取出来难度很大。根据高低分辨率图像各自的优势,各种物体信息在点云中不同,论文提出了多分辨率渐进式分类算法。
3、提出并实现了基于点线面模式的车载激光点云矢量化算法。与逆向工程等领域的点云处理方法不同,在测绘领域中,点云处理的一个重要的考虑因素是大量干扰点的剔除。相对于固定站点和机载激光点云,车载激光点云表现的地物更复杂,干扰点更多。论文利用基于扫描线矢量化的思想,对于车载散乱激光点云进行二维排序后,在每一条扫描线中进行线段拟合,在不同扫描线间将线段进行平面拟合,从而完成了基于点线面模式的车载激光点云矢量化算法。
Laser scanning, which is rapidly developing in recent years, is seen as a new technique revolution in surveying & mapping field. It can get density and definition point cloud of a large scene in a short time. Vehicle-borne laser data collection is a new technique in laser scanning, and the researchers begin to study it. Vehicle-borne laser data collection system integrates many sensors such as GPS, IMU, LS and CCD. It can get point cloud and image of the scene on the two sides of the street. Because the system integrates many sensors, and the point cloud has a large amount of information, there are many key techniques need study for data collection and data processing.
The main work in this paper is laser scanner calibration, the classification and the vectorization of the point cloud got by vehicle-borne laser scanner. The innovation works include:
1. Propose and realize an algorithm to calibrate a laser scanner and a camera. The algorithm is based on the homologous lines in the same plane. Design the calibration scene and made the calibration models. Analyze the algorithm result and give some advice to get better result.
2. Propose and realize an algorithm to classify the point cloud got by vehicle-borne laser scanner. The algorithm is based on multi-resolution and step-classification. Because there are various objects and disturbing information in the point cloud, it is difficult to extract the objects directly. In the algorithm, the advantages of high resolution image and low resolution image are used, and the objects are extracted in several steps.
3. Propose an algorithm to realize the vectorization of the point cloud got by vehicle-borne laser scanner based on point-line-face mode. The point cloud processing method in surveying & mapping field is different from that in reverse engineering field. The point cloud in surveying & mapping field has a lot of disturbing points, and the methods must remove them. The point cloud got by vehicle-borne laser scanner has more complex object structure and more disturbing points than the point cloud got by air-borne laser scanner or fixed station scanner. The vectorization in this paper is based on scan line. The algorithm of point-line-face vectorization includes the following steps. Firstly the points are sorted in two directions and the scan lines are formed. Then line segments are fitted in each scan line. Finally faces are fitted by the line segments.
引文
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