Vehicle positioning method, device, equipment and medium in multi-layer space transition area

Abstract

The present invention relates to a method, device, equipment, and medium for positioning a vehicle in a multi-layer spatial transition region, belonging to the field of vehicle positioning technology. The method comprises: discretizing the multi-layer spatial transition region into a sequence of nodes, collecting vehicle posture information, bird's-eye view image information, and altitude information corresponding to each node; collecting vehicle position information, associating the vehicle position information with the vehicle posture information, bird's-eye view image information, and altitude information to construct a posture feature map for the multi-layer spatial transition region; matching the posture feature map with the DTW algorithm to determine a candidate set of posture features; using nodes in the candidate set of posture features as particles and real-time vehicle posture features as observation information, calculating the likelihood probability between the observation information and the particles, and outputting the best matching node in the candidate set of posture features; and solving the vehicle's posture in a global coordinate system based on the best matching node. The method is used to improve high-precision and robust positioning of vehicles in transition regions.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle positioning, and in particular to a vehicle positioning method, device, equipment and medium in a multi-layer space transition area. Background Art

[0002] Multi-story parking lots are a crucial part of urban parking scenarios. Transition zones, the "throat" connecting indoor and outdoor spaces, directly determine whether a vehicle can achieve intelligent driving for the final mile. These transition zones are often structurally degraded, characterized by sudden changes in lighting, poor texture and structural features, and unique characteristics such as turns and ramps. Furthermore, these areas are satellite signal blind spots, making it difficult to obtain effective vehicle location information.

[0003] Among current positioning methods, V2X (Vehicle-to-Everything) has the disadvantages of high deployment and maintenance costs, SLAM (Simultaneous Localization and Mapping) and inertial navigation systems have the disadvantages of cumulative errors, and visual maps and laser maps have poor positioning accuracy and stability in multi-layer transition areas. These methods cannot yet effectively solve the positioning problem in transition areas.

[0004] Therefore, how to solve the positioning needs of vehicles in the "last mile" of the transition area is a technical problem that technicians in this field urgently need to solve. Summary of the Invention

[0005] In view of this, it is necessary to provide a vehicle positioning method, device, equipment and medium in a multi-layer space transition area to improve the high-precision and robust positioning of the vehicle in the multi-layer space transition area.

[0006] In order to solve the above problems, in a first aspect, the present invention provides a vehicle positioning method in a multi-layer spatial transition area, comprising:

[0007] Discretize the multi-layer spatial transition area into sequence nodes, and collect the vehicle posture information, bird's-eye view image information and altitude information corresponding to each node;

[0008] Collecting vehicle position information, correlating the vehicle position information with the vehicle posture information, bird's-eye view image information, and altitude information, and constructing a posture feature map database for the multi-layer spatial transition area;

[0009] Use the DTW algorithm to match the pose feature map to determine the candidate set of pose features;

[0010] Taking the nodes in the pose feature candidate set as particles and the real-time vehicle pose features as observation information, calculating the likelihood probability between the observation information and the particles, and outputting the best matching node in the pose feature candidate set;

[0011] The position and posture of the vehicle in the global coordinate system is calculated based on the best matching node.

[0012] In one possible implementation, collecting vehicle posture information, bird's-eye view image information, and altitude information corresponding to each node includes:

[0013] The barometer is used to collect the altitude information corresponding to each node, the IMU is used to collect the vehicle posture information, and the BEV is used to collect bird's-eye view image information;

[0014] The collecting of vehicle location information includes:

[0015] Use INS to collect vehicle location information.

[0016] In a possible implementation, after collecting the bird's-eye view image information, the method further includes:

[0017] Extracting lane line semantic information from the bird's-eye view image, and obtaining a deviation of the vehicle relative to the lane line based on the lane line semantic information;

[0018] The vehicle posture information is normalized using the deviation of the vehicle relative to the lane line.

[0019] In one possible implementation, after collecting the vehicle location information, the method further includes:

[0020] Structural information constraints of a multi-layer spatial transition region are obtained, and the vehicle position information is optimized based on the structural information constraints of the multi-layer spatial transition region.

[0021] In one possible implementation, associating the vehicle position information with the vehicle posture information, the bird's-eye view image information, and the altitude information to construct a posture feature map for the multi-layer spatial transition area includes:

[0022] Traverse the multi-layer space transition area and collect the data according to the formula Organize and store to complete the construction of the pose feature map for the multi-layer space transition area, wherein: x ( i ), y ( i ) is the vehicle in i The vehicle location information corresponding to each node, a ( i ), r ( i ), p ( i ) is the vehicle in i The pose information at each node, h( i ) is the vehicle in i The altitude information at each node, I ( i ) is the first i Bird’s-eye view image information of nodes, is the number of nodes.

[0023] In one possible implementation, before using the DTW algorithm to match the pose feature map to determine the pose feature candidate set, the method further includes:

[0024] Construct a short-term uniform motion model based on the vehicle's historical position information;

[0025] Determining a preliminary matching range of the vehicle in the posture feature map based on the short-term uniform motion model;

[0026] The DTW algorithm is used to match the pose feature map to determine the pose feature candidate set, specifically including:

[0027] The DTW algorithm is used to match the preliminary matching range in the pose feature map to determine the pose feature candidate set.

[0028] In one possible implementation, the method uses nodes in the pose feature candidate set as particles, uses real-time vehicle pose features as observation information, calculates likelihood probabilities between the observation information and the particles, and outputs the best matching nodes in the pose feature candidate set, including:

[0029] The likelihood probability between the real-time vehicle posture feature and each node in the posture feature candidate set is calculated, and the importance sampling of the particles is performed to output the best matching node in the posture feature candidate set.

[0030] In one possible implementation, calculating the vehicle's position and posture in a global coordinate system based on the best matching node includes:

[0031] The relative position relationship between the real-time vehicle posture feature and the posture feature of each node in the best matching node is calculated, and the posture of the vehicle in the global coordinate system is solved through a preset coordinate transformation relationship.

[0032] In a second aspect, the present invention provides a vehicle positioning device in a multi-layer space transition area, comprising:

[0033] The information collection module is used to discretize the multi-layer spatial transition area into sequence nodes and collect the vehicle posture information, bird's-eye view image information and altitude information corresponding to each node;

[0034] a map construction module, configured to collect vehicle position information, associate the vehicle position information with the vehicle posture information, bird's-eye view image information, and altitude information, and construct a posture feature map database for the multi-layer spatial transition area;

[0035] The matching module is used to match the pose feature map using the DTW algorithm to determine the candidate set of pose features;

[0036] A calculation module is used to use the nodes in the pose feature candidate set as particles and the real-time vehicle pose feature as observation information, calculate the likelihood probability between the observation information and the particles, and output the best matching node in the pose feature candidate set;

[0037] The vehicle positioning module is used to calculate the position and posture of the vehicle in the global coordinate system based on the best matching node.

[0038] In a third aspect, the present invention also provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps in the above-mentioned method for positioning a vehicle in a multi-layer spatial transition area are implemented.

[0039] In a fourth aspect, the present invention further provides a computer storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the vehicle positioning method in the multi-layer spatial transition area are implemented as described above.

[0040] The beneficial effects of the present invention are:

[0041] The present invention first discretizes the multi-layer spatial transition region into a sequence of nodes. Then, by using various sensors to collect vehicle posture information, bird's-eye view image information, altitude information, and vehicle position information corresponding to each node, the various information is correlated to construct a posture feature map for the multi-layer spatial transition region. By using sensors to collect information, hardware deployment and maintenance costs are reduced, and scene adaptability is improved. By correlating the various collected information, robust positioning can be achieved in multi-layer spatial structural degradation scenarios, improving the positioning accuracy of the posture feature map. Using the nodes on the posture feature map as particles and the real-time vehicle posture features as observation information, the DTW algorithm is used to determine the vehicle positioning result. By gradually narrowing the vehicle matching range on the posture feature map, the positioning accuracy of the vehicle positioning result is further improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0043] Figure 1 A method flow chart of an embodiment of a method for locating a vehicle in a multi-layer spatial transition area provided by the present invention;

[0044] Figure 2 A schematic diagram of normalization of vehicle posture information provided by one embodiment of the present invention;

[0045] Figure 3 A schematic diagram of constructing a posture feature map database provided by one embodiment of the present invention;

[0046] Figure 4 A schematic diagram of a vehicle posture feature sequence matching extracted according to an embodiment of the present invention;

[0047] Figure 5 A schematic structural diagram of an embodiment of a vehicle positioning device for a multi-layer space transition area provided by the present invention;

[0048] Figure 6 A schematic structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0049] The preferred embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, and are not used to limit the scope of the present invention.

[0050] The terms "first," "second," and so on, used in the embodiments of the present invention are for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, technical features designated as "first" or "second" may explicitly or implicitly include at least one such feature.

[0051] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present invention. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute a separate or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0052] A specific embodiment of the present invention, as Figure 1 As shown, Figure 1 A method flow chart of an embodiment of a vehicle positioning method in a multi-layer spatial transition area provided by the present invention includes:

[0053] S101: Discretize the multi-layer spatial transition area into sequence nodes, and collect vehicle posture information, bird's-eye view image information, and altitude information corresponding to each node;

[0054] S102: Collecting vehicle location information, associating the vehicle location information with vehicle posture information, bird's-eye view image information, and altitude information to construct a posture feature map database for multi-layer spatial transition areas;

[0055] S103: Using the nodes on the posture feature map as particles and the real-time vehicle posture features as observation information, the DTW algorithm is used to determine the vehicle positioning result.

[0056] The present invention first discretizes the multi-layer spatial transition region into a sequence of nodes. Then, by using various sensors to collect vehicle posture information, bird's-eye view image information, altitude information, and vehicle position information corresponding to each node, the various information is correlated to construct a posture feature map for the multi-layer spatial transition region. By using sensors to collect information, hardware deployment and maintenance costs are reduced, and scene adaptability is improved. By correlating the various collected information, robust positioning can be achieved in multi-layer spatial structural degradation scenarios, improving the positioning accuracy of the posture feature map. Using the nodes on the posture feature map as particles and the real-time vehicle posture features as observation information, the DTW algorithm is used to determine the vehicle positioning result. By gradually narrowing the vehicle matching range on the posture feature map, the positioning accuracy of the vehicle positioning result is further improved.

[0057] In one embodiment of the present invention, collecting vehicle posture information, bird's-eye view image information, and altitude information corresponding to each node includes:

[0058] The barometer is used to collect the altitude information corresponding to each node, the IMU is used to collect the vehicle posture information, and the BEV is used to collect bird's-eye view image information;

[0059] Collect vehicle location information, including:

[0060] Use INS to collect vehicle location information.

[0061] First, it's important to note that before collecting all the information, the multi-source sensors—the IMU (Inertial Measurement Unit), INS (Integrated Navigation System), and BEV (Bird's Eye View)—need to be calibrated for spatiotemporal consistency. Specifically, this involves using trajectory consistency methods to register the IMU and camera, and calibrating the extrinsic parameters of the INS to achieve spatial consistency of the data from these different sensors.

[0062] Among them, the sensor intrinsic parameter calibration consists of two parts: IMU intrinsic parameters and camera intrinsic parameters. The IMU intrinsic parameters include axis deviation, scale factor error, constant zero bias, etc.; the camera intrinsic parameters include parameters such as camera focal length, distortion coefficient and principal point coordinates.

[0063] Sensor extrinsic calibration includes spatial consistency calibration and temporal consistency calibration. For spatial consistency, the extrinsic parameter calibration between the IMU and camera is solved using the well-known Kaliber method, while the extrinsic parameter calibration between the camera and INS is performed using the trajectory consistency method, thereby achieving spatial consistency between heterogeneous sensors. For temporal consistency, linear interpolation is used.

[0064] In one embodiment of the present invention, after collecting the bird's-eye view image information, the method further includes:

[0065] Extracting the lane line semantic information of the BEV image and obtaining the vehicle's deviation from the lane line based on the lane line semantic information;

[0066] The vehicle's position information is normalized using the deviation of the vehicle relative to the lane line.

[0067] It should be noted that when using vehicle posture information to construct a vehicle posture feature map database, the vehicle posture information needs to be normalized. Specifically, BEVTransformer is used to extract the lane line semantic information of the bird's-eye view image, and based on the lane line semantic information, a linear regression method is used to obtain the vehicle's deviation from the lane line; then this deviation is used to correct the vehicle heading obtained by the IMU to achieve normalization of the vehicle posture information.

[0068] For example, if Figure 2 As shown, Figure 2 A schematic diagram of vehicle posture information normalization provided by one embodiment of the present invention. The lane line information in the bird's-eye view image is extracted, and the relative posture relationship with the lane line is calculated. Assume that "1" is the vehicle heading information stored in the posture map, and "2" is the actual heading of the vehicle during the positioning process. By calculating the angle between the vehicle and the lane line, as shown in the figure, θ , to achieve normalization of vehicle heading.

[0069] In one embodiment of the present invention, after collecting the vehicle location information, the method further includes:

[0070] Structural information constraints of multi-layer spatial transition regions are obtained, and vehicle position information is optimized based on the structural information constraints of multi-layer spatial transition regions.

[0071] It should be noted that the vehicle position information in the present invention is specifically high-precision vehicle position information. When using the vehicle position information to construct a vehicle posture feature map database, it is necessary to use the spatial structure information of the multi-layer transition area to optimize the position information collected by INS to obtain high-precision vehicle position information.

[0072] Specifically, by adding structural information constraints to the multi-layer spatial transition region, the impact of cumulative errors is reduced and the vehicle's positioning accuracy is further optimized. The structural information constraints in the multi-layer spatial transition region are structural features that pass through the multi-layer spatial transition region, such as the ramp structure at the entrance and exit, and the turning structure in the multi-layer space. By setting the vehicle pose as a node and the node constraints as edges for optimization, the vehicle's positioning accuracy is improved.

[0073] In one embodiment of the present invention, vehicle position information is associated with vehicle posture information, bird's-eye view image information, and altitude information to construct a posture feature map for a multi-layer spatial transition region, including:

[0074] Traverse the multi-layer spatial transition area and convert the collected data into Organize and store to complete the construction of the pose feature map for the multi-layer spatial transition area, where x ( i ), y ( i ) is the vehicle in i The vehicle location information corresponding to each node, a ( i ), r ( i ), p ( i ) is the vehicle in i The pose information at each node, h ( i ) is the vehicle in i The altitude information at each node, I ( i ) is the first i Bird’s-eye view image information of nodes, is the number of nodes.

[0075] It can be understood that by obtaining the node images collected by the vehicle at each node position, the global descriptor, local descriptor and local feature points of each node image are calculated as bird's-eye view image information, and associated with the vehicle position information, vehicle posture information and vehicle altitude information to construct a posture feature map database.

[0076] The data included in each node is: (1) x (i ), y ( i ) is the vehicle in i The vehicle position corresponding to each node, a ( i ), r ( i ), p ( i ) is the vehicle in i The pose information at each node, h ( i ) is the vehicle in i The altitude information at each node, I ( i ) is the first i By traversing all the areas to be located in the multi-layer space and organizing and storing the collected data according to the above formula, the pose feature map of the area to be located can be constructed. Figure 3 As shown, Figure 3 A schematic diagram of constructing a posture feature map database provided by one embodiment of the present invention.

[0077] In one embodiment of the present invention, before using the DTW algorithm to match the pose feature map to determine the pose feature candidate set, the method further includes:

[0078] Construct a short-term uniform motion model based on the vehicle's historical position information;

[0079] Determine the initial matching range of the vehicle in the posture feature map based on the short-term uniform motion model;

[0080] It's understandable that, given the complexity of multi-layered structural transition regions, historical vehicle position information can be used to predict the current vehicle position, narrowing the range of pose feature map matching and improving map matching efficiency and stability. Furthermore, during the localization process, the real-time vehicle pose features must be normalized. This normalization is performed in the same manner as used to construct the pose feature map and will not be further elaborated here.

[0081] Use the DTW algorithm to match the pose feature map to determine the candidate set of pose features, including:

[0082] The DTW algorithm is used to match the preliminary matching range in the pose feature map to determine the candidate set of pose features.

[0083] The DTW algorithm calculates the distance information between the real-time acquisition sequence and the map node sequence, and then constructs a distance information matrix. It uses the distance information matrix to find the map node sequence that matches the real-time acquisition sequence, and determines the candidate set by setting a threshold.

[0084] It can be understood that within the preliminary matching range, the DTW algorithm is used to determine the candidate set for further matching of the map, that is, the latest acquired posture features are matched with the historical posture features and the posture sequence in the posture feature map to determine the candidate set of posture features. This range is the coarse matching range, and its purpose is to improve the robustness and efficiency of map matching.

[0085] The map nodes in the pose feature map database are treated as particles in the particle filter algorithm. The pose and altitude information obtained from the vehicle are used as observations. The pose features are normalized using lane line information detected from BEV images. The normalized sequence pose and altitude information is then used to match the optimal node sequence in the pose feature map database using the DTW algorithm. The cosine algorithm is used to measure the similarity between nodes.

[0086] Specifically, for example, Figure 4 As shown, Figure 4 A schematic diagram of a vehicle pose feature sequence matching method extracted according to one embodiment of the present invention. The triangles represent historical vehicle pose features stored in the map, and the quadrilaterals represent real-time vehicle pose features during positioning. Using a sliding window model, the historically acquired vehicle pose feature sequences are matched with the pose sequences stored in the map to determine the optimal map matching range, i.e., the pose feature candidate set.

[0087] In one embodiment of the present invention, the nodes in the pose feature candidate set are taken as particles, the real-time vehicle pose features are taken as observation information, the likelihood probability between the observation information and the particles is calculated, and the best matching node in the pose feature candidate set is output, including:

[0088] The likelihood probability between the real-time vehicle posture feature and each node in the posture feature candidate set is calculated, and the importance sampling of the particles is performed to output the best matching node in the posture feature candidate set.

[0089] It is understandable that based on the candidate set of pose features, the normalized pose and altitude information is further used to calculate the likelihood probability of the current vehicle pose information and each particle, and then the particles are importance sampled. Based on the particle importance sampling, the particles with the largest likelihood probability are selected as the best matching nodes.

[0090] In one embodiment of the present invention, calculating the position and posture of the vehicle in the global coordinate system based on the best matching node includes:

[0091] The relative position relationship between the real-time vehicle posture features and the posture features of each node in the best matching node is calculated, and the posture of the vehicle in the global coordinate system is solved through the preset coordinate transformation relationship.

[0092] It can be understood that based on the best matching node on the map, the posture features obtained in real time are aligned with the posture features in the best matching node, and the relative position relationship between the posture features obtained in real time and the posture features in the best matching node is calculated, and then the posture of the vehicle in the global coordinate system is solved through coordinate transformation.

[0093] In order to better implement the vehicle positioning method in the multi-layer space transition area in the embodiment of the present invention, based on the vehicle positioning method in the multi-layer space transition area, correspondingly, Figure 5 As shown, an embodiment of the present invention further provides a vehicle positioning device for a multi-layer space transition area. The vehicle positioning device 500 for a multi-layer space transition area includes:

[0094] The information collection module 501 is used to discretize the multi-layer spatial transition area into sequence nodes and collect the vehicle posture information, bird's-eye view image information and altitude information corresponding to each node;

[0095] A map construction module 502 is used to collect vehicle location information, associate the vehicle location information with vehicle posture information, bird's-eye view image information, and altitude information, and construct a posture feature map database for multi-layer spatial transition areas;

[0096] Matching module 503, used to match the pose feature map using the DTW algorithm to determine a candidate pose feature set;

[0097] The calculation module 504 is used to use the nodes in the pose feature candidate set as particles and the real-time vehicle pose features as observation information, calculate the likelihood probability between the observation information and the particles, and output the best matching node in the pose feature candidate set;

[0098] The vehicle positioning module 505 is used to calculate the position and posture of the vehicle in the global coordinate system based on the best matching node.

[0099] The vehicle positioning device 500 for the multi-layer space transition area provided in the above embodiment can implement the technical solution described in the above embodiment of the vehicle positioning method for the multi-layer space transition area. The specific implementation principles of the above modules or units can be found in the corresponding contents in the above embodiment of the vehicle positioning method for the multi-layer space transition area, which will not be repeated here.

[0100] like Figure 6 As shown, the present invention also provides an electronic device 600. The electronic device 600 includes a processor 601, a memory 602 and a display 603. Figure 6 Only some of the components of the electronic device 600 are shown, but it should be understood that implementation of all of the shown components is not required, and more or fewer components may be implemented instead.

[0101] In some embodiments, the processor 601 can be a central processing unit (CPU), a microprocessor or other data processing chip, used to run the program code or process data stored in the memory 602, such as the vehicle positioning method in the multi-layer spatial transition area of ​​the present invention.

[0102] In some embodiments, processor 601 may be a single server or a server group. The server group may be centralized or distributed. In some embodiments, processor 601 may be local or remote. In some embodiments, processor 601 may be implemented on a cloud platform. In one embodiment, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an internal cloud, multiple clouds, or any combination thereof.

[0103] In some embodiments, the memory 602 may be an internal storage unit of the electronic device 600, such as a hard disk or memory of the electronic device 600. In other embodiments, the memory 602 may also be an external storage device of the electronic device 600, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device 600.

[0104] Furthermore, the memory 602 may include both an internal storage unit of the electronic device 600 and an external storage device. The memory 602 is used to store application software installed in the electronic device 600 and various data.

[0105] In some embodiments, the display 603 can be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen. The display 603 is used to display information about the electronic device 600 and to display a visual user interface. Components 601-603 of the electronic device 600 communicate with each other via a system bus.

[0106] In some embodiments, when the processor 601 executes the vehicle positioning program for the multi-layer spatial transition area in the memory 602, the following steps may be implemented:

[0107] Discretize the multi-layer spatial transition area into sequence nodes, and collect the vehicle posture information, bird's-eye view image information and altitude information corresponding to each node;

[0108] Collect vehicle location information, associate it with vehicle posture information, bird's-eye view image information, and altitude information, and build a posture feature map database for multi-layer spatial transition areas;

[0109] Use the DTW algorithm to match the pose feature map to determine the candidate set of pose features;

[0110] Taking the nodes in the pose feature candidate set as particles and the real-time vehicle pose features as observation information, the likelihood probability between the observation information and the particles is calculated, and the best matching node in the pose feature candidate set is output;

[0111] The vehicle's position in the global coordinate system is calculated based on the best matching node.

[0112] It should be understood that, when the processor 601 executes the vehicle positioning program for the multi-layer spatial transition area in the memory 602 , in addition to the above functions, it can also implement other functions. For details, please refer to the description of the corresponding method embodiment above.

[0113] Furthermore, the embodiment of the present invention does not specifically limit the type of the electronic device 600 mentioned. The electronic device 600 may be a portable electronic device such as a mobile phone, a tablet computer, a personal digital assistant (PDA), a wearable device, a laptop computer, or the like. Exemplary embodiments of portable electronic devices include but are not limited to portable electronic devices equipped with IOS, Android, Microsoft, or other operating systems. The above-mentioned portable electronic devices may also be other portable electronic devices, such as a laptop computer with a touch-sensitive surface (e.g., a touch panel). It should also be understood that in some other embodiments of the present invention, the electronic device 600 may not be a portable electronic device, but a desktop computer with a touch-sensitive surface (e.g., a touch panel).

[0114] Accordingly, an embodiment of the present application also provides a computer-readable storage medium, which is used to store computer-readable programs or instructions. When the program or instructions are executed by a processor, it can implement the steps or functions of the vehicle positioning method in the multi-layer spatial transition area provided by the above-mentioned method embodiments.

[0115] Those skilled in the art will appreciate that all or part of the process steps of the above-described embodiments can be implemented by instructing related hardware through a computer program, and the program can be stored in a computer-readable storage medium, such as a magnetic disk, an optical disk, a read-only memory, or a random access memory.

[0116] The above are only preferred specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or replacements that can be easily thought of by any technician familiar with this technical field within the technical scope disclosed by the present invention should be covered by the scope of protection of the present invention.

Claims

1. A vehicle positioning method in a multi-layer space transition area, characterized in that: include: Discretize the multi-layer spatial transition area into sequence nodes, and collect the vehicle posture information, bird's-eye view image information and altitude information corresponding to each node; Collecting vehicle position information, correlating the vehicle position information with the vehicle posture information, the bird's-eye view image information, and the altitude information, and constructing a posture feature map for the multi-layer spatial transition area; Use the DTW algorithm to match the pose feature map to determine the candidate set of pose features; Taking nodes in the pose feature candidate set as particles and real-time vehicle pose features as observation information, calculating the likelihood probability between the observation information and the particles, and outputting the best matching node in the pose feature candidate set; The position and posture of the vehicle in the global coordinate system is calculated based on the best matching node.

2. The vehicle positioning method in a multi-layer space transition area according to claim 1, characterized in that: After collecting the bird's-eye view image information, the method further includes: Extracting lane line semantic information from the bird's-eye view image, and obtaining a deviation of the vehicle relative to the lane line based on the lane line semantic information; The vehicle posture information is normalized using the deviation of the vehicle relative to the lane line.

3. The vehicle positioning method in a multi-layer space transition area according to claim 1, characterized in that: After collecting the vehicle location information, the method further includes: Structural information constraints of a multi-layer spatial transition region are obtained, and vehicle position errors are optimized based on the structural information constraints of the multi-layer spatial transition region.

4. The vehicle positioning method in a multi-layer space transition area according to claim 1, characterized in that: The step of associating the vehicle position information with the vehicle posture information, the bird's-eye view image information, and the altitude information to construct a posture feature map for the multi-layer spatial transition area includes: Traverse the multi-layer spatial transition area and convert the collected data into vectors Organize and store to complete the construction of the pose feature map for the multi-layer space transition area, wherein: x (i) , y (i) For the vehicle i The vehicle location information corresponding to each node, a (i) 、 r (i) 、 p (i) For the vehicle i The pose information at each node, h (i) For the vehicle i The altitude information at each node, I (i) For the collection of i Bird’s-eye view image information of nodes, is the number of nodes.

5. The vehicle positioning method in a multi-layer space transition area according to claim 1, characterized in that: Before using the DTW algorithm to match the pose feature map to determine the pose feature candidate set, the method further includes: Construct a short-term uniform motion model based on the vehicle's historical position information; Determining a preliminary matching range of the vehicle in the posture feature map based on the short-term uniform motion model; The DTW algorithm is used to match the pose feature map to determine the pose feature candidate set, specifically including: The DTW algorithm is used to match the preliminary matching range in the pose feature map to determine the pose feature candidate set.

6. The vehicle positioning method in a multi-layer space transition area according to claim 5, characterized in that: The method uses nodes in the pose feature candidate set as particles, uses real-time vehicle pose features as observation information, calculates likelihood probabilities between the observation information and the particles, and outputs the best matching nodes in the pose feature candidate set, including: The likelihood probability between the real-time vehicle posture feature and each node in the posture feature candidate set is calculated, and the importance sampling of the particles is performed to output the best matching node in the posture feature candidate set.

7. The vehicle positioning method in a multi-layer space transition area according to claim 6, characterized in that: Calculating the vehicle's position in a global coordinate system based on the best matching node includes: The relative position relationship between the real-time vehicle posture feature and the posture feature of each node in the best matching node is calculated, and the posture of the vehicle in the global coordinate system is solved through a preset coordinate transformation relationship.

8. A vehicle positioning device for a multi-layer space transition area, characterized in that: include: The information collection module is used to discretize the multi-layer spatial transition area into sequence nodes and collect the vehicle posture information, bird's-eye view image information and altitude information corresponding to each node; a map construction module, configured to collect vehicle position information, associate the vehicle position information with the vehicle posture information, the bird's-eye view image information, and the altitude information, and construct a posture feature map for the multi-layer spatial transition area; The matching module is used to match the pose feature map using the DTW algorithm to determine the candidate set of pose features; A calculation module is used to use the nodes in the pose feature candidate set as particles and the real-time vehicle pose feature as observation information, calculate the likelihood probability between the observation information and the particles, and output the best matching node in the pose feature candidate set; The vehicle positioning module is used to calculate the position and posture of the vehicle in the global coordinate system based on the best matching node.

9. An electronic device, characterized in that: comprising a memory and a processor, wherein, The memory is used to store programs; The processor is coupled to the memory and is used to execute the program stored in the memory to implement the steps of the vehicle positioning method in a multi-layer spatial transition area as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that Used to store computer-readable programs or instructions, which, when executed by a processor, can implement the steps of the vehicle positioning method in a multi-layer spatial transition area as described in any one of claims 1 to 7.

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