CN112649814B - Matching method, device, equipment and storage medium for laser positioning - Google Patents
Matching method, device, equipment and storage medium for laser positioning Download PDFInfo
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Abstract
The present invention relates to the field of image analysis technologies, and in particular, to a matching method, apparatus, device, and storage medium for laser positioning. Wherein, the matching method comprises the following steps: acquiring laser point cloud data based on a laser positioning image and raster data to be matched based on an image to be matched; filtering the laser point cloud data, and processing the laser point cloud data after noise filtering according to a rasterization algorithm to obtain laser raster data; matching the laser raster data with the raster data to be matched according to a phase correlation algorithm to obtain a rotation parameter and a translation parameter; and according to the rotation parameter, the translation parameter and the grid data to be matched. The method can quickly complete the matching process of the laser point cloud data and obtain a high-precision matching result.
Description
Technical Field
The present invention relates to the field of image analysis technologies, and in particular, to a matching method, apparatus, device, and storage medium for laser positioning.
Background
The high-precision positioning is one of important components of an automatic driving system, and not only can the position and the posture of a vehicle loaded with the automatic driving system be provided, but also the decision part of the automatic driving system can be ensured to smoothly plan a driving route for the vehicle; and the environment perception can be assisted to prejudge the periphery, and the high-precision map is facilitated to be manufactured.
In practical application, the high-precision positioning module of the automatic driving system needs to achieve the centimeter magnitude of positioning precision of a vehicle, and needs to be less than 0.2 degree of positioning precision of the vehicle posture.
Most of the conventional high-precision positioning modules complete the positioning work of the vehicle through a Global Navigation Satellite System (GNSS) differential positioning algorithm, and complete the inter-frame interpolation and the correction of a weak observation value by means of a gyroscope and an accelerometer.
However, in the practical application process, when facing to the non-open scenes such as tunnels, high buildings, forest and the like, the differential positioning algorithm is affected by multipath interference and error accumulation, so that the high-precision positioning effect on the vehicle cannot be maintained all the time.
The current common laser positioning scheme in the industry mainly includes that after a laser positioning three-dimensional point cloud base map is obtained, three-dimensional point cloud data are matched by utilizing an ICP (iterative closest point) algorithm, so that the position and the posture of a vehicle are obtained. The ICP algorithm generally needs to consider operations with 6 degrees of freedom, that is, translation with 3 degrees of freedom and rotation with 3 degrees of freedom, which makes the space-time complexity of the overall matching process higher, so the matching speed of the algorithm is slow; meanwhile, the ICP algorithm can not filter the three-dimensional point cloud data, so that the ICP algorithm is interfered by noise in the three-dimensional point cloud data, and the matching precision of the ICP algorithm is low.
Therefore, an enterprise needs to provide a new technical solution to solve the problems of low matching speed and low matching accuracy of three-dimensional point cloud data based on a laser positioning base map.
Disclosure of Invention
The invention provides a matching method, a matching device, matching equipment and a storage medium for laser positioning, which are used for quickly completing the matching process of three-dimensional point cloud data and obtaining the effect of high-precision matching results.
In a first aspect, an embodiment of the present invention provides a matching method for laser positioning, where the matching method for laser positioning includes:
acquiring laser point cloud data based on a laser positioning image and raster data to be matched based on an image to be matched;
filtering the laser point cloud data, and processing the laser point cloud data after noise filtering according to a rasterization algorithm to obtain laser raster data;
matching the laser raster data and the raster data to be matched according to a phase correlation algorithm to obtain rotation parameters, wherein the rotation parameters are used for explaining the rotation relation between the laser positioning image and the image to be matched;
rotating the raster data to be matched according to the rotation parameters to obtain rotated raster data;
matching the laser raster data and the rotated raster data according to the phase correlation algorithm to obtain translation parameters, wherein the translation parameters are used for explaining the translation relation between the laser positioning image and the image to be matched;
and acquiring actual positioning data according to the rotation parameters, the translation parameters and the to-be-matched raster data.
In a second aspect, an embodiment of the present invention provides a matching device for laser positioning, where the matching device for laser positioning includes:
the acquisition module is used for acquiring laser point cloud data based on a laser positioning image and grid data to be matched based on an image to be matched;
the rasterization module is used for filtering the laser point cloud data and processing the laser point cloud data after noise is filtered according to a rasterization algorithm to obtain laser raster data;
the rotation matching module is used for matching the laser raster data with the raster data to be matched according to a phase correlation algorithm to obtain rotation parameters, and the rotation parameters are used for explaining the rotation relation between the laser positioning image and the image to be matched;
the translation matching module is used for rotating the raster data to be matched according to the rotation parameters to obtain rotated raster data, matching the laser raster data with the rotated raster data according to the phase correlation algorithm to obtain translation parameters, and the translation parameters are used for explaining the translation relation between the laser positioning image and the image to be matched;
and the positioning module is used for obtaining actual positioning data according to the rotation parameter, the translation parameter and the to-be-matched grid data.
In a third aspect, an embodiment of the present invention provides a computer device, where the computer device includes:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement a matching method for laser positioning as described in any of the above.
In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, the computer program including program instructions, when executed by a processor, for implementing the matching method for laser positioning as described in any one of the above.
The invention provides a matching method for laser positioning, which comprises the steps of firstly, acquiring laser point cloud data based on a laser positioning image and raster data to be matched based on the image to be matched through data interaction between a laser radar and a server, then filtering the laser point cloud data, and processing the laser point cloud data after noise is filtered according to a rasterization algorithm to obtain laser raster data; then, according to a phase correlation algorithm, sequentially obtaining a rotation parameter and a translation parameter, and finally combining the raster data to be matched, the rotation parameter and the translation parameter to obtain actual positioning data;
in the process, the laser point cloud data are rasterized by virtue of a rasterization algorithm, the matching process is simplified by virtue of a phase correlation algorithm, the matching efficiency between the laser positioning image and the image to be matched can be effectively improved, and meanwhile, the three-dimensional point cloud data are not required to be processed in an iterative manner, so that the phase correlation algorithm can be used for carrying out high-precision estimation on the vehicle pose at a longer distance; meanwhile, the interference of noise to the matching process is reduced by filtering the laser positioning image, so that the precision of the matching result is further enhanced.
Drawings
FIG. 1 is a schematic flow chart of a matching method for laser positioning according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart illustrating a phase correlation algorithm for obtaining rotation parameters according to a first embodiment of the present invention;
FIG. 3 is a graph illustrating the data processing flow of the sub-pixel algorithm according to one embodiment of the present invention;
FIG. 4 is a schematic structural diagram of a matching device for laser positioning according to a second embodiment of the present invention;
fig. 5 is a schematic structural diagram of a matching device for laser positioning according to a third embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Before discussing exemplary embodiments in greater detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although a flowchart may describe the steps as a sequential process, many of the steps can be performed in parallel, concurrently or simultaneously. In addition, the order of the steps may be rearranged. A process may be terminated when its operations are completed, but may have additional steps not included in the figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc.
Example one
Fig. 1 is a flowchart of a matching method for laser positioning according to an embodiment of the present invention, which specifically includes the following steps:
1100. and acquiring laser point cloud data based on the laser positioning image and grid data to be matched based on the image to be matched.
Specifically, a laser radar is used for collecting laser positioning images around the vehicle, the laser positioning images are transmitted to a server, and the server obtains laser point cloud data based on the laser positioning images.
In addition, the server can also obtain the grid data to be matched corresponding to the image to be matched of the vehicle from a pre-established database.
1200. And filtering the laser point cloud data, and processing the laser point cloud data after noise filtering according to a rasterization algorithm to obtain laser raster data.
Specifically, after the server obtains the laser point cloud data, the laser point cloud data is filtered according to a Random Sampling Consensus (RANSAC) algorithm to obtain the laser point cloud data with the ground filtered.
The model of the RANSAC algorithm for filtering the ground is shown as follows:
P＝{p i |||z i -z i ransac ||>T h }
in the above formula, pi is used for indicating ith point cloud data in the laser point cloud data;
z i for indicating p i The z value of the point;
z i ransac for indicating p i The z value of the ground corresponding to the point;
T h for indicating the judgment p i A distance threshold of whether the point is located on the ground.
It should be noted that, in practical applications, the number of iterations of the above random consistency sampling algorithm is generally [50,100], and the number of iterations may be adaptively adjusted according to practical situations, and the specific number of iterations of the random consistency sampling algorithm is not limited in the embodiment of the present invention.
After the laser point cloud data of the ground is filtered and obtained through the RANSAC algorithm, the server filters the laser point cloud data of the ground after being filtered through the region-of-interest algorithm to obtain the laser point cloud data of the ground after being filtered and the dynamic object is filtered.
The process of filtering out the dynamic object is shown as follows:
Q＝{q i |p i ∈Region(static)}
in the above formula, p i The laser point cloud data processing method is used for indicating ith point cloud data in the laser point cloud data after the ground is filtered.
In the process of actually constructing the high-precision map and the laser positioning base map, marking each point cloud data by means of a region of interest (ROI) algorithm so as to distinguish static objects such as trees and the like from dynamic objects such as pedestrians, attaching static marks to the point cloud data corresponding to the static objects such as trees and the like, and attaching dynamic marks to the point cloud data corresponding to the dynamic objects such as pedestrians and the like; after the laser point cloud data with the filtered bottom surface is obtained, the laser point cloud data with the static marks are reserved, and the laser point cloud data without the static marks are screened, so that the laser point cloud data with the filtered bottom surface and the filtered dynamic objects is obtained.
After the laser point cloud data for filtering the ground and the dynamic object is obtained, the server performs rasterization processing on the filtered laser point cloud data according to a rasterization algorithm.
For example, the above rasterization process may be:
assuming that scale is the resolution (pixel/M) of the grid, the grid has M rows and N columns, and the filtered laser point cloud data is (x) i ,y i ,z i ) And correspondingly forming laser raster data (u, v) after the laser point cloud data are rasterized.
The laser raster data and the corresponding laser point cloud data have the following incidence relation:
meanwhile, the laser raster data meets the following limiting conditions:
it should be emphasized that, the ground filtering operation is completed before the dynamic object is filtered, on one hand, the data processing amount of the ROI algorithm is reduced, so as to achieve the purpose of improving the processing efficiency of the ROI algorithm; on the other hand, the probability of the adhesion of the point cloud data is reduced, so that the purpose of improving the filtering effect is achieved.
1300. And matching the laser raster data with raster data to be matched according to a phase correlation algorithm to obtain rotation parameters.
The rotation parameters are used for explaining the rotation relation between the laser positioning image and the image to be matched.
Specifically, as shown in fig. 2, the execution process of step 1300 may be:
1301. a mapping function is obtained.
The mapping function is used for indicating the rotation and translation relation between the laser positioning image and the image to be matched.
The mapping function is specifically:
f 2 (u,v)＝f 1 (ucosθ 0 -vsinθ 0 +△u,usinθ 0 +vcosθ 0 +△v)
in the formula (f) 2 (u, v) indicating raster data to be matched;
f 1 (u, v) for indicating laser raster data;
θ 0 the system is used for indicating a rotation parameter, namely the current heading angle of the vehicle in the high-precision map;
the delta u is used for indicating the offset of the abscissa of the laser raster data relative to the abscissa of the raster data to be matched;
and the delta v is used for indicating the offset of the vertical coordinate of the laser raster data relative to the vertical coordinate of the raster data to be matched.
1302. And carrying out Fourier transform on the mapping function to obtain a magnitude spectrum function.
Specifically, the server obtains a transform function after fourier transform for the mapping function:
in the formula, F 2 (U, V) is used for indicating the grid data to be matched after Fourier transform;
F 1 (U, V) indicates the fourier transformed laser raster data.
It is emphasized that θ in the transformation function 0 The definitions of Δ u, Δ v have been described in the above mapping function, and thus will not be repeated here, and in the present embodiment, θ 0 The three parameters of u, v are defined unchanged, and theta is described later 0 And the three parameters of delta u and delta v are interpreted according to the definition in the mapping function.
After the transformation function is obtained, the offset parameters in the transformation function are removed to obtain a magnitude spectrum function:
1303. and (4) carrying out interaction processing on the amplitude spectrum function to obtain a polar coordinate function.
Specifically, after obtaining the magnitude spectrum function, the magnitude spectrum function is converted from a rectangular coordinate to a polar coordinate, so as to obtain a polar coordinate function corresponding to the magnitude spectrum function:
G 2 (λ,θ)＝G 1 (λ+lna,θ+θ 0 )
in the formula, G 2 (λ, θ) for indicating the polar coordinate form of the raster data to be matched;
G 1 (λ, θ) is used to indicate the polar morphology of the laser raster data;
the parameter a is used to indicate the scaling factor, which satisfies the condition a > 0.
It should be noted that, since the value of the scale factor is close to 0, the rotation parameter θ is given to 0 Causes a certain interference, so that the rotation parameter theta is rotated in order to reduce the scale conversion factor 0 The interference caused by the solution process is filtered again through the high pass filter before step 1304 is performed to obtain the laser raster dataAnd the scale transformation factor corresponding to the filtered laser raster data meets the constraint of a = 1.
1304. And substituting the laser raster data and the raster data to be matched into a polar coordinate function to obtain the rotation parameters.
It should be noted that, in the practical application process, in order to enhance the solution accuracy of the rotation parameter, the laser grid data and the grid data to be matched are substituted into the polar coordinate function, and the rotation parameter θ is obtained correspondingly 0 The server will also later process the rotation parameter theta according to the Kalman filtering algorithm 0 Processing is performed to obtain a refined rotation parameter θ 0 The specific refinement process is as follows:
in the formula, theta 11 The rotation parameter is used for indicating the rotation parameter obtained according to the polar coordinate function;
θ 12 for indicating a rotation parameter during the refinement;
θ 22 for indicating the refined rotation parameters;
q is used to indicate a state error;
r is used to indicate the observation error.
1400. And rotating the raster data to be matched according to the rotation parameters to obtain the rotated raster data.
1500. And matching the laser raster data with the rotated raster data according to a phase correlation algorithm to obtain translation parameters.
The translation parameters are used for explaining the translation relation between the laser positioning image and the image to be matched.
In the embodiment of the present invention, the translation parameters are parameters Δ u and Δ v.
Specifically, the solving process of the translation parameter is as follows:
the existence of a translation relation between the laser raster data and the rotated raster data can be known as follows:
f′ 2 (u,v)＝f 1 (u+△u,v+△v)
of formula (II) to' 2 (u, v) indicating the rotated raster data;
f 1 (u, v) for indicating laser raster data.
And the translation relation is transformed according to the time shift characteristic of Fourier transform, so that the following steps are obtained:
F 2 ′(U,V)＝F 1 (U,V)exp{-2jπ(U△u+V△v)}
wherein, F 2 ' (U, V) is used to indicate the rotated raster data after Fourier transform.
The subsequent known calculation of the frequency domain cross power spectrum is:
in the formula, F 3 Is F 2 ' (U, V).
An Inverse discrete Fourier Transform (IFFT) is performed on the cross power spectrum to obtain an impulse function, and since the impulse function has a maximum value only at (Δ u, Δ v), the parameters Δ u and Δ v can be obtained accordingly.
Optionally, in order to further improve the accuracy of the obtained translation parameter, the server may further process the translation parameter according to a sub-pixel algorithm to obtain a refined translation parameter.
Specifically, the process of obtaining the refined translation parameter according to the sub-pixel algorithm and the translation parameter is as follows:
and shifting the translation parameter obtained according to the impulse function by one unit pixel to obtain a secondary translation parameter.
And obtaining a main weight and a secondary weight according to the amplitude spectrum function, the rotation parameter, the translation parameter and the secondary translation parameter.
The primary weight corresponds to the shift parameter, and the secondary weight corresponds to the secondary shift parameter.
And obtaining refined translation parameters according to the main weight, the translation parameters, the secondary weight and the secondary translation parameters.
Exemplarily, as shown in fig. 3, it is assumed that the maximum value of the above impulse function is (x) 0 ,y 0 ) And the corresponding impulse function amplitude value is A 0 Then, according to the above-mentioned sub-pixel algorithm, the maximum value is shifted by one unit pixel in the four adjacent domains to obtain (x) 1 ,y 0 )、(x -1 ,y 0 )、(x 0 ,y 1 ) And (x) 0 ,y -1 )。
Then order
And
at this time, the translation parameter obtained according to the impulse function is (x) 0 ,y 0 )；
The sub-translation parameter is
And
according to the amplitude spectrum function and the translation parameters, the following parameters can be obtained:
secondary translation parameter
Corresponding amplitude value of
Secondary translation parameter
Corresponding amplitude value of
Finally, the refined translation parameters are obtained as follows:
wherein, (x ', y') is the refined translation parameter;
the dominant weight is
And
the sub-weight is
And
1600. and acquiring actual positioning data according to the rotation parameters, the translation parameters and the to-be-matched raster data.
The embodiment provides a matching method for laser positioning, which comprises the steps of firstly obtaining laser point cloud data and raster data to be matched, and then filtering and rasterizing the laser point cloud data so as to facilitate the subsequent data matching operation; matching the raster data to be matched with the laser raster data according to a phase correlation algorithm to obtain a rotation parameter and a translation parameter for a rotation-translation relation between the laser positioning image and the image to be matched; and finally, transforming the grid data to be matched according to the rotation parameters and the translation parameters to finally obtain the actual positioning data of the vehicle.
In the process, in order to improve the precision of the actual positioning data as much as possible, after the rotating parameters are obtained, errors in the rotating parameter solving process are correspondingly processed through a Kalman filtering algorithm.
Similarly, after the translation parameter is obtained, the pixel limit of the rasterized data is broken through by a sub-pixel algorithm, so that the estimated translation parameter is as close to the real translation parameter as possible, and the precision of the final actual positioning data can be effectively improved.
Compared with a common iteration nearest point algorithm, the matching method for laser positioning provided by the invention has the advantages of higher matching efficiency and lower matching error. In practical applications, the evaluation matching error of the iterative closest point algorithm is generally 0.08m ± 0.03m, while the evaluation matching error of the matching method mentioned in the present invention is only 0.03m ± 0.01m.
Example two
Fig. 4 is a schematic structural diagram of a matching device for laser positioning according to a second embodiment of the present invention, where the matching device specifically includes:
an obtaining module 2100 is configured to obtain laser point cloud data based on the laser positioning image and grid data to be matched based on the image to be matched.
And a rasterizing module 2200 configured to filter the laser point cloud data, and process the laser point cloud data after noise filtering according to a rasterizing algorithm to obtain laser raster data.
And a rotation matching module 2300, configured to match the laser raster data with the raster data to be matched according to a phase correlation algorithm, to obtain a rotation parameter, where the rotation parameter is used to describe a rotation relationship between the laser positioning image and the image to be matched.
And a translation matching module 2400, configured to rotate the raster data to be matched according to the rotation parameter to obtain rotated raster data, and match the laser raster data and the rotated raster data according to the phase correlation algorithm to obtain a translation parameter, where the translation parameter is used to describe a translation relationship between the laser positioning image and the image to be matched.
And a positioning module 2500, configured to obtain actual positioning data according to the rotation parameter, the translation parameter, and the to-be-matched grid data.
Further, the rasterizing module 2200 specifically includes:
and the cutting unit is used for filtering the laser point cloud data according to a random consistency sampling algorithm to obtain the laser point cloud data after the ground is filtered.
And the filtering unit is used for filtering the laser point cloud data after the ground is filtered according to the region of interest algorithm to obtain the laser point cloud data after the ground and the dynamic object are filtered.
And the grid unit is used for processing the laser point cloud data after the ground and the dynamic object are filtered according to a rasterization algorithm to obtain laser grid data.
Further, the rotation matching module 2300 specifically includes:
the device comprises an acquisition unit, a matching unit and a matching unit, wherein the acquisition unit is used for acquiring a mapping function, and the mapping function is used for indicating the rotation and translation relation between a laser positioning image and an image to be matched.
And the phase correlation unit is used for carrying out Fourier transform on the mapping function to obtain a magnitude spectrum function.
And the coordinate system interaction unit is used for carrying out interaction processing on the amplitude spectrum function to obtain a polar coordinate function.
And the rotation parameter calculation unit is used for substituting the laser raster data and the raster data to be matched into a polar coordinate function to obtain the rotation parameter.
Further, the rotation parameter calculating unit specifically includes:
and the high-pass filtering subunit is used for filtering the laser raster data according to the high-pass filter to obtain the filtered laser raster data.
And the rotation parameter calculation subunit is used for substituting the raster data to be matched and the filtered laser raster data into a polar coordinate function to obtain a rotation parameter.
Further, the translation matching module 2400 specifically includes:
and the error processing unit is used for processing the rotation parameters according to a Kalman filtering algorithm to obtain the refined rotation parameters.
And the image rotation unit is used for rotating the to-be-matched raster data according to the refined rotation parameters to obtain the rotated raster data.
And the translation matching unit is used for matching the laser raster data with the rotated raster data according to a phase correlation algorithm to obtain translation parameters.
Further, the positioning module 2500 specifically includes:
and the refinement unit is used for processing the translation parameters according to the sub-pixel algorithm and obtaining the refined translation parameters.
And the positioning unit is used for obtaining actual positioning data according to the rotation parameters, the grid data to be matched and the refined translation parameters.
Further, the refinement unit specifically includes:
and the shifting subunit is used for shifting the translation parameter by one unit pixel to obtain a secondary translation parameter.
And the weighting subunit is used for obtaining a main weight and a secondary weight according to the amplitude spectrum function, the rotation parameter, the translation parameter and the secondary translation parameter, wherein the main weight corresponds to the translation parameter, and the secondary weight corresponds to the secondary translation parameter.
And the refinement subunit is used for obtaining the refined translation parameters according to the main weight, the translation parameters, the secondary weight and the secondary translation parameters.
The technical scheme of the embodiment solves the problems of low matching speed and low matching precision in the existing laser positioning process by providing the matching device for laser positioning.
EXAMPLE III
Fig. 5 is a schematic structural diagram of a computer device according to a third embodiment of the present invention, as shown in fig. 5, the computer device includes a memory 3100 and a processor 3200, the number of the processors 3200 in the computer device may be one or more, and one processor 3200 is taken as an example in fig. 5; the memory 3100 and the processor 3200 in the devices may be connected by a bus or other means, and fig. 5 illustrates the connection by a bus as an example.
The memory 3100 is a computer-readable storage medium, and can be used for storing software programs, computer-executable programs, and modules, such as program instructions/modules corresponding to the power adapter testing method in the embodiment of the present invention (for example, the receiving module 3100, the control module 3200, and the calculating module 3300 in the power adapter testing apparatus). The processor 3200 executes various functional applications and data processing of the device/terminal/device by running software programs, instructions and modules stored in the memory 3100, i.e. implements the matching method described above.
Wherein the processor 3200 is adapted to run a computer program stored in the memory 3100, the following steps are implemented:
acquiring laser point cloud data based on a laser positioning image and raster data to be matched based on an image to be matched;
filtering the laser point cloud data, and processing the laser point cloud data after noise filtering according to a rasterization algorithm to obtain laser raster data;
matching the laser raster data with raster data to be matched according to a phase correlation algorithm to obtain rotation parameters, wherein the rotation parameters are used for explaining the rotation relation between the laser positioning image and the image to be matched;
rotating the raster data to be matched according to the rotation parameters to obtain rotated raster data;
matching the laser raster data with the rotated raster data according to a phase correlation algorithm to obtain translation parameters, wherein the translation parameters are used for explaining the translation relation between the laser positioning image and the image to be matched;
and acquiring actual positioning data according to the rotation parameters, the translation parameters and the to-be-matched raster data.
In one embodiment, the computer program of the computer device provided in the embodiment of the present invention is not limited to the above method operations, and may also perform related operations in the matching method provided in any embodiment of the present invention.
The memory 3100 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required for at least one function; the storage data area may store data created according to the use of the terminal, and the like. Further, the memory 3100 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some examples, memory 3100 may further include memory located remotely from processor 3200, which may be connected to devices/terminals/devices through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
Example four
A fourth embodiment of the present invention further provides a storage medium containing computer-executable instructions, where a computer program is stored on the storage medium, the computer program includes program instructions, and when the program instructions are executed by a processor, the matching method for laser positioning is implemented, and the matching method includes:
acquiring laser point cloud data based on a laser positioning image and raster data to be matched based on an image to be matched;
filtering the laser point cloud data, and processing the laser point cloud data after noise filtering according to a rasterization algorithm to obtain laser raster data;
matching the laser raster data with raster data to be matched according to a phase correlation algorithm to obtain rotation parameters, wherein the rotation parameters are used for explaining the rotation relation between the laser positioning image and the image to be matched;
rotating the raster data to be matched according to the rotation parameters to obtain rotated raster data;
matching the laser raster data with the rotated raster data according to a phase correlation algorithm to obtain translation parameters, wherein the translation parameters are used for explaining the translation relation between the laser positioning image and the image to be matched;
and acquiring actual positioning data according to the rotation parameters, the translation parameters and the to-be-matched raster data.
Of course, the storage medium provided by the embodiment of the present invention includes computer-executable instructions, and the computer-executable instructions are not limited to the above method operations, and may also execute the relevant operations in the matching method provided by any embodiment of the present invention.
From the above description of the embodiments, it is obvious for those skilled in the art that the present invention can be implemented by software and necessary general hardware, and certainly, can also be implemented by hardware, but the former is a better embodiment in many cases. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as a floppy disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a FLASH Memory (FLASH), a hard disk or an optical disk of a computer, and includes several instructions for enabling a computer device (which may be a personal computer, a device, or a network device) to execute the methods according to the embodiments of the present invention.
It should be noted that, in the embodiment of the matching device for laser positioning, the included units and modules are merely divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be implemented; in addition, specific names of the functional units are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present invention.
It is to be noted that the foregoing description is only exemplary of the invention and that the principles of the technology may be employed. Those skilled in the art will appreciate that the present invention is not limited to the particular embodiments described herein, and that various obvious changes, rearrangements and substitutions will now be apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (7)
1. A matching method for laser positioning, the method comprising:
acquiring laser point cloud data based on a laser positioning image and raster data to be matched based on an image to be matched;
filtering the laser point cloud data, and processing the laser point cloud data after noise filtering according to a rasterization algorithm to obtain laser raster data;
matching the laser raster data and the raster data to be matched according to a phase correlation algorithm to obtain rotation parameters, wherein the rotation parameters are used for explaining the rotation relation between the laser positioning image and the image to be matched;
rotating the raster data to be matched according to the rotation parameters to obtain rotated raster data;
matching the laser raster data and the rotated raster data according to the phase correlation algorithm to obtain translation parameters, wherein the translation parameters are used for explaining the translation relation between the laser positioning image and the image to be matched;
acquiring actual positioning data according to the rotation parameter, the translation parameter and the to-be-matched grid data;
wherein, the matching the laser raster data and the raster data to be matched according to the phase correlation algorithm to obtain the rotation parameter comprises:
acquiring a mapping function, wherein the mapping function is used for indicating a rotational translation relation between the laser positioning image and the image to be matched;
carrying out Fourier transform on the mapping function to obtain a magnitude spectrum function;
carrying out interaction processing on the amplitude spectrum function to obtain a polar coordinate function;
substituting the laser raster data and the raster data to be matched into the polar coordinate function to obtain the rotation parameters;
after obtaining the translation parameters, the method further comprises:
shifting the translation parameter by one unit pixel to obtain a secondary translation parameter;
obtaining a main weight and a secondary weight according to the amplitude spectrum function, the translation parameter and the secondary translation parameter, wherein the main weight corresponds to the translation parameter, and the secondary weight corresponds to the secondary translation parameter;
obtaining refined translation parameters according to the main weight, the translation parameters, the secondary weight and the secondary translation parameters;
wherein, the translation parameter is (x) 0 ,y 0 ) In the case of (a), (b), (c) is obtained after the four-neighbor domains of the translation parameter are shifted by one unit pixel, respectively 1 ,y 0 )、(x -1 ,y 0 )、(x 0 ,y 1 ) And (x) 0 ,y -1 )；
The secondary translation parameter is
And
the main weight is
And
the secondary weight is
And
A 0 is (x) 0 ,y 0 ) Corresponding amplitude values in the amplitude spectrum function,
is composed of
The corresponding amplitude value in the amplitude spectrum function,
is composed of
Corresponding amplitude values in the amplitude spectrum function;
the refined translation parameters are (x ', y');
2. the method of claim 1, wherein the filtering the laser point cloud data comprises:
filtering the laser point cloud data according to a random consistency sampling algorithm to obtain laser point cloud data after the ground is filtered;
and filtering the laser point cloud data after the ground is filtered according to the region-of-interest algorithm to obtain the laser point cloud data after the ground and the dynamic object are filtered.
3. The method of claim 1, wherein prior to substituting the laser raster data and the raster data to be matched into the polar function, the method further comprises:
and filtering the laser raster data according to a high-pass filter to obtain the filtered laser raster data.
4. The method of claim 1, wherein prior to rotating the raster data to be matched according to the rotation parameters, the method further comprises:
and processing the rotation parameters according to a Kalman filtering algorithm to obtain refined rotation parameters.
5. A matching device for laser positioning, comprising:
the acquisition module is used for acquiring laser point cloud data based on a laser positioning image and grid data to be matched based on an image to be matched;
the rasterization module is used for filtering the laser point cloud data and processing the laser point cloud data after noise is filtered according to a rasterization algorithm to obtain laser raster data;
the rotation matching module is used for matching the laser raster data with the raster data to be matched according to a phase correlation algorithm to obtain rotation parameters, and the rotation parameters are used for explaining the rotation relation between the laser positioning image and the image to be matched;
the translation matching module is used for rotating the raster data to be matched according to the rotation parameters to obtain rotated raster data, matching the laser raster data with the rotated raster data according to the phase correlation algorithm to obtain translation parameters, and the translation parameters are used for explaining the translation relation between the laser positioning image and the image to be matched;
the positioning module is used for obtaining actual positioning data according to the rotation parameters, the translation parameters and the to-be-matched raster data;
wherein, rotatory matching module specifically includes:
the device comprises an acquisition unit, a matching unit and a matching unit, wherein the acquisition unit is used for acquiring a mapping function which is used for indicating the rotation and translation relation between a laser positioning image and an image to be matched;
the phase correlation unit is used for carrying out Fourier transform on the mapping function to obtain a magnitude spectrum function;
the coordinate system interaction unit is used for carrying out interaction processing on the amplitude spectrum function to obtain a polar coordinate function;
the positioning module specifically comprises:
the refinement unit is used for processing the translation parameters according to a sub-pixel algorithm and obtaining refined translation parameters;
the positioning unit is used for obtaining actual positioning data according to the rotation parameters, the grid data to be matched and the refined translation parameters;
the refinement unit specifically comprises:
an offset subunit, configured to offset the translation parameter by one unit pixel to obtain a sub-translation parameter;
the weighting subunit is used for obtaining a main weight and a secondary weight according to the amplitude spectrum function, the translation parameter and the secondary translation parameter, wherein the main weight corresponds to the translation parameter, and the secondary weight corresponds to the secondary translation parameter;
the refinement subunit is used for obtaining refined translation parameters according to the main weight, the translation parameters, the secondary weight and the secondary translation parameters;
wherein, the translation parameter is (x) 0 ,y 0 ) In the case of (2), the (x) is obtained after the four-neighbor domains of the translation parameter are respectively shifted by one unit pixel 1 ,y 0 )、(x -1 ,y 0 )、(x 0 ,y 1 ) And (x) 0 ,y -1 )；
The secondary translation parameter is
And
the main weight is
And
the secondary weight is
And
A 0 is (x) 0 ,y 0 ) The corresponding amplitude value in the amplitude spectrum function,
is composed of
Corresponding amplitude values in the amplitude spectrum function,
is composed of
Corresponding amplitude values in the amplitude spectrum function;
the refined translation parameters are (x ', y');
6. a computer device, characterized in that the computer device comprises:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the matching method for laser positioning as claimed in any one of claims 1-4.
7. A computer-readable storage medium, on which a computer program is stored, the computer program comprising program instructions, characterized in that the program instructions, when executed by a processor, implement the matching method for laser positioning according to any of claims 1-4.
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