Method for improving 2D laser radar point cloud data based on adaptive Fourier decomposition

Abstract

The invention relates to a method for improving 2D laser radar point cloud data based on adaptive Fourier decomposition, and the method comprises the following steps: carrying out the interpolation processing of obtained original low-precision 2D laser radar point cloud data, so as to match the data density of high-precision 2D laser radar point cloud data; converting the interpolated low-precision 2D laser radar point cloud data into a linear curve, performing decomposition through adaptive Fourier decomposition, and extracting M components; the M components are divided into N groups, each group comprises K components, one component group is gradually removed from the high-frequency component, and the remaining components are combined to obtain second low-precision 2D laser radar point cloud data; calculating a root mean square error between the second low-precision 2D laser radar point cloud data in different elimination stages and the obtained original high-precision 2D laser radar point cloud data; comparing root-mean-square errors corresponding to different rejection stages, and selecting a component combination of the root-mean-square errors as an optimal component combination; and recombining the low-precision 2D laser radar point cloud data by using the optimal component combination.

Description

Technical Field

[0001] The present invention relates to the field of laser radar technology, and in particular to a method for improving 2D laser radar point cloud data based on adaptive Fourier decomposition. Background Art

[0002] In the current technology field, 2D LIDAR is widely used in autonomous driving, robot navigation, environmental monitoring, terrain mapping and other fields because of its ability to provide environmental scanning and object detection. With the development of technology, the accuracy and reliability of LIDAR data are increasingly required, which has promoted the rapid development of high-precision LIDAR technology. However, the cost of high-precision LIDAR is relatively high, which limits its widespread application in cost-sensitive projects.

[0003] At the same time, although low-cost 2D LiDAR is widely used, it faces the problem of insufficient data quality, such as sparse point cloud data, large errors, and high noise. These problems seriously limit its use in precise application scenarios. Therefore, how to improve the data quality of low-cost LiDAR without significantly increasing costs has become a key technical problem that needs to be solved in this field of technology.

[0004] At present, in response to the above problems, academia and industry have tried a variety of data processing technologies, including but not limited to data interpolation, filtering and denoising, feature enhancement and other methods to improve the quality of low-precision lidar data. However, these methods often introduce new problems while improving data quality, such as over-smoothing and feature loss, and the processing efficiency and effect still need to be improved. Therefore, developing a new method that can effectively improve the quality of low-precision lidar data while maintaining data features and improving processing efficiency is an important research direction in this technical field. Summary of the invention

[0005] In order to solve the problem of poor processing efficiency and effect in the prior art when improving the quality of low-precision laser radar data, the purpose of the present invention is to provide a method for improving 2D laser radar point cloud data based on adaptive Fourier decomposition. The technical solution adopted is as follows:

[0006] A method for improving 2D laser radar point cloud data based on adaptive Fourier decomposition, the method comprising the following steps:

[0007] Obtain original high-precision 2D lidar point cloud data and original low-precision 2D lidar point cloud data under the same environment;

[0008] Interpolate the original low-precision 2D LiDAR point cloud data to increase the 2D LiDAR point cloud to match the data density of the high-precision 2D LiDAR point cloud data;

[0009] The low-precision 2D lidar point cloud data after interpolation is converted into a linear curve, and decomposed by adaptive Fourier decomposition to extract M components;

[0010] The extracted M components are divided into N groups, each group has K components, one group of components is gradually eliminated starting from the high-frequency components, and the remaining components are merged to obtain the second low-precision 2D lidar point cloud data;

[0011] Calculate the root mean square error between the second lowest precision 2D lidar point cloud data at different elimination stages and the original high precision 2D lidar point cloud data;

[0012] Compare the root mean square errors corresponding to different elimination stages, and select the component combination of the root mean square error as the optimal component combination;

[0013] Reconstruct low-precision 2D lidar point cloud data using optimal component combination.

[0014] Preferably, the cubic spline interpolation method is used for interpolation processing.

[0015] Preferably, the low-precision 2D laser radar point cloud data after interpolation processing is converted into a linear curve, and decomposed by adaptive Fourier decomposition to extract M components, including:

[0016] Use Adaptive Basis Each basis function B n is called the modified Blaschke product and is defined as:

[0017]

[0018] where a n ∈D={z∈C:|z|<1}, C is the complex plane;

[0019] Initialization: Set the initial signal to f0(t);

[0020] Iterative decomposition: In each iteration, an optimal parameter a is selected n Make the energy converge as fast as possible, and calculate the next signal f through the recursive formula n+1 (t):

[0021] f n+1 (t) = f n (t)- <f n ,B n >B n

[0022] in, <f n ,B n > represents the signal f n(t) and basis function B n (t) Inner product.

[0023] Preferably, a group of component groups are gradually eliminated starting from the high-frequency components, and the method further comprises: denoising in combination with a wavelet transform method and / or a median filter method.

[0024] A system for improving 2D laser radar point cloud data based on adaptive Fourier decomposition, the system comprising:

[0025] An acquisition module is used to acquire original high-precision 2D laser radar point cloud data and original low-precision 2D laser radar point cloud data under the same environment;

[0026] An interpolation processing module is used to interpolate the original low-precision 2D lidar point cloud data using cubic spline interpolation to increase the 2D lidar point cloud to match the data density of the high-precision 2D lidar point cloud data;

[0027] The adaptive Fourier decomposition module is used to convert the low-precision 2D lidar point cloud data after interpolation processing into a linear curve, and decompose it through adaptive Fourier decomposition to extract M components;

[0028] A grouping and elimination module is used to divide the extracted M components into N groups, each group has K components, gradually eliminate one group of component groups starting from the high-frequency components, and merge the remaining components to obtain the second low-precision 2D lidar point cloud data;

[0029] A root mean square error calculation module, used to calculate the root mean square error between the second low-precision 2D lidar point cloud data at different elimination stages and the original high-precision 2D lidar point cloud data;

[0030] A comparison module is used to compare the root mean square errors corresponding to different elimination stages, and select the component combination of the root mean square error as the optimal component combination;

[0031] Recombination optimization module, used to recombine low-precision 2D lidar point cloud data using optimal component combinations.

[0032] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for enhancing 2D laser radar point cloud data based on adaptive Fourier decomposition as described above is implemented.

[0033] A computer-readable storage medium stores a computer program, which, when executed by a processor, enables the processor to perform the method for enhancing 2D laser radar point cloud data based on adaptive Fourier decomposition as described above.

[0034] The present invention has the following beneficial effects:

[0035] The present invention introduces adaptive Fourier decomposition (AFD) to solve the problems of data sparsity and insufficient precision in the prior art, and the loss of some important signal information while denoising, making it difficult to find a good balance between noise reduction and retaining data features. As a powerful signal processing tool, adaptive Fourier decomposition AFD can carefully decompose signals, more accurately identify and remove noise components, while retaining key signal features. Through this method, the present invention can effectively improve the quality of low-precision 2D lidar point cloud data, solve the problems of data sparsity, large errors and noise, without the need for complex parameter adjustments, with relatively low computational cost and easy implementation. In addition, the AFD method does not rely on a large amount of training data, which makes the present invention have good applicability and generalization ability in different application scenarios.

[0036] The present invention uses an adaptive Fourier decomposition (AFD) processing method to improve the quality of point cloud data collected by low-cost, low-precision 2D LiDAR, making it closer to the performance of high-cost, high-precision LiDAR in terms of accuracy and data integrity. This technology is not only suitable for application scenarios in the fields of autonomous driving, robot navigation, environmental monitoring, etc. that have high requirements for LiDAR data accuracy, but can also provide support for LiDAR-based terrain mapping, three-dimensional reconstruction of buildings, and heritage protection. It especially demonstrates its important value and application potential in cost-sensitive projects. By improving the quality of low-precision LiDAR data, the present invention aims to broaden the scope of application of LiDAR technology and improve its practicality and economic benefits in multiple technical fields. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0038] Figure 1 A flowchart of a method for enhancing 2D lidar point cloud data based on adaptive Fourier decomposition provided by an embodiment of the present invention.

[0039] Figure 2 It is a schematic diagram of the experimental environment of the present invention.

[0040] Figure 3 It is a schematic diagram of the original low-precision 2D lidar point cloud data collected by the low-precision 2D lidar.

[0041] Figure 4 It is a schematic diagram of the original high-precision 2D lidar point cloud data collected by the high-precision 2D lidar.

[0042] Figure 5 It is a schematic diagram of low-precision 2D lidar point cloud data after interpolation processing.

[0043] Figure 6 It is to delete a set of components and merge the remaining components to obtain the point cloud data map.

[0044] Figure 7 It is an operation flow chart of the method of the present invention.

[0045] Figure 8 A schematic diagram of a system for enhancing 2D lidar point cloud data based on adaptive Fourier decomposition provided by an embodiment of the present invention.

[0046] Fig. 9 A functional block diagram of a computer device provided in accordance with an embodiment of the present invention. DETAILED DESCRIPTION

[0047] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the method for enhancing 2D lidar point cloud data based on adaptive Fourier decomposition proposed by the present invention, its specific implementation method, structure, features and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0048] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0049] As pointed out in the background technology, for low-precision 2D LiDAR point cloud data, there are problems of data sparsity and insufficient precision. Although the interpolation method is currently used to increase data density, it is often unable to effectively improve the accuracy and quality of low-cost 2D LiDAR data, especially when dealing with sparse point cloud data and large errors. Regarding the balance between noise removal and feature retention, existing signal denoising technologies (such as wavelet transform denoising) and feature-preserving denoising methods (such as PCA) often lose some important signal information while denoising, making it difficult to find a good balance between denoising and retaining data features.

[0050] Although the currently widely used deep learning-based denoising methods perform well in processing complex data structures, they have significant limitations. First, deep learning models rely on a large amount of training data, and their training process is usually time-consuming and computationally demanding. In addition, the generalization ability of these models strongly depends on the quality and diversity of the training data, which limits their effectiveness in different application scenarios.

[0051] In contrast, the adaptive Fourier decomposition (AFD) method used in the present invention shows obvious advantages in optimizing the processing of two-dimensional (2D) lidar point cloud data. AFD can analyze the local characteristics of the signal more finely and effectively remove noise by screening and reorganizing components while retaining key signal information. Importantly, the AFD method does not need to rely on a large amount of training data, thereby reducing computational costs and simplifying the implementation process. Therefore, the present invention can not only effectively improve the quality of low-cost lidar data, but is also particularly suitable for application scenarios with limited computing resources and costs, such as use in cost-sensitive projects.

[0052] The following is a specific solution of a method for improving 2D lidar point cloud data based on adaptive Fourier decomposition provided by the present invention, which is described in detail with reference to the accompanying drawings.

[0053] like Figure 1 As shown, a method for improving 2D laser radar point cloud data based on adaptive Fourier decomposition comprises the following steps:

[0054] Obtain original high-precision 2D lidar point cloud data and original low-precision 2D lidar point cloud data under the same environment;

[0055] Interpolate the original low-precision 2D LiDAR point cloud data to increase the 2D LiDAR point cloud to match the data density of the high-precision 2D LiDAR point cloud data;

[0056] The low-precision 2D lidar point cloud data after interpolation is converted into a linear curve, and decomposed by adaptive Fourier decomposition to extract M components;

[0057] The extracted M components are divided into N groups, each group has K components, one group of components is gradually eliminated starting from the high-frequency components, and the remaining components are merged to obtain the second low-precision 2D lidar point cloud data;

[0058] Calculate the root mean square error between the second lowest precision 2D lidar point cloud data at different elimination stages and the original high precision 2D lidar point cloud data;

[0059] Compare the root mean square errors corresponding to different elimination stages, and select the component combination of the root mean square error as the optimal component combination;

[0060] Reconstruct low-precision 2D lidar point cloud data using optimal component combination.

[0061] In a specific embodiment, two 2D laser radars with different accuracies (low-precision N10 and high-precision M10) are used to collect point cloud data in the same experimental environment. Figure 2 It is the experimental environment for low-precision N10 and high-precision M10.

[0062] The original low-precision 2D lidar point cloud data generated by the low-precision N10 lidar is relatively sparse (448 points), such as Figure 3 shown.

[0063] The M10 LiDAR provides denser raw high-precision 2D LiDAR point cloud data (1656 points), such as Figure 4 shown.

[0064] This embodiment uses cubic spline interpolation to interpolate the original low-precision 2D laser radar point cloud data collected by the low-precision N10 laser radar, increasing the data points to 1656 points to match the data density of the original high-precision 2D laser radar point cloud data acquired by the high-precision M10 laser radar. This step is intended to improve the sparsity of the point cloud data and provide a more continuous and uniform data basis for the subsequent adaptive Fourier decomposition processing. Figure 5 shown.

[0065] Among them, cubic spline interpolation is a commonly used interpolation method in mathematics and computer science, which is used to construct a smooth function through a set of discrete data points. Among many interpolation techniques, spline interpolation is often favored for its high flexibility and excellent approximation characteristics. It forms an overall smooth curve or surface by splicing multiple low-order polynomial functions to approximate or pass through all given data points. The following is the definition of the cubic spline interpolation function:

[0066] Suppose a partition is given on the interval [a,b]:

[0067] a=x0 <x1<…<x m-1 <x m =b,x m Indicates an interpolation point.

[0068] T(x) is a real-valued function if it satisfies the following conditions:

[0069] T(x) in the subinterval [x k ,x k+1 ], (k=0,1,…,m-1) is an m-degree polynomial;

[0070] T(x) has continuous derivatives up to order m-1 on [a,b], that is, T(x)∈C m-1 [a,b];

[0071] Then T(x) is called the m-order spline function, x0,x1,…,x m is an interpolation node

[0072] Features are as follows:

[0073] 1. The curve constructed by cubic spline interpolation not only has equal values ​​at all connection points, but also has continuous first-order and second-order derivatives, so the overall curve is very smooth.

[0074] 2. Local controllability: Each segment of the polynomial is determined only by the data points at its two ends, so when modifying or adding data points, it will only affect the adjacent curve segments.

[0075] 3. Flexibility and adaptability: Cubic spline interpolation is suitable for various scenarios that require smooth curve approximation, and is widely used in both scientific research and engineering design.

[0076] Cubic spline interpolation: Given m+1 interpolation nodes x on the interpolation interval [a,b] k (a=x0 <x1<…<x m-1 <x m = b), at node x k The function y at k =f(x k )(k=0,1,…,m), if the interpolation function s(x) satisfies the following conditions:

[0077] 1.s(x k )=y(x k )=y k (k=0,1,…,m);

[0078] 2. In each small interval [x k ,x k+1 ](k=0,1,…,m-1) where s(x) is a cubic polynomial s k (x);

[0079] 3.s(x) is second-order continuously differentiable on [a,b], that is, s(x)∈C 2 [a+b], C represents continuity, C 2 represents a second-order continuously differentiable function.

[0080] Then s(x) is called the cubic spline interpolation function of f(x).

[0081] It should be noted that the low-precision 2D lidar point cloud data after interpolation processing is also a continuous signal.

[0082] The adaptive Fourier decomposition (AFD) provided in this embodiment converts the low-precision 2D lidar point cloud data after interpolation processing into a linear curve, decomposes it through adaptive Fourier decomposition AFD, and extracts 100 components. The purpose of adaptive Fourier decomposition AFD is to separate the main components and noise components of the signal to facilitate subsequent noise removal and data quality improvement.

[0083] The 100 extracted components are divided into 10 groups, each with 10 components. Start by gradually eliminating the high-frequency component group that is most likely to contain noise. Generally, start deleting from the tail because high-frequency components usually contain more noise in adaptive Fourier decomposition (AFD). High-frequency components correspond to the fastest changing part of the signal, which is often the area where noise is concentrated. By deleting from the tail (high-frequency components), noise can be effectively removed without significantly affecting the main features of the original signal. In signal processing, noise usually appears as high-frequency components. High-frequency components correspond to the rapidly changing part of the signal, and noise is usually high-frequency. Starting from the high-frequency components, a group of component groups are gradually eliminated, that is, first remove the component groups of 91-100, and then merge the remaining components to obtain the second low-precision 2D lidar point cloud data, such as Figure 6 shown.

[0084] In this embodiment, the adaptive Fourier decomposition (AFD) is a signal processing method evolved from traditional Fourier decomposition, which achieves rapid energy convergence by using an adaptive basis. AFD is mainly used to decompose signals so that they can analyze and process local characteristics of signals in more detail. Adaptive Fourier decomposition (AFD) is a complex signal decomposition technology that plays an important role in digital signal processing (DSP). Unlike traditional Fourier transform, AFD can adapt to the local characteristics of signals and is particularly suitable for non-stationary signals whose frequency components change over time. This method adaptively interpolates the lidar data into a continuous signal, decomposes the signal into a linear combination of sine and cosine components of different frequencies, thereby enhancing its application in various fields such as lidar technology.

[0085] The low-precision 2D lidar point cloud data after interpolation is converted into a linear curve and decomposed through adaptive Fourier decomposition to extract M components, including:

[0086] Use Adaptive Basis Each basis function B n is called the modified Blaschke product and is defined as:

[0087]

[0088] where a n∈D={z∈C:|z|<1}, C is the complex plane;

[0089] D represents the unit disk on the complex plane, usually defined as D = {z∈C:|z|<1}; Z represents a complex variable; e jt Represents the complex exponential function, representing a complex number on the unit circle, often used to represent rotational or oscillatory signals, which can be viewed as a function of time t, often associated with Fourier transforms or frequency domain analysis.

[0090] Initialization: Set the continuous signal obtained after initial interpolation to f0(t);

[0091] Iterative decomposition: In each iteration, an optimal parameter a is selected n Make the energy converge as fast as possible, and calculate the next signal f through the recursive formula n+1 (t):

[0092] f n+1 (t) = f n (t)- <f n ,B n >B n

[0093] in, <f n ,B n > represents the signal f n (t) and basis function B n (t) Inner product.

[0094] The selection of adaptive basis is the key to AFD. The optimal basis function is selected through the matching pursuit algorithm. In each iteration, the basis function with the fastest energy convergence is selected according to the characteristics of the current signal, so that the signal can be quickly decomposed in a fewer number of iterations. By selecting an adaptive basis, AFD achieves faster energy convergence than traditional Fourier decomposition. This means that the main features of the signal can be captured in fewer iterations, thereby improving the efficiency and accuracy of signal processing.

[0095] In this embodiment, a group of component groups is gradually eliminated starting from the high-frequency component, and the method further includes: denoising in combination with a wavelet transform method and / or a median filter method.

[0096] Specifically, the operation flow chart of the method for improving 2D laser radar point cloud data based on adaptive Fourier decomposition provided by the present invention is as follows: Figure 7 Each step is as described above and will not be described in detail here.

[0097] Example 2

[0098] like Figure 8 As shown, a system for improving 2D laser radar point cloud data based on adaptive Fourier decomposition, the system comprising:

[0099] An acquisition module is used to acquire original high-precision 2D laser radar point cloud data and original low-precision 2D laser radar point cloud data under the same environment;

[0100] An interpolation processing module is used to interpolate the original low-precision 2D lidar point cloud data using cubic spline interpolation to increase the 2D lidar point cloud to match the data density of the high-precision 2D lidar point cloud data;

[0101] The adaptive Fourier decomposition module is used to convert the low-precision 2D lidar point cloud data after interpolation processing into a linear curve, and decompose it through adaptive Fourier decomposition to extract M components;

[0102] A grouping and elimination module is used to divide the extracted M components into N groups, each group has K components, gradually eliminate one group of component groups starting from the high-frequency components, and merge the remaining components to obtain the second low-precision 2D lidar point cloud data;

[0103] A root mean square error calculation module, used to calculate the root mean square error between the second low-precision 2D lidar point cloud data at different elimination stages and the original high-precision 2D lidar point cloud data;

[0104] A comparison module is used to compare the root mean square errors corresponding to different elimination stages, and select the component combination of the root mean square error as the optimal component combination;

[0105] Recombination optimization module, used to recombine low-precision 2D lidar point cloud data using optimal component combinations.

[0106] See also Fig. 9 , Fig. 9 5 is a schematic block diagram of a computer device provided by an embodiment of the present invention. The computer device 500 is a server, which can be an independent server or a server cluster composed of multiple servers.

[0107] See also Fig. 9 The computer device 500 includes a processor 502 , a memory and a network interface 505 connected via a system bus 501 , wherein the memory may include a non-volatile storage medium 503 and an internal memory 504 .

[0108] The non-volatile storage medium 503 can store an operating system 5031 and a computer program 5032. When the computer program 5032 is executed, the processor 502 can execute a multi-audio data intelligent processing method.

[0109] The processor 502 is used to provide computing and control capabilities to support the operation of the entire computer device 500 .

[0110] The internal memory 504 provides an environment for the operation of the computer program 5032 in the non-volatile storage medium 503. When the computer program 5032 is executed by the processor 502, the processor 502 can execute the multi-audio data intelligent processing method.

[0111] The network interface 505 is used for network communication, such as providing data information transmission, etc. Those skilled in the art will understand that Fig. 9 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device 500 to which the solution of the present invention is applied. The specific computer device 500 may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0112] The processor 502 is used to run a computer program 5032 stored in the memory to implement the multi-audio data intelligent processing method disclosed in the embodiment of the present invention.

[0113] Those skilled in the art will understand that Fig. 9 The embodiments of the computer device shown in the figure do not constitute a limitation on the specific composition of the computer device. In other embodiments, the computer device may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently. For example, in some embodiments, the computer device may only include a memory and a processor. In such embodiments, the structure and function of the memory and the processor are the same as those of the embodiment of the present invention. Fig. 9 The embodiments shown are consistent and will not be described again here.

[0114] It should be understood that in the embodiment of the present invention, the processor 502 may be a central processing unit (CPU), and the processor 502 may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0115] In another embodiment of the present invention, a computer-readable storage medium is provided. The computer-readable storage medium may be a non-volatile computer-readable storage medium. The computer-readable storage medium stores a computer program, wherein when the computer program is executed by a processor, the multi-audio data intelligent processing method disclosed in the embodiment of the present invention is implemented.

[0116] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, the specific working process of the above-described equipment, devices and units can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here. Those of ordinary skill in the art can appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented with electronic hardware, computer software or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to the function. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0117] In the several embodiments provided by the present invention, it should be understood that the disclosed equipment, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. Units with the same function may also be combined into one unit. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interfaces, devices or units, or may be an electrical, mechanical or other form of connection.

[0118] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments of the present invention.

[0119] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0120] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), disk or optical disk and other media that can store program codes.

[0121] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0122] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A method for improving 2D laser radar point cloud data based on adaptive Fourier decomposition, characterized by: The method comprises the following steps: Obtain original high-precision 2D lidar point cloud data and original low-precision 2D lidar point cloud data under the same environment; Interpolate the original low-precision 2D LiDAR point cloud data to increase the 2D LiDAR point cloud to match the data density of the high-precision 2D LiDAR point cloud data; The low-precision 2D lidar point cloud data after interpolation is converted into a linear curve, and decomposed by adaptive Fourier decomposition to extract M components; The extracted M components are divided into N groups, each group has K components, one group of components is gradually eliminated starting from the high-frequency components, and the remaining components are merged to obtain the second low-precision 2D lidar point cloud data; Calculate the root mean square error between the second lowest precision 2D lidar point cloud data at different elimination stages and the original high precision 2D lidar point cloud data; Compare the root mean square errors corresponding to different elimination stages, and select the component combination of the root mean square error as the optimal component combination; Reconstruct low-precision 2D lidar point cloud data using optimal component combination.

2. The method for improving 2D laser radar point cloud data based on adaptive Fourier decomposition according to claim 1, characterized in that: The cubic spline interpolation method is used for interpolation.

3. The method for improving 2D laser radar point cloud data based on adaptive Fourier decomposition according to claim 1, characterized in that: The low-precision 2D lidar point cloud data after interpolation is converted into a linear curve and decomposed through adaptive Fourier decomposition to extract M components, including: Use Adaptive Basis Each basis function B n is called the modified Blaschke product and is defined as: where a n ∈D={z∈C:|z|<1}, C is the complex plane; Initialization: Set the initial signal to f0(t); Iterative decomposition: In each iteration, an optimal parameter a is selected n Make the energy converge as fast as possible, and calculate the next step signal f through the recursive formula n+1 (t): f n+1 (t)=f n (t)-<f n ,B n >B n in, <f n ,B n > represents the signal f n (t) and basis function B n (t) Inner product.

4. The method for improving 2D laser radar point cloud data based on adaptive Fourier decomposition according to claim 1, characterized in that: A group of component groups is gradually eliminated starting from the high frequency component, and the method further comprises: denoising in combination with a wavelet transform method and / or a median filter method.

5. A system for improving 2D laser radar point cloud data based on adaptive Fourier decomposition, characterized by: The system comprises: An acquisition module is used to acquire original high-precision 2D laser radar point cloud data and original low-precision 2D laser radar point cloud data under the same environment; An interpolation processing module is used to interpolate the original low-precision 2D lidar point cloud data using cubic spline interpolation to increase the 2D lidar point cloud to match the data density of the high-precision 2D lidar point cloud data; The adaptive Fourier decomposition module is used to convert the low-precision 2D lidar point cloud data after interpolation processing into a linear curve, and decompose it through adaptive Fourier decomposition to extract M components; A grouping and elimination module is used to divide the extracted M components into N groups, each group has K components, gradually eliminate one group of component groups starting from the high-frequency components, and merge the remaining components to obtain the second low-precision 2D lidar point cloud data; A root mean square error calculation module, used to calculate the root mean square error between the second low-precision 2D lidar point cloud data at different elimination stages and the original high-precision 2D lidar point cloud data; A comparison module is used to compare the root mean square errors corresponding to different elimination stages, and select the component combination of the root mean square error as the optimal component combination; Recombination optimization module, used to recombine low-precision 2D lidar point cloud data using optimal component combinations.

6. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, it implements the method for enhancing 2D lidar point cloud data based on adaptive Fourier decomposition as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which, when executed by a processor, enables the processor to perform the method for enhancing 2D lidar point cloud data based on adaptive Fourier decomposition as described in any one of claims 1 to 4.

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