Two-dimensional laser synchronous positioning and mapping optimization method, system, equipment, medium and product

Abstract

The invention discloses a two-dimensional laser synchronous positioning and mapping optimization method, system and device, a medium and a product, and relates to the field of graph optimizing.The method comprises the steps that laser scanning equipment is adopted to obtain laser scanning data, and linear systems at different moments are obtained based on the laser scanning data; decomposing the linear system into a position linear system and an angle linear system; respectively solving the position linear system and the angle linear system by adopting a Cholesky decomposition method to obtain position data of the laser scanning equipment; and completing map construction based on the position data of the laser scanning equipment. According to the method and the device, optimization can be quickly completed in a relatively short time when a large-scale pose map is optimized or in a special occasion, so that the optimization rate is improved.

Description

Technical Field

[0001] The present application relates to the field of map optimization, and in particular to a two-dimensional laser synchronous positioning and map optimization method, system, equipment, medium and product. Background Art

[0002] Backend nonlinear optimization is a very important part of the entire framework, especially when building maps. From the SLAM (Simultaneous Localization And Mapping) algorithm proposed by Smith, Self and Cheeseman in 1988 to the present, the algorithms for backend optimization can be roughly divided into two categories. One is the filter-based method, and the other is the nonlinear optimization method based on graph optimization. Early backend research was basically under the framework of filters, such as the Extended Kalman Filter (EKF) method, Particle Filter (PF), etc. However, since the filter method must be applied to linear systems, it has great limitations in nonlinear systems and large-scale scenarios, and the algorithm assumes Markov properties, which makes it difficult for the filter method to handle loop problems, so people are committed to finding more efficient optimization methods. With the deepening of research, traditional filter methods are gradually replaced by graph optimization-based methods. Graph optimization was first proposed by Lu et al. in 1997. The so-called graph optimization is a graph constructed with the postures of rigid bodies at different times as nodes and the transformation relationships between postures as edges, so it is also called a pose graph. Errors will accumulate in the process of graph construction, and the accumulated errors in the process are optimized by the least squares method. Olson, Grisetti et al. used the gradient descent method to optimize the pose graph, and extended the gradient descent method based on tree parameterization, which improved the convergence speed to a certain extent. Later, with the discovery of the sparsity of the system matrix, Dellaert et al. adopted a sparse direct linear solver The algorithm was then improved by Kaess et al., who incrementally updated the linear matrix related to the nonlinear least squares problem. However, the time of nonlinear optimization is proportional to the size of the pose graph constructed, that is, proportional to the size of the linear matrix constructed. When optimizing large pose graphs or in special situations, such as fast scene switching and limited computing time, it is required to complete the optimization quickly in a shorter time, but traditional pose graph optimization cannot meet this requirement. Summary of the invention

[0003] The purpose of this application is to provide a two-dimensional laser synchronous positioning and mapping optimization method, system, equipment, medium and product, which can quickly complete the optimization in a shorter time when optimizing large pose graphs or in special occasions, so as to improve the optimization rate.

[0004] To achieve the above objectives, this application provides the following solutions:

[0005] In a first aspect, the present application provides a two-dimensional laser synchronous positioning and mapping optimization method, comprising:

[0006] Using a laser scanning device to acquire laser scanning data, and obtaining a linear system at different times based on the laser scanning data;

[0007] Decomposing the linear system into a position linear system and an angle linear system;

[0008] The position linear system and the angle linear system are respectively solved by using Cholesky decomposition method to obtain the position data of the laser scanning device;

[0009] Map construction is completed based on the position data of the laser scanning device.

[0010] Optionally, a laser scanning device is used to acquire laser scanning data, and a linear system at different times is obtained based on the laser scanning data, specifically including:

[0011] Taking the laser scanning device as a node, the rotation matrix of the node and the rotation matrix of the edge are obtained from the position and posture of the node and the transformation relationship between the nodes;

[0012] The front-end odometer is used to obtain the position and posture of adjacent nodes, and the transformation relationship between adjacent nodes is obtained by combining the node rotation matrix and the edge rotation matrix;

[0013] Based on the transformation relationship between adjacent nodes, the error value is obtained by combining the rotation matrix of the node and the rotation matrix of the edge;

[0014] Obtaining a Jacobian matrix based on the error value, and obtaining an H matrix and a b vector based on the Jacobian matrix;

[0015] The linear system is constructed based on the H matrix and the b vector.

[0016] Optionally, the linear system is: HΔx=b;

[0017] Where H represents the H matrix, b represents the b vector, and Δx represents the vector composed of unknown quantities.

[0018] Optionally, decomposing the linear system into a position linear system and an angle linear system specifically includes:

[0019] Performing graph optimization representation on the H matrix and the b vector respectively to obtain a system matrix;

[0020] The linear system is decomposed into the position linear system and the angle linear system based on the system matrix.

[0021] Optionally, the position linear system and the angle linear system are respectively solved by using a Cholesky decomposition method to obtain position data of the laser scanning device, specifically including:

[0022] The Cholesky decomposition method is used to decompose the position linear system and the angle linear system into a lower triangular matrix and a transposed matrix of the lower triangular matrix, respectively, and an iterative solution is performed to obtain the position data of the laser scanning device.

[0023] In a second aspect, the present application provides a two-dimensional laser synchronous positioning and mapping optimization system, which is used to implement the two-dimensional laser synchronous positioning and mapping optimization method provided above; the system includes:

[0024] Laser scanning equipment, used for acquiring laser scanning data;

[0025] The loop detection device and the front-end odometer are both connected to the laser scanning device and are used to obtain a linear system at different times based on the laser scanning data;

[0026] A back-end optimization device is connected to the loop detection device and the front-end odometer, respectively, and is used to decompose the linear system into a position linear system and an angle linear system, and is used to solve the position linear system and the angle linear system respectively by using a Cholesky decomposition method to obtain position data of a laser scanning device;

[0027] A mapping device is connected to the back-end optimization device and is used to complete map construction based on the position data of the laser scanning device.

[0028] In a third aspect, the present application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the two-dimensional laser synchronous positioning and mapping optimization method provided above.

[0029] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-mentioned two-dimensional laser synchronous positioning and mapping optimization method.

[0030] In a fifth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the above-mentioned two-dimensional laser synchronous positioning and mapping optimization method.

[0031] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0032] The present application provides a two-dimensional laser synchronous positioning and mapping optimization method, system, device, medium and product. By decomposing a linear system into a position linear system and an angle linear system, the scale of the linear system can be greatly reduced, thereby improving the optimization rate. By using the Cholesky decomposition method to solve the position linear system and the angle linear system respectively, the solution rate of the linear system can be significantly improved, thereby improving the optimization rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0034] Figure 1 This is an application environment diagram of a two-dimensional laser synchronous positioning and mapping optimization method in an embodiment of the present application;

[0035] Figure 2 A schematic diagram of a flow chart of a two-dimensional laser synchronous positioning and mapping optimization method provided in one embodiment of the present application;

[0036] Figure 3 A diagram for optimizing a graph provided in one embodiment of the present application;

[0037] Figure 4 A schematic diagram of a system matrix provided in an embodiment of the present application;

[0038] Figure 5 A schematic diagram of functional modules of a two-dimensional laser synchronous positioning and mapping optimization system provided in another embodiment of the present application.

[0039] Figure 6 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0040] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0041] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0042] The two-dimensional laser synchronous positioning and mapping optimization method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be set up separately, integrated on the server 104, or placed on the cloud or other servers. The terminal 102 can send the laser scanning data to the server 104. After the server 104 receives the laser scanning data, for the laser scanning data, the server 104 obtains a linear system at different times based on the laser scanning data. Decompose the linear system into a position linear system and an angle linear system. The Cholesky decomposition method is used to solve the position linear system and the angle linear system respectively to obtain the position data of the laser scanning device. The map is constructed based on the position data of the laser scanning device. The server 104 can feed back the obtained map to the terminal 102. In addition, in some embodiments, the two-dimensional laser synchronous positioning and mapping optimization method can also be implemented by the server 104 or the terminal 102 alone, such as the terminal 102 can directly perform two-dimensional laser synchronous positioning and mapping optimization on the laser scanning data, or the server 104 can obtain the laser scanning data from the data storage system, and perform two-dimensional laser synchronous positioning and mapping optimization on the laser scanning data.

[0043] The terminal 102 may be, but is not limited to, various desktop computers, laptop computers, smart phones, tablet computers, IoT devices, and portable wearable devices. The IoT devices may be smart speakers, smart TVs, smart air conditioners, smart vehicle-mounted devices, etc. The portable wearable devices may be smart watches, smart bracelets, head-mounted devices, etc. The server 104 may be implemented as an independent server or a server cluster consisting of multiple servers, or may be a cloud server.

[0044] In an exemplary embodiment, Figure 2 As shown, a two-dimensional laser synchronous positioning and mapping optimization method is provided. The method is executed by a computer device, and can be executed by a computer device such as a terminal or a server alone, or can be executed by a terminal and a server together. In the embodiment of the present application, the method is applied to Figure 1 The server 104 in the example is used as an example to illustrate, including the following steps 200 to 203. Among them:

[0045] Step 200: Using a laser scanning device to acquire laser scanning data, and obtaining a linear system at different times based on the laser scanning data.

[0046] Step 201: Decompose the linear system into a position linear system and an angle linear system.

[0047] Step 202: Use the Cholesky decomposition method to solve the position linear system and the angle linear system respectively to obtain the position data of the laser scanning device.

[0048] Step 203: Complete map construction based on the position data of the laser scanning device.

[0049] The implementation of the above steps 201 to 203 can greatly reduce the scale of the linear system, thereby improving the optimization rate. In addition, the present application can also significantly improve the solution rate of the linear system to improve the optimization rate.

[0050] In another exemplary embodiment of the present application, the graph optimization is a graph constructed with the laser scanning device as a node and the conversion relationship of each node as an edge, such as Figure 3 X1, X2, X3…X7 represent the position and posture of the laser scanning device. For two-dimensional laser SLAM, its position and posture can be expressed as X i =[x i ,y i ,θ i ] T , where x i ,y i represents the position of the node, θ i Indicates the angle of the node. Z 12 , Z 23 , Z 34 …、Z 67 Represents the conversion relationship between two adjacent nodes, which can be expressed as Z ij =[Δx ij ,Δy ij ,Δθ ij ] T ,θ ij represents the angle between node i and node j. The purpose of graph optimization is to find the position of each node as accurately as possible [x i ,y i ,θ i ], because the error is inevitable, but the smaller the error, the better. Based on this, in this embodiment, the implementation process of the above step 200 can be described as:

[0051] Step 1: Take the laser scanning device as a node, and obtain the node rotation matrix and edge rotation matrix from the node posture and the transformation relationship between nodes. The node rotation matrix and edge rotation matrix are expressed as:

[0052]

[0053] In the formula, R i represents the rotation matrix of the node, R ij Represents the rotation matrix of the edge.

[0054] Step 2: Use the front-end odometer to obtain the poses of adjacent nodes, and combine the node rotation matrix and the edge rotation matrix to obtain the transformation relationship between adjacent nodes. For example, the poses of adjacent nodes are [x i y i θ i ] and [x j y j θ j ], the transformation relationship between nodes is [Δx ij Δy ij Δθ ij ], then the relationship between the three is:

[0055]

[0056] Where t represents translation.

[0057] Step 3: Based on the transformation relationship between adjacent nodes, the error value is obtained by combining the node rotation matrix and the edge rotation matrix. Assume that the transformation relationship between nodes obtained by actual measurement is [Δx ij ′, Δy ij ′, Δθ ij ′], then the error function constructed is ij (x) is:

[0058] e ij Indicates the error value.

[0059] Step 4: Obtain the Jacobian matrix based on the error value, and obtain the H matrix and b vector based on the Jacobian matrix, for example:

[0060] After obtaining the error function, calculate the error value e ij x i and x j The partial derivative of , we get the corresponding Jacobian matrix. This calculation process is described as:

[0061]

[0062] Since the error function is only related to x i and x j Related, therefore:

[0063]

[0064] make Get J ij=(0,...,A ij ,...,B ij ,...,0).

[0065] In the formula, J ij represents the Jacobian matrix.

[0066] Iteratively update the H matrix and b vector, expressed as:

[0067]

[0068] In the formula, Ω ij Represents the information matrix, which is an identity matrix.

[0069] Step 5: Construct a linear system based on the H matrix and b vector.

[0070] From the specific expression of the Jacobian matrix, we can get that the Jacobian matrix is ​​sparse and contains a large number of zero items. This is because a node is connected to a limited number of edges, and a node does not form edges with all nodes. Therefore, the H matrix is ​​also sparse, and the sparsity of the H matrix comes from the Jacobian matrix. The sparsity of the H matrix is ​​considered to be the most important discovery in the history of SLAM development, and this structure can be represented by graph optimization.

[0071] In order to conveniently represent sparsity, in this embodiment, colored squares are used to represent places where there are values ​​in the matrix, and the remaining uncolored areas represent that the values ​​of the matrix at that place are 0. The obtained system matrix is ​​as follows: Figure 4 As shown. Figure 4 It can be seen that the system matrix has a total of 7×7 matrix blocks. The matrix blocks on the diagonal represent nodes, and the matrix blocks on the off-diagonal lines, if non-zero, represent the edges constructed between nodes. Figure 4 It can be clearly seen that each matrix block is a 3×3 matrix, so the overall system matrix is ​​a 21×21 matrix. Based on this, after obtaining the H matrix and b vector, the final linear system is constructed as:

[0072] HΔx=b.

[0074] Where H represents the H matrix, b represents the b vector, and Δx represents the vector composed of unknown quantities.

[0075] In another exemplary embodiment of the present application, the implementation process of step 201 can be described as follows:

[0076] Step 1: Perform graph optimization on the H matrix and b vector respectively to obtain the system matrix.

[0077] Step 2: Decompose the linear system into a position linear system and an angle linear system based on the system matrix.

[0078] Assume that Figure 4 For example, Δx is a vector composed of 21 unknown quantities, that is, Δx = [Δx1, Δy1, Δθ1, Δx2, Δy2, Δθ2… Δx7, Δy7, Δθ7]. Each matrix block of the constructed system matrix is ​​a three-dimensional matrix, in which the first two dimensions are related to the calculation of the position term of the pose, namely [Δx1, Δy1, Δx2, Δy2,… Δx7, Δy7], and the third dimension is related to the calculation of the angle term of the pose, namely [Δθ1, Δθ2,… Δθ7]. Therefore, the system matrix can be divided into a 14×14 matrix and a 7×7 matrix. The 14×14 matrix is ​​used to calculate the position term of the pose, and the 7×7 matrix is ​​used to calculate the angle term of the pose.

[0079] Similar to the system matrix, the b vector is also composed of 7 3×1 matrix blocks, where the first two dimensions of each matrix block are related to the position term of the pose, and the third dimension is related to the angle term of the pose. Therefore, it can be divided into a 2×1 matrix and a 1×1 matrix. The 2×1 matrix is ​​used to calculate the position term of the pose, and the 1×1 matrix is ​​used to calculate the angle term of the pose. Based on this, the decomposed linear system (i.e., the position linear system and the angle linear system) is as follows:

[0080] H xy Δx xy =b xy .

[0081] H θ Δx θ =b θ .

[0082] In the formula, H xy represents the first two dimensions of the H matrix, Δx xy represents the first two dimensions of the vector composed of unknown quantities, b xy represents the first two dimensions of the b vector, H θ represents the third dimension of the H matrix, Δx θ represents the third dimension of the vector composed of unknown quantities, b θ Represents the third dimension of the b vector.

[0083] The decomposed system becomes two linear systems, and the size of its system matrix is ​​greatly reduced compared to the original system, thereby reducing the calculation time.

[0084] In another exemplary embodiment of the present application, the coefficient matrix of the constructed linear system is symmetric and positive definite, and even if it is decomposed into two small linear systems, its coefficient matrix is ​​still symmetric and positive definite. When solving a linear system of equations whose coefficient matrix is ​​symmetric and positive definite, the Cholesky decomposition method has a great advantage. Based on this, the main process of the above step 202 is to decompose a symmetric positive definite matrix (such as an H matrix) into a lower triangular matrix and its transposed matrix, and the specific calculation process is as follows:

[0085] H=LL T .

[0086] Where L is a lower triangular matrix, L T is the transposed matrix of L. At this time, the linear system becomes:

[0087] LL T Δx=b.

[0089] So the final result is:

[0090] LL T Δx=b.

[0092] Finally, the iterations are continued until convergence.

[0093] In practical applications, the position data of the laser scanning device can be solved by replacing the H matrix in the above calculation process with the decomposed position linear system and angle linear system.

[0094] In summary, this application proposes a method of dividing a rigid body pose vector (i.e., a linear system) into two parts, namely, a position term and an angle term, based on a 2D laser SLAM graph optimization algorithm. The decomposed position term and angle term are optimized separately, which greatly reduces the size of the constructed linear matrix and improves the optimization rate. And because the constructed system matrix is ​​positive definite, the Cholesky decomposition can be combined with the solution speed when solving the linear equations of the positive definite matrix, realizing the rapid solution of the state increment Δx, thereby accelerating the convergence speed.

[0095] Based on the same inventive concept, the embodiment of the present application also provides a two-dimensional laser synchronous positioning and mapping optimization system for implementing the two-dimensional laser synchronous positioning and mapping optimization method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more two-dimensional laser synchronous positioning and mapping optimization system embodiments provided below can refer to the limitations of the two-dimensional laser synchronous positioning and mapping optimization method above, and will not be repeated here.

[0096] In an exemplary embodiment, Figure 5 As shown, a two-dimensional laser synchronous positioning and mapping optimization system is provided, including: a laser scanning device, a loop detection device, a front-end odometer, a back-end optimization device and a mapping device.

[0097] The loop detection device and the front-end odometer are connected to the laser scanning device. The back-end optimization device is connected to the loop detection device and the front-end odometer respectively. The mapping device is connected to the back-end optimization device.

[0098] The laser scanning device is mainly responsible for collecting laser scanning data in the actual environment. The front-end odometer is responsible for estimating the relative position of the rigid body at different times, also known as the front end. The loop detection device is mainly used to eliminate spatial cumulative errors. The combined effect of the loop detection device and the front-end odometer can obtain a linear system at different times based on the laser scanning data.

[0099] The back-end optimization device is mainly responsible for optimizing the errors caused by the odometer and loop detection. It decomposes the linear system into a position linear system and an angle linear system, and uses the Cholesky decomposition method to solve the position linear system and the angle linear system respectively to obtain the position data of the laser scanning device.

[0100] The mapping equipment mainly completes map construction based on the location data of the laser scanning equipment.

[0101] As an optional implementation, the laser scanning device may be a laser sensor.

[0102] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 6 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store two-dimensional laser synchronous positioning and mapping optimization data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a two-dimensional laser synchronous positioning and mapping optimization method is implemented.

[0103] Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components. In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0104] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0105] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0106] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0107] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0108] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0109] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0110] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A two-dimensional laser synchronous positioning and mapping optimization method, characterized in that: The two-dimensional laser synchronous positioning and mapping optimization method comprises: Using a laser scanning device to acquire laser scanning data, and obtaining a linear system at different times based on the laser scanning data; Decomposing the linear system into a position linear system and an angle linear system; The position linear system and the angle linear system are respectively solved by using Cholesky decomposition method to obtain the position data of the laser scanning device; Map construction is completed based on the position data of the laser scanning device.

2. The two-dimensional laser synchronous positioning and mapping optimization method according to claim 1, characterized in that: A laser scanning device is used to obtain laser scanning data, and a linear system at different times is obtained based on the laser scanning data, specifically including: Taking the laser scanning device as a node, the rotation matrix of the node and the rotation matrix of the edge are obtained from the position and posture of the node and the transformation relationship between the nodes; The front-end odometer is used to obtain the position and posture of adjacent nodes, and the transformation relationship between adjacent nodes is obtained by combining the node rotation matrix and the edge rotation matrix; Based on the transformation relationship between adjacent nodes, the error value is obtained by combining the rotation matrix of the node and the rotation matrix of the edge; Obtaining a Jacobian matrix based on the error value, and obtaining an H matrix and a b vector based on the Jacobian matrix; The linear system is constructed based on the H matrix and the b vector.

3. The two-dimensional laser synchronous positioning and mapping optimization method according to claim 2, characterized in that: The linear system is: HΔx=b; Where H represents the H matrix, b represents the b vector, and Δx represents the vector composed of unknown quantities.

4. The two-dimensional laser synchronous positioning and mapping optimization method according to claim 2, characterized in that: Decomposing the linear system into a position linear system and an angle linear system specifically includes: Performing graph optimization representation on the H matrix and the b vector respectively to obtain a system matrix; The linear system is decomposed into the position linear system and the angle linear system based on the system matrix.

5. The two-dimensional laser synchronous positioning and mapping optimization method according to claim 1, characterized in that: The position linear system and the angle linear system are respectively solved by using the Cholesky decomposition method to obtain the position data of the laser scanning device, which specifically includes: The Cholesky decomposition method is used to decompose the position linear system and the angle linear system into a lower triangular matrix and a transposed matrix of the lower triangular matrix, respectively, and an iterative solution is performed to obtain the position data of the laser scanning device.

6. A two-dimensional laser synchronous positioning and mapping optimization system, characterized in that: The two-dimensional laser synchronous positioning and mapping optimization system is used to implement the two-dimensional laser synchronous positioning and mapping optimization method according to any one of claims 1 to 5; the system comprises: Laser scanning equipment, used for acquiring laser scanning data; The loop detection device and the front-end odometer are both connected to the laser scanning device and are used to obtain a linear system at different times based on the laser scanning data; A back-end optimization device is connected to the loop detection device and the front-end odometer, respectively, and is used to decompose the linear system into a position linear system and an angle linear system, and is used to solve the position linear system and the angle linear system respectively by using a Cholesky decomposition method to obtain position data of a laser scanning device; A mapping device is connected to the back-end optimization device and is used to complete map construction based on the position data of the laser scanning device.

7. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the two-dimensional laser synchronous positioning and mapping optimization method according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the two-dimensional laser synchronous positioning and mapping optimization method described in any one of claims 1 to 5 is implemented.

9. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the two-dimensional laser synchronous positioning and mapping optimization method described in any one of claims 1 to 5 is implemented.

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