The airborne LiDAR integrates GPS, inertial navigation system and laser technology, which can quickly and accurately obtain massive 3D spatial data of the desired target.
1 LiDAR
1. 1 LiDAR system
The LiDAR system includes a laser transmitter that can actively emit laser pulse signals. While transmitting the signal, it uses the global positioning system (GPS) to obtain the actual space position of the carrier, and uses the inertial measurement unit (IMU) to determine the attitude parameters of the scanning device , Through the laser scanner (LS) to measure the distance between the carrier and the ground target point, so as to obtain the ground target point three-dimensional information, laser echo intensity information and digital image and other spatial information, and can quickly generate Digital surface model (DSM), digital elevation model (DEM) and digital orthophoto (DOM) provide rich data sources for the research of 3D geospatial information ].
Building Materials Technology and Application
The LiDAR system can be divided into portable, vehicle-mounted, airborne and space-borne LiDAR according to the different platforms it carries . Airborne LiDAR can be carried by helicopters or drones as a platform, and its data collection methods are more flexible. Compared with portable and vehicle-mounted LiDAR, it has a larger scanning scene range, and obtains richer three-dimensional spatial information, and has more advantages than spaceborne LiDAR Higher spatial resolution, so it has been widely used in power, transportation, pipelines and other fields.
1. 2 Airborne LiDAR technology
The airborne LiDAR is equipped with diverse platforms and flexible flight route selection. The actively emitted laser pulse signal is not easily affected by light conditions and meteorological conditions. It can quickly, accurately and massively obtain surface three-dimensional spatial information, intensity information, echo information and other data Information is widely used in urban three-dimensional visualization modeling, forest resource investigation, urban road network extraction, and power grid inspection (selection) routes. Especially in power engineering, through the comparative analysis of DSM and DEM, the distribution of vegetation and structures (buildings) around the power grid can be understood; the three-dimensional information and color information contained in the DOM can be used to intuitively understand the real terrain and landforms ; With the help of the generated 3D scene graph, it can directly and efficiently perform 3D viewing of the scene, view the cross-section and other operations, and realize the optimized line selection operation of the designer and the power line inspection operation of the inspector [4, 8] in the room. The earth reduces the intensity and cost of power grid inspection (selection) line work, improves the efficiency of operation and inspection, and has high practical application value.
2 Application in power engineering
2. 1 Power line (optional)
With the rapid development of high-resolution sensors and unmanned aerial vehicles in recent years, the use of airborne LiDAR technology for power line inspection (selection) has become a research hotspot at home and abroad. The basic operation process is shown in Figure 1. Foreign research on airborne LiDAR technology started earlier and the technology is relatively mature. Some countries have built relatively complete airborne LiDAR power line selection and line inspection systems, such as the United States' integrated Vegetation Management, PLS-CADD, and TIMS systems, and Germany The FM-Profil, IHCM system, and the Portuguese PLMI system [9]. In addition, companies such as the German GHA company have formed a complete operation process for the design and maintenance of the power grid using LiDAR technology, and achieved good results.
my country has also actively explored the use of airborne LiDAR technology for power line inspection (selection), and has achieved certain results. The Guangxi Electric Power Industry Survey and Design Institute used the airborne LiDAR technology to realize the optimal selection of the Daxin-Nanning 500 kV power line, saving more than 5 million yuan in costs; Chongqing UHV Power Bureau used LiDAR technology to establish a three-dimensional ultra-high voltage (UHV) transmission line corridor Visualized management and monitoring system [2]; State Grid Hunan Electric Power Company has actively explored and researched "the technology of site selection for power transmission and transformation projects with complex terrain based on'UAV + precision laser three-dimensional digital model'".
Using airborne LiDAR technology to conduct research on power line inspections (selected), the key lies in how to quickly and accurately identify the power line data set from the massive point cloud data, and distinguish a single power line point set from it, which is also airborne LiDAR data processing The main purpose of point cloud filtering, point cloud clustering and 3D reconstruction are the three key links of data processing.
2. 2 Point cloud filtering
The point cloud data obtained by airborne LiDAR is a collection of all scanned points in the scanned scene, including both ground points and non-ground points. The process of removing non-ground points to obtain a digital terrestrial model (DTM) is filtering. At present, scholars at home and abroad have successively proposed a variety of filtering methods, mainly including morphological filtering algorithms, moving window filtering algorithms, iterative least squares filtering algorithms, terrain-based slope filtering algorithms, progressive encryption triangulation filtering algorithms, etc.
The analysis and comparison of various filtering methods shows that the filtering algorithm with simple principle has strong operability, but the filtering accuracy is not high; the morphological filtering algorithm uses moving windows and regular grid data to improve the calculation rate and filtering effect. However, the loss of important information is caused during interpolation; the moving window filtering algorithm has better filtering effect on complex terrain, but has poor adaptability; although the iterative least squares filtering algorithm has higher filtering accuracy and can eliminate gross errors, it is more effective for terrain The filtering efficiency and effect in more complicated areas are not ideal; the filtering algorithm based on the terrain slope can better retain the characteristics of the terrain slope, but the filtering error is larger and the adaptability is poor; the filtering effect of the progressive encryption triangulation filtering algorithm is better, But it is easy to cause misjudgment of ground points, which will affect the filtering accuracy
The classic power-related LiDAR data processing software Terrasolid uses the progressive encryption triangulation filter method, but due to the defects of its filtering method, related scholars have proposed a variety of improved methods. For example, Wu Jun et al. proposed fusion of morphological gray-scale reconstruction methods, Yu Shuang et al. used fuzzy C-means clustering method to improve the filtering effect, KANG XC et al. proposed multi-core parallel computing to improve filtering efficiency, ZHU LC et al. The establishment of grid index and other means to improve the classification precision.
2. 3 Point cloud clustering
The point cloud data filtering process only separates the ground points from the ground feature points. In order to further extract the ground feature information, it is necessary to classify the ground feature foot points. At present, the existing related clustering methods include: Hough transform method, 3D connected component analysis method, power line model growth and merging method. The Hough transform method has vertical, mixed and staggered arrangement of power lines, especially when multiple power lines are arranged vertically, it will not be able to separate the LiDAR points of a single power line, and it is difficult to meet the needs of power line point cloud clustering with multiple configurations. Model growth and merging methods and 3D connected component analysis methods are both susceptible to gross errors and irregular fractures of LiDAR point clouds.
In order to overcome the shortcomings of the existing point cloud clustering methods and improve the recognition accuracy, automation and applicability of power line LiDAR points, related scholars have proposed a variety of improved algorithms. For example, Lin Xiangguo et al. proposed a fusion stratified random sampling method, Duan Minyan used feature space k-means clustering method, Zhao Chuan et al. used normal vector density clustering method to improve the effect of LiDAR point cloud clustering, Xu Ying et al. conducted integrated clustering Exploration of filtering methods.www.isurestar.net