CN103018728B - Laser radar real-time imaging and building characteristic extracting method - Google Patents
Laser radar real-time imaging and building characteristic extracting method Download PDFInfo
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Abstract
The invention discloses a laser radar real-time imaging and building characteristic extracting method comprising the following steps of: mapping laser pin spots of a scanning line obtained by a laser radar to an ideal scanning line; carrying out rasterization on the ideal scanning line; obtaining elevation information at each grid position on the ideal scanning line through a linear interpolation method; carrying out graying treatment on the elevation information in the ideal scanning line, so that a probing area corresponding to the scanning line is imaged; and extracting building characteristic information in the ideal scanning line by using discrete stationary wavelet transform. By utilizing the method provided by the invention, the scanning lines of the laser pin spots in point cloud data of the laser radar can be processed one by one, so that the operations of real-time imaging, building characteristic extraction and the like in a scanning process of the laser radar can be realized.
Description
[ technical field ] A method for producing a semiconductor device
The invention relates to the field of remote sensing mapping, in particular to a method for laser radar real-time imaging and building feature extraction.
[ background of the invention ]
Buildings, which are important components of three-dimensional geographic information systems, are often used as important landmarks in lidar point cloud data due to their relatively fixed position and relatively regular shape. Therefore, in the processing process of the laser radar point cloud data, how to accurately extract the building position information and the top surface type information contained in the laser radar point cloud data in real time is an important problem which needs to be solved urgently. At present, methods for filtering and segmenting point cloud data of a laser radar basically adopt a post-processing mode, such as: a mathematical morphology filtering method, a triangular grid filtering method, a region growing method, a random sampling consistency method, a K-means cluster analysis method and the like. The methods are more suitable for processing the sheet-shaped point cloud data, and although the method has higher precision, the required operation time is longer, and the requirements of real-time imaging and building feature extraction in the scanning process are difficult to meet.
In the method described in patent application (application No. 201110337099.0) of the patent application of huxiang and lissaka, the point cloud data of the laser radar is filtered according to each scan line contained therein. By means of the method, the point cloud data can be processed in real time in the scanning process of the laser radar, but the method cannot meet the processing requirements of real-time imaging, building feature extraction and the like. In the method described in the invention of the Helverso application (application number 200710045734.1), a discrete wavelet transform is applied to the two-dimensional lidar point cloud data processing. However, in the method, wavelet transformation is only used as a denoising method, and does not involve processes such as ground and building classification and building feature extraction.
In the method, for one scanning line which finishes scanning in the scanning process of the laser radar, an ideal scanning straight line corresponding to the scanning line is calculated by using a least square fitting method, and laser foot points on the scanning line are mapped to the ideal scanning straight line. And calculating the elevation value of the grid position on each ideal scanning line according to the elevation value of the laser foot point on each ideal scanning line by a rasterization and linear interpolation method, thereby converting the position and the elevation value of the laser foot point on each scanning line into a group of regular elevation value sequences. And analyzing the elevation value sequence by utilizing the stationary discrete wavelet transform, and extracting building position information, top surface type information and the like from the obtained wavelet detail coefficients. And through the analysis of space-time correlation, the extraction result of each scanning line is verified by using the elevation value, the wavelet detail coefficient and the like on the adjacent scanning lines, so that the accuracy of data extraction is ensured. Compared with the traditional point cloud data processing method, the method provided by the invention has the characteristic that the point cloud data can be processed in real time in the scanning process of the laser radar, and the data updating frequency of the obtained processing result is equal to the line scanning frequency of the adopted laser radar.
[ summary of the invention ]
The invention relates to a method for laser radar real-time imaging and building feature extraction. And calculating an ideal straight line expression of each scanning line in the laser radar point cloud data by using a least square fitting method. And calculating the elevation value corresponding to each grid position on the ideal straight line by using a rasterization and linear interpolation method. And extracting elevation change information of each scanning line elevation value sequence by utilizing discrete stationary wavelet transformation, and extracting the edge position information of the building and the top surface type information of the building from the elevation change information. And performing gray value calculation on the elevation value sequence and the wavelet detail coefficient of each scanning line by using a graying method to obtain real-time image information of the corresponding detection area. And analyzing the elevation value, the wavelet detail coefficient, the building position information and the building top surface type information of the corresponding grid position on the adjacent scanning lines by using a space-time correlation method, and checking the obtained point cloud data extraction result to improve the accuracy of building feature extraction.
The technical scheme of the invention is realized as follows:
the invention discloses a method for laser radar real-time imaging and building feature extraction, which comprises the following steps:
step one, laser foot points which belong to the same scanning line in point cloud data are subjected to linearization processing in an xoy plane.
Under ideal conditions, laser foot points on one scanning line obtained by the laser radar through the rotation of the prism in the scanning mechanism are uniformly distributed on the same straight line on an xoy plane in a Cartesian three-dimensional xyz coordinate system. However, in practical situations, due to elevation changes of ground objects and heading angle, roll angle and pitch angle deviations of the laser radar load platform under the influence of complex airflow, certain distance deviations exist between laser foot points on each scanning line and ideal scanning lines of the laser foot points. In the scanning process of the laser radar, an ideal scanning line equation corresponding to the actually obtained scanning line is calculated by using a least square fitting method, so that the sum of squares of distances from all laser foot points on the scanning line to the ideal straight line in the xoy plane is minimum. And calculating the perpendicular line of each laser foot point and the ideal straight line in the xoy plane, and taking the coordinates of the intersection point of each perpendicular line and the ideal straight line in the xoy plane as the coordinates of the x axis and the y axis of each laser foot point after the laser foot point is subjected to linearization treatment.
And secondly, performing elevation value rasterization on the laser foot points subjected to the linearization treatment on the ideal scanning line.
After the linearization treatment, the laser foot points on the ideal scanning line are distributed in a straight line, and the average value of the distances of all the laser foot points on the straight line is taken as the distance value of the grid. The straight line is divided into a plurality of grids from the first laser foot point by the distance value. And calculating the elevation value of each grid position on the ideal straight line by using the elevation value of each laser foot point on the straight line and adopting a linear interpolation method.
And step three, performing real-time imaging of the laser radar and extraction processing of building features based on the stationary wavelet transform.
And converting the laser foot points on the scanning lines into a regular discrete elevation data sequence through operations such as linearization, elevation value rasterization and the like in the first step and the second step, processing the regular discrete elevation data sequence by adopting discrete stationary wavelet transform, and extracting the edge position information of the building and the top surface type information of the building contained in the regular discrete elevation data sequence according to the characteristics of the obtained wavelet detail coefficients. And calculating the gray values of the rasterized regular discrete elevation data sequence and the wavelet detail coefficient by a graying method. And in the scanning process of the laser radar, processing the obtained laser foot point data on each scanning line in real time according to the method, thereby realizing real-time imaging of the corresponding detection area.
And step four, checking the extraction result of each scanning line by adopting a space-time correlation analysis method.
Through the operation of the third step, the building edge position information and the building top surface type information contained in the laser foot points of each scanning line can be extracted in real time, and the corresponding detection area can be imaged in real time according to the elevation information and the wavelet detail coefficient of each scanning line. But the elevation information on each scan line only comes from the scanning direction of the lidar and does not contain information on the traveling direction of the load platform. Therefore, the elevation value and the wavelet detail coefficient of the corresponding grid position on each adjacent scanning line in the scanning process and the position and top surface type information of each building contained in the grid position and the wavelet detail coefficient are analyzed and verified by using a space-time correlation analysis method, so that the accuracy of building feature extraction is improved.
The invention has the beneficial effects that: and converting disordered and irregular laser foot points on each scanning line in the point cloud data into a group of regular elevation discrete sequences by using processing methods such as plane linearization, elevation value rasterization and the like. And analyzing the elevation discrete sequence by using the discrete stationary wavelet. Compared with the traditional laser radar point cloud data processing method, the method can perform imaging and building feature extraction processing in real time in the laser radar scanning process. By means of space-time correlation analysis, the obtained information such as the building position on each scanning line, the building top surface type and the like is verified by using the elevation value and the wavelet detail coefficient of the corresponding grid position on the adjacent scanning lines, and the accuracy of processing the laser radar point cloud data by using the method is ensured.
[ description attached drawings ]
FIG. 1 is a schematic diagram of a method for lidar real-time imaging and building feature extraction according to the present disclosure;
FIG. 2 is a schematic diagram of an embodiment of processing a real lidar point cloud data using the method of the present invention.
[ detailed description ] embodiments
The invention discloses a method for laser radar real-time imaging and building feature extraction, which comprises the following steps:
step one, laser foot points which belong to the same scanning line in point cloud data are subjected to linearization processing in an xoy plane.
Ideally, laser foot points on one scanning line obtained by the laser radar through rotation of the prism in the scanning mechanism should be distributed on the same straight line on the xoy plane more uniformly. However, in practical situations, due to the elevation change of the ground object and the deviation of the course angle, the roll angle and the pitch angle of the loading platform of the scanning mechanism under the influence of complex airflow, a certain distance deviation exists between the laser foot point on each scanning line and the ideal scanning line. Under the influence of complex external conditions, the laser foot points on the k-th scanning line are generally distributed around the ideal scanning line. Ideal scanning line L corresponding to k-th scanning linekThe mathematical expression on the xoy plane can be defined as: and y is ax + b. Laser foot point Pi(i∈[0，N]And the distance between i ∈ Z) and the ideal scanning line to which it belongs on the xoy plane is recorded as dist (L)k，Pi). Defining the sum of squares of distances between all the N +1 laser foot points on the k-th scanning line and an ideal scanning line as an objective function sumdistLk：
Calculating a current objective function sumdistL by using a least square fitting methodkIdeal scanning line L when taking minimum valuekThe relevant parameters of (2): the values of a and b. Mapping the x-axis and y-axis coordinates of the N +1 laser foot points to the ideal scanning line respectively, and mapping the laser foot points Pi(xi，yi) At the scanning line LkThe result of the upper linearization is defined as P'i(x′i，y′i). According to Pi，P′iAnd LkThe relation between the two sets of equations is established: <math>
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</math> p 'is calculated by solving the equation set'iX-axis and y-axis coordinates of points: (x'i，y′i). And (4) processing the N +1 laser foot points on the k-th scanning line in such a way to finish the linearization processing process of the k-th scanning line on the xoy plane.
And secondly, performing elevation value rasterization on the laser foot points subjected to the linearization treatment.
After the linearization treatment, the horizontal distances among the laser foot points on the k-th ideal scanning line are in discrete and uneven distribution. In order to simplify the calculation complexity and improve the calculation speed, the three-dimensional coordinates of each mapped laser foot point are simplified to include LkAnd the two-dimensional coordinates on the cross section plane perpendicular to the xoy plane and the elevation information contained in each laser foot point after the linear transformation are rasterized. Laser foot point P 'at ideal scanning line starting position'0(x′0，y′0，z0) Is defined as P 'in two-dimensional coordinate form'0(X0，z0) And using it as the starting position G of the grid0(0，z0) Wherein z is0Is P'0Elevation values of the points. Calculating the distance between other laser foot points and the initial position of the grid to obtain the respective abscissa X on the section planei：
Defining the grid spacing grid as the ideal scan line LkAverage dot spacing of N +1 laser foot dots above:
and mapping the elevation value information of the laser foot points on the ideal scanning line to each grid position distributed at equal intervals by a linear interpolation method. Grid position GiThe abscissa of (A) is i.grid, and the elevation information thereof is defined as z'i。GiAnd the nearest laser foot point P 'on both sides of the ideal scanning line'i-1(Xi-1，zi-1) And P'i(Xi，zi) May be expressed as:
then solve for GiElevation information z'i：
According to the method, for LkAnd calculating the elevation values of all the grid positions, wherein the elevation values of the nearest laser foot points on the left end point and the right end point of the grid sequence are directly assigned according to the elevation values of the nearest laser foot points of the grid sequence because the conditions that the nearest laser foot points on the two sides exist are not met. By the above-mentioned treatment method, LkThe laser foot points originally unevenly and dispersedly distributed in the three-dimensional space are mapped to a regular discrete mathematical sequence in the two-dimensional space.
And step three, real-time imaging and building feature extraction based on stationary wavelet transform.
And converting the laser foot points on the k-th scanning line into a regular discrete elevation data sequence through xoy plane linearization, elevation value rasterization and other operations in the first step and the second step. And processing the data by adopting discrete stationary wavelet transform, and extracting the building position information and the top surface type information of the building contained in the scanning line according to the characteristics of the obtained wavelet detail coefficients. According to the shape of the top surface of a building, the buildings contained in the point cloud data can be divided into: flat roof buildings, slope roof buildings and herringbone roof buildings. Scanning the three kinds of buildings, and processing the laser foot points on each obtained scanning line by linearization, rasterization and the likeInto a regular discrete sequence. And analyzing the Discrete sequence of the laser foot points by adopting a Discrete Stationary Wavelet Transform (DSWT) method, and extracting elevation change information contained in the Discrete sequence. Besides the fast operation capability of the discrete wavelet transform, the DSWT transform has important characteristics of redundancy and translation invariance relative to other discrete wavelet transforms. The signal length of each scale of wavelet detail coefficient obtained by DSWT transform is equal to the signal length of original data, so that the position of the characteristic signal represented in the wavelet detail coefficient in the original discrete sequence can be quickly and accurately determined by the corresponding time shift parameter. Will be based on the scanning line LkLaser foot point P oni(i∈[0，N]And i ∈ Z) calculated grid position data Gi(i∈[0，N]And i belongs to Z), and a regular discrete sequence formed by the sequence i belongs to Gk, and a low-pass filter h and a high-pass filter g are adopted to perform smooth discrete wavelet transform on the sequence Gk: <math>
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</math> wherein,
indicating convolution operation, dhGk and dgGk indicate wavelet approximation coefficients and wavelet detail coefficients obtained by DSWT transform, respectively. Analyzing the property of discrete wavelet transform, the elevation change information included in the sequence Gk is mainly included in the wavelet detail coefficient dgGk. The laser foot points of the top surfaces of buildings with flat top surfaces, buildings with slope top surfaces and buildings with herringbone top surfaces are processed by adopting the linearization and rasterization methodsThree discrete sequences are obtained. And performing first-order DSWT transformation on the three groups of discrete sequences to obtain three groups of wavelet detail coefficients. And analyzing the obtained wavelet detail coefficients, wherein the waveforms of the wavelet detail coefficients can generate impact signals corresponding to the edge positions of the top surface of the building, the elevation rising edge position of the building corresponds to a negative impact signal, and the elevation falling edge position corresponds to a positive impact signal. Because the DSWT transformation adopts a linear filter, the elevation value of the building can be reversely calculated according to the amplitude of the impact signal. There is no elevation change in the top surface portion of the flat-top building, and correspondingly, the wavelet detail coefficients between the negative impact signal and the positive impact signal are both 0 values. If the slope is a fixed positive value, the wavelet detail coefficient between the negative impact signal and the positive impact signal is a fixed negative value correspondingly; on the contrary, if the slope is a fixed negative value, the corresponding wavelet detail coefficient is a fixed positive value. A gable roof is understood to be composed of two sloping roofs with mutually opposite slopes, the wavelet coefficients of the roof portion being a sequence of negative values and a corresponding sequence of positive values. By combining and expanding the three typical building top surface models, a large number of complex building top surface models can be obtained. By adopting the method, the boundary of the top surface of the building is judged according to the position of the impact signal, and the type of the top surface of the building is judged according to the wavelet detail coefficient of the internal area of the top surface of the building, so that the type information of the top surface of the building in the laser radar point cloud data is obtained. And calculating the gray values of the rasterized regular discrete elevation data sequence and the wavelet detail coefficient by a graying method, so as to realize real-time imaging processing of the corresponding detection area.
And step four, checking the extraction result of each scanning line by adopting a space-time correlation analysis method.
Through the operation of the third step, the building position information and the building top surface type information contained in the laser foot points of each scanning line can be extracted in real time, and the corresponding detection area is imaged in real time according to the elevation information and the wavelet detail coefficient of each scanning line. However, the elevation information on each scanning line only comes from the scanning direction of the laser radar and does not contain the information on the traveling direction of the loading platform, so that the elevation values and the wavelet detail coefficients of the corresponding grid positions on each adjacent scanning line in the scanning process and the position and top surface type information of each building contained in the wavelet detail coefficients are analyzed and verified by utilizing the space-time correlation analysis. Taking wavelet coefficients as an example, classifying the wavelet detail coefficients of each scanning line, and marking the corresponding grid positions by using the obtained result. In the wavelet detail coefficient, if there is a section with a negative impact signal on one side and a positive impact signal on the other side, the section is determined as a building. Wherein, the initial position of the negative impact signal represents the rising edge of the top surface of the building, and the grid position corresponding to the initial position is marked as 1; the start position of a positive impact signal represents the falling edge of the building roof, with its corresponding grid position marked 2. And (3) carrying out classification marking on the grid positions inside the interval according to the corresponding wavelet detail coefficient values: when the wavelet coefficient is 0 value, no elevation change exists at the position, and the position is marked as 3 to represent a flat top surface; when the wavelet coefficient is a negative value, marking the wavelet coefficient as 4, and indicating that the slope is the top surface of the slope with positive slope; when the wavelet coefficient is a positive value, marking the wavelet coefficient as 5, and indicating that the wavelet coefficient is a slope top surface with a negative slope; when the wavelet coefficients are made up of a combination of positive and negative numbers, they are labeled 4&5, indicating a chevron top surface. And performing space-time correlation analysis on the marked grid values of all the scanning lines, comparing the marked values of the corresponding grid positions of the forward and backward scanning lines with the marked value of the obtained building feature extraction result, and verifying the obtained building feature extraction result.
The method of the present invention is further illustrated by the specific embodiment of the method of the present invention for real-time processing of a real lidar point cloud data:
the real lidar point cloud data and the remote sensing images of the corresponding scanning areas are respectively shown in fig. 2(a) and 2 (b). The laser foot points in the point cloud data are processed according to the scanning lines to which the laser foot points belong by using the method, and the obtained elevation image of the point cloud data is shown in fig. 2(c), and the wavelet detail coefficient image is shown in fig. 2 (d). Analyzing the wavelet detail coefficients of each obtained scanning line by using the method of the invention to obtain the building characteristics, marking according to the method in the fourth step, and verifying the obtained result by using a space-time correlation analysis method to obtain the building position and top surface type information in the point cloud data, as shown in fig. 2 (e). As can be seen from the observation of the figure 2(e), the method can well process the laser radar point cloud data, and realize the operations of real-time imaging, building feature extraction and the like.
The above description is only a basic scheme of the specific implementation method of the present invention, but the protection scope of the present invention is not limited thereto, and any changes or substitutions that can be conceived by those skilled in the art within the technical scope of the present invention disclosed herein are all covered within the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims. All changes which come within the meaning and range of equivalency of the claims are to be embraced within their scope.
Claims (1)
1. A method for laser radar real-time imaging and building feature extraction is characterized by comprising the following four steps:
firstly, carrying out linearization processing on laser foot points which belong to the same scanning line in point cloud data in an xoy plane;
under an ideal condition, laser foot points on one scanning line obtained by a laser radar through rotation of a prism in a scanning mechanism are uniformly distributed on the same straight line on an xoy plane in a Cartesian three-dimensional xyz coordinate system, but in an actual situation, due to the height change of a ground object and the deviation of course angle, roll angle and pitch angle of a laser radar load platform under the influence of complex airflow, a certain distance deviation exists between the laser foot points on each scanning line and an ideal scanning line thereof, in the scanning process of the laser radar, an ideal scanning line equation corresponding to the completed scanning line is calculated by using a least square fitting method, so that the sum of squares of distances from all the laser foot points on the scanning line to the ideal straight line is minimum in the xoy plane, the perpendicular line of each laser foot point and the ideal straight line in the xoy plane is calculated, and the intersection point coordinate of each perpendicular line and the ideal straight line in the xoy plane is used as the position where each laser foot point passes through the linearization position The processed x-axis and y-axis coordinates;
secondly, performing elevation value rasterization on the laser foot points subjected to the linearization treatment on the ideal scanning line;
after linearization processing, distributing the laser foot points on the ideal scanning line in a linear mode, taking the average value of the distance of each laser foot point on the linear as a grid distance value, dividing the linear from the first laser foot point into a plurality of grids by using the distance value, and calculating the elevation value of each grid position on the ideal linear by using the elevation value of each laser foot point on the linear and adopting a linear interpolation method;
thirdly, performing real-time imaging of the laser radar and extraction processing of building features based on the stationary wavelet transform;
converting laser foot points on scanning lines into a regular discrete elevation data sequence through linearization and elevation value rasterization operations in the first step and the second step, processing the discrete elevation data sequence by adopting discrete stationary wavelet transform, extracting building edge position information and building top surface type information contained in the discrete elevation data sequence according to the characteristics of an obtained wavelet detail coefficient, calculating a rasterized regular discrete elevation data sequence and a gray value of the wavelet detail coefficient by a graying method, and processing the obtained laser foot point data on each scanning line in real time according to the method in the scanning process of the laser radar so as to realize real-time imaging of a corresponding detection area;
step four, checking the extraction result of each scanning line by adopting a space-time correlation analysis method;
through the operation of the third step, building edge position information and building top surface type information contained in each scanning line laser foot point are extracted in real time, and real-time imaging processing is carried out by utilizing elevation information and wavelet detail coefficients of each scanning line, but the elevation information on each scanning line is only from the scanning direction of a laser radar and does not contain information on the advancing direction of a load platform, so that the elevation value of the corresponding grid position on each adjacent scanning line in the scanning process, the wavelet detail coefficients and the position and top surface type information of each building contained in each scanning line are analyzed and verified by utilizing space-time correlation analysis, and the accuracy of feature extraction is improved.
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| CN102645209A (en) * | 2012-04-24 | 2012-08-22 | 长江勘测规划设计研究有限责任公司 | Joint positioning method for spatial points by means of onboard LiDAR point cloud and high resolution images |
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