CN111968253A - Point cloud data-based road surface extraction method and system - Google Patents
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Abstract
The embodiment of the invention provides a road surface extraction method and a system based on point cloud data, wherein the method comprises the following steps: acquiring initial three-dimensional point cloud data of a target pavement under different visual angles; converting the initial three-dimensional point cloud data under different visual angles to the same visual angle through an iterative closest point algorithm, and acquiring the converted initial three-dimensional point cloud data; and performing multiple filtering on the converted initial three-dimensional point cloud data, and extracting the point cloud data of the target pavement. The embodiment of the invention establishes the spatial index of the three-dimensional point cloud data through the iterative closest point algorithm, filters the initial three-dimensional point cloud data of the target pavement by a method of combining multiple filtering, removes noise and non-pavement point cloud data, and finally extracts the pavement point cloud data, thereby improving the pavement extraction precision.
Description
Technical Field
The invention relates to the technical field of surveying and mapping science, in particular to a road surface extraction method and system based on point cloud data.
Background
The road center line can clearly represent the topological structure of the road, and has important functions on determining the road position, reconstructing and expanding and updating the map. Therefore, the method for road surface extraction and centerline fitting based on the vehicle-mounted LiDAR point cloud has important practical significance and research value.
The classical measurement means, such as a total station, collects data on a normally running road, which easily causes traffic jam and also harms the safety of measurement personnel. The GPS data can only provide track data with limited density, and can not show the full view of the road comprehensively. Street view data, while providing a clear image of the road, lacks direct elevation information. The LiDAR point cloud and other information acquired by the vehicle-mounted laser scanning technology can quickly acquire massive high-density road coordinates and color data, and provides reliable data for road surface extraction and midline fitting.
Although the vehicle-mounted LiDAR point cloud can rapidly and objectively obtain massive three-dimensional data of the road surface, the disordered point cloud is lack of a topological structure, has more noise points and the like, and therefore the road surface extraction and fitting effects are poor.
Therefore, a road surface extraction method based on point cloud data is needed.
Disclosure of Invention
The embodiment of the invention provides a road surface extraction method and system based on point cloud data, which are used for solving the defect of poor road surface extraction effect in the prior art and realizing high-precision road surface extraction.
The embodiment of the invention provides a road surface extraction method based on point cloud data, which comprises the following steps:
acquiring initial three-dimensional point cloud data of a target pavement under different visual angles;
converting the initial three-dimensional point cloud data under different visual angles to the same visual angle through an iterative closest point algorithm, and acquiring the converted initial three-dimensional point cloud data;
and performing multiple filtering on the converted initial three-dimensional point cloud data, and extracting the point cloud data of the target pavement.
According to the road surface extraction method based on point cloud data, the multiple filtering is performed on the converted initial three-dimensional point cloud data, and the point cloud data of the target road surface is extracted, and the method specifically comprises the following steps:
filtering the converted initial three-dimensional point cloud data through a gradient filtering algorithm to obtain first road point cloud data;
filtering the first road point cloud data through a Gaussian filtering algorithm to obtain second road point cloud data;
and filtering the second road point cloud data through a bilateral filtering algorithm, and extracting the point cloud data of the target road surface.
According to the road surface extraction method based on point cloud data of one embodiment of the invention, the initial three-dimensional point cloud data after conversion is filtered through a gradient filtering algorithm to obtain first road point cloud data, and the method specifically comprises the following steps:
setting the gradient threshold range of the gradient filtering as a preset threshold range, and filtering the converted initial three-dimensional point cloud data through the preset threshold range;
and reducing the range of the preset threshold value, repeating the process until the gradient filtering times reach the preset times, and finally obtaining the first road point cloud data.
According to the road surface extraction method based on the point cloud data, the preset threshold range is 0-0.05.
According to the road surface extraction method based on the point cloud data, the threshold range of the Gaussian filter is 3-10.5.
According to the point cloud data-based road surface extraction method, the threshold range of bilateral filtering is 0-0.09.
The road surface extraction method based on the point cloud data further comprises the following steps:
and obtaining a fitted road center line of the target road surface by a B-spline curve fitting algorithm.
The embodiment of the invention also provides a road surface extraction system based on the point cloud data, which comprises the following steps:
the acquisition module is used for acquiring initial three-dimensional point cloud data of a target road surface under different visual angles;
the matching module is used for converting the initial three-dimensional point cloud data under different visual angles into the same visual angle through an iterative closest point algorithm and acquiring the converted initial three-dimensional point cloud data;
and the extraction module is used for performing multiple filtering on the converted initial three-dimensional point cloud data and extracting the point cloud data of the target pavement.
The embodiment of the present invention further provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the processor implements any one of the steps of the above-mentioned road surface extraction method based on point cloud data when executing the program.
Embodiments of the present invention further provide a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of any one of the above-mentioned point cloud data-based road surface extraction methods.
According to the point cloud data-based road surface extraction method and system provided by the embodiment of the invention, the spatial index of the three-dimensional point cloud data is established through the iterative closest point algorithm, and the relation between the three-dimensional point cloud data is established, so that the subsequent filtering operation is facilitated; in addition, the initial three-dimensional point cloud data of the road surface is filtered by combining the spatial distribution characteristics of the three-dimensional point cloud data in the road surface scene through a method of combining multiple filtering, noise and non-road surface point cloud data are removed, and finally, the road surface point cloud data are extracted, so that the road surface extraction precision is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
Fig. 1 is a flowchart of a road surface extraction method based on point cloud data according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a road surface extraction system based on point cloud data according to an embodiment of the present invention;
fig. 3 illustrates a physical structure diagram of an electronic device.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The technical solution of the embodiment of the present invention is described below with reference to fig. 1 to 3.
Fig. 1 is a flowchart of a road surface extraction method based on point cloud data according to an embodiment of the present invention, and as shown in fig. 1, the method includes:
s1, acquiring initial three-dimensional point cloud data of the target road surface under different visual angles;
specifically, the initial three-dimensional point cloud data of the target road surface is acquired by the vehicle-mounted LiDAR device, and the embodiment of the invention takes the example of extracting the road surface of the expressway as an example.
The three-dimensional point cloud data can obtain accurate topological structure and geometric structure of an object with low storage cost, so that the three-dimensional point cloud data is more and more focused. In the actual acquisition process, because the size of the object to be measured is too large, the surface of the object is shielded or the scanning angle of the three-dimensional scanning device is equal, and the like, complete geometric information of the object cannot be obtained by single scanning. Therefore, in order to obtain complete geometric information of the measured object, two or more sets of three-dimensional point cloud data under different viewing angles, i.e. different reference coordinates, need to be unified to the same coordinate system for point cloud registration. In the registration algorithm, an Iterative Closest Point (ICP) algorithm is used most.
S2, converting the initial three-dimensional point cloud data under different viewing angles to the same viewing angle through an iterative closest point algorithm, and acquiring the converted initial three-dimensional point cloud data;
and converting the initial three-dimensional point cloud data under different visual angles to the same visual angle through an iterative closest point algorithm, namely converting the initial three-dimensional point cloud data under different coordinate systems to the same coordinate system to obtain the converted initial three-dimensional point cloud data.
In addition, by the ICP algorithm, a spatial index of the initial three-dimensional point cloud data may be established, which includes coordinates of the initial three-dimensional point cloud data and distances from other adjacent three-dimensional point cloud data. Spatial indexes among the three-dimensional point cloud data are established through an ICP (inductively coupled plasma) algorithm, so that subsequent filtering operation is facilitated, the three-dimensional point cloud data can be visually displayed, and the operation of inquiring the coordinates of the adjacent points in a certain neighborhood of the three-dimensional point cloud data is realized.
In addition, due to the fact that the discrete three-dimensional point cloud data lack of a topological structure, the space index is established by adopting an ICP point cloud matching algorithm, the relation among the three-dimensional point cloud data is established, and the coordinate query of the adjacent point in a certain neighborhood of the three-dimensional point cloud data is achieved.
And S3, performing multiple filtering on the converted initial three-dimensional point cloud data, and extracting the point cloud data of the target road surface.
And finally, performing multiple filtering on the converted initial three-dimensional point cloud data, wherein the multiple filtering refers to combining multiple filtering modes, and each filtering mode can complement the advantages, so that the filtering precision and the extraction effect are improved to the maximum extent.
In conclusion, the method and the device aim at the spatial distribution characteristics of the three-dimensional point cloud data in the road scene, combine multiple filtering to filter the initial three-dimensional point cloud data, remove noise and non-road surface point cloud data, finally extract the road surface point cloud data, and improve the road surface extraction precision.
On the basis of the foregoing embodiment, preferably, the performing multiple filtering on the converted initial three-dimensional point cloud data to extract the point cloud data of the target road surface specifically includes:
filtering the converted initial three-dimensional point cloud data through a gradient filtering algorithm to obtain first road point cloud data;
the initial three-dimensional point cloud data is acquired through vehicle-mounted LiDAR equipment and comprises street lamp three-dimensional point cloud data, green belt three-dimensional point cloud data and noise of the vehicle-mounted LiDAR equipment, and the non-road point cloud and the noise can be well filtered through a gradient filtering algorithm to obtain first road point cloud data.
Filtering the first road point cloud data through a Gaussian filtering algorithm to obtain second road point cloud data;
the non-road surface point cloud data distribution in the first road point cloud data after the non-road point cloud is filtered accords with the Gaussian distribution, so that the non-road surface point cloud in the first road point cloud can be well filtered through a Gaussian filtering algorithm, and the second road point cloud data is extracted.
And filtering the second road point cloud data through a bilateral filtering algorithm, and extracting the point cloud data of the target road surface.
The boundary between the road surface point cloud and other non-road surface point clouds in the second road point cloud data is obvious, and the bilateral filtering algorithm has a good segmentation effect on the point clouds with obvious boundaries, so that the point cloud data of the target road surface is extracted through the bilateral filtering algorithm.
On the basis of the foregoing embodiment, preferably, the filtering processing is performed on the converted initial three-dimensional point cloud data through a gradient filtering algorithm to obtain first road point cloud data, and specifically includes:
setting the gradient threshold range of the gradient filtering as a preset threshold range, and filtering the converted initial three-dimensional point cloud data through the preset threshold range;
when the gradient filtering algorithm is used for filtering, firstly, the threshold range of the gradient filtering algorithm is set as a preset threshold range, and the gradient of the preset threshold range is used for filtering the converted initial three-dimensional point cloud data for the first time.
And reducing the range of the preset threshold value, repeating the process until the gradient filtering times reach the preset times, and finally obtaining the first road point cloud data.
And then gradually reducing the range of a preset threshold value, and filtering the converted initial three-dimensional point cloud data for multiple times until the filtering times reach the prediction times, thereby finally obtaining the first road point cloud data.
By executing the gradient filtering algorithm for multiple times, the preset threshold range of the gradient filtering is gradually reduced, and the non-road point cloud is gradually filtered, so that the filtering precision of the non-road point cloud is improved, the extraction precision of the first road point cloud data is improved, and the subsequent target pavement extraction precision is further improved.
On the basis of the above embodiment, preferably, the preset threshold range is 0-0.05.
Through a large number of experiments, the filtering precision of the gradient filtering algorithm is good when the preset threshold range is 0-0.05, so that the preset threshold range is 0-0.05 in the embodiment of the invention.
On the basis of the above embodiment, the threshold value of the gaussian filtering is preferably in the range of 3-10.5.
In the embodiment of the invention, the Gaussian filtering is carried out by selecting the range of 3-10.5 threshold values.
On the basis of the above embodiment, preferably, the threshold value of the bilateral filtering is in the range of 0-0.09.
Specifically, the threshold range for bilateral filtering is 0-0.09.
On the basis of the above embodiment, it is preferable to further include:
and extracting a road skeleton line by a B-spline curve fitting algorithm to obtain a fitted road center line of the target road surface.
In order to find a B-spline curve that can correctly reflect the shape and trend of the point cloud of the highway surface, the problem is usually transformed into an optimization problem solution: and for the point cloud data of the target road surface after the multiple filtering, finding a group of B sample bands to minimize the target function.
The target function comprises the square distance from the point cloud to the spline curve, an energy function for controlling the smoothness of the curve and a corresponding energy factor. And obtaining the target function after calculation.
Based on the method, multi-curve fitting is carried out on the point cloud data of the target pavement to obtain a fitting road center line of the target pavement. The method for extracting the point cloud of the highway pavement and fitting the center line can serve the fields of map updating, road maintenance, asset management and the like.
The main advantages of the embodiments of the invention are as follows:
(1) because the discrete three-dimensional point cloud data lack a topological structure, the embodiment of the invention adopts an ICP point cloud matching algorithm to establish a spatial index and establish the relation between the three-dimensional point cloud data, thereby realizing the coordinate query of the adjacent point in a certain neighborhood of the three-dimensional point cloud data.
(2) According to the spatial distribution characteristics of point clouds in the expressway scene, a method of setting a height threshold value is adopted to carry out rough extraction on the road points. Based on the statistical analysis of Gaussian distribution, non-pavement points can be further removed effectively, and the method has a good filtering effect particularly on low-density discrete points outside the pavement range. The bilateral filtering principle is that point cloud edge segmentation is firstly carried out, and then space weight is calculated, so that a good filtering effect is obtained for roads with obvious boundaries such as expressways.
(3) And (4) completing fitting extraction of the highway pavement central line by using a B-spline curve fitting algorithm. The method has accurate effect particularly in the process of fitting the road surface center line of a road with a short length of straight line section.
The point cloud data-based road surface extraction system provided by the embodiment of the invention is described below, and the point cloud data-based road surface extraction system described below and the point cloud data-based road surface extraction method described above can be referred to in a corresponding manner.
Fig. 2 is a schematic structural diagram of a road surface extraction system based on point cloud data according to an embodiment of the present invention, as shown in fig. 2, the system includes an obtaining module 201, a matching module 202, and an extracting module 203, where:
the acquisition module 201 is configured to acquire initial three-dimensional point cloud data of a target road surface at different viewing angles;
the matching module 202 is configured to convert the initial three-dimensional point cloud data at different viewing angles to the same viewing angle through an iterative closest point algorithm, and acquire the converted initial three-dimensional point cloud data;
the extraction module 203 is configured to perform multiple filtering on the converted initial three-dimensional point cloud data, and extract point cloud data of the target road surface.
The system embodiment corresponds to the method embodiment, and reference may be made to the method embodiment specifically, and the system embodiment is not described herein again.
Fig. 3 illustrates a physical structure diagram of an electronic device, which may include, as shown in fig. 3: a processor (processor)310, a communication interface (communication interface)320, a memory (memory)330 and a communication bus 340, wherein the processor 310, the communication interface 320 and the memory 330 communicate with each other via the communication bus 340. The processor 310 may invoke logic instructions in the memory 330 to perform a method of road surface extraction based on point cloud data, the method comprising:
acquiring initial three-dimensional point cloud data of a target pavement under different visual angles;
converting the initial three-dimensional point cloud data under different visual angles to the same visual angle through an iterative closest point algorithm, and acquiring the converted initial three-dimensional point cloud data;
and performing multiple filtering on the converted initial three-dimensional point cloud data, and extracting the point cloud data of the target pavement.
In addition, the logic instructions in the memory 330 may be implemented in the form of software functional units and stored in a computer readable storage medium when the software functional units are sold or used as independent products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
In another aspect, an embodiment of the present invention further provides a computer program product, where the computer program product includes a computer program stored on a non-transitory computer-readable storage medium, the computer program includes program instructions, and when the program instructions are executed by a computer, the computer is capable of executing a road surface extraction method based on point cloud data provided by the above-mentioned method embodiments, where the method includes:
acquiring initial three-dimensional point cloud data of a target pavement under different visual angles;
converting the initial three-dimensional point cloud data under different visual angles to the same visual angle through an iterative closest point algorithm, and acquiring the converted initial three-dimensional point cloud data;
and performing multiple filtering on the converted initial three-dimensional point cloud data, and extracting the point cloud data of the target pavement.
In still another aspect, an embodiment of the present invention further provides a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program is implemented by a processor to execute a method for extracting a road surface based on point cloud data provided in the foregoing embodiments, where the method includes: acquiring initial three-dimensional point cloud data of a target pavement under different visual angles;
converting the initial three-dimensional point cloud data under different visual angles to the same visual angle through an iterative closest point algorithm, and acquiring the converted initial three-dimensional point cloud data;
and performing multiple filtering on the converted initial three-dimensional point cloud data, and extracting the point cloud data of the target pavement.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A road surface extraction method based on point cloud data is characterized by comprising the following steps:
acquiring initial three-dimensional point cloud data of a target pavement under different visual angles;
converting the initial three-dimensional point cloud data under different visual angles to the same visual angle through an iterative closest point algorithm, and acquiring the converted initial three-dimensional point cloud data;
and performing multiple filtering on the converted initial three-dimensional point cloud data, and extracting the point cloud data of the target pavement.
2. The method for extracting a road surface based on point cloud data according to claim 1, wherein the step of performing multiple filtering on the converted initial three-dimensional point cloud data to extract the point cloud data of the target road surface specifically comprises:
filtering the converted initial three-dimensional point cloud data through a gradient filtering algorithm to obtain first road point cloud data;
filtering the first road point cloud data through a Gaussian filtering algorithm to obtain second road point cloud data;
and filtering the second road point cloud data through a bilateral filtering algorithm, and extracting the point cloud data of the target road surface.
3. The method for extracting a road surface based on point cloud data according to claim 2, wherein the step of filtering the converted initial three-dimensional point cloud data by a gradient filtering algorithm to obtain first road point cloud data specifically comprises:
setting the gradient threshold range of the gradient filtering as a preset threshold range, and filtering the converted initial three-dimensional point cloud data through the preset threshold range;
and reducing the range of the preset threshold value, repeating the process until the gradient filtering times reach the preset times, and finally obtaining the first road point cloud data.
4. The method for extracting a road surface based on point cloud data of claim 3, wherein the preset threshold range is 0-0.05.
5. The method of claim 2, wherein the threshold range of the gaussian filter is 3-10.5.
6. The method of claim 2, wherein the threshold range of the bilateral filtering is 0-0.09.
7. The method for extracting a road surface based on point cloud data according to claim 1, further comprising:
and obtaining a fitted road center line of the target road surface by a B-spline curve fitting algorithm.
8. A road surface extraction system based on point cloud data is characterized by comprising:
the acquisition module is used for acquiring initial three-dimensional point cloud data of a target road surface under different visual angles;
the matching module is used for converting the initial three-dimensional point cloud data under different visual angles into the same visual angle through an iterative closest point algorithm and acquiring the converted initial three-dimensional point cloud data;
and the extraction module is used for performing multiple filtering on the converted initial three-dimensional point cloud data and extracting the point cloud data of the target pavement.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the steps of the point cloud data-based road surface extraction method according to any one of claims 1 to 7.
10. A non-transitory 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 for road surface extraction based on point cloud data according to any one of claims 1 to 7.
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