CN115953604B - Real estate geographic information mapping data acquisition method - Google Patents
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Abstract
The invention relates to the technical field of electric digital processing, in particular to a method for acquiring real estate geographic information mapping data. The method comprises the following steps: acquiring a point cloud data set of a real estate building, and screening characteristic points according to Thiessen polygons corresponding to data points in each acquisition graph, normal vectors of the data points in each acquisition graph and normal vectors of key points in each acquisition graph; based on the HOG operator corresponding to each feature point in each acquisition graph and the gray value of each feature point, euclidean distance between each feature point and the feature point in the preset neighborhood of each feature point, and structural similarity of the structural graph corresponding to each feature point and the structural graph corresponding to the feature point in the preset neighborhood of each feature point, obtaining a feature descriptor; and matching the point cloud data set of the real estate building with the standard point cloud data set based on the feature descriptors, so as to determine the position information of the point cloud data of the real estate building. The invention improves the accuracy and the credibility of real estate building geographic information mapping data acquisition.
Description
Technical Field
The invention relates to the technical field of electric digital processing, in particular to a method for acquiring real estate geographic information mapping data.
Background
The basic data of mapping geographic information is carried in various forms, and can be various types of data, such as satellite images, aerial images, various scale maps and the like, and the images can form a grid map database, a digital elevation model database, an orthographic image database and the like.
For real estate buildings, mapping data of the buildings are often obtained through three-dimensional models, the traditional real estate buildings generate corresponding three-dimensional models through professional software to collect the mapping data, orthographic images are needed, correction is needed to be carried out on the orthographic images through DEM data, three-dimensional modeling is carried out through combining with CAD data, and the implementation process is complicated. At present, a three-dimensional scanner is generally utilized to collect mapping data by applying a point cloud technology of a real estate building. The point cloud is a massive point set of surface features of the real estate building, the denser the point cloud is, the more image detail information is reflected, but the primarily acquired elevation mapping point cloud data of the building possibly contains wrong or abnormal data points, the existence of the data points can influence the matching result of the point cloud data of the real estate building and standard point cloud data, further, the acquisition result of the integral mapping data of the point cloud data of the real estate building is caused to generate errors, the accuracy of the mapping data influences the credibility of the geographic information storage result, and therefore, the point cloud data in the three-dimensional model needs to be processed, and the accuracy and the stability of the acquisition of the geographic information mapping data of the real estate are improved.
Disclosure of Invention
In order to solve the problem of lower accuracy in the prior art when acquiring real estate building geographic information mapping data, the invention aims to provide a real estate geographic information mapping data acquisition method, which adopts the following technical scheme:
the invention provides a method for acquiring real estate geographic information mapping data, which comprises the following steps:
acquiring a point cloud data set of a real estate building;
acquiring each acquisition graph based on the space coordinates of each data point in the point cloud data set; obtaining the characteristic stability of each data point in the point cloud data set according to the Thiessen polygon corresponding to each data point in each acquisition chart, the normal vector of the key point in the acquisition chart where each data point is located, and the normal vector of the acquisition chart where each data point is located; screening feature points based on the feature stability;
constructing a structure diagram corresponding to each feature point based on the feature points and the feature points in the preset neighborhood of the feature points, and constructing a point cloud descriptor of each feature point based on a HOG operator corresponding to each feature point in each acquisition diagram, a normal vector of the feature point in the acquisition diagram where each feature point is located and a gray value of each feature point; obtaining feature descriptors of all the feature points according to Euclidean distance between each feature point and the feature points in the preset neighborhood of the feature point, the point cloud descriptors of all the feature points, the point cloud descriptors of the feature points in the preset neighborhood of the feature points, and the structural similarity of the structural graph corresponding to each feature point and the structural graph corresponding to the feature points in the preset neighborhood of the feature points;
And matching the characteristic points in the point cloud data set of the real estate building with the data points in the standard point cloud data set based on the characteristic descriptors of the characteristic points in the point cloud data set of the real estate building and the characteristic descriptors of the data points in the standard point cloud data set, and determining the position information of the point cloud data of the real estate building based on the matching result.
Preferably, the obtaining each acquisition chart based on the spatial coordinates of each data point in the point cloud data set includes:
the acquisition graph comprises a transverse acquisition graph and a longitudinal acquisition graph;
the origin of the space coordinate system is an O point, three coordinate axes of the coordinate system are an X axis, a Y axis and a Z axis respectively, each plane parallel to a plane YOZ is marked as a first plane, each plane parallel to the plane XOZ is marked as a second plane, the first plane of the abscissa of any data point in the point cloud data set of the real estate building is used as a transverse acquisition diagram, and the second plane of the ordinate of any data point in the point cloud data set of the real estate building is used as a longitudinal acquisition diagram.
Preferably, the obtaining the feature stability of each data point in the point cloud data set according to the corresponding Thiessen polygon of each data point in each collection chart, the normal vector of the key point in the collection chart where each data point is located, and the normal vector of the collection chart where each data point is located includes:
For an a-th data point in the point cloud dataset:
according to the Thiessen polygon corresponding to the a-th data point in each acquisition chart and the Thiessen polygon corresponding to each data point in the preset neighborhood of the a-th data point, obtaining the transverse angle difference and the longitudinal angle difference corresponding to each data point in the a-th data point and the preset neighborhood;
calculating the area difference of the Thiessen polygon corresponding to the a-th data point in the transverse acquisition chart and the Thiessen polygon corresponding to each data point in the preset neighborhood of the a-th data point in the transverse acquisition chart, and marking the area difference as the transverse area difference; calculating the area difference of the Thiessen polygon corresponding to the a-th data point in the longitudinal acquisition chart and the Thiessen polygon corresponding to each data point in the preset neighborhood of the a-th data point in the longitudinal acquisition chart, and marking the area difference as the longitudinal area difference; calculating the spatial dispersion of an a-th data point based on the transverse area difference, the longitudinal area difference, the transverse angle difference and the longitudinal angle difference, wherein the transverse area difference, the longitudinal area difference, the transverse angle difference and the longitudinal angle difference are in positive correlation with the spatial dispersion;
Respectively calculating cosine similarity of normal vector of each key point in each acquisition graph where the a data point is positioned and normal vector of each acquisition graph where the a data point is positioned, and obtaining structural stability of the a data point based on the cosine similarity, wherein the cosine similarity and the structural stability are in positive correlation;
taking the ratio of the structural stability to the spatial dispersion as the characteristic stability of the a data point.
Preferably, the method for acquiring the transverse angle difference between the a-th data point and each data point in the preset neighborhood comprises the following steps:
respectively calculating absolute values of differences between maximum internal angles of the a-th data point in the corresponding Thiessen polygons in the transverse acquisition diagram and maximum internal angles of the i-th data point in the corresponding Thiessen polygons in the transverse acquisition diagram, and marking the absolute values as the differences of the maximum internal angles; respectively calculating the absolute value of the difference value between the minimum internal angle of the a-th data point in the corresponding Thiessen polygon in the transverse acquisition diagram and the minimum internal angle of the i-th data point in the corresponding Thiessen polygon in the transverse acquisition diagram, and recording the absolute value as the difference of the minimum internal angles; the ith data point is a data point in a preset neighborhood of the a data point; and marking the sum of the difference of the maximum internal angle and the difference of the minimum internal angle as the transverse angle difference corresponding to the (a) data point and the (i) data point in the preset adjacent area.
Preferably, the screening feature points based on the feature stability includes: and obtaining the minimum value of the characteristic stability of all data points in the standard point cloud data set, taking the minimum value as a screening threshold value, and taking the data points with the characteristic stability larger than or equal to the screening threshold value in the point cloud data set of the real estate building as the characteristic points.
Preferably, the construction of the point cloud descriptor of each feature point based on the HOG operator corresponding to each feature point in each collection chart, the normal vector of the feature point in the collection chart where each feature point is located, and the gray value of each feature point includes:
for the q-th feature point:
calculating the sum of an HOG operator in the transverse acquisition graph where the q-th feature point is located and an HOG operator in the longitudinal acquisition graph where the q-th feature point is located;
calculating the normal vector of the q-th feature point in the transverse acquisition graph where the q-th feature point is located and the variances of the normal vectors of all the feature points in the preset neighborhood of the q-th feature point, and marking the variances as first variances; calculating the normal vector of the q-th feature point in the longitudinal acquisition graph where the q-th feature point is located and the variance of the normal vector of all the feature points in the preset neighborhood of the q-th feature point, marking the variance as a second variance, and taking the sum of the first variance and the second variance as the normal vector distribution variance of the q-th feature point;
And combining the sum value, the normal vector distribution variance and the gray value of the q-th characteristic point together to serve as a point cloud descriptor of the q-th characteristic point.
Preferably, the feature descriptors of the feature points are calculated using the following formula:
wherein ,
a feature descriptor for the q-th feature point,
distance of kth feature point in preset neighborhood for qth feature pointThe weight of the separation is calculated,
for the Euclidean distance between the q-th feature point and the k-th feature point in the preset neighborhood,
for the number of feature points in the preset neighborhood of the q-th feature point,
for a radius of the preset neighborhood,
the average value of Euclidean distances between the q-th feature point and all feature points in the preset neighborhood of the q-th feature point is obtained,
a point cloud descriptor that is the q-th feature point,
a point cloud descriptor of a kth feature point in a preset neighborhood of the qth feature point,
is a natural constant which is used for the production of the high-temperature-resistant ceramic material,
in order to preset the adjustment parameters, the adjustment parameters are set,
is the structure diagram corresponding to the q-th feature point,
is a structure diagram corresponding to the kth feature point in the preset neighborhood of the qth feature point,
is of a structure diagram
And structure diagram
Is a function of the structural similarity of the (c) to the (c),
to take absolute value symbols.
Preferably, the construction of the structure diagram corresponding to each feature point based on the feature points and the feature points in the preset neighborhood of the feature points includes: and taking each characteristic point as a center, and respectively connecting each characteristic point with each characteristic point in the preset adjacent area to obtain a structure diagram corresponding to each characteristic point.
The invention has at least the following beneficial effects:
according to the method, firstly, a point cloud data set of a real estate building is obtained, the characteristic stability of each data point in the point cloud data set of the real estate building is determined, the characteristic stability considers the distribution condition of the data points in each acquisition graph, when a scanner scans a region with severe surface change of the real estate building, the influence caused by the fact that the local density of adjacent point cloud data points is reduced due to the fact that the scanning angle is formed, the data point coordinate error is increased, the influence caused by the matching of the point cloud data is avoided, the characteristic stability is used for representing the stability degree of the characteristics of the data points on the corresponding acquisition graph in the point cloud data set, the characteristic stability is larger, the more uniform distribution of the corresponding data points and surrounding data points in space is shown, the higher the similarity of the corresponding data points and key points is, the characteristic is more remarkable, therefore, the corresponding data points are more likely to be normal data points, the characteristic points are selected from the point cloud data set of the real estate building based on the characteristic stability, the characteristic points in the real estate building are matched later, the interference of abnormal points is eliminated, and the reliability of the subsequent matching results can be improved; according to the Euclidean distance between each characteristic point and the characteristic point in the preset neighborhood of the characteristic point, the point cloud descriptor of each characteristic point, the point cloud descriptor of the characteristic point in the preset neighborhood of each characteristic point, the structural similarity of the structural diagram corresponding to each characteristic point and the structural diagram corresponding to the characteristic point in the preset neighborhood of each characteristic point, the characteristic descriptor of each characteristic point is constructed, the influence of the distance is considered by the characteristic descriptor, the data points with different distances are analyzed, the problem that the data points with far distances are ignored when the traditional description is constructed is avoided, the structural similarity of the characteristic point and the data points in the neighborhood is also considered by the characteristic descriptor, accurate characteristic information can be acquired in the local area with complex structural lines, the influence of isolated interference points is avoided, and the matching precision between the follow-up data points is improved; according to the method, the characteristic descriptors of the characteristic points in the point cloud data set of the real estate building and the characteristic descriptors of the data points in the standard point cloud data set are matched with the point cloud data, so that the position information of the point cloud data of the real estate building is determined, the interference of abnormal points on a matching result is eliminated, and the accuracy and the reliability of acquisition of geographic information mapping data of the real estate building are improved.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions and advantages of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of a real estate geographic information mapping data acquisition method provided by an embodiment of the present invention;
fig. 2 is a structural diagram corresponding to the q-th feature point.
Detailed Description
In order to further describe the technical means and effects adopted by the invention to achieve the preset aim, the following detailed description is given to a real estate geographic information mapping data acquisition method according to the invention by combining the attached drawings and the preferred embodiment.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The following specifically describes a specific scheme of the real estate geographic information mapping data acquisition method provided by the invention with reference to the accompanying drawings.
An embodiment of a real estate geographic information mapping data acquisition method comprises the following steps:
the embodiment provides a real estate geographic information mapping data acquisition method, as shown in fig. 1, which comprises the following steps:
and step S1, acquiring a point cloud data set of the real estate building.
The specific scene aimed at by this embodiment is: the three-dimensional model of the real estate building is obtained by utilizing the three-dimensional scanner, the corresponding point cloud data set is obtained from the three-dimensional model, the obtained point cloud data set is matched with the standard point cloud data set, the influence of abnormal data points or error data points on the acquisition of the real estate building mapping data is eliminated, and the acquisition precision of the real estate building mapping data is improved.
The point cloud data is generally obtained through a three-dimensional laser scanner, the three-dimensional laser scanner can obtain three-dimensional coordinate information of the surface of the real estate building in a large area and high resolution, in the embodiment, the real estate building is scanned by using the Trimble three-dimensional scanner, in the process of Trimble three-dimensional laser scanning, the effect of factors such as shielding and uneven illumination of objects around the real estate building is often caused, scanning blind spots exist in the area of the object with a complex shape easily, holes are formed, meanwhile, the real estate building cannot be completely measured at one time usually due to limited scanning measurement range, only local data of the real estate building can be obtained at one scanning position, and the position must be replaced for multiple scanning measurements. According to the method, firstly, a plurality of scanning positions are determined according to the appearance characteristics of the real estate building, all data acquired by all the scanning positions can contain all data of the surface of the real estate building, a Trimble three-dimensional scanner is sequentially arranged at each scanning position or one Trimble three-dimensional scanner is arranged at each scanning position, a plurality of initial data with different coordinate systems are obtained, therefore, coordinate transformation is needed to be carried out to unify all point cloud data into the same coordinate system, the coordinate transformation can be realized by utilizing a rotation matrix, the process is a known technology, and redundant description is omitted here. In order to improve accuracy of point cloud data and eliminate noise influence, the point cloud data needs to be preprocessed, in this embodiment, an outlier detection method is adopted to preprocess the collected point cloud data, and outlier detection is a known technology and will not be described herein.
So far, the point cloud data set of the real estate building can be obtained according to the outlier detection and used for matching with the standard point cloud data set in the follow-up process.
Step S2, acquiring each acquisition graph based on the space coordinates of each data point in the point cloud data set; obtaining the characteristic stability of each data point in the point cloud data set according to the Thiessen polygon corresponding to each data point in each acquisition chart, the normal vector of the key point in the acquisition chart where each data point is located, and the normal vector of the acquisition chart where each data point is located; and screening feature points based on the feature stability.
The purpose of this embodiment is to accurately match the point cloud data set of the real estate building with the standard point cloud data set, and collect real estate building geographic information mapping data according to the matching result of the point cloud data set. Because the probability of changing the data points corresponding to the internal positions of the building is extremely small, the data points are not necessarily all matched, the embodiment is to match the data points with certain characteristics, respectively acquire a transverse acquisition chart and a longitudinal acquisition chart corresponding to the point cloud data sets of the real estate building, respectively analyze the transverse acquisition chart and the longitudinal acquisition chart to acquire the characteristic stability of each data point, screen normal data points based on the characteristic stability, match the point cloud data sets of the real estate building with the standard point cloud data sets, if the data points are normal point cloud data, the matching result of the normal data points and the matching result of the data points adjacent to the space positions or with the same dimension should be relatively stable, and if the data points are abnormal or wrong point cloud data, the matching result of the data points adjacent to the space positions or with the same dimension should have certain difference; for example, data points located on the line of a real estate building structure, there are data points belonging to the same dimension as the data points, and the matching results of the data points have a certain similarity. And fusing different matching results to obtain final screening point cloud data, and adaptively acquiring initial values in an ICP algorithm for different areas, thereby improving the accuracy and stability of point cloud matching.
The point cloud data set of the real estate building contains normal data points and abnormal data points, the reason that the abnormal data points are generated is that the scanning angle of a scanner does not reach an ideal angle, or the data points are positioned at the vertex angle and the complex structure of the real estate building, the data points in the areas have larger different direction changes, and the abnormal condition easily occurs in the scanning process, so that before the point cloud data set of the real estate building is matched with the standard point cloud data set, the characteristic point extraction is required to be carried out on the point cloud data set, and the interference of the abnormal data on the matching result is reduced on the premise that the matching precision is not reduced.
Each data point in the point cloud data set contains coordinate information in a three-dimensional space, when the abscissa is fixed, a plurality of data points exist on the same plane, the data points are located in the same space plane in the real estate building, the origin of the space coordinate system is an O point, three coordinate axes of the coordinate system are an X axis, a Y axis and a Z axis respectively, each plane parallel to the plane YOZ is marked as a first plane, each plane parallel to the plane XOZ is marked as a second plane, the first plane of the abscissa of any data point in the point cloud data set of the real estate building is used as a transverse acquisition diagram, and the second plane of the ordinate of any data point in the point cloud data set of the real estate building is used as a longitudinal acquisition diagram; to this end, a plurality of transverse acquisition maps and a plurality of longitudinal acquisition maps are obtained, wherein one transverse acquisition map or one longitudinal acquisition map may contain a plurality of data points.
The real estate building comprises a large number of structural lines which are distributed in each acquisition graph according to the design requirements of the real estate building and are represented by line segments with different lengths and different slopes. The vertex angle of the real estate building is an area where a plurality of structural lines are intersected and is also an area where local characteristics in the real estate building change greatly, for example, the normal vector of data points on the same plane in the vertex angle area changes greatly, so that the matching point pairs formed by the data points in the vertex angle area of the real estate building can be given a larger weight because of the matching points formed by the data points and standard point cloud dataThe matching point pairs corresponding to surrounding adjacent data points are obviously different and cannot be replaced by other data points; the local characteristics of adjacent point cloud data points on the same plane change less in a relatively flat local area on the real estate building, so that the normal vector directions of the data points in the area are almost consistent, and after the matching point pairs formed by the data points are replaced by the matching point pairs formed by the adjacent data points, the local matching result does not change significantly. In addition, in the transverse acquisition chart or the longitudinal acquisition chart, if the data points are areas with larger local variation and the adjacent data points are also more likely to be in the areas with larger local variation, the local characteristics of the data points are more remarkable in the three-dimensional model corresponding to the point cloud data set of the real estate building. Based on this, for the a-th data point in the point cloud dataset of the real estate building: any data point in the point cloud data set is simultaneously positioned in one transverse acquisition image and one longitudinal acquisition image, so that a transverse acquisition image in which the a data point is positioned and a longitudinal acquisition image in which the a data point is positioned are obtained; considering that the shape of the polygon in the Thiessen polygon reflects the distribution characteristics of the adjacent data points in each acquisition surface, the smaller the area of the polygon is, the denser the distribution of the data points in the polygon is; therefore, for the transverse acquisition graph in which the a-th data point is located, acquiring the Thiessen polygon corresponding to the a-th data point in the transverse acquisition graph according to the a-th data point and the data points in the preset neighborhood of the a-th data point 
Simultaneously acquiring a Thiessen polygon corresponding to each data point in the preset neighborhood of the a-th data point in the transverse acquisition chart, wherein the Thiessen polygon corresponding to the i-th data point in the preset neighborhood of the a-th data point in the transverse acquisition chart is
The method comprises the steps of carrying out a first treatment on the surface of the Acquiring Thiessen polygons
The maximum internal angle and the minimum internal angle of the (B) are obtained to obtain a Thiessen polygon
The maximum internal angle and the minimum internal angle of (3) are calculated as Thiessen polygons
The largest interior angle and Thiessen polygon of (2) are
The absolute value of the difference of the maximum internal angles in the polygon is recorded as the difference of the maximum internal angles, and the Thiessen polygon is calculated
The minimum interior angle and Thiessen polygon of (a) are
The absolute value of the difference value of the minimum internal angle is recorded as the difference of the minimum internal angle, and the sum of the difference of the maximum internal angle and the difference of the minimum internal angle is recorded as the transverse angle difference corresponding to the a-th data point and the i-th data point in the preset adjacent area; for a longitudinal acquisition chart of the a data point, acquiring a Thiessen polygon corresponding to the a data point in the longitudinal acquisition chart according to the a data point and the data points in the preset neighborhood of the a data point
Simultaneously acquiring a Thiessen polygon corresponding to each data point in the preset neighborhood of the a-th data point in the longitudinal acquisition chart, wherein the Thiessen polygon corresponding to the j-th data point in the preset neighborhood of the a-th data point in the longitudinal acquisition chart is 
Similarly, the method is analogized to obtain the longitudinal angle difference corresponding to the a data point and the j data point in the preset neighborhood; performing key point extraction on a transverse acquisition diagram where an a data point is located and a longitudinal acquisition diagram where the a data point is located by adopting a SIFT algorithm to obtain a plurality of key points in the transverse acquisition diagram where the a data point is located and a plurality of key points in the longitudinal acquisition diagram where the a data point is located; SIFT algorithm and TayThe process of acquiring the forest polygon is known in the art, and will not be repeated here. In this embodiment, the preset neighborhood refers to a circular area with a radius r taken by a data point as a center point, r is a radius of the preset neighborhood, in this embodiment, the value of r is set to 9, and in a specific application, an implementer can set according to a specific situation. The greater the similarity between the normal vector of the collection diagram where the data point is and the normal vector of the key point in the collection diagram where the data point is, the more likely the data point is to be a normal data point, and the more stable the structure is. Therefore, the embodiment determines the structural stability of each data point based on the cosine similarity between the normal vector of the acquisition graph where each data point is located and the normal vector of the key point in the acquisition graph where each data point is located, and determines the spatial dispersion of each data point based on the area difference of the Thiessen polygon corresponding to each data point in each acquisition graph in the Thiessen polygon corresponding to each data point in each acquisition graph and the Thiessen polygon corresponding to each data point in the preset neighborhood of each data point, the transverse angle difference corresponding to each data point in each data point and the preset neighborhood of each data point, and the longitudinal angle difference corresponding to each data point in each data point and the preset neighborhood of each data point; and constructing feature stability according to the structural stability and the feature stability, wherein the feature stability is used for representing the stability of the features of the data points on the corresponding acquisition graph in the point cloud data set, and the specific expression of the feature stability of the a-th data point is as follows:
By adopting the method, the characteristic stability of each data point in the point cloud data set of the real estate building can be obtained; in a point cloud data set of a real estate building, the more uneven the distribution of data points adjacent to the space position is, the more complex the region where the data points are located is, the worse the stability of the data points is, and the more inaccurate the result of matching the point cloud is; the area where the key points are located in the real estate building is often an area with obvious structural characteristics, and abnormal data points are not easy to appear in the scanning process, so that the more similar the data points are to the key points, the stronger the characteristic stability of the data points is.
Further, obtaining the minimum value of the feature stability of all data points in the standard point cloud data set according to the steps, and taking the minimum value as a screening threshold
The smaller the characteristic stability of the point cloud data set of the real estate building, the more likely the data points are abnormal data points, so that the characteristic stability of the point cloud data set of the real estate building is smaller than
The data points of the real estate building are judged to be abnormal data points, all abnormal data points are deleted, and the remaining data points in the point cloud data set of the real estate building are marked as characteristic points, namely, the characteristic stability in the point cloud data set of the real estate building is more than or equal to 
And (3) judging the data points as normal data points, recording the normal data points as characteristic points, and matching based on the characteristic points. The characteristic stability is achieved by deleting abnormal data points in the point cloud data set of the real estate building through evaluating the characteristic stability of the data points, and the influence of the increase of data point coordinate errors on the matching of the point cloud data due to the reduction of local density of adjacent data points caused by the scanning angle when the scanner scans the area with more intense surface change of the real estate building is avoided.
So far, the data points in the point cloud data set of the real estate building are screened, and all the characteristic points are obtained.
Step S3, constructing a structure diagram corresponding to each feature point based on the feature points and the feature points in the preset neighborhood of the feature points, and constructing a point cloud descriptor of each feature point based on a HOG operator corresponding to each feature point in each acquisition diagram, a normal vector of the feature point in the acquisition diagram where each feature point is located and a gray value of each feature point; and obtaining the feature descriptors of the feature points according to the Euclidean distance between the feature points and the feature points in the preset neighborhood, the point cloud descriptors of the feature points in the preset neighborhood of the feature points, and the structural similarity of the structural graph corresponding to the feature points and the structural graph corresponding to the feature points in the preset neighborhood of the feature points.
In this embodiment, feature points are selected in step S2, and then, the present embodiment will acquire feature descriptors of each feature point for matching calculation with data points in the standard point cloud data set. In the conventional ICP algorithm, features are generally calculated by using a small number of data points with short distances in a neighborhood of feature points, namely when the q-th feature point is far away from the data points in the preset neighborhood, the feature descriptors of the q-th feature point are not influenced by the corresponding neighborhood data points; when the distance between the q-th feature point and a data point in a preset neighborhood of the q-th feature point is short, the feature descriptors of the q-th feature point are greatly influenced by the corresponding neighborhood data point; this approach ignores data points that are far away, whereas in large-area structurally complex areas of the real estate building surface, the characteristics of the data points may be affected by the far away data points, e.g., abnormal data points appear at the corner vertices of an engraving window, and the corner vertices on the same structural line, although far away, may still be affected. Considering that the similarity of the structural diagram constructed by taking the feature points as the center and the structural diagram constructed by taking the neighborhood feature points as the center can reflect the consistency degree of the structural features of the local area where the feature points are positioned; therefore, in this embodiment, the feature descriptors of the feature points are constructed by combining the distance weights between the feature points and the feature points in the preset neighborhood and the similarity of the structure diagram, so as to characterize the feature information of the feature points in the point cloud data set of the real estate building.
For the q-th feature point:
taking the characteristic point as a center, respectively connecting the characteristic point with each characteristic point in the preset neighborhood to obtain a structural diagram
The structure diagram is marked as a structure diagram corresponding to the q-th characteristic point, as shown in fig. 2, q in the diagram represents the q-th characteristic point, and k in the diagram represents the k-th characteristic point in the preset neighborhood of the q-th characteristic point; similarly, the method is analogized to obtain a structure diagram corresponding to each feature point in the preset neighborhood of the q-th feature point; then respectively calculateStructural similarity of a structural diagram corresponding to the q-th feature point and a structural diagram corresponding to each feature point in a preset neighborhood of the q-th feature point is obtained by adopting an existing SSIM structural similarity algorithm; respectively acquiring Euclidean distance between the q-th feature point and each feature point in a preset adjacent area of the q-th feature point; the calculation method of the euclidean distance is a well-known technique, and will not be repeated here. Respectively acquiring an HOG operator in a transverse acquisition graph in which the q-th feature point is positioned and an HOG operator in a longitudinal acquisition graph in which the q-th feature point is positioned, and calculating the sum value of the HOG operator in the transverse acquisition graph in which the q-th feature point is positioned and the HOG operator in the longitudinal acquisition graph in which the q-th feature point is positioned 
The method comprises the steps of carrying out a first treatment on the surface of the Acquiring normal vectors of a q-th feature point in a transverse acquisition diagram where the q-th feature point is located and normal vectors of each feature point in a preset neighborhood of the q-th feature point, calculating normal vectors of the q-th feature point in the transverse acquisition diagram where the q-th feature point is located and variances of normal vectors of all feature points in the preset neighborhood of the q-th feature point, and marking the variances as first variances; acquiring normal vectors of the q-th feature point in the longitudinal acquisition diagram of the q-th feature point and normal vectors of each feature point in the preset neighborhood of the q-th feature point, calculating variances of normal vectors of the q-th feature point in the longitudinal acquisition diagram of the q-th feature point and normal vectors of all feature points in the preset neighborhood of the q-th feature point, marking the variances as second variances, and summing the first variances and the second variances
Normal vector distribution variance as the q-th feature point. Will be
Normal vector distribution variance of the q-th feature point
Gray value of the q-th feature point
Combined together as the (q) thPoint cloud descriptors of feature points, i.e
As a point cloud descriptor for the qth feature point, the acquiring process of the HOG operator is a well-known technique, and will not be described in detail here, in this embodiment, the value of m is 31, that is 
The operator is a 31-dimensional vector; the normal vector distribution variance and the gray value are one-dimensional data, so the point cloud descriptor is a 33-dimensional vector, and the characteristic descriptor
And is also a 33-dimensional feature vector.
The reason for constructing the feature descriptors in the embodiment is that the closest point corresponding to the q-th feature point is found in the standard point cloud set in the ICP matching process, and the similarity degree of certain features can be met between the closest point and the feature points, so that the features of the q-th feature point in the point cloud data set of the real estate building can be represented by using the normal vector, the gray value and the structural similarity of the q-th feature point. The feature descriptors of the q-th feature point are specifically:
wherein ,
a feature descriptor for the q-th feature point,
the distance weight of the kth feature point in the preset neighborhood of the qth feature point is determined,
for the Euclidean distance between the q-th feature point and the k-th feature point in the preset neighborhood,
for the number of feature points in the preset neighborhood of the q-th feature point,
for a radius of the preset neighborhood,
the average value of Euclidean distances between the q-th feature point and all feature points in the preset neighborhood of the q-th feature point is obtained,
a point cloud descriptor that is the q-th feature point,
a point cloud descriptor of a kth feature point in a preset neighborhood of the qth feature point, 
Is a natural constant which is used for the production of the high-temperature-resistant ceramic material,
in order to preset the adjustment parameters, the adjustment parameters are set,
is the structure diagram corresponding to the q-th feature point,
is a structure diagram corresponding to the kth feature point in the preset neighborhood of the qth feature point,
is of a structure diagram
And structure diagram
Is a function of the structural similarity of the (c) to the (c),
to take absolute value symbols.
The preset adjustment parameters are introduced to prevent denominator0, in this embodiment, a preset adjustment parameter is set
The value of (2) is 0.01, and in a specific application, the practitioner can set the value according to the specific situation.
The structural significance representing the q characteristic point is reflected to the point cloud data set of the real estate building and is represented as the structural significance of the q characteristic point and adjacent data points in the local area of the three-dimensional model of the real estate building,
the larger the description structure diagram
And structure diagram
The more similar the q-th feature point is, the higher the structural feature consistency of the local area where the q-th feature point is located is, and the more remarkable the features in the three-dimensional model are.
The closer the distance between a certain feature point and a feature point in the neighborhood is, the larger the influence of the corresponding neighborhood feature point on the feature point is, and the smaller the influence of the distance on the feature point is; the greater the structural similarity between the feature points and the feature points in the neighborhood, the more stable the region where the feature points are located, the more remarkable the features, and the more accurate the matching result. The feature descriptors are feature vectors formed by carrying image information by feature points in the point cloud data set, and the influence of the feature points with the closer distribution distance to the q-th feature points in the neighborhood on the q-th feature point feature descriptors is larger, namely 
The larger the size of the container,
the larger; the greater the structural similarity between the q-th feature point and the structure diagram corresponding to the neighborhood feature point is, the higher the significance degree of the local region feature where the q-th feature point is located is, namely
The larger the q-th feature point is, the higher the structural feature consistency of the local area where the q-th feature point is located is, and the more remarkable the structural feature consistency is in the point cloud data set of the real estate building. The feature descriptor has the beneficial effects that a distance weight which cannot be ignored is obtained for feature points with different distances, so that the problem that long-distance data points are ignored when the traditional ICP builds the descriptor is avoided; the feature descriptors consider the structural similarity of the structural graph corresponding to the feature points and the structural graph corresponding to the feature points in the neighborhood, accurate feature information can be obtained in a local area with complex structural lines, the influence of isolated interference points is avoided, and the accuracy of the follow-up matching result is improved.
So far, by adopting the method, the feature descriptors of each feature point can be obtained.
And S4, matching the characteristic points in the point cloud data set of the real estate building with the data points in the standard point cloud data set based on the characteristic descriptors of the characteristic points in the point cloud data set of the real estate building and the characteristic descriptors of the data points in the standard point cloud data set, and determining the position information of the point cloud data of the real estate building based on the matching result.
In the embodiment, a feature descriptor of each feature point is obtained in step S3, a point cloud data set of a real estate building is used as an input of a matching algorithm, the point cloud data set of the real estate building and a standard point cloud data set are matched, and position information of the point cloud data of the real estate building is determined based on a matching result.
The improved matching process in this embodiment is as follows: inputting a point cloud data set and a standard point cloud data set of a real estate building, respectively extracting feature descriptors of feature points in the point cloud data set of the real estate building and feature descriptors of data points in the standard point cloud data set, forming a matching point pair by data points with the most similar feature descriptors, and measuring the similarity of an objective function of the matching point pair to the feature descriptors through the matching point pair, wherein the objective function is specifically as follows:
wherein ,
as a function of the object to be processed,
for the number of feature points in the point cloud dataset of the real estate building,
for the number of data points in the standard point cloud data set, y is the y-th characteristic point in the point cloud data set of the real estate building,
is the first of the standard point cloud data set
A data point is provided for each of the data points,
for the feature descriptors of the y-th feature points in the point cloud data set of the real estate building, 
Point cloud data set for real estate building
The feature descriptors of the data points,
feature descriptor and the y-th feature point
Similarity of feature descriptors of data points. In this embodiment, the similarity of the two feature descriptors is represented by using cosine similarity, and the calculation process of the cosine similarity is a known technology, and the specific process is not repeated.
Solving for rotation matrix by SVD decomposition
Translation vector
The point cloud data set of the real estate building is according to the rotation matrix
Translation vector
Performing transformation, namely matching the transformed point cloud data set with the standard point cloud data set, iterating continuously until the iteration stop condition is met, wherein the iteration stop condition is set to be that the iteration times reach 50 times in the embodiment, and in the specific application, an implementer can set according to specific conditions; after iteration stops, the rotation matrix is output
Translation vector
. The matching effect of the matching point pair is optimal when the objective function takes the maximum value, the matching cost of the data point is minimum, the corresponding matching result when the objective function takes the maximum value is obtained, the corresponding matching result when the objective function takes the maximum value is recorded as the optimal matching result, and the embodiment obtains the matching characteristics of the data point through the fusion processing of a plurality of characteristic values of the data point.
In the embodiment, the point cloud data set and the standard point cloud data set of the real estate building are used as the input of an improved ICP algorithm, the actual information of the data points in the acquired point cloud data set is acquired according to the matching result, and the acquisition of the mapping data of the real estate building is completed.
According to the steps, the ICP matching algorithm flow in the embodiment is obtained, the point cloud data set and the standard point cloud data set of the real estate building are used as input of the ICP matching algorithm, the matching result of the point cloud data set and the standard point cloud data set of the real estate building is obtained according to the specific flow of the ICP algorithm, after the matching result is obtained, the matching result comprises data points with coincident space positions and data points with non-coincident space positions, the ICP algorithm is the prior art, and redundant description is omitted here; for the data points with the overlapped space positions, taking the actual position information corresponding to the standard point cloud data as the position information of the point cloud data on the real estate building; and calculating the difference value between the data point and the matched standard point cloud data on each coordinate axis for the data point with the difference of the spatial positions, and acquiring the position information of the point cloud data on the real estate building by using the actual position information and the difference value corresponding to the standard point cloud data. After the position information of all the data points is obtained, according to the statistical requirements of the real estate building, the needed mapping data are collected and uploaded to the cloud for storage.
Thus, the acquisition of the position information of the point cloud data of the real estate building is completed.
According to the embodiment, a point cloud data set of a real estate building is firstly obtained, the characteristic stability of each data point in the point cloud data set of the real estate building is determined, the characteristic stability considers the distribution condition of the data points in each acquisition graph, when a scanner scans a region with severe surface change of the real estate building, the influence caused by the fact that the local density of adjacent point cloud data points is reduced due to the fact that the scanning angle is changed, the data point coordinate error is increased, the influence caused by the matching of the point cloud data is increased, the characteristic stability is used for representing the stability degree of the characteristics of the data points on the corresponding acquisition graph in the point cloud data set, the larger the characteristic stability is, the more uniform the distribution of the corresponding data points and surrounding data points in space is, the higher the similarity of the corresponding data points and key points is, the more remarkable the characteristics are, so that the corresponding data points are more likely to be normal data points are selected from the point cloud data set of the real estate building, the characteristic points in the point cloud data set of the real estate building are matched based on the characteristic stability, the characteristic points of the real estate building are eliminated, and the reliability of the follow-up matching result is improved; according to the embodiment, according to Euclidean distance between each feature point and the feature point in the preset neighborhood of the feature point, the point cloud descriptor of each feature point, the point cloud descriptor of the feature point in the preset neighborhood of each feature point, the structural similarity of the structural diagram corresponding to each feature point and the structural diagram corresponding to the feature point in the preset neighborhood of each feature point, the feature descriptor of each feature point is constructed, the influence of the distance is considered by the feature descriptor, data points with different distances are analyzed, the problem that data points with far distances are ignored when the traditional description is constructed is avoided, the structural similarity of the feature point and the data points in the neighborhood is also considered by the feature descriptor, accurate feature information can be acquired in a local area with complex structural line, the influence of isolated interference points is avoided, and the matching precision between subsequent data points is improved; according to the embodiment, the point cloud data are matched based on the feature descriptors of the feature points in the point cloud data set of the real estate building and the feature descriptors of the data points in the standard point cloud data set, so that the position information of the point cloud data of the real estate building is determined, the interference of abnormal points on the matching result is eliminated, and the accuracy and the reliability of acquisition of geographic information mapping data of the real estate building are improved.
Claims (4)
1. The method for collecting real estate geographic information mapping data is characterized by comprising the following steps:
acquiring a point cloud data set of a real estate building;
acquiring each acquisition graph based on the space coordinates of each data point in the point cloud data set; obtaining the characteristic stability of each data point in the point cloud data set according to the Thiessen polygon corresponding to each data point in each acquisition chart, the normal vector of the key point in the acquisition chart where each data point is located, and the normal vector of the acquisition chart where each data point is located; screening feature points based on the feature stability;
constructing a structure diagram corresponding to each feature point based on the feature points and the feature points in the preset neighborhood of the feature points, and constructing a point cloud descriptor of each feature point based on a HOG operator corresponding to each feature point in each acquisition diagram, a normal vector of the feature point in the acquisition diagram where each feature point is located and a gray value of each feature point; obtaining feature descriptors of all the feature points according to Euclidean distance between each feature point and the feature points in the preset neighborhood of the feature point, the point cloud descriptors of all the feature points, the point cloud descriptors of the feature points in the preset neighborhood of the feature points, and the structural similarity of the structural graph corresponding to each feature point and the structural graph corresponding to the feature points in the preset neighborhood of the feature points;
Based on the feature descriptors of the feature points in the point cloud data set of the real estate building and the feature descriptors of the data points in the standard point cloud data set, matching the feature points in the point cloud data set of the real estate building with the data points in the standard point cloud data set, and determining the position information of the point cloud data of the real estate building based on the matching result;
the obtaining each acquisition graph based on the space coordinates of each data point in the point cloud data set includes:
the acquisition graph comprises a transverse acquisition graph and a longitudinal acquisition graph;
the origin of the space coordinate system is an O point, three coordinate axes of the coordinate system are an X axis, a Y axis and a Z axis respectively, each plane parallel to a plane YOZ is marked as a first plane, each plane parallel to the plane XOZ is marked as a second plane, the first plane of the abscissa of any data point in the point cloud data set of the real estate building is used as a transverse acquisition diagram, and the second plane of the ordinate of any data point in the point cloud data set of the real estate building is used as a longitudinal acquisition diagram;
obtaining the characteristic stability of each data point in the point cloud data set according to the Thiessen polygon corresponding to each data point in each acquisition chart, the normal vector of the key point in the acquisition chart where each data point is located, and the normal vector of the acquisition chart where each data point is located, comprising:
For an a-th data point in the point cloud dataset:
according to the Thiessen polygon corresponding to the a-th data point in each acquisition chart and the Thiessen polygon corresponding to each data point in the preset neighborhood of the a-th data point, obtaining the transverse angle difference and the longitudinal angle difference corresponding to each data point in the a-th data point and the preset neighborhood;
calculating the area difference of the Thiessen polygon corresponding to the a-th data point in the transverse acquisition chart and the Thiessen polygon corresponding to each data point in the preset neighborhood of the a-th data point in the transverse acquisition chart, and marking the area difference as the transverse area difference; calculating the area difference of the Thiessen polygon corresponding to the a-th data point in the longitudinal acquisition chart and the Thiessen polygon corresponding to each data point in the preset neighborhood of the a-th data point in the longitudinal acquisition chart, and marking the area difference as the longitudinal area difference; calculating the spatial dispersion of an a-th data point based on the transverse area difference, the longitudinal area difference, the transverse angle difference and the longitudinal angle difference, wherein the transverse area difference, the longitudinal area difference, the transverse angle difference and the longitudinal angle difference are in positive correlation with the spatial dispersion;
Respectively calculating cosine similarity of normal vector of each key point in each acquisition graph where the a data point is positioned and normal vector of each acquisition graph where the a data point is positioned, and obtaining structural stability of the a data point based on the cosine similarity, wherein the cosine similarity and the structural stability are in positive correlation;
taking the ratio of the structural stability to the spatial dispersion as the characteristic stability of an a-th data point;
the method for acquiring the transverse angle difference between the a-th data point and each data point in the preset neighborhood comprises the following steps:
respectively calculating absolute values of differences between maximum internal angles of the a-th data point in the corresponding Thiessen polygons in the transverse acquisition diagram and maximum internal angles of the i-th data point in the corresponding Thiessen polygons in the transverse acquisition diagram, and marking the absolute values as the differences of the maximum internal angles; respectively calculating the absolute value of the difference value between the minimum internal angle of the a-th data point in the corresponding Thiessen polygon in the transverse acquisition diagram and the minimum internal angle of the i-th data point in the corresponding Thiessen polygon in the transverse acquisition diagram, and recording the absolute value as the difference of the minimum internal angles; the ith data point is a data point in a preset neighborhood of the a data point; the sum of the difference of the maximum internal angle and the difference of the minimum internal angle is recorded as a transverse angle difference corresponding to an a-th data point and an i-th data point in a preset adjacent area;
Calculating a feature descriptor of each feature point by adopting the following formula:
wherein ,
feature descriptor for the q-th feature point, < >>
Distance weight of the kth feature point in the preset neighborhood of the qth feature point,/>
For the Euclidean distance between the q-th feature point and the k-th feature point in its preset neighborhood,/>
For the number of feature points in the preset neighborhood of the q-th feature point, < >>
For the radius of the preset neighborhood>
For the mean value of Euclidean distance between the q-th feature point and all feature points in the preset neighborhood of the q-th feature point,/>
Point cloud descriptor for the q-th feature point,>
a point cloud descriptor for the kth feature point in the preset neighborhood of the qth feature point, +.>
Is natural constant (18)>
For presetting the adjustment parameters, < >>
For the structure diagram corresponding to the q-th feature point, < >>
Is a structure diagram corresponding to the kth feature point in the preset neighborhood of the qth feature point,
is a structural diagram->
Structure of->
Structural similarity of->
To take absolute value symbols.
2. The real estate geographic information mapping data acquisition method according to claim 1, characterized in that the screening feature points based on the feature stability comprises: and obtaining the minimum value of the characteristic stability of all data points in the standard point cloud data set, taking the minimum value as a screening threshold value, and taking the data points with the characteristic stability larger than or equal to the screening threshold value in the point cloud data set of the real estate building as the characteristic points.
3. The method for collecting real estate geographic information mapping data according to claim 1, wherein constructing a point cloud descriptor of each feature point based on a HOG operator corresponding to each feature point in each collection map, a normal vector of the feature point in the collection map where each feature point is located, and a gray value of each feature point, comprises:
for the q-th feature point:
calculating the sum of an HOG operator in the transverse acquisition graph where the q-th feature point is located and an HOG operator in the longitudinal acquisition graph where the q-th feature point is located;
calculating the normal vector of the q-th feature point in the transverse acquisition graph where the q-th feature point is located and the variances of the normal vectors of all the feature points in the preset neighborhood of the q-th feature point, and marking the variances as first variances; calculating the normal vector of the q-th feature point in the longitudinal acquisition graph where the q-th feature point is located and the variance of the normal vector of all the feature points in the preset neighborhood of the q-th feature point, marking the variance as a second variance, and taking the sum of the first variance and the second variance as the normal vector distribution variance of the q-th feature point;
and combining the sum value, the normal vector distribution variance and the gray value of the q-th characteristic point together to serve as a point cloud descriptor of the q-th characteristic point.
4. The method for acquiring real estate geographic information mapping data according to claim 1, wherein the constructing a structure diagram corresponding to each feature point based on each feature point and the feature point in the preset neighborhood comprises: and taking each characteristic point as a center, and respectively connecting each characteristic point with each characteristic point in the preset adjacent area to obtain a structure diagram corresponding to each characteristic point.
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