A memory parking positioning method and related device

Abstract

The present application discloses a memory parking positioning method and related devices. In the present application, the driving trajectory of the vehicle in the target scene is first obtained, and a semantic map and a feature point map are constructed based on the driving trajectory. The semantic map and the feature point map are constructed based on the posture state information of the vehicle. Then, in response to the vehicle performing a memory parking operation in the target scene, the posture state information of the current vehicle is obtained. The memory parking operation is used to instruct the vehicle to park according to the driving trajectory, and the semantic information of the current vehicle is matched with the semantic information in the semantic map, and the feature point information of the current vehicle is matched with the feature point information in the feature point map. Finally, the vehicle is initialized and positioned based on the matching results, so that the vehicle enters the initialization state of memory parking. The present application realizes accurate and efficient initialization positioning of vehicles with memory parking.

Description

Technical Field

[0001] The present application relates to the field of big data technology, and in particular to a memory parking positioning method and related devices. Background Art

[0002] With the development of society, autonomous driving technology is becoming increasingly important in daily life. Autonomous driving technology includes the function of memory parking, which can realize automatic parking and exiting of the vehicle without human intervention.

[0003] When performing memory parking, the vehicle first needs to be initialized and positioned. However, in related technologies, the method for initializing and positioning the vehicle requires a large amount of computing resources, and the accuracy of the initialization positioning is low.

[0004] Therefore, in order to accurately and efficiently initialize and locate a memory-parked vehicle, a memory parking positioning method is currently in urgent need. Summary of the Invention

[0005] In view of this, embodiments of the present application provide a memory parking positioning method and related devices, aiming to achieve accurate and efficient initialization positioning of memory parked vehicles.

[0006] In a first aspect, an embodiment of the present application provides a memory parking positioning method, the method comprising:

[0007] Acquire a driving trajectory of a vehicle in a target scene, and construct a semantic map and a feature point map based on the driving trajectory, wherein the semantic map and the feature point map are constructed based on the posture state information of the vehicle;

[0008] In response to the vehicle performing a memory parking operation in the target scene, obtaining current posture state information of the vehicle, wherein the memory parking operation is used to instruct the vehicle to park according to the driving trajectory, and the posture state information is obtained based on semantic information and feature point information;

[0009] Matching the semantic information of the current vehicle with the semantic information in the semantic map, and matching the feature point information of the current vehicle with the feature point information in the feature point map;

[0010] The vehicle is initialized and positioned based on the matching result, so that the vehicle enters an initialization state of memory parking.

[0011] Optionally, constructing a semantic map and a feature point map based on the driving trajectory includes:

[0012] extracting semantic information from surround view data generated in the driving trajectory using a surround view camera, inputting the extracted semantic information and vehicle body information into an error state Kalman filter (ESKF) model, and constructing the semantic map using vehicle posture state information output by the ESKF model, wherein the vehicle body information includes inertial sensor (IMU) information and wheel encoder information;

[0013] A front surround view camera is used to extract feature point information from the front surround view data generated in the driving trajectory, the extracted feature point information and the vehicle body information are input into the ESKF model, and the vehicle posture state information output by the ESKF model is used to construct the feature point map.

[0014] Optionally, obtaining the driving trajectory of the vehicle in the target scene includes:

[0015] Acquiring global navigation satellite system (GNSS) information and vehicle body information of the vehicle, wherein the vehicle body information includes inertial sensor (IMU) information;

[0016] Performing noise calibration on the IMU to obtain initialized IMU information, and performing coordinate system initialization on the GNSS to obtain initialized GNSS information, thereby completing an initialization operation on the vehicle;

[0017] Obtaining the driving trajectory of the vehicle in the target scene after completing the initialization operation.

[0018] Optionally, in response to the vehicle performing a memory parking operation in the target scene, obtaining current posture state information of the vehicle includes:

[0019] In response to the vehicle performing a memory parking operation in the target scene, acquiring semantic information and feature point information of the current vehicle;

[0020] Based on the semantic information and the feature point information, the current posture state information of the vehicle is obtained.

[0021] Optionally, the initializing the positioning of the vehicle based on the matching result so that the vehicle enters an initialization state of memory parking includes:

[0022] When the matching result shows that the semantic information of the vehicle matches the first semantic information in the semantic map, and the feature point information of the vehicle matches the first feature point information in the feature point map, determining the first position state information of the vehicle based on the first semantic information and the first feature point information;

[0023] The vehicle is initialized and positioned based on the first posture state information, so that the vehicle enters an initialization state of memory parking.

[0024] Optionally, obtaining a driving trajectory of a vehicle in a target scene and constructing a semantic map and a feature point map based on the driving trajectory includes:

[0025] Set the preset distance;

[0026] A target driving trajectory of the vehicle in a target scene is obtained, and a semantic map and a feature point map are constructed based on the target driving trajectory, where the trajectory distance of the target driving trajectory is the preset distance.

[0027] Optionally, the method further includes:

[0028] At least one operation of adding, deleting, modifying and searching is performed on the acquired driving trajectory.

[0029] In a second aspect, an embodiment of the present application provides a memory parking positioning device, the device comprising: a construction module, an acquisition module, a matching module, and a positioning module;

[0030] The construction module is used to obtain the driving trajectory of the vehicle in the target scene, and to construct a semantic map and a feature point map based on the driving trajectory, wherein the semantic map and the feature point map are constructed based on the posture state information of the vehicle;

[0031] The acquisition module is configured to acquire the current posture state information of the vehicle in response to the vehicle performing a memory parking operation in the target scene, wherein the memory parking operation is used to instruct the vehicle to park according to the driving trajectory;

[0032] The matching module is used to match the semantic information of the current vehicle with the semantic information in the semantic map, and to match the feature point information of the current vehicle with the feature point information in the feature point map;

[0033] The positioning module is used to initialize the positioning of the vehicle based on the matching result, so that the vehicle enters an initialization state of memory parking.

[0034] In a third aspect, the present application provides an electronic device, the device comprising: a processor, a memory, and a system bus;

[0035] The processor and the memory are connected via the system bus;

[0036] The memory is used to store one or more programs, and the one or more programs include instructions, which, when executed by the processor, enable the processor to execute the method described in the first aspect.

[0037] In a fourth aspect, an embodiment of the present application provides a computer storage medium, wherein the computer storage medium stores a code. When the code is executed, the device executing the code implements any of the methods described in the first aspect.

[0038] The present application provides a memory parking positioning method and related device. When executing the method, the vehicle's driving trajectory in the target scene is first obtained, and a semantic map and a feature point map are constructed based on the driving trajectory. The semantic map and feature point map are constructed based on the vehicle's posture state information. Then, in response to the vehicle performing a memory parking operation in the target scene, the current vehicle's posture state information is obtained. The memory parking operation is used to instruct the vehicle to park according to the driving trajectory. The semantic information of the current vehicle is matched with the semantic information in the semantic map, and the feature point information of the current vehicle is matched with the feature point information in the feature point map. Finally, based on the matching results, the vehicle is initialized and positioned, so that the vehicle enters the initialization state of memory parking. In this way, by simultaneously obtaining the vehicle's driving trajectory in the target scene and constructing a corresponding semantic map and feature point map based on the driving trajectory, when the vehicle needs to perform a memory parking operation in the target scene, the current vehicle's posture state information can be obtained. Based on the posture state information, the semantic information and feature point information in the semantic map are matched to achieve initial positioning of the vehicle. The implementation is simple, avoiding significant waste of computing resources. Furthermore, the simultaneous use of semantic maps and feature point maps enhances the accuracy of initial positioning in scenarios with weak GNSS signals. Furthermore, since the semantic and feature point maps contain semantic and feature point information from the entire driving trajectory, the initial positioning position can be determined by matching the pose state information regardless of the vehicle's current location, ensuring, to a certain extent, the accuracy of initial positioning during memory parking operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions in this embodiment or the prior art, the following briefly introduces the drawings required for use in the embodiment or the prior art description. 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 creative work.

[0040] Figure 1 A flowchart of a method for memorizing parking positioning provided in an embodiment of the present application;

[0041] Figure 2 A schematic diagram of trajectory acquisition provided in an embodiment of the present application;

[0042] Figure 3 A flow chart of trajectory acquisition provided in an embodiment of the present application;

[0043] Figure 4 A schematic diagram of updating a map list provided in an embodiment of the present application;

[0044] Figure 5 A schematic diagram of initial positioning provided in an embodiment of the present application;

[0045] Figure 6 A flowchart of initialization positioning provided in an embodiment of the present application;

[0046] Figure 7 A schematic structural diagram of a memory parking positioning device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0047] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0048] In this application, the terms "comprises," "comprising," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not preclude the presence of additional identical elements in the process, method, article, or apparatus that includes the element.

[0049] Research into related technologies has revealed that, with the development of society, the memory parking function in autonomous driving technology requires increasingly precise initial positioning. To achieve completely unassisted memory parking, the autonomous driving system must provide even more accurate positioning to ensure vehicle safety and reliability in various complex environments. Furthermore, when performing memory parking, the vehicle must first be initialized and positioned. However, the methods used in related technologies for initializing vehicle positioning consume significant computing resources and suffer from low initial positioning accuracy.

[0050] Based on this, this application proposes a memory parking positioning method and related device. The simultaneous use of semantic maps and feature point maps helps enhance the accuracy of initial positioning in scenarios with weak GNSS signals. Furthermore, because the semantic map and feature point map contain semantic information and feature point information throughout the driving trajectory, the initial positioning position can be determined by matching the posture state information regardless of the current vehicle position, ensuring, to a certain extent, the accuracy of the initial positioning during the memory parking operation.

[0051] In order to help those skilled in the art better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0052] Figure 1 This is a flowchart of a method for memorizing parking positioning provided in an embodiment of the present application, see Figure 1 As shown, a memory parking positioning method provided in an embodiment of the present application includes:

[0053] S11: Acquire a driving trajectory of a vehicle in a target scene, and construct a semantic map and a feature point map based on the driving trajectory.

[0054] The vehicle's driving trajectory in the target scenario is a predetermined trajectory for memory parking. This trajectory is then used as the parking trajectory when the vehicle subsequently needs to perform memory parking in the target scenario. Multiple driving trajectories may be included, and different driving trajectories may differ from one another. The target scenario may be an outdoor parking lot, an indoor parking lot (e.g., an underground parking lot), etc., without limitation.

[0055] The aforementioned S11 mentions "obtaining the driving trajectory of the vehicle in the target scenario". The method of obtaining the driving trajectory requires initializing the vehicle, and then obtaining the driving trajectory based on the initialized vehicle. The method can be: first obtaining the global navigation satellite system GNSS information and vehicle body information of the vehicle, the vehicle body information including the inertial sensor IMU information. Then, the IMU is noise calibrated to obtain the initialized IMU information, and the coordinate system of the GNSS is initialized to obtain the initialized GNSS information, completing the initialization operation of the vehicle. Finally, the driving trajectory of the vehicle that has completed the initialization operation in the target scenario is obtained.

[0056] That is, the GNSS information and body information of the vehicle are obtained, and the body information may include but is not limited to inertial sensor IMU information and wheel encoder information. When the GNSS signal is normal, the IMU is noise calibrated to obtain initialized IMU information, and the GNSS coordinate system is initialized to obtain initialized GNSS information, completing the initialization operation of the vehicle. After the initialization operation is completed, the driving trajectory of the vehicle in the target scenario can be obtained. Among them, the inertial sensor (Inertial Measurement Unit, IMU), also known as the inertial measurement unit, is a sensor mainly used to detect and measure acceleration and rotational angular velocity.

[0057] In the embodiment of the present application, it is necessary to solve the posture state information of the vehicle based on the GNSS data. When initializing the GNSS, the longitude and latitude need to be converted into a rectangular coordinate system to provide a coordinate origin for the solution process and to use the position information obtained by GNSS in the ESKF (Error State Kalman Filter) model to update the ESKF model state. That is, when obtaining the driving trajectory of the vehicle in the target scene, the ESKF and GNSS information can be combined for real-time positioning, and the vehicle body information (IMU information) can be combined to estimate the state of the vehicle, wherein the IMU data is subjected to noise calibration and inertial solution to obtain high-frequency dynamic information provided by the IMU, and the GNSS provides relatively low-frequency single-precision absolute position information. By fusing IMU information and GNSS information, the ESKF model can achieve high-precision positioning and following of the vehicle.

[0058] By initializing the vehicle, the vehicle will not be interfered with by existing data, ensuring that the acquired driving trajectory is adaptable, which is conducive to the direct use of the driving trajectory when the vehicle needs to perform memory parking.

[0059] It should be noted that, in the embodiment of the present application, data generated by the wheel encoder of the vehicle can also be obtained, and the speed of the data can be resolved to provide the ESKF model with vehicle speed observation.

[0060] The aforementioned S11 also mentions "constructing a semantic map and a feature point map based on the driving trajectory". The semantic map and feature point map are constructed based on the vehicle's posture status information. In one possible implementation, the method is: using a surround-view camera to extract semantic information from the surround-view data generated in the driving trajectory, combining the extracted semantic information with the vehicle's posture status information output by the ESKF model, and constructing the semantic map. Using a front surround-view camera to extract feature point information from the front surround-view data generated in the driving trajectory, combining the extracted feature point information with the vehicle's posture status information output by the ESKF model, and constructing the feature point map.

[0061] The Error-State Kalman Filter (ESKF) model refers to an Error-State Kalman Filter (ESKF) model, a technique used in autonomous systems such as robots and drones to filter and estimate variables such as position, velocity, acceleration, and attitude. In the embodiments of this application, the ESKF model is used to determine the vehicle's position and attitude state information in conjunction with data acquired or generated during the driving trajectory.

[0062] In the embodiment of the present application, the vehicle's posture state information refers to the position information and posture information of the vehicle itself with a memory parking function, wherein the position information includes longitude, latitude and altitude, and the posture information includes roll angle, pitch angle and heading angle.

[0063] In the embodiment of the present application, the vehicle is equipped with a surround-view camera, wherein the surround-view camera includes four surround-view cameras of front, back, left and right. In the process of acquiring the driving trajectory, the surround-view camera processes the data generated in the process and constructs a map in combination with the vehicle posture state information output by the ESKF model. Specifically, the surround-view camera is used to extract semantic information from the surround-view data generated in the process of forming the driving trajectory, and the extracted semantic information is input into the error state Kalman filter ESKF model in combination with the vehicle body information. The vehicle posture state information output by the ESKF model is used to construct a semantic map (that is, the semantic information and the vehicle body information are input into the ESKF model as input data, so that the ESKF model outputs the corresponding vehicle posture state information according to the input data to construct a semantic map). The front surround-view camera is used to extract feature point information from the front surround-view data (including but not limited to ORB (Oriented FAST and Rotated BRIEF) features) generated in the process of forming the driving trajectory, and the extracted feature point information is input into the ESKF model in combination with the vehicle body information. The vehicle posture state information output by the ESKF model is used to construct a feature point map. It should be noted that the number of front surround-view cameras and surround-view cameras can be one or more, and is not limited here. For example, there can be four surround-view cameras and one front surround-view camera. Feature point information can include ORB feature information or optical flow information, and vehicle body information can include but is not limited to inertial sensor IMU information, wheel encoder information, and high-precision global positioning sensor RTK (Real-time Kinematic) information.

[0064] In actual application scenarios, a state machine exists within the vehicle. When this state machine issues the "Start Path Learning" command, it extracts feature point information from the front surround-view camera and semantic information from the surround-view camera. This semantic information can include point cloud information and XML documents. The point cloud information can include ground landmarks (such as lane markings and objects), while the XML document can include road and storage location information. Semantic maps and feature point maps are then constructed based on the extracted semantic and feature point information. When the state machine issues the "End Path Learning" command, the driving trajectory is saved and the process ends.

[0065] Figure 2 A schematic diagram of trajectory acquisition provided in an embodiment of the present application is shown in FIG. Figure 2As shown, the GNSS (Global Navigation Satellite System) data is position-resolved to provide the coordinate origin and GNSS observations for the ESKF (Error State Kalman Filter). The wheel encoder data is velocity-resolved to provide velocity observations for the ESKF. The IMU data is noise-calibrated and inertial-resolved to provide state predictions for the ESKF, enabling state updates. Visual feature points are acquired through the front surround-view camera. After initialization, the camera is used to perform pose calculations based on the acquired feature point information. The front surround-view camera provides feature observations for the ESKF. Visual semantics are acquired through the surround-view camera and pose calculations based on the acquired semantic information. The surround-view camera provides semantic observations for the ESKF. When an observation is detected, the ESKF updates the observations and calculates the posterior pose. The acquired vehicle state is cleared and the pose output is converted into a memory trajectory. The output pose is combined with the feature point information to generate a pose map (i.e., a feature point map). The output pose is then combined with the semantic information to generate a pose map.

[0066] It should also be noted that in the embodiments of the present application, when obtaining the vehicle's driving trajectory in the target scenario and constructing a semantic map and a feature point map based on the driving trajectory, the distance of the driving trajectory can be pre-set. When the distance of the vehicle's driving trajectory in the target scenario reaches a preset value, the semantic map and the feature point map can be directly constructed. In one possible implementation, the method is as follows: first, a preset distance is set; then, a target driving trajectory of the vehicle in the target scenario is obtained; and based on the target driving trajectory, a semantic map and a feature point map are constructed, where the trajectory distance of the target driving trajectory is the preset distance.

[0067] The preset distance can be set by those skilled in the art according to actual conditions and application scenarios and is not limited here. For example, the preset distance can be 300 meters. That is, when obtaining the driving trajectory, the target driving trajectory with a trajectory distance of the preset distance is obtained.

[0068] This method of obtaining driving trajectories can avoid unlimited acquisition of driving trajectories, which causes the memorized driving trajectories to occupy a large amount of storage space. At the same time, it avoids a large amount of waste of computing power resources in the process of obtaining driving trajectories, which is conducive to improving the efficiency of driving trajectory acquisition.

[0069] Figure 3 A flow chart of trajectory acquisition provided in an embodiment of the present application is provided in Figure 3As shown, first input the GNSS (Global Navigation Satellite System) and vehicle information to determine whether the initialization is successful. If so, perform ESKF (Error State Kalman Filter) trajectory estimation to determine whether to start memorizing the path. If so, obtain visual semantics (i.e. semantic information) and features (i.e. feature information), and perform pose (i.e. pose state information) solution. When it is determined that the memory distance is met, save the feature map and semantic map. If not, repeat the above steps. When initialization is unsuccessful, determine whether the GNSS signal is normal. If so, perform IMU (Inertial Sensor) noise calibration and coordinate initialization. If the GNSS signal is abnormal, re-enter the GNSS and vehicle information. During the ESKF trajectory estimation process, it can be determined whether to end path memory. If so, save the memory trajectory and end the process.

[0070] S12: In response to the vehicle performing a memory parking operation in the target scene, obtaining current posture state information of the vehicle.

[0071] Among them, the memory parking operation is used to instruct the vehicle to perform automatic parking according to the driving trajectory. The driving trajectory is the driving trajectory of the vehicle in the target scene obtained in the aforementioned S11, and the posture state information is obtained based on semantic information and feature point information.

[0072] In one possible implementation method, step S12 is implemented by first acquiring semantic information and feature point information of the current vehicle in response to the vehicle performing a memory parking operation in the target scene. Then, based on the semantic information and the feature point information, acquiring the current posture state information of the vehicle.

[0073] Specifically, when a vehicle needs to perform a memorized parking maneuver in a target scene, it obtains its current semantic information and feature point information. Similar to the previous description, semantic information can be obtained by performing semantic extraction on the surround view data captured by the surround view camera, while feature point information can be obtained by performing feature extraction on the front surround view data captured by the front surround view camera. Based on this acquired semantic and feature point information, the ESKF model is updated to obtain the corresponding current vehicle pose information.

[0074] S13: Matching the semantic information of the current vehicle with the semantic information in the semantic map, and matching the feature point information of the current vehicle with the feature point information in the feature point map.

[0075] The semantic map and the feature point map are constructed based on the vehicle's driving trajectory in the aforementioned step S11.

[0076] S14: Initializing the positioning of the vehicle based on the matching result, so that the vehicle enters an initialization state of memory parking.

[0077] The method for initializing the positioning of the vehicle mentioned in step S14 may include: first, when the matching result shows that the semantic information of the vehicle matches the first semantic information in the semantic map, and the feature point information of the vehicle matches the first feature point information in the feature point map, determining the first posture state information of the vehicle based on the first semantic information and the first feature point information. Then, initializing the positioning of the vehicle based on the first posture state information to put the vehicle into the initialization state of memory parking.

[0078] Specifically, the semantic information of the current vehicle is matched with the semantic information in the semantic map to determine a match with the first semantic information in the semantic map. Furthermore, the feature point information of the current vehicle is matched with the feature point information in the feature point map to determine a match with the first feature point information in the feature point map. Based on the first semantic information and the first feature point information, the first position state of the vehicle can be determined. Initial positioning of the vehicle is then performed based on the first position state information, placing the vehicle in the initial state for memory parking.

[0079] It should be noted that in an embodiment of the present application, at least one of the operations of adding, deleting, modifying and searching can be performed on the driving trajectory obtained in step S11. After acquisition, the driving trajectory can be stored in a module, in which a map list is created, and the correspondence between the map origin and the map name is stored in the map list. During the stage of acquiring the driving trajectory in step S11, the module will store the acquired driving trajectory. In step S13, the appropriate driving trajectory can be determined from the map list based on the determined posture status information of the current vehicle. It should be noted that at least one of the operations of adding, deleting, modifying and searching the driving trajectory can be performed automatically by the module, or by the user of the vehicle, which is not limited here.

[0080] Figure 4 A schematic diagram of updating a map list provided in an embodiment of the present application is shown in FIG. Figure 4 As shown, when the map list is loaded, the vehicle's position (i.e., the current vehicle's position and state information) is obtained, and the state machine command is obtained to determine whether to perform path (i.e., driving trajectory) maintenance. If so, the path list can be added, deleted, modified, and searched to update the map list. If path maintenance is not required, the map list is reloaded.

[0081] Figure 5 A schematic diagram of initialization positioning provided in an embodiment of the present application is shown in FIG. Figure 5As shown, the GNSS (Global Navigation Satellite System) data is position-resolved, providing the coordinate origin and GNSS observations for the ESKF (Error State Kalman Filter). The wheel encoder data is velocity-resolved, providing velocity observations for the ESKF. The IMU data is noise-calibrated and inertial-resolved, providing state predictions for the ESKF to update the ESKF predictions. When observations are made, the ESKF updates the observations and calculates the posterior pose (i.e., the current vehicle pose state information). The acquired state is then cleared and the pose (i.e., pose state information) is output. This output pose state information is feature-matched with the visual feature points (i.e., feature point information) in the feature map (i.e., feature point map) to achieve coarse localization of the visual feature points. The output pose state information is semantically matched with the visual semantics (i.e., semantic information) in the semantic map to achieve semantic information matching. Based on the matching results, the vehicle pose is resolved, resulting in the visual pose, which is then transmitted to the ESKF as a visual observation.

[0082] Figure 6 A flowchart of initialization positioning provided in an embodiment of the present application first loads the map and path, then inputs the GNSS (Global Navigation Satellite System) and vehicle body information to determine whether the initialization is successful. If so, perform ESKF (Error State Kalman Filter) trajectory estimation to determine whether to start cruising (turn on the memory parking operation). If so, obtain visual semantics (i.e. semantic information) and features (i.e. feature information), match the pose solution with the map (i.e. feature point map and semantic map), and output the pose. Determine whether the vehicle is under control. If so, end the initialization process. If not, re-enter the GNSS and vehicle body information and repeat the above operation. When the above-mentioned initialization is unsuccessful, determine whether the GNSS signal is normal. If so, perform IMU (Inertial Sensor) noise calibration and coordinate initialization. If the GNSS signal is abnormal, re-enter the GNSS and vehicle body information and repeat the above operation. If it is determined that cruising has not started, it is also necessary to re-enter the GNSS and vehicle body information and repeat the above operation.

[0083] It should be noted that in above-ground parking lots, due to the relatively strong GNSS signal, positioning initialization can be performed effectively even without visual observation updates (i.e., matching semantic information of the semantic map to update the ESKF). Therefore, visual updates are primarily used for positioning initialization scenarios in underground parking lots where GNSS signals are weak. As the vehicle progresses deeper into the underground parking lot, the IMU's pose prediction error increases. When the vehicle reaches the memory starting point, it becomes difficult to determine its specific position within the memory path (i.e., driving trajectory). At this time, the visual map (i.e., semantic map) is used to update the vehicle's pose state information, accurately positioning the vehicle within the memory path and preparing for subsequent cruise control.

[0084] In this embodiment, a method for memory parking positioning is proposed. The method first obtains a vehicle's driving trajectory in a target scene and constructs a semantic map and a feature point map based on the driving trajectory. The semantic map and feature point map are constructed based on the vehicle's posture state information. Then, in response to the vehicle performing a memory parking operation in the target scene, the current vehicle posture state information is obtained. The memory parking operation is used to instruct the vehicle to park according to the driving trajectory. The current vehicle's semantic information is matched with the semantic information in the semantic map, and the current vehicle's feature point information is matched with the feature point information in the feature point map. Finally, based on the matching results, the vehicle is initialized and positioned, entering the initialization state for memory parking. In this way, by simultaneously obtaining the vehicle's driving trajectory in the target scene and constructing a corresponding semantic map and feature point map based on the driving trajectory, when the vehicle needs to perform a memory parking operation in the target scene, the current vehicle posture state information can be obtained. The posture state information is matched with the semantic information and feature point information in the semantic map to achieve initial positioning of the vehicle. The implementation is simple, avoiding significant waste of computing resources. Furthermore, the simultaneous use of semantic maps and feature point maps enhances the accuracy of initial positioning in scenarios with weak GNSS signals. Furthermore, since the semantic and feature point maps contain semantic and feature point information from the entire driving trajectory, the initial positioning position can be determined by matching the pose state information regardless of the vehicle's current location, ensuring, to a certain extent, the accuracy of initial positioning during memory parking operations.

[0085] Figure 7 A schematic diagram of the structure of a memory parking positioning device provided in an embodiment of the present application is shown in FIG. Figure 7 As shown, a memory parking positioning device specifically includes: a construction module 100, an acquisition module 200, a matching module 300 and a positioning module 400;

[0086] The construction module 100 is used to obtain a driving trajectory of a vehicle in a target scene, and to construct a semantic map and a feature point map based on the driving trajectory, wherein the semantic map and the feature point map are constructed based on the posture state information of the vehicle;

[0087] The acquisition module 200 is configured to acquire the current posture state information of the vehicle in response to the vehicle performing a memory parking operation in the target scene, wherein the memory parking operation is used to instruct the vehicle to park according to the driving trajectory;

[0088] The matching module 300 is configured to match the semantic information of the current vehicle with the semantic information in the semantic map, and to match the feature point information of the current vehicle with the feature point information in the feature point map;

[0089] The positioning module 400 is configured to initialize the positioning of the vehicle based on the matching result, so that the vehicle enters an initialization state of memory parking.

[0090] In a possible implementation, the building block 100 is configured to:

[0091] extracting semantic information from surround view data generated in the driving trajectory using a surround view camera, inputting the extracted semantic information and vehicle body information into an error state Kalman filter (ESKF) model, and constructing the semantic map using the vehicle posture state information output by the ESKF model;

[0092] A front surround view camera is used to extract feature point information from the front surround view data generated in the driving trajectory, the extracted feature point information and the vehicle body information are input into the ESKF model, and the vehicle posture state information output by the ESKF model is used to construct the feature point map.

[0093] In a possible implementation, the building block 100 is used to:

[0094] Acquiring global navigation satellite system (GNSS) information and vehicle body information of the vehicle, wherein the vehicle body information includes inertial sensor (IMU) information;

[0095] Performing noise calibration on the IMU to obtain initialized IMU information, and performing coordinate system initialization on the GNSS to obtain initialized GNSS information, thereby completing an initialization operation on the vehicle;

[0096] Obtaining the driving trajectory of the vehicle in the target scene after completing the initialization operation.

[0097] In a possible implementation, the acquisition module 200 is configured to:

[0098] In response to the vehicle performing a memory parking operation in the target scene, acquiring semantic information and feature point information of the current vehicle;

[0099] Based on the semantic information and the feature point information, the current posture state information of the vehicle is obtained.

[0100] In a possible implementation, the positioning module 400 is configured to:

[0101] When the matching result shows that the semantic information of the vehicle matches the first semantic information in the semantic map, and the feature point information of the vehicle matches the first feature point information in the feature point map, determining the first position state information of the vehicle based on the first semantic information and the first feature point information;

[0102] The vehicle is initialized and positioned based on the first posture state information, so that the vehicle enters an initialization state of memory parking.

[0103] In a possible implementation, the building block 100 is used to:

[0104] Set the preset distance;

[0105] A target driving trajectory of the vehicle in a target scene is obtained, and a semantic map and a feature point map are constructed based on the target driving trajectory, where the trajectory distance of the target driving trajectory is the preset distance.

[0106] In a possible implementation, the apparatus further includes an operating module, wherein the operating module is configured to:

[0107] At least one operation of adding, deleting, modifying and searching is performed on the acquired driving trajectory.

[0108] In this embodiment, a device for memory parking positioning is proposed. The device includes: a construction module, an acquisition module, a matching module, and a positioning module. The construction module is configured to acquire a vehicle's driving trajectory in a target scene and construct a semantic map and a feature point map based on the driving trajectory. The semantic map and feature point map are constructed based on the vehicle's posture state information. The acquisition module is configured to acquire the vehicle's current posture state information in response to the vehicle performing a memory parking operation in the target scene. The memory parking operation is configured to instruct the vehicle to park according to the driving trajectory. The matching module is configured to match the vehicle's semantic information with the semantic information in the semantic map and the feature point information of the vehicle with the feature point information in the feature point map. The positioning module is configured to initialize the vehicle's positioning based on the matching results, so that the vehicle enters the initialization state for memory parking. Thus, by simultaneously acquiring the vehicle's driving trajectory in the target scene and constructing a corresponding semantic map and feature point map based on the driving trajectory, when the vehicle needs to perform a memory parking operation in the target scene, the vehicle's current posture state information can be acquired and matched with the semantic information and feature point information in the semantic map to achieve initial positioning of the vehicle. The implementation is simple, avoiding significant waste of computing resources. Furthermore, the simultaneous use of semantic maps and feature point maps enhances the accuracy of initial positioning in scenarios with weak GNSS signals. Furthermore, since the semantic and feature point maps contain semantic and feature point information from the entire driving trajectory, the initial positioning position can be determined by matching the pose state information regardless of the vehicle's current location, ensuring, to a certain extent, the accuracy of initial positioning during memory parking operations.

[0109] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the devices and methods according to various embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the module, program segment, or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of the boxes in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.

[0110] The embodiments of the present application also provide corresponding devices and computer-readable storage media for implementing the solutions provided by the embodiments of the present application.

[0111] The device includes a memory and a processor, the memory is used to store instructions or codes, and the processor is used to execute the instructions or codes, so that the device executes a memory parking positioning method described in any embodiment of the present application.

[0112] In practical applications, the computer-readable storage medium can adopt any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or component, or any combination thereof. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present embodiment, the computer-readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it.

[0113] A computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0114] Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0115] Computer program code for performing the operations of the present invention may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0116] It should also be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or device comprising the element.

[0117] The above is merely one specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A memory parking positioning method, characterized in that: The method comprises: Acquire a driving trajectory of a vehicle in a target scene, and construct a semantic map and a feature point map based on the driving trajectory, wherein the semantic map and the feature point map are constructed based on the posture state information of the vehicle; In response to the vehicle performing a memory parking operation in the target scene, obtaining current posture state information of the vehicle, wherein the memory parking operation is used to instruct the vehicle to park according to the driving trajectory, and the posture state information is obtained based on semantic information and feature point information; Matching the semantic information of the current vehicle with the semantic information in the semantic map, and matching the feature point information of the current vehicle with the feature point information in the feature point map; Initializing the positioning of the vehicle based on the matching result, so that the vehicle enters an initialization state of memory parking; The obtaining of the driving trajectory of the vehicle in the target scene includes: Acquiring global navigation satellite system (GNSS) information and vehicle body information of the vehicle, wherein the vehicle body information includes inertial sensor (IMU) information; Performing noise calibration on the IMU to obtain initialized IMU information, and performing coordinate system initialization on the GNSS to obtain initialized GNSS information, thereby completing an initialization operation on the vehicle; Obtaining the driving trajectory of the vehicle in the target scene after completing the initialization operation.

2. The method according to claim 1, characterized in that The constructing of a semantic map and a feature point map based on the driving trajectory includes: extracting semantic information from surround view data generated in the driving trajectory using a surround view camera, inputting the extracted semantic information and vehicle body information into an error state Kalman filter (ESKF) model, and constructing the semantic map using vehicle posture state information output by the ESKF model, wherein the vehicle body information includes inertial sensor (IMU) information and wheel encoder information; A front surround view camera is used to extract feature point information from the front surround view data generated in the driving trajectory, the extracted feature point information and the vehicle body information are input into the ESKF model, and the vehicle posture state information output by the ESKF model is used to construct the feature point map.

3. The method according to claim 1, characterized in that In response to the vehicle performing the memory parking operation in the target scene, obtaining the current posture state information of the vehicle includes: In response to the vehicle performing a memory parking operation in the target scene, acquiring semantic information and feature point information of the current vehicle; Based on the semantic information and the feature point information, the current posture state information of the vehicle is obtained.

4. The method according to claim 1, wherein Initializing the positioning of the vehicle based on the matching result so that the vehicle enters an initialization state of memory parking includes: When the matching result shows that the semantic information of the vehicle matches the first semantic information in the semantic map, and the feature point information of the vehicle matches the first feature point information in the feature point map, determining the first position state information of the vehicle based on the first semantic information and the first feature point information; The vehicle is initialized and positioned based on the first posture state information, so that the vehicle enters an initialization state of memory parking.

5. The method according to claim 1, characterized in that The obtaining of the driving trajectory of the vehicle in the target scene and constructing a semantic map and a feature point map based on the driving trajectory include: Set the preset distance; A target driving trajectory of the vehicle in a target scene is obtained, and a semantic map and a feature point map are constructed based on the target driving trajectory, where the trajectory distance of the target driving trajectory is the preset distance.

6. The method according to claim 1, characterized in that The method further comprises: At least one operation of adding, deleting, modifying and searching is performed on the acquired driving trajectory.

7. A memory parking positioning device, characterized in that: The device includes: a construction module, an acquisition module, a matching module and a positioning module; The construction module is used to obtain the driving trajectory of the vehicle in the target scene, and to construct a semantic map and a feature point map based on the driving trajectory, wherein the semantic map and the feature point map are constructed based on the posture state information of the vehicle; The acquisition module is configured to acquire the current posture state information of the vehicle in response to the vehicle performing a memory parking operation in the target scene, wherein the memory parking operation is used to instruct the vehicle to park according to the driving trajectory; The matching module is used to match the semantic information of the current vehicle with the semantic information in the semantic map, and to match the feature point information of the current vehicle with the feature point information in the feature point map; The positioning module is configured to initialize the positioning of the vehicle based on the matching result, so that the vehicle enters an initialization state of memory parking; The construction module is specifically used to: obtain global navigation satellite system GNSS information and vehicle body information of the vehicle, wherein the vehicle body information includes inertial sensor IMU information; Performing noise calibration on the IMU to obtain initialized IMU information, and performing coordinate system initialization on the GNSS to obtain initialized GNSS information, thereby completing an initialization operation on the vehicle; Obtaining the driving trajectory of the vehicle in the target scene after completing the initialization operation.

8. An electronic device, characterized in that: The device includes: a processor, a memory, and a system bus; The processor and the memory are connected via the system bus; The memory is used to store one or more programs, wherein the one or more programs include instructions, and when the instructions are executed by the processor, the processor executes the memory parking positioning method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores an implementation program for implementing the memory parking positioning method. When the implementation program for implementing the memory parking positioning method is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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