CN111680263A - Laser radar shaking error compensation method - Google Patents
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Abstract
The invention relates to a laser radar shaking error compensation method which includes the steps of firstly carrying out nonlinear operation on laser radar point cloud data, then carrying out point cloud intensity homogenization and point selection, carrying out cyclic displacement, carrying out fast Fourier transform after windowing, carrying out iterative error compensation according to phase gradient estimation, and finally outputting compensated point cloud data. The method designs an algorithm from the aspect of a software system, starts from the characteristics of the laser radar by utilizing a nonlinear phase gradient self-focusing method, saves the consumption of hardware resources, reduces the requirements on other sensing precision and the real-time property, reduces the cost of error compensation, can enhance the precision of the error compensation under the severe environment of generating jitter during the running of a ship, and improves the effectiveness of data.
Description
Technical Field
The invention relates to the technical field of radar information, in particular to a nonlinear phase gradient self-focusing laser radar shaking error compensation method.
Background
Under the complex condition of a water surface environment, particularly a marine environment, a laser radar installed on a ship can generate certain jitter along with the running of the ship, point cloud data generated by the laser radar partially lacks data discontinuity due to jitter, and the data missing can cause misjudgment of the ship on obstacles, so that the navigation safety is threatened. The existing solution is to compensate the jitter of the laser radar in hardware, install a six-degree-of-freedom servo system, and keep the laser radar from being affected by the jitter of the ship through the hardware. The six-degree-of-freedom servo system is high in cost and cannot solve data errors generated when the laser radar rotates.
In summary, the existing software system method compensates from the six-degree-of-freedom motion direction of the ship, and the obtained result still has a certain difference from the real data and needs real-time six-degree-of-freedom basic data information. The existing error compensation method in a hardware mode is high in cost, limited in hardware resources, and more things need to be done under the limited resources, the traditional error compensation method has high requirements on the precision of other sensors and the real-time performance of communication, and the problems of time efficiency reduction, inaccurate precision and the like of an information terminal are easily caused under the severe environment that jitter is generated in the running process of a ship.
Disclosure of Invention
The present invention is directed to a method for compensating for a laser radar shaking error, so as to solve the above-mentioned problems encountered in the background art.
In order to achieve the purpose, the technical scheme of the invention is as follows:
a laser radar shaking error compensation method comprises the following steps:
s1, inputting laser radar point cloud data intensity matrix X ═ X1,x2,...,xn]Wherein x is1,x2,...,xnCarrying out nonlinear operation on the intensity of the point cloud data for the intensity information of each point of the point cloud data, wherein the nonlinear operation is lnx2Obtaining a nonlinear matrix Y ═ Y1,y1,...,yn]Wherein y is lnx2；
S2, dividing the whole area of the point cloud data equally by a grid method, selecting the point with the maximum y in each cell in the grid,
wherein i is a small square grid obtained by a grid, i is 1,2, …, N, and m is the number of point clouds in the small square grid;
s3, step S2
Performing cyclic displacement to make the cyclic displacement be in the center of the square in the grid;
s4, windowing the transverse center of each square grid, and selecting the initial window length N when windowingαThe specific window function is f (x) exp (j pi λ T), j is an imaginary function, λ is the scanning frequency of the lidar, T is a window variable,
the matrix data obtained after windowing is Yp＝YiF (x), wherein p ═ 1,2, …, N, thus Yp＝[yp1,yp2,...,ypm]；
S5, carrying out Fast Fourier Transform (FFT), Y on the windowed dataω＝FFT(Yp),ω＝1,2,...,N，Yω＝[yω1,yω2,...,yωm]；
S6, estimating the phase error by utilizing the maximum likelihood estimation to obtain the gradient value phi (omega) of the phase error,
s7, integrating the gradient of the phase estimation error obtained in step S6 to obtain a phase error function;
s8, multiplying the phase error function and the original phase data frequency spectrum to obtain the frequency spectrum of the iterative error compensation data
Φ(0)＝FFT(Xω)，XωFor the matrix data after the trellis according to S2, Xω＝[xω1,xω2,...,xωm]；
S9, carrying out inverse Fourier transform on the frequency spectrum data to obtain the compensated point cloud data intensity
Further, in step S2, the grid method includes the steps of:
a: the side length L of the squares in the grid is determined,
wherein λ is a scale factor, s is a scale coefficient, and g is the number of point cloud data;
b: dividing the point cloud data into each grid in the grid, wherein m × n × L grid point cloud data are shared, m is the number of long-edge grids, and n is the number of short-edge grids;
c: according to a coding calculation formula
Solving the grid code of each data point of the point cloud data model, carrying out hash chain listing on the code, establishing the spatial topological relation of the point cloud data space, and determining the adjacent point of each data point;
d: and establishing an index table for the grid where each data point of the point cloud data is located, and determining that the grid index corresponds to the point cloud data points one to one.
Further, in step S2, the grid method is used so that the side length L is 10 m.
Further, in step S4, the window length decreases as the number of iterations t increases, and the window length N at different iterationst＝Nα/3t-1。
Compared with the prior art, the invention has the beneficial effects that: the method designs an algorithm from the aspect of a software system, starts from the characteristics of the laser radar by utilizing a nonlinear phase gradient self-focusing method, saves the consumption of hardware resources, reduces the requirements on other sensing precision and the real-time requirement, and reduces the cost of error compensation. Through analysis of radar point cloud data, non-model phase nonlinear compensation is carried out on the data by a nonlinear phase gradient self-focusing method, so that the error of the laser radar is compensated. Through the characteristics of the laser radar, the requirement for real-time transmission of six-degree-of-freedom data information is lowered, consumption of limited hardware resources is reduced, accuracy of error compensation can be enhanced under the severe environment that the ship shakes during running, and effectiveness of data is improved.
Drawings
FIG. 1 is a schematic flow chart of a laser radar shaking error compensation method according to the present invention;
Detailed Description
The technical solution of the present invention is further described in detail with reference to the accompanying drawings and examples.
As shown in fig. 1, a laser radar shake error compensation method includes the following steps:
s1, inputting laser radar point cloud data intensity matrix X ═ X1,x2,...,xn]Wherein x is1,x2,...,xnCarrying out nonlinear operation on the intensity of the point cloud data for the intensity information of each point of the point cloud data, wherein the nonlinear operation is lnx2Obtaining a nonlinear matrix Y ═ Y1,y1,...,yn]Wherein y is lnx2。
S2, dividing the whole area of the point cloud data equally by a grid method, selecting the point with the maximum y in each cell in the grid,
wherein i is a small grid obtained by a grid, i is 1,2, …, N, and m is the number of point clouds in the small grid.
S3, step S2
A cyclic shift is performed so that it is centered on the square in the grid. The cyclic displacement is to carry out left displacement on the maximum value of the data energy in the matrix and lead the maximum value to be subjected to multiple cyclic displacements
And is positioned in the center of the square to eliminate Doppler shift caused by different positions of the reflection points.
S4, windowing the transverse center of each square grid in the grid, and selecting the transverse center in the windowing processInitial window length NαThe specific window function is f (x) exp (j pi λ T), j is an imaginary function, λ is the scanning frequency of the lidar, T is a window variable,
the matrix data obtained after windowing is Yp＝YiF (x), wherein p ═ 1,2, …, N, thus Yp＝[yp1,yp2,...,ypm]；
S5, carrying out Fast Fourier Transform (FFT), Y on the windowed dataω＝FFT(Yp),ω＝1,2,...,N，Yω＝[yω1,yω2,...,yωm]The fast fourier transform method is a common processing method for those skilled in the art, and is not described in detail herein.
S6, estimating the phase error by utilizing the maximum likelihood estimation to obtain the gradient value phi (omega) of the phase error,
S7, integrating the gradient of the phase estimation error obtained in step S6 to obtain a phase error function;
s8, multiplying the phase error function and the original phase data frequency spectrum to obtain the frequency spectrum of the iterative error compensation data
Φ(0)＝FFT(Xω)，XωFor the matrix data after the trellis according to S2, Xω＝[xω1,xω2,...,xωm]；
S9, carrying out inverse Fourier transform on the frequency spectrum data to obtain the compensated point cloud data intensity
Further, in step S2, the grid method includes the steps of:
a: the side length L of the squares in the grid is determined,
wherein, lambda is a scale factor used for adjusting the side length of the grid, s is a scale factor, and g is the number of point cloud data;
b: dividing the point cloud data into each grid in the grid, wherein m × n × L grid point cloud data are shared, m is the number of long-edge grids, and n is the number of short-edge grids;
c: according to a coding calculation formula
Solving the grid code of each data point of the point cloud data model, carrying out hash chain listing on the code, establishing the spatial topological relation of the point cloud data space, and determining the adjacent point of each data point;
d: and establishing an index table for the grid where each data point of the point cloud data is located, and determining that the grid index corresponds to the point cloud data points one to one.
Further, in step S2, the grid method is used so that the side length L is 10 m.
Further, in step S4, the window length decreases as the number of iterations t increases, and the window length N at different iterationst＝Nα/3t-1To preserve the ambiguity region due to phase error and to remove the phase error estimate of the noisy points.
The method designs an algorithm from the aspect of a software system, starts from the characteristics of the laser radar by utilizing a nonlinear phase gradient self-focusing method, saves the consumption of hardware resources, reduces the requirements on other sensing precision and the real-time requirement, and reduces the cost of error compensation. Through analysis of radar point cloud data, non-model phase nonlinear compensation is carried out on the data by a nonlinear phase gradient self-focusing method, so that the error of the laser radar is compensated. Through the characteristics of the laser radar, the requirement for real-time transmission of six-degree-of-freedom data information is lowered, consumption of limited hardware resources is reduced, accuracy of error compensation can be enhanced under the severe environment that the ship shakes during running, and effectiveness of data is improved.
The above-mentioned embodiments, objects, technical solutions and advantages of the present invention are further described in detail, it should be understood that the above-mentioned embodiments are only specific embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (4)
1. A laser radar shaking error compensation method is characterized by comprising the following steps:
s1, inputting laser radar point cloud data intensity matrix X ═ X1,x2,...,xn]Wherein x is1,x2,...,xnCarrying out nonlinear operation on the intensity of the point cloud data for the intensity information of each point of the point cloud data, wherein the nonlinear operation is lnx2Obtaining a nonlinear matrix Y ═ Y1,y1,...,yn]Wherein y is lnx2；
S2, dividing the whole area of the point cloud data equally by a grid method, selecting the point with the maximum y in each cell in the grid,
wherein i is a small square grid obtained by a grid, i is 1,2, …, N, and m is the number of point clouds in the small square grid;
s3, step S2
Performing cyclic displacement to make the cyclic displacement be in the center of the square in the grid;
s4, windowing the transverse center of each square grid, and selecting the initial window length N when windowingαThe specific window function is f (x) exp (j pi λ T), j is an imaginary function, λ is the scanning frequency of the lidar, T is a window variable,
the matrix data obtained after windowing is Yp＝YiF (x), wherein p ═ 1,2, …, N, thus Yp＝[yp1,yp2,...,ypm]；
S5, carrying out Fast Fourier Transform (FFT), Y on the windowed dataω＝FFT(Yp),ω＝1,2,...,N，Yω＝[yω1,yω2,...,yωm]；
S6, estimating the phase error by utilizing the maximum likelihood estimation to obtain the gradient value phi (omega) of the phase error,
s7, integrating the gradient of the phase estimation error obtained in step S6 to obtain a phase error function;
s8, multiplying the phase error function and the original phase data frequency spectrum to obtain the frequency spectrum of the iterative error compensation data
Φ(0)＝FFT(Xω)，XωFor the matrix data after the trellis according to S2, Xω＝[xω1,xω2,...,xωm]；
S9, carrying out inverse Fourier transform on the frequency spectrum data to obtain the compensated point cloud data intensity
2. The lidar shake error compensation method of claim 1, wherein: in step S2, the grid method includes the steps of:
a: the side length L of the squares in the grid is determined,
wherein λ is a scale factor, s is a scale coefficient, and g is the number of point cloud data;
b: dividing the point cloud data into each grid in the grid, wherein m × n × L grid point cloud data are shared, m is the number of long-edge grids, and n is the number of short-edge grids;
c: according to a coding calculation formula
Solving the grid code of each data point of the point cloud data model, carrying out hash chain listing on the code, establishing the spatial topological relation of the point cloud data space, and determining the adjacent point of each data point;
d: and establishing an index table for the grid where each data point of the point cloud data is located, and determining that the grid index corresponds to the point cloud data points one to one.
3. The lidar shake error compensation method of claim 1, wherein: in step S2, the grid method is used so that the side length L becomes 10 m.
4. The lidar shake error compensation method of claim 1, wherein: in step S4, the window length decreases as the number of iterations t increases, and the window length N at different iterationst＝Nα/3t-1。
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Citations (3)
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| JP2003194932A (en) * | 2001-12-27 | 2003-07-09 | Mitsubishi Electric Corp | Synthetic aperture radar apparatus and image reproducing method for synthetic aperture radar |
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| CN110531338A (en) * | 2019-10-12 | 2019-12-03 | 南京航空航天大学 | Multimode SAR self-focusing immediate processing method and system based on FPGA |
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Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2003194932A (en) * | 2001-12-27 | 2003-07-09 | Mitsubishi Electric Corp | Synthetic aperture radar apparatus and image reproducing method for synthetic aperture radar |
| CN105468375A (en) * | 2015-11-30 | 2016-04-06 | 扬州大学 | Surface structure light point cloud data oriented corresponding point search structure construction method |
| CN110531338A (en) * | 2019-10-12 | 2019-12-03 | 南京航空航天大学 | Multimode SAR self-focusing immediate processing method and system based on FPGA |
Non-Patent Citations (1)
| Title |
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| 张洁 等: "相位梯度自聚焦算法在合成孔径激光雷达中的应用与改进", 《激光与光电子学进展》 * |
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