CN113989447A - Three-dimensional model indoor and outdoor integrated construction method and system - Google Patents
Three-dimensional model indoor and outdoor integrated construction method and system Download PDFInfo
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Abstract
The invention relates to the technical field of geographic information, in particular to a three-dimensional model indoor and outdoor integrated construction method and a system, wherein the method comprises the steps of obtaining outdoor point cloud data; screening outdoor contour points of a building, determining the contour points of outdoor doors and windows, taking 4 corner points which are not coplanar in the contour points of the outdoor doors and windows as outdoor calibration points, determining a central point through the outdoor calibration points, establishing a space rectangular coordinate system by taking the central point as an original point, taking indoor calibration points as homonymous points of the outdoor calibration points in the room of the building, acquiring indoor point cloud data of the building through the indoor calibration points, and establishing an indoor high-precision model; performing rotary translation on the indoor calibration point to the outdoor calibration point through a least square algorithm to enable the indoor calibration point to be overlapped with the outdoor calibration point, so as to form an indoor and outdoor integrated three-dimensional model; compared with the scanner which is generally adopted to integrally model indoors and outdoors, the method has higher precision.
Description
Technical Field
The invention relates to the technical field of geographic information, in particular to a three-dimensional model indoor and outdoor integrated construction method and system.
Background
The digital city system is a personal-ground (geographical environment) relationship system, which embodies the interaction and interrelation of people to people, ground to ground and people to ground, and the system is composed of subsystems which are relatively independent and closely related, such as governments, enterprises, citizens, geographical environments and the like. Government administration, business activities of enterprises and production and life of citizens do not reflect the people-ground relationship of cities. The CUDI national institute of urban development believes that the informatization of cities is essentially the digitization of urban human-ground relationship systems, embodies the leading position of people, better grasps the motion state and the law of the urban systems through the urban informatization, regulates and controls the urban human-ground relationship, realizes the system optimization, and enables the cities to become a space which is beneficial to human survival and sustainable development. The city informatization process is represented by informatization of earth surface mapping and statistics (digital survey and map), informatization of government management and decision (digital government), informatization of enterprise management, decision and service (digital enterprise), informatization of citizen life (digital city life), and the above four informatization processes are digital cities.
The digital city is a research hotspot in recent years, and the reconstruction of the building three-dimensional model has extremely important significance in aspects of building method research, safety monitoring, building repair, city planning and design and the like. For indoor and outdoor integrated modeling, at present, a three-dimensional laser scanner is generally adopted to collect indoor and outdoor point cloud data, then three-dimensional modeling is carried out according to the collected data, the accuracy of the built model is often not high, and the absolute position coordinates inside the building cannot be known.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to solve the problems that: how to provide a three-dimensional model indoor and outdoor integrated construction method to solve the problems of poor indoor and outdoor integrated modeling precision and unclear absolute coordinates in a building in the prior art.
In order to solve the problems, the invention adopts the following technical scheme:
a three-dimensional model indoor and outdoor integrated construction method comprises the steps of obtaining outdoor point cloud data of a building and establishing an outdoor model; screening outdoor contour points of the building according to the outdoor point cloud data, determining the contour points of outdoor doors and windows, taking 4 corner points which are not coplanar in the contour points of the outdoor doors and windows as outdoor calibration points, determining a central point through the outdoor calibration points, and establishing a space rectangular coordinate system by taking the central point as an origin, wherein the distance between any two calibration points is larger than a set threshold; in the indoor of the building, an indoor calibration point is a homonymous point of the outdoor calibration point, indoor point cloud data of the building are obtained through the indoor calibration point, and an indoor high-precision model is established; and performing rotary translation on the indoor calibration point to the outdoor calibration point by using a least square algorithm to enable the indoor calibration point to coincide with the outdoor calibration point, so that registration is completed, and an indoor and outdoor integrated three-dimensional model is formed.
Specifically, the specific steps of screening the outdoor contour points of the building according to the outdoor point cloud data are as follows:
A. projecting the outdoor point cloud data to a three-dimensional coordinate system to obtain a projection base surface;
B. dividing projection points on the projection base surface into different projection surface point cloud block sets at intervals of 1.5 times of average point spacing;
C. calculating the height difference of the adjacent projection surface point cloud block sets according to the highest point of each projection surface point cloud block set, and judging whether the two projection surface point cloud block sets belong to the same cluster according to the calculation result;
the highest point of the YOZ projection surface point cloud block set is a projection point which is farthest away along the Z-axis direction in a plane; if the height difference of the adjacent projection surface point cloud block sets is smaller than a threshold value, the two projection surface point cloud block sets are continuous ground objects and belong to a cluster, otherwise, the two projection surface point cloud block sets are used as a new category;
D. fitting the projection point clouds of each cluster through a random sampling consistency algorithm, extracting a building contour line segment from each clustered projection point cloud, and recording the coordinates of the starting point and the end point of the building contour line segment to obtain building contour point cloud data;
E. carrying out three-dimensional gridding on the collected outdoor point cloud data, carrying out segmentation clustering on grids through connectivity analysis, and obtaining potential rod-shaped object categories and non-rod-shaped object categories according to a clustering region with a section two-dimensional area smaller than a threshold value and screening of a section shape and a main shaft length;
F. calculating the centroid coordinate of each cluster in potential rod-shaped object categories, setting an inner radius and an outer radius by taking the centroid coordinate as a dot, wherein the inner radius contains all point clouds of the cluster, and no point cloud exists in a circular ring range formed by the inner radius and the outer radius;
G. setting a minimum height threshold of a rod, if the height data of a certain clustered point cloud in the potential rod type is larger than the minimum height threshold, judging the rod, recording the coordinates of a starting point and an end point of the rod, extracting a line segment of the rod, and obtaining rod point cloud data;
H. matching the extracted features of the building outline point cloud data or the rod-shaped object point cloud data, and judging whether the extracted features are the same building outline or the same rod-shaped object;
and registering the acquired point cloud data through adjustment calculation to obtain a registration fusion result.
Further, the establishing of the outdoor model comprises: and acquiring the outdoor point cloud data by utilizing an oblique shooting measurement technology, and establishing the outdoor model, wherein the outdoor model comprises an outdoor structure parameter, an outdoor position parameter and an outdoor color parameter.
Further, the establishing of the indoor high-precision model comprises: acquiring indoor point cloud data by using a three-dimensional laser scanner, and establishing an indoor model; acquiring texture pictures of an indoor space, and splicing the texture pictures to the indoor model to form the indoor high-precision model; the indoor model includes an indoor structure parameter, an indoor position parameter, and an indoor color parameter.
Further, the establishing of the indoor high-precision model comprises: obtaining indoor internal scanning attribute parameters, wherein the internal scanning attribute parameters comprise internal structure parameters, spatial layout and characteristic articles of a building; planning acquisition routes according to the internal scanning attribute parameters, and splicing internal three-dimensional scanning data of each acquisition route by indoor mobile scanning equipment according to the indoor calibration points to form the indoor high-precision model.
The invention also provides an indoor and outdoor integrated construction system of the three-dimensional model, which comprises the following components:
an outdoor point cloud data acquisition and processing module: the system comprises a data acquisition module, a data storage module and a data processing module, wherein the data acquisition module is used for acquiring outdoor point cloud data of a building and establishing an outdoor model; screening outdoor contour points of the building according to the outdoor point cloud data, determining the contour points of outdoor doors and windows, taking 4 corner points which are not coplanar in the contour points of the outdoor doors and windows as outdoor calibration points, determining a central point through the outdoor calibration points, and establishing a space rectangular coordinate system by taking the central point as an origin, wherein the distance between any two calibration points is larger than a set threshold;
the indoor point cloud data acquisition and processing module: the indoor calibration point is used for acquiring indoor point cloud data of the building through the indoor calibration point and establishing an indoor high-precision model;
an indoor and outdoor model registration module: and the indoor calibration point is rotationally translated to the outdoor calibration point by using a least square algorithm, so that the indoor calibration point and the outdoor calibration point are superposed to complete registration and form an indoor and outdoor integrated three-dimensional model.
Specifically, the outdoor point cloud data acquisition processing module comprises an outdoor contour point acquisition unit, which is used for screening the outdoor contour points of the building according to the outdoor point cloud data; the method comprises the following specific steps:
A. projecting the outdoor point cloud data to a three-dimensional coordinate system to obtain a projection base surface;
B. dividing projection points on the projection base surface into different projection surface point cloud block sets at intervals of 1.5 times of average point spacing;
C. calculating the height difference of the adjacent projection surface point cloud block sets according to the highest point of each projection surface point cloud block set, and judging whether the two projection surface point cloud block sets belong to the same cluster according to the calculation result;
the highest point of the YOZ projection surface point cloud block set is a projection point which is farthest away along the Z-axis direction in a plane; if the height difference of the adjacent projection surface point cloud block sets is smaller than a threshold value, the two projection surface point cloud block sets are continuous ground objects and belong to a cluster, otherwise, the two projection surface point cloud block sets are used as a new category;
D. fitting the projection point clouds of each cluster through a random sampling consistency algorithm, extracting a building contour line segment from each clustered projection point cloud, and recording the coordinates of the starting point and the end point of the building contour line segment to obtain building contour point cloud data;
E. carrying out three-dimensional gridding on the collected outdoor point cloud data, carrying out segmentation clustering on grids through connectivity analysis, and obtaining potential rod-shaped object categories and non-rod-shaped object categories according to a clustering region with a section two-dimensional area smaller than a threshold value and screening of a section shape and a main shaft length;
F. calculating the centroid coordinate of each cluster in potential rod-shaped object categories, setting an inner radius and an outer radius by taking the centroid coordinate as a dot, wherein the inner radius contains all point clouds of the cluster, and no point cloud exists in a circular ring range formed by the inner radius and the outer radius;
G. setting a minimum height threshold of a rod, if the height data of a certain clustered point cloud in the potential rod type is larger than the minimum height threshold, judging the rod, recording the coordinates of a starting point and an end point of the rod, extracting a line segment of the rod, and obtaining rod point cloud data;
H. matching the extracted features of the building outline point cloud data or the rod-shaped object point cloud data, and judging whether the extracted features are the same building outline or the same rod-shaped object;
and registering the acquired point cloud data through adjustment calculation to obtain a registration fusion result.
8. The system for building the three-dimensional model according to claim 6, wherein the outdoor point cloud data acquisition and processing module further comprises an outdoor model building unit for building the outdoor model by acquiring the outdoor point cloud data by oblique photogrammetry, and the outdoor model comprises an outdoor structure parameter, an outdoor position parameter and an outdoor color parameter.
9. The system for building the three-dimensional model according to claim 6, wherein the indoor point cloud data acquisition and processing module comprises an indoor model building and processing unit for building an indoor model by acquiring indoor point cloud data with a three-dimensional laser scanner; acquiring texture pictures of an indoor space, and splicing the texture pictures to the indoor model to form the indoor high-precision model; the indoor model includes an indoor structure parameter, an indoor position parameter, and an indoor color parameter.
10. The three-dimensional model indoor and outdoor integrated construction system according to claim 6, wherein the indoor point cloud data acquisition processing module further comprises an internal scanning attribute parameter acquisition processing unit for acquiring indoor internal scanning attribute parameters, wherein the internal scanning attribute parameters comprise internal structure parameters, spatial layout and characteristic items of a building; planning acquisition routes according to the internal scanning attribute parameters, and splicing internal three-dimensional scanning data of each acquisition route by indoor mobile scanning equipment according to the indoor calibration points to form the indoor high-precision model.
The invention has the beneficial effects that: the invention provides a three-dimensional model indoor and outdoor integrated construction method, which comprises the steps of calculating outdoor contour points based on outdoor point cloud data of a building, determining outdoor door and window contour points, selecting 4 outdoor calibration points from the outdoor door and window contour points, wherein the distance between any two points in the 4 outdoor calibration points is larger than a set threshold value, the 4 outdoor calibration points are corner points in the outdoor door and window contour points and are not coplanar, in the indoor of the building, the indoor calibration points are the same-name points of the outdoor calibration points, acquiring indoor point cloud data of the building through the indoor calibration points, and establishing an indoor high-precision model; performing rotary translation on the indoor calibration point to the outdoor calibration point through a least square algorithm to enable the indoor calibration point to be overlapped with the outdoor calibration point, finishing registration and forming an indoor and outdoor integrated three-dimensional model; compared with the common three-dimensional laser scanner, the method has higher precision for indoor and outdoor integrated modeling and data integrated acquisition.
Drawings
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described in detail with reference to the accompanying drawings, in which:
FIG. 1 is a schematic flow chart of an indoor and outdoor integrated construction method of a three-dimensional model according to the present invention;
FIG. 2 is a schematic diagram of an indoor and outdoor integrated construction system of a three-dimensional model according to the present invention.
Detailed Description
The present invention will be described in further detail with reference to specific examples.
It should be noted that these examples are only for illustrating the present invention, and not for limiting the present invention, and the simple modification of the method based on the idea of the present invention is within the protection scope of the present invention.
Referring to fig. 1, a three-dimensional model indoor and outdoor integrated construction method includes acquiring outdoor point cloud data of a building, and establishing an outdoor model; screening outdoor contour points of a building according to outdoor point cloud data, determining the contour points of outdoor doors and windows, taking 4 corner points which are not coplanar in the contour points of the outdoor doors and windows as outdoor calibration points, determining a central point through the outdoor calibration points, and establishing a space rectangular coordinate system by taking the central point as an origin, wherein the distance between any two calibration points is greater than a set threshold; in the indoor of the building, an indoor calibration point is a homonymous point of the outdoor calibration point, indoor point cloud data of the building are obtained through the indoor calibration point, and an indoor high-precision model is established; and performing rotary translation on the indoor calibration point to the outdoor calibration point by using a least square algorithm to enable the indoor calibration point to be superposed with the outdoor calibration point, finishing registration and forming an indoor and outdoor integrated three-dimensional model.
As an implementable manner, the specific steps of screening the outdoor contour points of the building according to the outdoor point cloud data are as follows:
A. projecting the outdoor point cloud data to a three-dimensional coordinate system to obtain a projection base surface;
B. dividing projection points on the projection base surface into different projection surface point cloud block sets at intervals of 1.5 times of average point spacing;
C. calculating the height difference of the adjacent projection surface point cloud block sets according to the highest point of each projection surface point cloud block set, and judging whether the two projection surface point cloud block sets belong to the same cluster according to the calculation result;
for example, the highest point of the cloud block set of the YOZ projection surface points is the projection point with the farthest distance along the Z-axis direction in the plane; if the height difference of the adjacent projection surface point cloud block sets is smaller than a threshold value, the two projection surface point cloud block sets are continuous ground objects and belong to a cluster, otherwise, the two projection surface point cloud block sets are used as a new category;
D. fitting the projection point clouds of each cluster through a random sampling consistency algorithm, extracting a building contour line segment from each clustered projection point cloud, and recording the coordinates of the starting point and the end point of the building contour line segment to obtain building contour point cloud data;
E. carrying out three-dimensional gridding on the collected outdoor point cloud data, carrying out segmentation clustering on grids through connectivity analysis, and obtaining potential rod-shaped object categories and non-rod-shaped object categories according to a clustering region with a section two-dimensional area smaller than a threshold value and screening of a section shape and a main shaft length;
F. calculating the centroid coordinate of each cluster in potential rod-shaped object categories, setting an inner radius and an outer radius by taking the centroid coordinate as a dot, wherein the inner radius contains all point clouds of the cluster, and no point cloud exists in a circular ring range formed by the inner radius and the outer radius;
G. setting a minimum height threshold of a rod, if the height data of a certain clustered point cloud in the potential rod type is larger than the minimum height threshold, judging the rod, recording the coordinates of a starting point and an end point of the rod, extracting a line segment of the rod, and obtaining rod point cloud data;
H. matching the extracted features of the building outline point cloud data or the rod-shaped object point cloud data, and judging whether the extracted features are the same building outline or the same rod-shaped object;
and registering the acquired point cloud data through adjustment calculation to obtain a registration fusion result.
Wherein, establishing the outdoor model comprises: and acquiring outdoor point cloud data by using an oblique shooting measurement technology, and establishing an outdoor model, wherein the outdoor model comprises an outdoor structure parameter, an outdoor position parameter and an outdoor color parameter.
Wherein, the indoor high-precision model establishment comprises the following steps: acquiring indoor point cloud data by using a three-dimensional laser scanner, and establishing an indoor model; acquiring texture pictures of an indoor space, and splicing the texture pictures to an indoor model to form an indoor high-precision model; the indoor model includes an indoor structure parameter, an indoor position parameter, and an indoor color parameter.
Wherein, the indoor high-precision model establishment comprises the following steps: obtaining indoor internal scanning attribute parameters, wherein the internal scanning attribute parameters comprise internal structure parameters, spatial layout and characteristic articles of a building; and planning the acquisition routes according to the internal scanning attribute parameters and the indoor calibration points of each acquisition route, so that the indoor mobile scanning equipment splices the internal three-dimensional scanning data according to the indoor calibration points to form an indoor high-precision model.
The invention also provides an indoor and outdoor integrated construction system of the three-dimensional model, which comprises the following components:
the outdoor point cloud data acquisition processing module 100: the system comprises a data acquisition module, a data storage module and a data processing module, wherein the data acquisition module is used for acquiring outdoor point cloud data of a building and establishing an outdoor model; screening outdoor contour points of a building according to outdoor point cloud data, determining the contour points of outdoor doors and windows, taking 4 corner points which are not coplanar in the contour points of the outdoor doors and windows as outdoor calibration points, determining a central point through the outdoor calibration points, and establishing a space rectangular coordinate system by taking the central point as an origin, wherein the distance between any two calibration points is greater than a set threshold;
the outdoor point cloud data acquisition processing module 100 includes an outdoor contour point acquisition unit 110 for screening outdoor contour points of a building according to the outdoor point cloud data; the method comprises the following specific steps:
A. projecting the outdoor point cloud data to a three-dimensional coordinate system to obtain a projection base surface;
B. dividing projection points on the projection base surface into different projection surface point cloud block sets at intervals of 1.5 times of average point spacing;
C. calculating the height difference of the adjacent projection surface point cloud block sets according to the highest point of each projection surface point cloud block set, and judging whether the two projection surface point cloud block sets belong to the same cluster according to the calculation result;
the highest point of the YOZ projection surface point cloud block set is a projection point which is farthest away along the Z-axis direction in a plane; if the height difference of the adjacent projection surface point cloud block sets is smaller than a threshold value, the two projection surface point cloud block sets are continuous ground objects and belong to a cluster, otherwise, the two projection surface point cloud block sets are used as a new category;
D. fitting the projection point clouds of each cluster through a random sampling consistency algorithm, extracting a building contour line segment from each clustered projection point cloud, and recording the coordinates of the starting point and the end point of the building contour line segment to obtain building contour point cloud data;
E. carrying out three-dimensional gridding on the collected outdoor point cloud data, carrying out segmentation clustering on grids through connectivity analysis, and obtaining potential rod-shaped object categories and non-rod-shaped object categories according to a clustering region with a section two-dimensional area smaller than a threshold value and screening of a section shape and a main shaft length;
F. calculating the centroid coordinate of each cluster in potential rod-shaped object categories, setting an inner radius and an outer radius by taking the centroid coordinate as a dot, wherein the inner radius contains all point clouds of the cluster, and no point cloud exists in a circular ring range formed by the inner radius and the outer radius;
G. setting a minimum height threshold of a rod, if the height data of a certain clustered point cloud in the potential rod type is larger than the minimum height threshold, judging the rod, recording the coordinates of a starting point and an end point of the rod, extracting a line segment of the rod, and obtaining rod point cloud data;
H. matching the extracted features of the building outline point cloud data or the rod-shaped object point cloud data, and judging whether the extracted features are the same building outline or the same rod-shaped object;
and registering the acquired point cloud data through adjustment calculation to obtain a registration fusion result.
The outdoor point cloud data obtaining and processing module 100 further includes an outdoor model establishing unit 120, configured to obtain outdoor point cloud data by using an oblique photogrammetry technique, and establish an outdoor model, where the outdoor model includes an outdoor structure parameter, an outdoor location parameter, and an outdoor color parameter.
The indoor point cloud data acquisition processing module 200: the indoor calibration point is used for indoor of the building, the indoor calibration point is the same name point of the outdoor calibration point, indoor point cloud data of the building are obtained through the indoor calibration point, and an indoor high-precision model is built.
The indoor point cloud data acquiring and processing module 200 includes an indoor model establishing and processing unit 210, configured to acquire indoor point cloud data by using a three-dimensional laser scanner and establish an indoor model; acquiring texture pictures of an indoor space, and splicing the texture pictures to an indoor model to form an indoor high-precision model; the indoor model includes an indoor structure parameter, an indoor position parameter, and an indoor color parameter.
The indoor point cloud data acquiring and processing module 200 further includes an internal scanning attribute parameter acquiring and processing unit 220, configured to acquire indoor internal scanning attribute parameters, where the internal scanning attribute parameters include internal structure parameters, spatial layout, and feature items of a building; and planning the acquisition routes according to the internal scanning attribute parameters and the indoor calibration points of each acquisition route, so that the indoor mobile scanning equipment splices the internal three-dimensional scanning data according to the indoor calibration points to form an indoor high-precision model.
Indoor-outdoor model registration module 300: the method is used for performing rotary translation on the indoor calibration point to the outdoor calibration point by using a least square algorithm, so that the indoor calibration point and the outdoor calibration point are overlapped, registration is completed, and an indoor and outdoor integrated three-dimensional model is formed.
Finally, it is noted that the above-mentioned embodiments illustrate rather than limit the invention, and that, while the invention has been described with reference to preferred embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. A three-dimensional model indoor and outdoor integrated construction method is characterized by comprising the steps of obtaining outdoor point cloud data of a building and establishing an outdoor model; screening outdoor contour points of the building according to the outdoor point cloud data, determining the contour points of outdoor doors and windows, taking 4 corner points which are not coplanar in the contour points of the outdoor doors and windows as outdoor calibration points, determining a central point through the outdoor calibration points, and establishing a space rectangular coordinate system by taking the central point as an origin, wherein the distance between any two calibration points is larger than a set threshold; in the indoor of the building, an indoor calibration point is a homonymous point of the outdoor calibration point, indoor point cloud data of the building are obtained through the indoor calibration point, and an indoor high-precision model is established; and performing rotary translation on the indoor calibration point to the outdoor calibration point by using a least square algorithm to enable the indoor calibration point to coincide with the outdoor calibration point, so that registration is completed, and an indoor and outdoor integrated three-dimensional model is formed.
2. The method for building the three-dimensional model by integrating the indoor space and the outdoor space according to the claim 1, wherein the specific steps of screening the outdoor contour points of the building according to the external point cloud data are as follows:
A. projecting the outdoor point cloud data to a three-dimensional coordinate system to obtain a projection base surface;
B. dividing projection points on the projection base surface into different projection surface point cloud block sets at intervals of 1.5 times of average point spacing;
C. calculating the height difference of the adjacent projection surface point cloud block sets according to the highest point of each projection surface point cloud block set, and judging whether the two projection surface point cloud block sets belong to the same cluster according to the calculation result;
the highest point of the YOZ projection surface point cloud block set is a projection point which is farthest away along the Z-axis direction in a plane; if the height difference of the adjacent projection surface point cloud block sets is smaller than a threshold value, the two projection surface point cloud block sets are continuous ground objects and belong to a cluster, otherwise, the two projection surface point cloud block sets are used as a new category;
D. fitting the projection point clouds of each cluster through a random sampling consistency algorithm, extracting a building contour line segment from each clustered projection point cloud, and recording the coordinates of the starting point and the end point of the building contour line segment to obtain building contour point cloud data;
E. carrying out three-dimensional gridding on the collected outdoor point cloud data, carrying out segmentation clustering on grids through connectivity analysis, and obtaining potential rod-shaped object categories and non-rod-shaped object categories according to a clustering region with a section two-dimensional area smaller than a threshold value and screening of a section shape and a main shaft length;
F. calculating the centroid coordinate of each cluster in potential rod-shaped object categories, setting an inner radius and an outer radius by taking the centroid coordinate as a dot, wherein the inner radius contains all point clouds of the cluster, and no point cloud exists in a circular ring range formed by the inner radius and the outer radius;
G. setting a minimum height threshold of a rod, if the height data of a certain clustered point cloud in the potential rod type is larger than the minimum height threshold, judging the rod, recording the coordinates of a starting point and an end point of the rod, extracting a line segment of the rod, and obtaining rod point cloud data;
H. matching the extracted features of the building outline point cloud data or the rod-shaped object point cloud data, and judging whether the extracted features are the same building outline or the same rod-shaped object;
and registering the acquired point cloud data through adjustment calculation to obtain a registration fusion result.
3. The method for building the three-dimensional model by integrating indoor and outdoor functions as claimed in claim 1, wherein the building of the outdoor model comprises: and acquiring the outdoor point cloud data by utilizing an oblique shooting measurement technology, and establishing the outdoor model, wherein the outdoor model comprises an outdoor structure parameter, an outdoor position parameter and an outdoor color parameter.
4. The method for building the indoor and outdoor integration of the three-dimensional model according to claim 1, wherein the building of the indoor high-precision model comprises the following steps: acquiring indoor point cloud data by using a three-dimensional laser scanner, and establishing an indoor model; acquiring texture pictures of an indoor space, and splicing the texture pictures to the indoor model to form the indoor high-precision model; the indoor model includes an indoor structure parameter, an indoor position parameter, and an indoor color parameter.
5. The method for building the indoor and outdoor integration of the three-dimensional model according to claim 4, wherein the building of the indoor high-precision model comprises the following steps: obtaining indoor internal scanning attribute parameters, wherein the internal scanning attribute parameters comprise internal structure parameters, spatial layout and characteristic articles of a building; planning acquisition routes according to the internal scanning attribute parameters, and splicing internal three-dimensional scanning data of each acquisition route by indoor mobile scanning equipment according to the indoor calibration points to form the indoor high-precision model.
6. A three-dimensional model indoor and outdoor integrated construction system is characterized by comprising:
an outdoor point cloud data acquisition and processing module: the system comprises a data acquisition module, a data storage module and a data processing module, wherein the data acquisition module is used for acquiring outdoor point cloud data of a building and establishing an outdoor model; screening outdoor contour points of the building according to the outdoor point cloud data, determining the contour points of outdoor doors and windows, taking 4 corner points which are not coplanar in the contour points of the outdoor doors and windows as outdoor calibration points, determining a central point through the outdoor calibration points, and establishing a space rectangular coordinate system by taking the central point as an origin, wherein the distance between any two calibration points is larger than a set threshold;
the indoor point cloud data acquisition and processing module: the indoor calibration point is used for acquiring indoor point cloud data of the building through the indoor calibration point and establishing an indoor high-precision model;
an indoor and outdoor model registration module: and the indoor calibration point is rotationally translated to the outdoor calibration point by using a least square algorithm, so that the indoor calibration point and the outdoor calibration point are superposed to complete registration and form an indoor and outdoor integrated three-dimensional model.
7. The three-dimensional model indoor and outdoor integrated construction system according to claim 6, wherein the outdoor point cloud data acquisition and processing module comprises a building outdoor contour point acquisition unit for screening outdoor contour points of the building according to the external point cloud data; the method comprises the following specific steps:
A. projecting the outdoor point cloud data to a three-dimensional coordinate system to obtain a projection base surface;
B. dividing projection points on the projection base surface into different projection surface point cloud block sets at intervals of 1.5 times of average point spacing;
C. calculating the height difference of the adjacent projection surface point cloud block sets according to the highest point of each projection surface point cloud block set, and judging whether the two projection surface point cloud block sets belong to the same cluster according to the calculation result;
the highest point of the YOZ projection surface point cloud block set is a projection point which is farthest away along the Z-axis direction in a plane; if the height difference of the adjacent projection surface point cloud block sets is smaller than a threshold value, the two projection surface point cloud block sets are continuous ground objects and belong to a cluster, otherwise, the two projection surface point cloud block sets are used as a new category;
D. fitting the projection point clouds of each cluster through a random sampling consistency algorithm, extracting a building contour line segment from each clustered projection point cloud, and recording the coordinates of the starting point and the end point of the building contour line segment to obtain building contour point cloud data;
E. carrying out three-dimensional gridding on the collected outdoor point cloud data, carrying out segmentation clustering on grids through connectivity analysis, and obtaining potential rod-shaped object categories and non-rod-shaped object categories according to a clustering region with a section two-dimensional area smaller than a threshold value and screening of a section shape and a main shaft length;
F. calculating the centroid coordinate of each cluster in potential rod-shaped object categories, setting an inner radius and an outer radius by taking the centroid coordinate as a dot, wherein the inner radius contains all point clouds of the cluster, and no point cloud exists in a circular ring range formed by the inner radius and the outer radius;
G. setting a minimum height threshold of a rod, if the height data of a certain clustered point cloud in the potential rod type is larger than the minimum height threshold, judging the rod, recording the coordinates of a starting point and an end point of the rod, extracting a line segment of the rod, and obtaining rod point cloud data;
H. matching the extracted features of the building outline point cloud data or the rod-shaped object point cloud data, and judging whether the extracted features are the same building outline or the same rod-shaped object;
and registering the acquired point cloud data through adjustment calculation to obtain a registration fusion result.
8. The system for building the three-dimensional model according to claim 6, wherein the outdoor point cloud data acquisition and processing module further comprises an outdoor model building unit for building the outdoor model by acquiring the outdoor point cloud data by oblique photogrammetry, and the outdoor model comprises an outdoor structure parameter, an outdoor position parameter and an outdoor color parameter.
9. The system for building the three-dimensional model according to claim 6, wherein the indoor point cloud data acquisition and processing module comprises an indoor model building and processing unit for building an indoor model by acquiring indoor point cloud data with a three-dimensional laser scanner; acquiring texture pictures of an indoor space, and splicing the texture pictures to the indoor model to form the indoor high-precision model; the indoor model includes an indoor structure parameter, an indoor position parameter, and an indoor color parameter.
10. The three-dimensional model indoor and outdoor integrated construction system according to claim 6, wherein the indoor point cloud data acquisition processing module further comprises an internal scanning attribute parameter acquisition processing unit for acquiring indoor internal scanning attribute parameters, wherein the internal scanning attribute parameters comprise internal structure parameters, spatial layout and characteristic items of a building; planning acquisition routes according to the internal scanning attribute parameters, and splicing internal three-dimensional scanning data of each acquisition route by indoor mobile scanning equipment according to the indoor calibration points to form the indoor high-precision model.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116051734A (en) * | 2022-12-28 | 2023-05-02 | 中建三局集团华南有限公司 | Engineering quantity rapid statistical method based on three-dimensional laser scanning and live-action three-dimensional modeling |
| CN116152151A (en) * | 2022-11-12 | 2023-05-23 | 重庆数字城市科技有限公司 | Building deformation information extraction method and system |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105303521A (en) * | 2015-10-16 | 2016-02-03 | 重庆市勘测院 | Heaven/earth and indoor/outdoor panorama automatic correlation analysis method |
| CN108090959A (en) * | 2017-12-07 | 2018-05-29 | 中煤航测遥感集团有限公司 | Indoor and outdoor one modeling method and device |
| CN109147030A (en) * | 2018-07-05 | 2019-01-04 | 厦门大学 | Indoor and outdoor scene based on line feature combines modeling method |
| CN111598823A (en) * | 2020-05-19 | 2020-08-28 | 北京数字绿土科技有限公司 | Multi-source mobile measurement point cloud data air-ground integrated fusion method and storage medium |
| CN112652003A (en) * | 2020-10-29 | 2021-04-13 | 西北工业大学 | Three-dimensional point cloud registration method based on RANSAC measure optimization |
-
2021
- 2021-10-14 CN CN202111198617.5A patent/CN113989447A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105303521A (en) * | 2015-10-16 | 2016-02-03 | 重庆市勘测院 | Heaven/earth and indoor/outdoor panorama automatic correlation analysis method |
| CN108090959A (en) * | 2017-12-07 | 2018-05-29 | 中煤航测遥感集团有限公司 | Indoor and outdoor one modeling method and device |
| CN109147030A (en) * | 2018-07-05 | 2019-01-04 | 厦门大学 | Indoor and outdoor scene based on line feature combines modeling method |
| CN111598823A (en) * | 2020-05-19 | 2020-08-28 | 北京数字绿土科技有限公司 | Multi-source mobile measurement point cloud data air-ground integrated fusion method and storage medium |
| CN112652003A (en) * | 2020-10-29 | 2021-04-13 | 西北工业大学 | Three-dimensional point cloud registration method based on RANSAC measure optimization |
Non-Patent Citations (3)
| Title |
|---|
| 明镜 等: "天地及室内外一体实景地图系统研究与应用", 《地理信息世界》 * |
| 朱庆 等: "面向三维城市建模的多点云数据融合方法综述", 《武汉大学学报（信息科学版）》 * |
| 陈昌鸣 等: "基于车载激光雷达的行道树提取研究", 《北京测绘》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116152151A (en) * | 2022-11-12 | 2023-05-23 | 重庆数字城市科技有限公司 | Building deformation information extraction method and system |
| CN116051734A (en) * | 2022-12-28 | 2023-05-02 | 中建三局集团华南有限公司 | Engineering quantity rapid statistical method based on three-dimensional laser scanning and live-action three-dimensional modeling |
| CN116051734B (en) * | 2022-12-28 | 2023-11-17 | 中建三局集团华南有限公司 | Engineering quantity rapid statistical method based on three-dimensional laser scanning and live-action three-dimensional modeling |
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