Multi-sensor fusion positioning method and system based on extended Kalman filtering

Abstract

The invention relates to a multi-sensor fusion positioning method and system based on extended Kalman filtering, and the method comprises the steps: R1, enabling a vehicle to run on a road, obtaining the point cloud data information of the road in real time based on a vehicle-mounted laser radar, obtaining the image data information of the road in real time based on a vehicle-mounted camera, and obtaining the point cloud data information of the road; the absolute positioning data information of the vehicle is obtained in real time based on vehicle-mounted integrated navigation, the pose data information of the vehicle is obtained in real time based on a vehicle-mounted IMU, and the data information of the vehicle speed and the wheel turning angle of the vehicle is obtained in real time based on a vehicle-mounted wheel speed meter; and R2, based on the point cloud data information of the road and the image data information of the road, fusing the point cloud and the image of the road by adopting a fusion algorithm of an Archimedes optimized neural network model. The invention not only provides more robust navigation estimation than using a single sensor by combining complementary or redundant information from different sensors, but also provides stable and reliable positioning information in the whole operating scene.

Description

Technical Field

[0001] The present invention relates to the technical field of multi-sensor fusion positioning technology, and in particular to a multi-sensor fusion positioning method and system based on extended Kalman filtering. Background Art

[0002] Unmanned container trucks need to accurately stop at designated locations in the yard and under the quay cranes during operation, waiting for loading and unloading operations, and the positioning accuracy is required to be at the centimeter level. High-precision positioning can ensure accurate parking of vehicles, improve handling efficiency and reduce road blockage accidents caused by braking. Existing positioning methods mainly include combined navigation based on observation satellite signals, map matching positioning based on high-precision maps, laser SLAM using lidar, and visual SLAM using cameras. Due to the characteristics of the port environment, such as complex road structure, frequent environmental changes, unfixed operation routes, and mixed operations of manned and unmanned container trucks, the use of a single sensor and positioning source cannot guarantee continuous, stable and reliable high-precision positioning in all operating scenarios. Therefore, the present invention comprehensively considers the particularity of the port environment and proposes a multi-sensor fusion positioning method to realize the fusion processing of observation data from different sensors, providing accurate and reliable positioning information for downstream modules such as environmental perception, planning and collision avoidance of unmanned container trucks.

[0003] In the prior art, a Chinese patent (application number: 202311571021.4, publication number: CN 117584989A) discloses a laser radar / IMU / vehicle kinematic constraint tightly coupled SLAM system and algorithm. First, the vehicle kinematic constraint is constructed using IMU, rear axle wheel speed and front wheel steering angle. By decoupling the displacement and attitude information of the vehicle kinematic constraint, the displacement and attitude constraints are constructed respectively, thereby improving the accuracy of the optimization result; secondly, according to the number of feature points and the steering angle, an adaptive coefficient is introduced to adjust the weight of the vehicle kinematic constraint in real time. However, it is not completely applicable to the port environment. The goods carried by unmanned container trucks are loaded in containers and circulate between cargo ships and container stacking areas. The containers in the container stacking area are arranged in an orderly manner, and the number of containers on both sides of the stacking area is exactly the same. For laser SLAM, this structured scene has less available feature information, and the lack of sufficient constraints is prone to degradation, and the continuity and accuracy reliability of positioning information cannot be guaranteed. Summary of the invention

[0004] In view of the above shortcomings of the prior art, the present invention provides a multi-sensor fusion positioning method and system based on extended Kalman filtering, which not only provides more robust navigation estimation than using a single sensor by combining complementary or redundant information from different sensors, but also provides stable and reliable positioning information in all operating scenarios.

[0005] In order to achieve the above-mentioned purpose and other related purposes, the technical solution provided by the present invention is as follows: a multi-sensor fusion positioning method based on extended Kalman filtering, the method comprising:

[0006] R1. When a vehicle is driving on a road, it obtains point cloud data information of the road in real time based on the on-board laser radar, obtains image data information of the road in real time based on the on-board camera, obtains absolute positioning data information of the vehicle in real time based on the on-board integrated navigation, obtains posture data information of the vehicle in real time based on the on-board IMU, and obtains vehicle speed and wheel angle data information of the vehicle in real time based on the on-board wheel speedometer;

[0007] R2. Based on the point cloud data information of the road and the image data information of the road, the point cloud and the image of the road are fused using a fusion algorithm of an Archimedes optimized neural network model to obtain fused state data information of the road;

[0008] R3. Based on the absolute positioning data information of the vehicle, the posture data information of the vehicle, and the data information of the vehicle speed and wheel angle, the motion state data of the vehicle is fused using a multimodal feature fusion algorithm based on the zebra optimization algorithm to obtain the fused state data information of the vehicle;

[0009] R4. Based on the fused vehicle status data information and the fused road status data information, the extended Kalman filter algorithm fused with the golden sine factor is used to fuse the vehicle and road status data, and a fusion state matrix is ​​constructed to obtain the fusion state matrix data information of the vehicle and the road;

[0010] R5. Based on the fusion state matrix data information of the vehicle and the road, construct the vehicle's fusion positioning function W, characterize the vehicle's fusion positioning data, and obtain the vehicle's fusion positioning data information.

[0011] Furthermore, the fusion positioning function W of the vehicle is:

[0012]

[0013] Among them, x is the fusion state matrix data information of the vehicle and the road, and α1, α2 and α3 are the fusion positioning factors of the vehicle.

[0014] Furthermore, the constraint function f of the fusion positioning factors α1, α2 and α3 of the vehicle is,

[0015]

[0016] The value range of the constraint function f is (2,3).

[0017] Furthermore, in step R2, the fusion algorithm of the Archimedean optimized neural network model is used to fuse the point cloud and the image of the road, including:

[0018] R21. Based on the point cloud data information of the road, construct a point cloud parameter matrix of the road, based on the image data information of the road, construct an image parameter matrix of the road, and obtain the point cloud and image parameter matrix data information of the road;

[0019] R22. Based on the parameter matrix data information of the point cloud and image of the road, construct the Archimedean target optimization function G,

[0020]

[0021] Among them, y1 is the parameter matrix data information of the road point cloud, y2 is the parameter matrix data information of the road image, β1, β2 and β3 are Archimedes' target optimization factors, and the point cloud and image parameters of the road are optimized to obtain the optimized point cloud and image parameter data information of the road;

[0022] R23. Input the optimized road point cloud and image parameter data information into the neural network model for training and learning, and determine the feature fusion function H,

[0023]

[0024] Among them, z1 is the point cloud parameter data information of the optimized road, z2 is the image parameter data information of the optimized road, δ1, δ2 and δ3 are the optimization factors of the point cloud and image parameters of the road. The point cloud and image of the road are fused to obtain the state data information of the fused road.

[0025] Furthermore, the constraints of the optimization factors δ1, δ2 and δ3 of the point cloud and image parameters of the road are:

[0026]

[0027] The Archimedes' target optimization factors β1, β2 and β3 are,

[0028]

[0029] Among them, y1 is the parameter matrix data information of the point cloud of the road, and y2 is the parameter matrix data information of the image of the road.

[0030] Furthermore, in step R3, the multimodal feature fusion algorithm based on the zebra optimization algorithm is used to fuse the motion state data of the vehicle, including:

[0031] R31. Based on the absolute positioning data information of the vehicle, the posture data information of the vehicle, and the data information of the vehicle speed and wheel angle, a vehicle motion state relationship function L is established.

[0032]

[0033] Among them, a1 is the absolute positioning data information of the vehicle, a2 is the posture data information of the vehicle, a3 is the data information of the vehicle speed and wheel angle, γ1 and γ2 are relationship factors, which characterize the relationship of the vehicle's motion state data to obtain the relationship data information of the vehicle's motion state data;

[0034] R32. Based on the relationship data information of the vehicle's motion state data, a zebra optimization function Q of the relationship between the vehicle's motion state data is established,

[0035]

[0036] Wherein, b is the relational data information of the vehicle's motion state data, η1, η2 and η3 are the relational optimization factors of the vehicle's motion state, and the relation of the vehicle's motion state data is optimized to obtain the optimized relational data information of the vehicle's motion state data;

[0037] R33. Based on the relational data information of the optimized vehicle motion state data, construct a multimodal fusion function P of the vehicle state,

[0038]

[0039] Among them, c is the relational data information of the optimized vehicle motion state data, λ1, λ2 and λ3 are the multimodal fusion feature factors of the vehicle motion state, and the vehicle motion state data is fused to obtain the fused vehicle state data information.

[0040] Furthermore, the constraint function g of the multimodal fusion characteristic factors λ1, λ2 and λ3 of the vehicle motion state is,

[0041]

[0042] Among them, the value range of the constraint function g is (1,2).

[0043] Furthermore, in step R4, the use of the extended Kalman filter algorithm integrated with the golden sine factor to fuse the vehicle and road state data includes:

[0044] R41. Based on the fused vehicle status data information and the fused road status data information, establish the state variable function S of the extended Kalman filter for vehicle fusion positioning,

[0045]

[0046] Among them, r1 is the state data information of the fused vehicle, r2 is the state data information of the fused road, A is the state matrix, B is the control matrix, and the state parameters of the extended Kalman filter of the vehicle fusion positioning are characterized to obtain the state parameter data information of the extended Kalman filter of the vehicle fusion positioning;

[0047] R42. Based on the fused vehicle status data information and the fused road status data information, establish the observation variable function R of the extended Kalman filter for vehicle fusion positioning,

[0048]

[0049] Among them, r1 is the state data information of the fused vehicle, r2 is the state data information of the fused road, and U is the observation matrix, which characterizes the observation parameters of the extended Kalman filter of the vehicle fusion positioning to obtain the observation parameter data information of the extended Kalman filter of the vehicle fusion positioning;

[0050] R43. Based on the observation parameter data information of the extended Kalman filter of the vehicle fusion positioning and the state parameter data information of the extended Kalman filter of the vehicle fusion positioning, a fusion function O is established.

[0051]

[0052] Among them, g1 is the observation parameter data information of the extended Kalman filter for vehicle fusion positioning, g2 is the state parameter data information of the extended Kalman filter for vehicle fusion positioning, μ1, μ2 and μ3 are the golden sine factors of the extended Kalman filter. The state data of the vehicle and the road are fused, and the fusion state matrix is ​​constructed to obtain the fusion state matrix data information of the vehicle and the road.

[0053] Furthermore, the golden sine factors μ1, μ2 and μ3 of the extended Kalman filter are:

[0054]

[0055] Among them, g1 is the observation parameter data information of the extended Kalman filter for vehicle fusion positioning, and g2 is the state parameter data information of the extended Kalman filter for vehicle fusion positioning.

[0056] In order to achieve the above-mentioned purpose and other related purposes, the present invention also provides a computer-readable storage medium, which stores a computer program programmed or configured to execute any one of the multi-sensor fusion positioning methods based on extended Kalman filtering.

[0057] The present invention has the following positive effects:

[0058] 1. The present invention fuses the point cloud and image of the road by adopting a fusion algorithm of a neural network model optimized by Archimedes, and fuses the motion state data of the vehicle by combining a multimodal feature fusion algorithm based on a zebra optimization algorithm. It not only provides a more robust navigation estimate than using a single sensor by combining complementary or redundant information from different sensors, but also provides stable and reliable positioning information in all operating scenarios.

[0059] 2. The present invention fuses the status data of vehicles and roads by adopting the extended Kalman filter algorithm integrated with the golden sine factor, and characterizes the fused positioning data of the vehicle by combining the construction of the vehicle's fused positioning function W. The fused output positioning information not only includes global absolute positioning information, but also local relative positioning information, and participates in the operation processes such as container loading and unloading, container transfer between container area and quay crane, automatic parking, charging, etc., and provides stable and reliable positioning information for full-scene operations in port environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 It is a schematic diagram of the method flow of the present invention;

[0061] Figure 2 A schematic diagram of a fusion algorithm for the Archimedes-optimized neural network model of the present invention;

[0062] Figure 3 It is a flow chart of a multimodal feature fusion algorithm based on the zebra optimization algorithm of the present invention;

[0063] Figure 4 The figure is a flow chart of the extended Kalman filter algorithm integrated with the golden sine factor of the present invention. DETAILED DESCRIPTION

[0064] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0065] Example 1: Figure 1 As shown, a multi-sensor fusion positioning method based on extended Kalman filtering includes:

[0066] R1. When a vehicle is driving on a road, it obtains point cloud data information of the road in real time based on the on-board laser radar, obtains image data information of the road in real time based on the on-board camera, obtains absolute positioning data information of the vehicle in real time based on the on-board integrated navigation, obtains posture data information of the vehicle in real time based on the on-board IMU, and obtains vehicle speed and wheel angle data information of the vehicle in real time based on the on-board wheel speedometer;

[0067] R2. Based on the point cloud data information of the road and the image data information of the road, the point cloud and the image of the road are fused using a fusion algorithm of an Archimedes optimized neural network model to obtain fused state data information of the road;

[0068] R3. Based on the absolute positioning data information of the vehicle, the posture data information of the vehicle, and the data information of the vehicle speed and wheel angle, the motion state data of the vehicle is fused using a multimodal feature fusion algorithm based on the zebra optimization algorithm to obtain the fused state data information of the vehicle;

[0069] R4. Based on the fused vehicle status data information and the fused road status data information, the extended Kalman filter algorithm fused with the golden sine factor is used to fuse the vehicle and road status data, and a fusion state matrix is ​​constructed to obtain the fusion state matrix data information of the vehicle and the road;

[0070] R5. Based on the fusion state matrix data information of the vehicle and the road, construct the vehicle's fusion positioning function W, characterize the vehicle's fusion positioning data, and obtain the vehicle's fusion positioning data information.

[0071] In this embodiment, the fusion positioning function W of the vehicle is:

[0072]

[0073] Among them, x is the fusion state matrix data information of the vehicle and the road, and α1, α2 and α3 are the fusion positioning factors of the vehicle.

[0074] In this embodiment, the constraint function f of the fusion positioning factors α1, α2 and α3 of the vehicle is,

[0075]

[0076] The value range of the constraint function f is (2,3).

[0077] In this embodiment, if Figure 2 As shown, in step R2, the fusion algorithm of the Archimedean optimized neural network model is used to fuse the point cloud and the image of the road, including:

[0078] R21. Based on the point cloud data information of the road, construct a point cloud parameter matrix of the road, based on the image data information of the road, construct an image parameter matrix of the road, and obtain the point cloud and image parameter matrix data information of the road;

[0079] R22. Based on the parameter matrix data information of the point cloud and image of the road, construct the Archimedean target optimization function G,

[0080]

[0081] Among them, y1 is the parameter matrix data information of the road point cloud, y2 is the parameter matrix data information of the road image, β1, β2 and β3 are Archimedes' target optimization factors, and the point cloud and image parameters of the road are optimized to obtain the optimized point cloud and image parameter data information of the road;

[0082] R23. Input the optimized road point cloud and image parameter data information into the neural network model for training and learning, and determine the feature fusion function H,

[0083]

[0084] Among them, z1 is the point cloud parameter data information of the optimized road, z2 is the image parameter data information of the optimized road, δ1, δ2 and δ3 are the optimization factors of the point cloud and image parameters of the road. The point cloud and image of the road are fused to obtain the state data information of the fused road.

[0085] In this embodiment, the constraints of the optimization factors δ1, δ2 and δ3 of the point cloud and image parameters of the road are:

[0086]

[0087] The Archimedes' target optimization factors β1, β2 and β3 are,

[0088]

[0089] Among them, y1 is the parameter matrix data information of the point cloud of the road, and y2 is the parameter matrix data information of the image of the road.

[0090] Embodiment 2: Based on the multi-sensor fusion positioning method based on extended Kalman filtering in Embodiment 1, the present invention is further illustrated and described below.

[0091] like Figure 1 As shown, a multi-sensor fusion positioning method based on extended Kalman filtering includes:

[0092] R1. When a vehicle is driving on a road, it obtains point cloud data information of the road in real time based on the on-board laser radar, obtains image data information of the road in real time based on the on-board camera, obtains absolute positioning data information of the vehicle in real time based on the on-board integrated navigation, obtains posture data information of the vehicle in real time based on the on-board IMU, and obtains vehicle speed and wheel angle data information of the vehicle in real time based on the on-board wheel speedometer;

[0093] R2. Based on the point cloud data information of the road and the image data information of the road, the point cloud and the image of the road are fused using a fusion algorithm of an Archimedes optimized neural network model to obtain fused state data information of the road;

[0094] R3. Based on the absolute positioning data information of the vehicle, the posture data information of the vehicle, and the data information of the vehicle speed and wheel angle, the motion state data of the vehicle is fused using a multimodal feature fusion algorithm based on the zebra optimization algorithm to obtain the fused state data information of the vehicle;

[0095] R4. Based on the fused vehicle status data information and the fused road status data information, the extended Kalman filter algorithm fused with the golden sine factor is used to fuse the vehicle and road status data, and a fusion state matrix is ​​constructed to obtain the fusion state matrix data information of the vehicle and the road;

[0096] R5. Based on the fusion state matrix data information of the vehicle and the road, construct the vehicle's fusion positioning function W, characterize the vehicle's fusion positioning data, and obtain the vehicle's fusion positioning data information.

[0097] In this embodiment, if Figure 3 As shown, in step R3, the multimodal feature fusion algorithm based on the zebra optimization algorithm is used to fuse the motion state data of the vehicle, including:

[0098] R31. Based on the absolute positioning data information of the vehicle, the posture data information of the vehicle, and the data information of the vehicle speed and wheel angle, a vehicle motion state relationship function L is established.

[0099]

[0100] Among them, a1 is the absolute positioning data information of the vehicle, a2 is the posture data information of the vehicle, a3 is the data information of the vehicle speed and wheel angle, γ1 and γ2 are relationship factors, which characterize the relationship of the vehicle's motion state data to obtain the relationship data information of the vehicle's motion state data;

[0101] R32. Based on the relationship data information of the vehicle's motion state data, a zebra optimization function Q of the relationship between the vehicle's motion state data is established,

[0102]

[0103] Wherein, b is the relational data information of the vehicle's motion state data, η1, η2 and η3 are the relational optimization factors of the vehicle's motion state, and the relation of the vehicle's motion state data is optimized to obtain the optimized relational data information of the vehicle's motion state data;

[0104] R33. Based on the relational data information of the optimized vehicle motion state data, construct a multimodal fusion function P of the vehicle state,

[0105]

[0106] Among them, c is the relational data information of the optimized vehicle motion state data, λ1, λ2 and λ3 are the multimodal fusion feature factors of the vehicle motion state, and the vehicle motion state data is fused to obtain the fused vehicle state data information.

[0107] In this embodiment, the constraint function g of the multimodal fusion characteristic factors λ1, λ2 and λ3 of the vehicle motion state is,

[0108]

[0109]

[0110] Among them, the value range of the constraint function g is (1,2).

[0111] In this embodiment, if Figure 4 As shown, in step R4, the use of the extended Kalman filter algorithm integrated with the golden sine factor to fuse the vehicle and road state data includes:

[0112] R41. Based on the fused vehicle status data information and the fused road status data information, establish the state variable function S of the extended Kalman filter for vehicle fusion positioning,

[0113]

[0114] Among them, r1 is the state data information of the fused vehicle, r2 is the state data information of the fused road, A is the state matrix, B is the control matrix, and the state parameters of the extended Kalman filter of the vehicle fusion positioning are characterized to obtain the state parameter data information of the extended Kalman filter of the vehicle fusion positioning;

[0115] R42. Based on the fused vehicle status data information and the fused road status data information, establish the observation variable function R of the extended Kalman filter for vehicle fusion positioning,

[0116]

[0117] Among them, r1 is the state data information of the fused vehicle, r2 is the state data information of the fused road, and U is the observation matrix, which characterizes the observation parameters of the extended Kalman filter of the vehicle fusion positioning to obtain the observation parameter data information of the extended Kalman filter of the vehicle fusion positioning;

[0118] R43. Based on the observation parameter data information of the extended Kalman filter of the vehicle fusion positioning and the state parameter data information of the extended Kalman filter of the vehicle fusion positioning, a fusion function O is established.

[0119]

[0120] Among them, g1 is the observation parameter data information of the extended Kalman filter for vehicle fusion positioning, g2 is the state parameter data information of the extended Kalman filter for vehicle fusion positioning, μ1, μ2 and μ3 are the golden sine factors of the extended Kalman filter. The state data of the vehicle and the road are fused, and the fusion state matrix is ​​constructed to obtain the fusion state matrix data information of the vehicle and the road.

[0121] In this embodiment, the golden sine factors μ1, μ2 and μ3 of the extended Kalman filter are,

[0122]

[0123] Among them, g1 is the observation parameter data information of the extended Kalman filter for vehicle fusion positioning, and g2 is the state parameter data information of the extended Kalman filter for vehicle fusion positioning.

[0124] In this embodiment, the present invention provides a computer-readable storage medium, on which is stored a computer program programmed or configured to execute any one of the multi-sensor fusion positioning methods based on extended Kalman filtering.

[0125] In this embodiment, the present invention provides a computer-readable storage medium, on which is stored a computer program programmed or configured to execute any one of the multi-sensor fusion positioning based on extended Kalman filtering.

[0126] Any reference to memory, storage, database or other media used in the embodiments provided in the present application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM) and memory bus dynamic RAM (RDRAM).

[0127] In summary, the present invention not only provides a more robust navigation estimate than using a single sensor by combining complementary or redundant information from different sensors, but also provides stable and reliable positioning information in all operational scenarios.

[0128] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A multi-sensor fusion positioning method based on extended Kalman filtering, characterized in that: The method comprises: R1. When a vehicle is driving on a road, it obtains point cloud data information of the road in real time based on the on-board laser radar, obtains image data information of the road in real time based on the on-board camera, obtains absolute positioning data information of the vehicle in real time based on the on-board integrated navigation, obtains posture data information of the vehicle in real time based on the on-board IMU, and obtains vehicle speed and wheel angle data information of the vehicle in real time based on the on-board wheel speedometer; R2. Based on the point cloud data information of the road and the image data information of the road, the point cloud and the image of the road are fused using a fusion algorithm of an Archimedes optimized neural network model to obtain fused state data information of the road; R3. Based on the absolute positioning data information of the vehicle, the posture data information of the vehicle, and the data information of the vehicle speed and wheel angle, the motion state data of the vehicle is fused using a multimodal feature fusion algorithm based on the zebra optimization algorithm to obtain the fused state data information of the vehicle; R4. Based on the fused vehicle status data information and the fused road status data information, the extended Kalman filter algorithm fused with the golden sine factor is used to fuse the vehicle and road status data, and a fusion state matrix is ​​constructed to obtain the fusion state matrix data information of the vehicle and the road; R5. Based on the fusion state matrix data information of the vehicle and the road, construct the vehicle's fusion positioning function W, characterize the vehicle's fusion positioning data, and obtain the vehicle's fusion positioning data information.

2. The multi-sensor fusion positioning method based on extended Kalman filtering according to claim 1 is characterized in that: The fusion positioning function W of the vehicle is: Among them, x is the fusion state matrix data information of the vehicle and the road, and α1, α2 and α3 are the fusion positioning factors of the vehicle.

3. The multi-sensor fusion positioning method based on extended Kalman filtering according to claim 2 is characterized in that: The constraint function f of the fusion positioning factors α1, α2 and α3 of the vehicle is, The value range of the constraint function f is (2,3).

4. The multi-sensor fusion positioning method based on extended Kalman filtering according to claim 1 is characterized in that: In step R2, the fusion algorithm of the Archimedean optimized neural network model is used to fuse the point cloud and the image of the road, including: R21. Based on the point cloud data information of the road, construct a point cloud parameter matrix of the road, based on the image data information of the road, construct an image parameter matrix of the road, and obtain the point cloud and image parameter matrix data information of the road; R22. Based on the parameter matrix data information of the point cloud and image of the road, construct the Archimedean target optimization function G, Among them, y1 is the parameter matrix data information of the road point cloud, y2 is the parameter matrix data information of the road image, β1, β2 and β3 are Archimedes' target optimization factors, and the point cloud and image parameters of the road are optimized to obtain the optimized point cloud and image parameter data information of the road; R23. Input the optimized road point cloud and image parameter data information into the neural network model for training and learning, and determine the feature fusion function H, Among them, z1 is the point cloud parameter data information of the optimized road, z2 is the image parameter data information of the optimized road, δ1, δ2 and δ3 are the optimization factors of the point cloud and image parameters of the road. The point cloud and image of the road are fused to obtain the state data information of the fused road.

5. The multi-sensor fusion positioning method based on extended Kalman filtering according to claim 4 is characterized in that: The constraints of the optimization factors δ1, δ2 and δ3 of the point cloud and image parameters of the road are: The Archimedes' target optimization factors β1, β2 and β3 are, Among them, y1 is the parameter matrix data information of the point cloud of the road, and y2 is the parameter matrix data information of the image of the road.

6. The multi-sensor fusion positioning method based on extended Kalman filtering according to claim 1 is characterized in that: In step R3, the multimodal feature fusion algorithm based on the zebra optimization algorithm is used to fuse the motion state data of the vehicle, including: R31. Based on the absolute positioning data information of the vehicle, the posture data information of the vehicle, and the data information of the vehicle speed and wheel angle, a vehicle motion state relationship function L is established. Among them, a1 is the absolute positioning data information of the vehicle, a2 is the posture data information of the vehicle, a3 is the data information of the vehicle speed and wheel angle, γ1 and γ2 are relationship factors, which characterize the relationship of the vehicle's motion state data to obtain the relationship data information of the vehicle's motion state data; R32. Based on the relationship data information of the vehicle's motion state data, a zebra optimization function Q of the relationship between the vehicle's motion state data is established, Wherein, b is the relational data information of the vehicle's motion state data, η1, η2 and η3 are the relational optimization factors of the vehicle's motion state, and the relation of the vehicle's motion state data is optimized to obtain the optimized relational data information of the vehicle's motion state data; R33. Based on the relational data information of the optimized vehicle motion state data, construct a multimodal fusion function P of the vehicle state, Among them, c is the relational data information of the optimized vehicle motion state data, λ1, λ2 and λ3 are the multimodal fusion feature factors of the vehicle motion state, and the vehicle motion state data is fused to obtain the fused vehicle state data information.

7. The multi-sensor fusion positioning method based on extended Kalman filtering according to claim 6 is characterized in that: The constraint function g of the multimodal fusion characteristic factors λ1, λ2 and λ3 of the vehicle motion state is: Among them, the value range of the constraint function g is (1,2).

8. The multi-sensor fusion positioning method based on extended Kalman filtering according to claim 1 is characterized in that: In step R4, the method of fusing the vehicle and road state data using the extended Kalman filter algorithm fused with the golden sine factor includes: R41. Based on the fused vehicle status data information and the fused road status data information, establish the state variable function S of the extended Kalman filter for vehicle fusion positioning, Among them, r1 is the state data information of the fused vehicle, r2 is the state data information of the fused road, A is the state matrix, B is the control matrix, and the state parameters of the extended Kalman filter of the vehicle fusion positioning are characterized to obtain the state parameter data information of the extended Kalman filter of the vehicle fusion positioning; R42. Based on the fused vehicle status data information and the fused road status data information, establish the observation variable function R of the extended Kalman filter for vehicle fusion positioning, Among them, r1 is the state data information of the fused vehicle, r2 is the state data information of the fused road, and U is the observation matrix, which characterizes the observation parameters of the extended Kalman filter of the vehicle fusion positioning to obtain the observation parameter data information of the extended Kalman filter of the vehicle fusion positioning; R43. Based on the observation parameter data information of the extended Kalman filter of the vehicle fusion positioning and the state parameter data information of the extended Kalman filter of the vehicle fusion positioning, establish a fusion function O, Among them, g1 is the observation parameter data information of the extended Kalman filter for vehicle fusion positioning, g2 is the state parameter data information of the extended Kalman filter for vehicle fusion positioning, μ1, μ2 and μ3 are the golden sine factors of the extended Kalman filter. The state data of the vehicle and the road are fused, and the fusion state matrix is ​​constructed to obtain the fusion state matrix data information of the vehicle and the road.

9. The multi-sensor fusion positioning method based on extended Kalman filtering according to claim 8, characterized in that: The golden sine factors μ1, μ2 and μ3 of the extended Kalman filter are, Among them, g1 is the observation parameter data information of the extended Kalman filter for vehicle fusion positioning, and g2 is the state parameter data information of the extended Kalman filter for vehicle fusion positioning.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program that is programmed or configured to execute the multi-sensor fusion positioning method based on extended Kalman filtering as described in any one of claims 1 to 9.

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