CN114387488A - Road extraction system and method based on Potree point cloud image fusion - Google Patents
Road extraction system and method based on Potree point cloud image fusion Download PDFInfo
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Abstract
The invention discloses a road extraction system and a method based on a Potree point cloud image fusion, belonging to the technical field of surveying and mapping geographic information, wherein the road extraction system comprises the following steps: collecting three-dimensional point cloud data and panoramic image data along a lane by a multi-feature constrained mobile measurement system; extracting homonymous features in the three-dimensional point cloud data and the panoramic image data; fusing three-dimensional point cloud data and panoramic image data after homonymy feature extraction through a point cloud image display technology of a Potree and a serialized image data fusion method; performing serialization conversion on the fused three-dimensional point cloud data and panoramic image data to obtain a point cloud file and a panoramic image file; and outputting the point cloud file and the panoramic image file after sequentially carrying out three-dimensional measurement, POI marking and road vectorization operation. The invention solves the difficult problems of point cloud and image fusion and road element extraction, and improves the efficiency and the accuracy of point cloud and image fusion.
Description
Technical Field
The invention relates to a road extraction system and method based on a Potree point cloud image fusion, and belongs to the technical field of mapping geographic information.
Background
In the development of the technology of mapping geographic information, laser radar and photogrammetry are permanently and irrevocably topics, and have important roles in the fields of road detection, agriculture and forestry, high-precision maps and environment monitoring. The laser radar is measurement equipment integrating laser scanning and a positioning and attitude determination system, can quickly obtain high-precision and high-density point cloud data with irregular spatial distribution of attributes such as geometric coordinates (X, Y, Z) and reflection intensity, and can penetrate through vegetation to measure the terrain covered by the vegetation; the photogrammetry can cover the whole photographic area, and two-dimensional and three-dimensional image data containing texture, spectrum and other information can be obtained. With the continuous development and progress of society and science and technology, the demand for the society and the technology also becomes larger and larger.
Lidar and photogrammetry, although widely used in a variety of industries, have their own drawbacks. Although the laser radar has the characteristics of high coordinate precision and strong penetrability, and is suitable for high-precision topographic survey and engineering survey application with high precision requirement, the point cloud data is difficult to distinguish for the specific form, time and color of an object. Photogrammetry has a history of more than 170 years, the technical development is relatively mature, and the photogrammetry has the characteristics of high drawing speed, high precision and uniformity, but is difficult to grasp for some buildings, structures of the number and the density.
Nowadays, with the rapid development of software integration technology and hardware integration technology, laser radar systems are also continuously innovated, and the strong combination of point cloud and image becomes an urgent need at present. The point cloud and image fusion is used for three-dimensional reconstruction of a target model, point cloud image fusion data integrating high precision and high pixels can be obtained, and the precision and efficiency of three-dimensional modeling and mapping measurement and the accuracy of road element extraction are greatly improved.
Disclosure of Invention
The invention aims to overcome the defects in the prior art, provides a road extraction system and method based on the polytree point cloud image fusion, solves the problems of point cloud and image fusion and road element extraction, and improves the efficiency and accuracy of point cloud and image fusion.
In order to achieve the purpose, the invention is realized by adopting the following technical scheme:
in a first aspect, the invention provides a road extraction method based on a polytree point cloud image fusion, which comprises the following steps:
collecting three-dimensional point cloud data and panoramic image data along a lane by a multi-feature constrained mobile measurement system;
extracting homonymous features in the three-dimensional point cloud data and the panoramic image data;
fusing three-dimensional point cloud data and panoramic image data after homonymy feature extraction through a point cloud image display technology of a Potree and a serialized image data fusion method;
performing serialization conversion on the fused three-dimensional point cloud data and panoramic image data to obtain a point cloud file and a panoramic image file;
and outputting the point cloud file and the panoramic image file after sequentially carrying out three-dimensional measurement, POI marking and road vectorization operation.
Further, through the mobile survey system of many feature constraints along lane collection three-dimensional point cloud data and panoramic image data, include:
determining an initial value of a camera in the multi-feature constrained mobile measurement system by a direct linear transformation method;
solving the optimal parameter estimation value of the camera in the multi-feature constrained mobile measurement system by adopting a space rear intersection method based on the initial value;
and collecting three-dimensional point cloud data and panoramic image data along the lane by the multi-feature-constrained mobile measuring system after the parameter optimal estimation is confirmed.
Further, the spatial backward intersection method includes:
calculating a camera parameter value by adopting integral least square based on the initial value;
judging whether the camera parameter value meets the iteration precision requirement or not;
in response to the camera parameter value satisfying the iterative accuracy requirement, outputting the camera parameter value as the optimal parameter estimate;
in response to the fact that the camera parameter value does not meet the iteration precision requirement, residual errors in the camera parameter value are solved, and all distance deviations are solved;
calculating a deviation standard value based on all the distance deviations;
only the camera parameter value with the residual error not more than two times of the standard deviation is reserved, the overall least square is adopted again for calculation, and whether the iteration precision requirement is met or not is judged.
Further, extracting homonymous features in the three-dimensional point cloud data and the panoramic image data, including: the method is characterized in that three-dimensional point cloud data is used as a standard, a prism table device is used as a bridge, point cloud automation and an image recognition algorithm are combined, and homonymous characteristic fine extraction of two kinds of heterogeneous data of the three-dimensional point cloud data and panoramic image data is carried out.
Further, the method for fusing the three-dimensional point cloud data and the panoramic image data after the homonymy feature extraction through the point cloud image display technology of the polytree and the serialized image data fusion method comprises the following steps:
repairing the three-dimensional point cloud data and the panoramic image data by a neighborhood image repairing technology;
carrying out coordinate conversion on the three-dimensional point cloud data;
establishing a corresponding relation between point cloud points in the three-dimensional point cloud data after coordinate conversion and image pixel points in panoramic image data by utilizing a collinear relation of a panoramic ball coordinate system;
determining a fusion area according to the marking time of the single panoramic image;
and performing serialized three-dimensional point cloud data and panoramic image data fusion by utilizing time segmentation based on the fusion area.
Further, coordinate conversion is performed on the three-dimensional point cloud data, and the coordinate conversion includes:
the earth center in the three-dimensional point cloud data is fixedly transferred to a local horizontal coordinate system;
transferring the local horizontal coordinate system to an inertial navigation coordinate system;
transferring the inertial navigation coordinate system to a panoramic spherical coordinate system, and solving the spherical coordinates by utilizing a collinear equation;
and acquiring rgb values corresponding to the spherical coordinates.
Further, the point cloud file and the panoramic image file are output after three-dimensional measurement, POI labeling and road vectorization operation are sequentially performed, and the method comprises the following steps:
the point cloud file and the panoramic image file are published to a web server;
realizing road vectorization operation at a Web page end in a manual interaction mode to obtain a vector file;
outputting the vector file to the client in dwg format;
the vectoring operation includes:
acquiring a mouse operating point and a trackball center in a manual interaction mode;
calculating the distance between the operating point and the center of the track ball to obtain a coordinate point;
and matching type information corresponding to the coordinate points, and creating vector types and quantity sizes to obtain the vector file.
In a second aspect, the present invention provides a road extraction system based on a polytree point cloud image fusion, including:
an acquisition module: the system comprises a mobile measuring system, a lane, a three-dimensional point cloud data acquisition unit, a lane tracking unit and a panoramic image data acquisition unit, wherein the mobile measuring system is used for acquiring the three-dimensional point cloud data and the panoramic image data through a multi-feature constrained mobile measuring system along the lane;
a feature extraction module: the system is used for extracting homonymous features in the three-dimensional point cloud data and the panoramic image data;
a data fusion module: the method is used for fusing the three-dimensional point cloud data and the panoramic image data after the homonymy features are extracted through a point cloud image display technology of a lattice and a serialized image data fusion method;
a serialization conversion module: the system comprises a data processing module, a data storage module, a data processing module and a data processing module, wherein the data processing module is used for performing serialization conversion on fused three-dimensional point cloud data and panoramic image data to obtain a point cloud file and a panoramic image file;
an interaction module: and the system is used for outputting the point cloud file and the panoramic image file after sequentially carrying out three-dimensional measurement, POI marking and road vectorization operations.
In a third aspect, the invention provides a road extraction device based on the fusion of a Potree point cloud image, which comprises a processor and a storage medium;
the storage medium is used for storing instructions;
the processor is configured to operate in accordance with the instructions to perform the steps of the method according to any of the above.
In a fourth aspect, the invention provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of any of the methods described above.
Compared with the prior art, the invention has the following beneficial effects:
the method solves the problem of precise extraction of two isomerous data homonymous features of the laser image by using a multi-feature constrained collimation axis error self-checking model of the mobile measuring system; meanwhile, a time-based serialized image data fusion algorithm and a Potree-based point cloud image display technology are provided, the problems encountered by the fusion of a point cloud and a panoramic image are solved, the point cloud image panoramic image is displayed at a Web end, and the functions of three-dimensional measurement, POI labeling, vectorization operation and file output of the Web end point cloud image are realized.
Drawings
Fig. 1 is a general flowchart provided in an embodiment of the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
The first embodiment is as follows:
the road extraction method based on the Potree point cloud image fusion comprises the following steps:
step 1, firstly, a high-density laser point cloud and a high-definition panoramic image are collected along a lane by a three-dimensional laser mobile measurement system, and a multi-feature-constrained mobile measurement system collimation axis error self-checking model is used, so that the problems of poor adaptability of a single feature constraint model and low automation degree of a checking process are solved.
Three-dimensional laser removes measurement system includes camera, laser scanner etc. examines the school in-process, and the camera is the carrier of acquireing the photo, and photo quality and definition can influence to a certain extent and examine the extraction of school data, so need follow such several aspects when shooing the photo and consider: resolution of the camera, camera CCD or CMOS dimensions, distortion parameters, focal length, structural stability. Thereby ensuring the qualification of data quality in the internal parameter checking process. According to the description of the internal orientation elements and the distortion parameters, the whole equation is found to involve more parameters, and if the linear calculation is directly carried out according to the least square rule, the inaccuracy of the initial value can cause the equation to be easy to fall into the local optimal solution, thereby causing the failure of calibration.
The specific embodiment is as follows:
1. determining an initial value of a camera;
the initial value is determined by adopting a direct linear transformation method, the requirement on the initial value is low, and the precision requirement on the initially obtained parameters is also high. The method is used for establishing the relation between the image plane coordinates and the space coordinates, and a parameter estimation value with higher precision can be obtained without initial values of the inner orientation element and the outer orientation element during calculation.
2. Solving parameter values in the camera by a space rear intersection method;
(1) integral least squares solution
The collinearity equation is given according to the principle of similar triangle, and the collinearity equation is the basic equation of the photogrammetry, and the embodiment is not described in detail herein.
(2) Gross error rejection
In the data acquisition process, there are some inevitable factors which can cause gross errors or abnormal points of the data. Therefore, in the resolving process, in order to improve resolving accuracy, a space back intersection method is designed to remove gross errors in data so as to obtain an optimal solution.
a. Calculating a camera parameter value through integral least square according to the initial value;
b. judging whether the camera parameter value meets the iteration precision requirement, if so, obtaining an optimal estimated value, and if not, continuing the next step;
c. resolving a residual error in a camera parameter value and solving a distance deviation;
d. calculating the deviation of all points, and calculating the standard deviation of the deviation, wherein the content of the part is a calculation formula in the prior art, and the description of the embodiment is omitted;
e. sequentially judging each point participating in calculation, and when the residual error is more than two times of the standard deviation, considering the point as a coarse difference point, deleting the coarse difference point and reserving data meeting the conditions;
f. and re-executing the step a by using the reserved point.
The method for calibrating the parameters in the stable camera by using the integral least square solves the problem that a single calibration device is used for overcoming the homonymous feature precision extraction of two kinds of heterogeneous data, namely image (2D) and laser point cloud (3D). Aiming at the conditions that the camera internal parameter calibration method is easy to fall into a local optimal solution due to excessive parameters and errors exist in an observed value and a coefficient array, the method is used for solving the problem that the camera internal parameter linear solution is easy to fall into the local optimal solution in the calculation process by combining a direct linear transformation method and a space back intersection method; and performing gross error elimination by constructing an integral least square in the resolving process by using an image point displacement residual error and a standard deviation as thresholds so as to obtain reliable estimated values of parameters in the camera. On the basis of precise calibration of camera internal parameters, in order to realize a mobile measurement integrated data mode, three-dimensional point cloud data is used as a reference, a prism table device is used as a bridge, point cloud automation and an image recognition algorithm are combined, so that the same-name characteristic precise extraction of two kinds of heterogeneous data, namely image (2D) -laser point cloud (3D), is realized, and the high-precision calibration of camera external parameters is effectively improved.
And 2, solving the problem of true color point cloud loss caused by mismatching of camera field angles by a time-based serialized image data fusion method through a neighborhood image repairing technology, and realizing high-precision fusion of laser point cloud and image data.
Firstly, a series of conversion is carried out on the point cloud, and then the corresponding relation between the point cloud and the image pixel point is established by utilizing the collinear relation of a panoramic ball coordinate system. And then, determining a fusion area according to the marking time of the single panoramic image, and realizing the fusion of the serialized panoramic image and the laser point cloud by utilizing time segmentation.
The specific embodiment is as follows:
in the process of fusing the point cloud and the image, the corresponding color values of the point cloud point can be obtained only by carrying out coordinate conversion for many times and utilizing the collinear condition under a spherical coordinate system.
The conversion process comprises 4 steps:
1. the earth center is fixedly rotated to a local horizontal coordinate system;
2. the local horizontal coordinate system is transferred to an inertial navigation coordinate system;
3. the inertial navigation coordinate system is transferred to a panoramic spherical coordinate system, and the spherical coordinates are solved by utilizing a collinear equation;
4. and acquiring an rgb value corresponding to the spherical coordinates, wherein the rgb value is used for coloring the point cloud to generate a colored point cloud.
According to a time synchronization mechanism of a mobile measurement system, by taking image-triggered GNSS time as a reference and combining the characteristics of laser point cloud acquisition continuity and image acquisition interval, the rapid high-precision fusion of laser point cloud and serialized images is realized by a time domain segmentation method, and serialization is to load point cloud and image files of different layers in batches. By the image repairing method in the design field, the problem of true color point cloud information loss caused by mismatching of the field angle of the camera sensor and the laser radar is solved, and the completeness of the true color point cloud information is effectively improved.
And 3, converting the point cloud data in the las format into a serialized folder (containing bin and hrc files) by using PotreeConvereras, and converting the panoramic image data into a serialized jpe file by using PotreeConverter.
In order to speed up the interval calculation process, Potree also divides the node into a plurality of units, so that only the same unit and adjacent units need to be calculated. Meanwhile, the smaller unit size increases the overhead of the memory, while the larger unit size cannot achieve the effect of accelerating the calculation, and the experiment is required to be carried out in the specific implementation.
The specific embodiment is as follows:
the Potree data structure is constructed by the following steps:
1. inputting each point in the fused three-dimensional point cloud and sequentially placing each point in a root node;
2. a point falls into a certain node, and falls into a child node if the minimum distance is not kept between other points in the node (because the occupied space of the node is reduced along with the rise of the node hierarchy, the minimum distance of the node is halved every higher hierarchy);
3. similar to an MNO, points that fall into a leaf node (which contains no nodes below) do not immediately build internal nodes, but are first deposited into the leaf node until the number of points reaches a threshold;
4. in order to reduce the memory usage amount in the construction process, the current data is written into the hard disk every 10million points are processed, and then the current data is read back again if necessary.
The Potree adopts a Poisson-disk down-sampling method, which ensures the minimum distance between each point and other points. The implementation of the Poisson-disk may apply many algorithms, such as the darttthwwing algorithm, etc. The basic idea is to check the distance between the point and other points every time the point is added, and discard the point if the distance is less than the interval.
And 4, publishing the point cloud file and the panoramic image file obtained in the step 3 to a Web server, realizing road vectorization operation on a Web webpage end in a manual interaction mode, and outputting the obtained vector file to a client in a dwg format.
Converting the point cloud data in the las format into a serialized folder (containing bin and hrc files) by using PotreeConvereras, and converting the panoramic image data into a serialized jpe file by using PotreeConverter;
the specific embodiment is as follows:
and releasing the point cloud file and the panoramic image file which are analyzed and processed to a web server. The method comprises the steps of realizing road vectorization operation at a Web webpage end in a manual interaction mode, obtaining the distance between an operation point and the center of a track ball according to the radius of the track ball by depending on capturing of mouse operation in the mouse track ball by a polytree in the vectorization process, calculating the coordinate point of the operation point, calculating the type and the quantity of a vector by matching with corresponding type information, and outputting the obtained vector file to a client in a dwg format after the operation is finished.
And performing three-dimensional measurement, POI labeling, vectorization operation and file output on the Web endpoint cloud image in a manual interaction mode so as to realize the fusion process of the laser point cloud and the panoramic image and the shareability, transferability, exchangeability and openness of result display.
The invention improves the efficiency and the accuracy of point cloud and image fusion, realizes the functions of three-dimensional measurement, POI labeling, vectorization operation and file output of the Web endpoint cloud image, and greatly improves the efficiency of road element extraction.
Example two:
the road extraction system based on the polytree point cloud image fusion can realize the road extraction method based on the polytree point cloud image fusion in the first embodiment, and comprises the following steps:
an acquisition module: the system comprises a mobile measuring system, a lane, a three-dimensional point cloud data acquisition unit, a lane tracking unit and a panoramic image data acquisition unit, wherein the mobile measuring system is used for acquiring the three-dimensional point cloud data and the panoramic image data through a multi-feature constrained mobile measuring system along the lane;
a feature extraction module: the system is used for extracting homonymous features in the three-dimensional point cloud data and the panoramic image data;
a data fusion module: the method is used for fusing the three-dimensional point cloud data and the panoramic image data after the homonymy features are extracted through a point cloud image display technology of a lattice and a serialized image data fusion method;
a serialization conversion module: the system comprises a data processing module, a data storage module, a data processing module and a data processing module, wherein the data processing module is used for performing serialization conversion on fused three-dimensional point cloud data and panoramic image data to obtain a point cloud file and a panoramic image file;
an interaction module: and the system is used for outputting the point cloud file and the panoramic image file after sequentially carrying out three-dimensional measurement, POI marking and road vectorization operations.
Example three:
the embodiment of the invention also provides a road extraction device based on the polytree point cloud image fusion, which can realize the road extraction method based on the polytree point cloud image fusion in the first embodiment and comprises a processor and a storage medium;
the storage medium is used for storing instructions;
the processor is configured to operate in accordance with the instructions to perform the steps of the method of:
collecting three-dimensional point cloud data and panoramic image data along a lane by a multi-feature constrained mobile measurement system;
extracting homonymous features in the three-dimensional point cloud data and the panoramic image data;
fusing three-dimensional point cloud data and panoramic image data after homonymy feature extraction through a point cloud image display technology of a Potree and a serialized image data fusion method;
performing serialization conversion on the fused three-dimensional point cloud data and panoramic image data to obtain a point cloud file and a panoramic image file;
and outputting the point cloud file and the panoramic image file after sequentially carrying out three-dimensional measurement, POI marking and road vectorization operation.
Example four:
the embodiment of the present invention further provides a computer-readable storage medium, which can implement the road extraction method based on the Potree point cloud image fusion in the first embodiment, wherein a computer program is stored thereon, and when the program is executed by a processor, the following steps of the method are implemented:
collecting three-dimensional point cloud data and panoramic image data along a lane by a multi-feature constrained mobile measurement system;
extracting homonymous features in the three-dimensional point cloud data and the panoramic image data;
fusing three-dimensional point cloud data and panoramic image data after homonymy feature extraction through a point cloud image display technology of a Potree and a serialized image data fusion method;
performing serialization conversion on the fused three-dimensional point cloud data and panoramic image data to obtain a point cloud file and a panoramic image file;
and outputting the point cloud file and the panoramic image file after sequentially carrying out three-dimensional measurement, POI marking and road vectorization operation.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.
Claims (10)
1. The road extraction method based on the Potree point cloud image fusion is characterized by comprising the following steps:
collecting three-dimensional point cloud data and panoramic image data along a lane by a multi-feature constrained mobile measurement system;
extracting homonymous features in the three-dimensional point cloud data and the panoramic image data;
fusing three-dimensional point cloud data and panoramic image data after homonymy feature extraction through a point cloud image display technology of a Potree and a serialized image data fusion method;
performing serialization conversion on the fused three-dimensional point cloud data and panoramic image data to obtain a point cloud file and a panoramic image file;
and outputting the point cloud file and the panoramic image file after sequentially carrying out three-dimensional measurement, POI marking and road vectorization operation.
2. The road extraction method based on the polytree point cloud image fusion of claim 1, wherein the step of collecting the three-dimensional point cloud data and the panoramic image data along a lane by a multi-feature constrained mobile measurement system comprises the following steps:
determining an initial value of a camera in the multi-feature constrained mobile measurement system by a direct linear transformation method;
solving the optimal parameter estimation value of the camera in the multi-feature constrained mobile measurement system by adopting a space rear intersection method based on the initial value;
and collecting three-dimensional point cloud data and panoramic image data along the lane by the multi-feature-constrained mobile measuring system after the parameter optimal estimation is confirmed.
3. The road extraction method based on the polytree point cloud image fusion as claimed in claim 2, wherein the space backward intersection method comprises:
calculating a camera parameter value by adopting integral least square based on the initial value;
judging whether the camera parameter value meets the iteration precision requirement or not;
in response to the camera parameter value satisfying the iterative accuracy requirement, outputting the camera parameter value as the optimal parameter estimate;
in response to the fact that the camera parameter value does not meet the iteration precision requirement, residual errors in the camera parameter value are solved, and all distance deviations are solved;
calculating a deviation standard value based on all the distance deviations;
only the camera parameter value with the residual error not more than two times of the standard deviation is reserved, the overall least square is adopted again for calculation, and whether the iteration precision requirement is met or not is judged.
4. The method for extracting road based on the Potree point cloud image fusion as claimed in claim 1, wherein the extracting of homonymous features in the three-dimensional point cloud data and the panoramic image data comprises: the method is characterized in that three-dimensional point cloud data is used as a standard, a prism table device is used as a bridge, point cloud automation and an image recognition algorithm are combined, and homonymous characteristic fine extraction of two kinds of heterogeneous data of the three-dimensional point cloud data and panoramic image data is carried out.
5. The method for extracting a road based on the fusion of a Potree point cloud image as claimed in claim 1, wherein the fusion of the three-dimensional point cloud data and the panoramic image data after the extraction of the homonymous features is performed by a Potree point cloud image display technology and a serialized image data fusion method, comprising the following steps:
repairing the three-dimensional point cloud data and the panoramic image data by a neighborhood image repairing technology;
carrying out coordinate conversion on the three-dimensional point cloud data;
establishing a corresponding relation between point cloud points in the three-dimensional point cloud data after coordinate conversion and image pixel points in panoramic image data by utilizing a collinear relation of a panoramic ball coordinate system;
determining a fusion area according to the marking time of the single panoramic image;
and performing serialized three-dimensional point cloud data and panoramic image data fusion by utilizing time segmentation based on the fusion area.
6. The road extraction method based on the polytree point cloud image fusion as claimed in claim 1, wherein the coordinate transformation of the three-dimensional point cloud data comprises:
the earth center in the three-dimensional point cloud data is fixedly transferred to a local horizontal coordinate system;
transferring the local horizontal coordinate system to an inertial navigation coordinate system;
transferring the inertial navigation coordinate system to a panoramic spherical coordinate system, and solving the spherical coordinates by utilizing a collinear equation;
and acquiring rgb values corresponding to the spherical coordinates.
7. The road extraction method based on the Potree point cloud image fusion of claim 1, wherein the outputting of the point cloud file and the panoramic image file after sequentially performing three-dimensional measurement, POI labeling and road vectorization operations comprises:
the point cloud file and the panoramic image file are published to a web server;
realizing road vectorization operation at a Web page end in a manual interaction mode to obtain a vector file;
outputting the vector file to the client in dwg format;
the vectoring operation includes:
acquiring a mouse operating point and a trackball center in a manual interaction mode;
calculating the distance between the operating point and the center of the track ball to obtain a coordinate point;
and matching type information corresponding to the coordinate points, and creating vector types and quantity sizes to obtain the vector file.
8. Road extraction system based on polytree point cloud image fuses, characterized by includes:
an acquisition module: the system comprises a mobile measuring system, a lane, a three-dimensional point cloud data acquisition unit, a lane tracking unit and a panoramic image data acquisition unit, wherein the mobile measuring system is used for acquiring the three-dimensional point cloud data and the panoramic image data through a multi-feature constrained mobile measuring system along the lane;
a feature extraction module: the system is used for extracting homonymous features in the three-dimensional point cloud data and the panoramic image data;
a data fusion module: the method is used for fusing the three-dimensional point cloud data and the panoramic image data after the homonymy features are extracted through a point cloud image display technology of a lattice and a serialized image data fusion method;
a serialization conversion module: the system comprises a data processing module, a data storage module, a data processing module and a data processing module, wherein the data processing module is used for performing serialization conversion on fused three-dimensional point cloud data and panoramic image data to obtain a point cloud file and a panoramic image file;
an interaction module: and the system is used for outputting the point cloud file and the panoramic image file after sequentially carrying out three-dimensional measurement, POI marking and road vectorization operations.
9. The road extraction device based on the Potree point cloud image fusion is characterized by comprising a processor and a storage medium;
the storage medium is used for storing instructions;
the processor is configured to operate in accordance with the instructions to perform the steps of the method according to any one of claims 1 to 7.
10. Computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
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