Positioning method, movement control method, chip, controller, equipment and medium

Abstract

The invention provides a positioning method, a movement control method, a chip, a controller, equipment and a medium. The positioning method comprises the steps of obtaining first cruise scene information describing scene features based on a first cruise data set collected by the mobile device in a target scene at first time; searching a first map fragment associated with the first cruise scene information in a target scene map; deriving a second map segment based on at least two cruise data sets including the first cruise data set; determining a corresponding relationship between the map elements in the first map segment and the map elements in the second map segment; and according to the corresponding relationship and the position of the mobile device in the second map fragment at the first time, determining the first position of the mobile device in the target scene map at the first time.

Description

Technical Field

[0001] The present disclosure relates to the field of automatic control technology, and more specifically, to a positioning method for a movable device, a movement control method, a chip, a controller, a movable device, and a computer-readable storage medium. Background Art

[0002] In autonomous driving technology, when a vehicle is driving in a target scene without a Global Navigation Satellite System (GNSS) signal, it can be positioned through data collected by on-board sensors. However, the positioning accuracy of existing positioning methods either depends on environmental conditions or on high-cost hardware such as radar, which has strong application limitations. Summary of the invention

[0003] In view of this, an embodiment of the present disclosure proposes a new technical solution that can perform positioning in a target scene without a GNSS signal.

[0004] According to a first aspect of an embodiment of the present disclosure, a method for positioning a movable device is provided, the method comprising:

[0005] Based on a first cruise data set collected by the mobile device in the target scene at the first time, first cruise scene information describing scene characteristics is obtained;

[0006] Searching for a first map segment associated with the first cruising scene information in a target scene map;

[0007] Obtaining a second map segment based on at least two cruise data sets; wherein different cruise data sets correspond to different timestamps, and data in the same cruise data set have the same timestamp, and the at least two cruise data sets include the first cruise data set and at least one other cruise data set collected before the first time;

[0008] Determining a correspondence between map elements in the first map segment and map elements in the second map segment;

[0009] According to the corresponding relationship and the position of the movable device in the second map segment at the first time, the first position of the movable device in the target scene map at the first time is determined.

[0010] Optionally, searching the target scene map for a first map segment associated with the first cruise scene information includes:

[0011] In the mapping scene information set of the target scene, search for target mapping scene information matching the first cruise scene information; wherein the mapping scene information set includes multiple frames of mapping scene information corresponding to different timestamps, each frame of the mapping scene information is associated with a map fragment constituting the target scene map, the associated mapping scene information and the map fragment are respectively obtained based on a mapping data set and multiple mapping data sets including the mapping data set, different mapping data sets correspond to different timestamps, and data in the same mapping data set have the same timestamp;

[0012] A map segment associated with the target mapping scene information is determined as the first map segment.

[0013] Optionally, the cruise data set includes a plurality of images corresponding to the same timestamp, and the plurality of images are acquired by different cameras carried by the movable device;

[0014] The first cruise scene information is obtained based on the first image in the first cruise data set; and the second map fragment is obtained based on all images in the at least two cruise data sets.

[0015] Optionally, the first cruise data set includes a first image, and the first cruise scene information describing scene characteristics is obtained based on the first cruise data set collected by the movable device in the target scene at the first time, including:

[0016] Image features are extracted from the first image to obtain a feature vector of the first image as the first cruise scene information.

[0017] Optionally, the target scene map and the second map fragment are map elements represented by vector graphics, mapping scene elements in the target scene.

[0018] Optionally, the determining a correspondence between a map element in the first map segment and a map element in the second map segment includes:

[0019] Based on the graphics of the map elements, the element categories, and the relative positions of different map elements in the same map segment, a corresponding relationship between the map elements in the first map segment and the map elements in the second map segment is determined.

[0020] Optionally, searching the target mapping scene information matching the first cruise scene information in the mapping scene information set of the target scene includes:

[0021] In the mapping scene information set, searching for first mapping scene information whose similarity with the first cruise scene information meets a set condition;

[0022] Determining a consistency score of the first mapping scene information according to whether the first mapping scene information and the second mapping scene information have an association relationship; wherein the second mapping scene information is mapping scene information whose similarity with the second cruise scene information satisfies the set condition, and the collection time of the second cruise data set corresponding to the second cruise scene information is before the first time;

[0023] In the first mapping scene information whose consistency score is greater than or equal to a set value, a frame of mapping scene information is selected as the target mapping scene information.

[0024] Optionally, determining whether the first mapping scene information and the second mapping scene information have an association relationship includes:

[0025] Constructing a first mapping scene information set by using the first mapping scene information and mapping scene information of adjacent frames of the first mapping scene information;

[0026] When the first mapping scene information set and the second mapping scene information set have the same mapping scene information, determining that the first mapping scene information in the first mapping scene information set and the second mapping scene information in the second mapping scene information set have an association relationship;

[0027] The second mapping scene information set is constructed by the second mapping scene information and adjacent frame mapping scene information of the second mapping scene information.

[0028] Optionally, constructing a first mapping scene information set by using the first mapping scene information and the mapping scene information of adjacent frames of the first mapping scene information includes:

[0029] For each frame of first mapping scene information, setting the first mapping scene information and the mapping scene information of adjacent frames of the first mapping scene information to form a first mapping scene information set;

[0030] Different first mapping scene information sets having the same mapping scene information are merged to obtain an updated first mapping scene information set.

[0031] Optionally, the cruise data set further includes movement data reflecting the movement speed of the movable device at the corresponding timestamp; after determining the first position of the movable device in the target scene map at the first time, the method further includes:

[0032] Determine a first movement distance of the movable device in the target scene map based on the first position and a second position of the movable device in the target scene map; wherein the second position is determined based on a third cruise data set collected by the movable device at a third time, and the third time is before the first time;

[0033] determining a second movement distance of the movable device based on the movement data of the first cruise data set and the movement data of the third cruise data set;

[0034] The first moving distance is compared with the second moving distance, and the validity of the first position is determined according to the comparison result.

[0035] According to a second aspect of the present disclosure, a method for controlling movement of a movable device is also provided, the method comprising:

[0036] Based on a first cruise data set collected by the mobile device in the target scene at the first time, first cruise scene information describing scene characteristics is obtained;

[0037] Searching for a first map segment associated with the first cruising scene information in a target scene map;

[0038] Obtaining a second map segment based on at least two cruise data sets; wherein different cruise data sets correspond to different timestamps, and data in the same cruise data set have the same timestamp, and the at least two cruise data sets include the first cruise data set and at least one other cruise data set whose acquisition time is before the first timestamp;

[0039] Determining a correspondence between map elements in the first map segment and map elements in the second map segment;

[0040] Determine a first position of the movable device in the target scene map at the first time according to the corresponding relationship and the position of the movable device in the second map segment at the first time;

[0041] determining a moving path of the movable device according to the first position and a target position of the movable device in the target scene;

[0042] Based on the determined movement path, the movable device is controlled to move.

[0043] According to a third aspect of the present disclosure, a chip according to some embodiments is provided, which may include:

[0044] a storage unit for storing a computer program; and,

[0045] A processing unit, wherein the processing unit is configured to implement the method according to the first aspect and / or the second aspect of the present disclosure when executing the computer program stored in the storage unit.

[0046] According to a fourth aspect of an embodiment of the present disclosure, a controller is provided, comprising a memory and a processor, wherein the memory is used to store computer instructions, and the processor is used to call the computer instructions from the memory to execute the method described in the first aspect and / or the second aspect.

[0047] According to a fifth aspect of the present disclosure, a mobile device according to some embodiments is provided, and the mobile device may include a chip according to the third aspect of the present disclosure; or include a controller according to the fourth aspect of the present disclosure; or may include:

[0048] processor;

[0049] a memory for storing processor-executable instructions;

[0050] The processor is configured to implement the method according to the first aspect and / or the second aspect of the present disclosure when executing the instructions stored in the memory.

[0051] According to a sixth aspect of the present disclosure, a non-volatile computer-readable storage medium is also provided, on which computer program instructions are stored, and the computer program instructions implement the above method when executed by a processor.

[0052] According to the positioning method provided by the embodiment of the present disclosure, the positioning of the movable device in the cruise is achieved by matching the elements between the first map fragment in the target scene map obtained by mapping and the second map fragment created in the cruise. Moreover, when matching the elements between the map fragments, the single-frame map fragment obtained from the first cruise data set is also expanded to obtain the second map fragment. Compared with the independent feature recognition of each element, this can effectively reduce the influence of environmental conditions on the positioning accuracy, so that the application limitation problem that the positioning accuracy is affected by environmental conditions or hardware configuration can be improved.

[0053] Other features and advantages of the present disclosure will become apparent from the following detailed description of exemplary embodiments of the present disclosure with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0055] Figure 1 is a schematic diagram of an intelligent network connection system to which the method provided by the embodiment of the present disclosure can be applied;

[0056] Figure 2 is based on Figure 1 A schematic diagram of a movable device provided by the illustrated embodiment;

[0057] Figure 3 is a flowchart of a positioning method according to some embodiments;

[0058] Figure 4 is a schematic diagram of a process for matching target mapping scene information according to some embodiments;

[0059] Figure 5 is a schematic diagram of a process for determining a map segment according to some embodiments;

[0060] Figure 6 is a schematic diagram of a process of matching map segments according to some embodiments;

[0061] Figure 7 is a flowchart of a positioning method according to some other embodiments;

[0062] Figure 8 is a flow chart of a mobile control method according to some embodiments;

[0063] Fig. 9 is a schematic diagram of the composition structure of a chip according to some embodiments;

[0064] Fig.10 is a schematic diagram of a component structure of a controller according to some embodiments;

[0065] Fig.11 is a schematic diagram of the composition structure of a movable device according to some embodiments. DETAILED DESCRIPTION

[0066] Various exemplary embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. It should be noted that the relative arrangement of components and steps, numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present disclosure unless otherwise specifically stated.

[0067] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the present disclosure, its application, or uses.

[0068] Technologies, methods and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the above-mentioned technologies, methods and equipment should be considered as part of the specification.

[0069] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not limiting. Therefore, other examples of the exemplary embodiments may have different values.

[0070] It should be noted that like reference numerals and letters refer to similar items in the following figures, and therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0071] The present disclosure relates to a positioning solution for a mobile device in a scenario without GNSS signals. Taking a vehicle parking in an underground parking garage without GNSS signals as an example, by initializing the positioning of the vehicle in the parking garage, the parking path from the current position of the vehicle to the target parking space can be determined, thereby achieving accurate parking of the vehicle.

[0072] In the related art, the vehicle positioning can be realized based on the environmental data of the parking garage collected by the on-board sensor and the vehicle's own motion data, as well as the historical mapping results for the parking garage. Based on such a concept, there are a variety of optional implementations. For example, in one implementation, the environmental image can be collected by a camera, and feature points can be extracted from the environmental image, and the position of the vehicle in the scene map can be determined by identifying the corresponding feature points in the scene map obtained by mapping; for this implementation, since the recognition accuracy of the feature points is sensitive to the environment and lighting, when there are many dynamic objects in the scene or the lighting conditions are poor, the positioning accuracy cannot be guaranteed, that is, the positioning accuracy of this implementation depends on the environmental conditions and the robustness is poor. For another example, in another implementation, the scene can also be scanned by radar, and the position of the vehicle in the scene map can be determined based on the point cloud features of the scene scan; compared with feature point recognition, although this implementation reduces the dependence on environmental conditions, the hardware cost is also significantly increased, and the implementation of the solution depends on the vehicle configuration, and there are also application limitations.

[0073] In the related art, there are some other conceived positioning solutions. For example, based on the GNSS prior results, the sensing data of the inertial measurement unit (IMU), wheel speed meter and other sensors are integrated to infer the current position of the vehicle; for this positioning solution, even if the vehicle has a small angle deviation when entering the parking garage, after a long period of calculation, the position will have a large deviation, and the positioning accuracy is relatively low. For another example, the vehicle is initialized based on the ramp information and the distance between the vehicle and the walls on both sides of the ramp. This requires that there must be a ramp in the scene. The assumption of the scene is too strong and the usage scenario is relatively limited.

[0074] It can be seen that in the related art, the positioning accuracy without GNSS signals is highly dependent on environmental conditions or hardware configurations, and has application limitations. To this end, the embodiments of the present disclosure propose a positioning method that can at least reduce this limitation.

[0075] The method of the embodiment of the present disclosure can be applied to a mobile device, and the mobile device can be communicatively connected with a server and / or a user terminal to form an intelligent network connection system. Figure 1 An intelligent network connection system 100 to which the method provided by the embodiment of the present disclosure can be applied is schematically provided. Figure 1 As shown, the intelligent network connection system 100 may include: a movable device 101, a server 102, and a user terminal 103.

[0076] In some examples, the movable device 101 may be a vehicle with an automatic driving function, a robot that can move autonomously, and the like. Among them, automatic driving is also called unmanned driving or intelligent driving. Vehicles with automatic driving functions can achieve driving tasks such as environmental perception, decision planning, and control execution. The level of automatic driving can refer to the automobile intelligence classification standard formulated by the Society of Automotive Engineers (SAE). For example, L0 is manual driving, L1 is assisted driving, L2 is partial automatic driving, L3 is conditional automatic driving, L4 is highly automatic driving, and L5 is fully automatic driving. The above classification method of automatic driving levels is only used as an example, and the embodiments of the present disclosure do not limit the classification standards and levels of automatic driving.

[0077] In some examples, the server 102 may be a single server or a distributed server cluster consisting of multiple servers, and its deployment method may include a local server and / or a cloud server. The server 102 may communicate with the mobile device 101 and / or the user terminal 103 based on a communication network, and provide various services for the mobile device 101 and / or the user terminal 103. For example, the server may receive the perception data sent by the mobile device, and provide the mobile device with services such as high-precision maps, data analysis, and decision-making planning. For another example, the server may receive query instructions or control instructions sent by the user terminal, and provide corresponding services to the user.

[0078] In some examples, the user terminal 103 may be any form of electronic device that provides services to users, such as a personal computer, a laptop, a smart tablet, a smart phone, a smart wearable device, etc. The user may interact with the mobile device or server through the human-computer interaction terminal configured by the mobile device 101, or may interact with the mobile device or server through the user terminal 103, for example, querying the status and / or parameters of the mobile device through the user terminal, or controlling the mobile device to perform set tasks and / or modify configuration parameters, etc.; wherein the user terminal 103 runs an application based on the intelligent network connection system to achieve interaction with the mobile device or server, and the application may be a local application, a web application, a small program, etc., which is not limited here.

[0079] In some examples, the above application running on the user terminal 103 can provide authentication or authorization services for the user, and the user who is successfully authenticated and granted the corresponding authority can query and / or control the removable device within the scope of the granted authority.

[0080] The mobile device 101, the server 102 and the user terminal 103 can communicate with each other through the communication link provided by the communication network 104. The communication network 104 may include one or more networks of any type, for example, the communication network 104 may include the Internet, a local area network (LAN), a wide area network (WAN), a virtual private network (VPN), a public switched telephone network (PSTN), a satellite communication network, Wi-Fi, 2G, 3G, 4G, 5G, 6G, NB-IoT, eMTC, infrared, Bluetooth, NFC and other networks providing communication, or a combination of the above multiple networks. The communication networks between the mobile device 101 and the server 102, between the user terminal 103 and the server 102, and between the user terminal 103 and the mobile device 101 may be the same or different.

[0081] It should be noted that Figure 1 The structure of the intelligent network system 100 shown in the figure is only illustrative. The intelligent network system in the embodiment of the present disclosure is not limited to the above structure, and may include more or less devices as needed, and may also combine or split the devices. For example, the intelligent network system may not include Figure 1 The user terminal in.

[0082] The positioning method of the embodiment of the present disclosure may be independently implemented by the mobile device 101, may be implemented by the server 102, or may be jointly implemented by the mobile device 101 and the server 102, which is not limited here.

[0083] Figure 2 is based on Figure 1 The embodiment shown is a schematic diagram of a mobile device 101. Figure 2 As shown, the mobile device 101 may include a perception component 1011, a computing platform 1012, an execution component 1013, etc. The perception component 1011, the computing platform 1012, and the execution component 1013 may be connected via a bus or other means.

[0084] In some examples, the perception component 1011 can be used to collect information about the movable device itself or outside, and the perception component 1011 can include a visual sensing unit and a motion sensing unit. The visual sensing unit can include one or more cameras. The motion sensing unit can include a wheel speed meter and / or an inertial measurement unit (IMU). In another example, the perception component 1011 can also include a radar, a positioning and navigation unit, etc., which are not limited here. The radar can include at least one of a laser radar, a millimeter wave radar, an ultrasonic radar, or other radars. The wheel speed meter can be of any type, such as a magnetoelectric wheel speed meter, a photoelectric wheel speed meter, a mechanical wheel speed meter, a Hall effect wheel speed meter, or a visual wheel speed meter. The positioning and navigation unit can include at least one of a GPS system, a Beidou system, or other global positioning systems.

[0085] In some examples, the computing platform 1012 may include a device with computing capabilities, which is used to process the perception information collected by the perception component 1011 to obtain control information, and send corresponding control instructions to the execution component 1013, so that the execution component 1013 performs corresponding actions, thereby realizing control of the mobile device 101. For example, the computing platform 1012 can perform positioning and mapping (Simultaneous Localization and Mapping, SLAM), path planning, behavior decision-making and other behaviors on the mobile device, thereby realizing autonomous control of the mobile device. The computing platform 1012 may include at least one processor and at least one memory, and each processor may execute instructions stored in the memory alone or together to implement the method provided in the embodiment of the present disclosure. The processor in the embodiment of the present disclosure may include at least one of a central processing unit (CPU), a graphic processing unit (GPU), a field programmable gate array (FPGA), a system on chip (SOC), an application specific integrated circuit (ASIC), a microcontroller unit (MCU) or other processors. The memory can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. In addition to storing instructions, the memory can also store data, such as high-precision maps, path information, location, direction, speed and other data of movable devices. The data stored in the memory can be obtained and used by the processor.

[0086] In some examples, the computing platform of the mobile device can independently perform computing tasks, or can communicate with a server to complete computing tasks. For example, the computing platform of the mobile device can cooperate with the server to complete corresponding computing tasks.

[0087] The computing platform 1012 may be set in the mobile device 101, and part or all of the computing platform 1012 may also be set in a server corresponding to the mobile device. For example, some functions of the computing platform 1012 with higher real-time requirements are set in the mobile device, and other functions with lower real-time requirements are set in the server corresponding to the mobile device.

[0088] In some examples, the execution component 1013 is used to execute corresponding actions based on the control of the computing platform 1012, so that the movable device 101 completes the moving task. The execution component 1013 may include, for example, a power component, a brake component, a transmission component, a steering component, and the like.

[0089] It should be noted that Figure 2 The structure of the mobile device 101 shown in the figure is only illustrative. The mobile device in the embodiment of the present disclosure is not limited to the above structure, and may include more or fewer components as needed, and the device may be combined or split. For example, the mobile device may not include the above computing platform. For another example, the mobile device may also include a communication component, an interface component, a multimedia component, an input component, an output component, etc.

[0090] Combine the following Figure 1 The shown system illustrates various embodiments of the present disclosure.

[0091] <First Embodiment>

[0092] Figure 1 The positioning method of a movable device according to some embodiments is shown. The positioning method according to the embodiment of the present disclosure can be used to complete the positioning initialization of the movable device in the target scene, and can also be used to perform continuous tracking and positioning after completing the positioning initialization, which is not limited here.

[0093] like Figure 1 As shown, the method of the embodiment of the present disclosure may include the following steps S310 to S350:

[0094] Step S310, based on a first cruise data set collected by the movable device in the target scene at a first time, obtain first cruise scene information describing scene characteristics.

[0095] In this embodiment, the target scene may be a scene without GNSS signals, and the method of this embodiment can perform reliable positioning in the target scene without GNSS signals. For example, the target scene is an underground space such as an underground parking garage, or an outdoor scene where the GNSS signal is blocked by obstructions such as high-rise buildings.

[0096] by Figure 2Taking the mobile device 101 in the example, the mobile device 101 is provided with a sensing component 1011, and the sensing component 1011 can collect cruise data sets at a set sampling frequency. Different cruise data sets correspond to different timestamps, and the data in the same cruise data set correspond to the same timestamp. The timestamp is used to mark the sampling time of the data set. For example, the timestamp corresponding to the first cruise data set is the first timestamp used to mark the first time. It can be understood that the data in the same cruise data set correspond to the same timestamp, which does not mean that the sampling times of these data are strictly aligned. In practical applications, a time window for alignment can be set, that is, the data collected within a time window can be considered to correspond to the same timestamp.

[0097] In this embodiment, the cruise data set may include scene data. Scene data is data that feeds back the content of the target scene. The scene data in each cruise data set can be used to determine the cruise scene information describing the scene characteristics, and to generate a map segment about the target scene. The map segment generated based on the scene data of a cruise data set is referred to as a single-frame map segment.

[0098] In this embodiment, the cruise data set may also include movement data of the movable device 101. The movement data in each cruise data set may feedback the movement speed of the movable device 101 at the corresponding timestamp, wherein the movement speed is a vector including movement rate and movement direction, and the movement speed may include linear speed and / or angular speed, and the movement data may be the movement speed itself or any data that can be used to determine the movement speed, which is not limited here. The movement data may be used to splice single-frame map fragments generated based on scene scanning data of different timestamps to obtain map fragments of a larger range.

[0099] In some examples, the scene data may be a scene image captured by one or more cameras. In other words, the method of this embodiment can reduce the impact of environmental conditions on positioning accuracy and is suitable for collecting scene data through a camera. Here, since the camera is the basic hardware configuration of a mobile device and has a significant cost advantage over radar, the implementation of the method of this embodiment is not limited by the hardware configuration and can be adapted to any vehicle with intelligent driving functions.

[0100] Of course, those skilled in the art will appreciate that, when the vehicle is equipped with a radar for scene scanning, the method of this embodiment also supports collecting scene data through radar, or collecting scene data through a combination of radar and camera, etc.

[0101] In some examples, the movement data can be provided by a wheel speed meter and / or an IMU, which can be flexibly selected according to the vehicle hardware configuration without affecting the positioning accuracy. In fact, both the wheel speed meter and the IMU are basic hardware configurations of the mobile device, so the implementation of the method of this embodiment is not limited by the hardware configuration.

[0102] The first time in step S310 may be any sampling time. In some examples, the processing corresponding to the method of this embodiment may be performed on each cruise data set collected by the sensing component.

[0103] In another example, due to the limited movement of the movable device, the scene data collected by the sensing component in two samplings will not have changes that affect the positioning accuracy, wherein the movement includes distance and angle. For example, in the scene of the underground parking lot, the scene elements are mainly parking spaces and pillars. If the movement of the vehicle is limited, the scene elements within the radiation range of the sensing component 1011 will not change substantially. Therefore, it is also possible to select a key data set based on the movement of the movable device 101 in the target scene for processing corresponding to the method of this embodiment, without having to process each collected data set, so as to reduce the amount of data processing and improve the response speed. For example, the first cruise data set C1 collected by the movable device 101 at sampling time t1 is set as the key data set; at sampling time t2, if the movement of the movable device 101 is less than or equal to the first set threshold, the second cruise data set C2 collected at sampling time t2 can be ignored; at sampling time t3, if one of the moving distance and angle of the movable device 101 relative to sampling time t1 is greater than the corresponding first set threshold, the third cruise data set C3 collected at sampling time t3 is determined to be the key data set, and so on. In this example, the first cruise data set may be any key data set.

[0104] In this embodiment, by performing feature extraction on the scene data in the first cruise data set, first cruise scene information describing scene features can be obtained. The first cruise scene information can be represented by a feature vector.

[0105] Step S320: searching the target scene map for a first map segment associated with the first cruise scene information.

[0106] The vehicle is cruising in the target scene this time, and needs to be positioned based on the target scene map as a result of historical mapping. Taking the removable device as an example, in this embodiment, the mapping data set required to create the target scene map can be collected by the current vehicle, other vehicles, or a dedicated collection vehicle in the target scene at a historical time, which is not limited here. Taking the current vehicle for historical mapping as an example, the target scene map can be created by the current vehicle, or it can be created by the server based on the mapping data set uploaded by the current vehicle, and sent to the current vehicle for use. Taking other vehicles or collection vehicles as examples, the target scene map can be created by the vehicle and uploaded to the server for sharing between vehicles, or it can be created by the server based on the mapping data set uploaded by the vehicle, and sent to networked vehicles for use, etc.

[0107] In some examples, the scene data of the first cruise data set includes a first image, and the first cruise scene information is a feature vector of the first image obtained by extracting image features from the first image. Representing the first cruise scene information by a feature vector can reduce storage space usage, achieve lightweight detection, and improve image comparison detection efficiency.

[0108] The first image is, for example, an image captured by a forward-looking camera of the movable device 101. Visual scene detection is performed based on scene information of the forward-looking field of view of the movable device in the direction of travel, which helps to reduce the amount of data processing for coordinate conversion in vehicle positioning and improve data processing accuracy.

[0109] The feature vector of the first image is, for example, a global feature descriptor vector of the first image. The global feature descriptor can better capture the overall characteristics of the image, thereby concisely and comprehensively representing the features of the image, so as to more effectively complete visual scene detection.

[0110] In this embodiment, since the first cruise scene information is obtained based on the first cruise data set collected by the vehicle at the current position, the first cruise scene information can reflect the scene characteristics of the scene area where the vehicle's current position is located. In this way, based on the first cruise scene information, a map segment corresponding to the scene area where the vehicle's current position is located can be found in the target scene map as the first map segment to perform primary positioning of the movable device. The primary positioning here can also be called coarse positioning.

[0111] In some examples, in addition to the target scene map, the map database may also include a mapping scene information set of the target scene, the mapping scene information set including multiple frames of mapping scene information corresponding to different timestamps, and each frame of mapping scene information is associated with a map fragment in the target scene map. Similar to the cruise scene information, the mapping scene information is obtained based on a mapping data set collected by the vehicle during historical mapping. Similar to the cruise data set, different mapping data sets collected by the vehicle during historical mapping correspond to different timestamps, and the data in the same mapping data set have the same timestamp. The mapping data set may also include scene data and mobile data, which will not be elaborated here. In this way, when performing the first-level positioning, based on the similarity between the first cruise scene information and each frame of mapping scene information in the mapping scene information set, a frame of mapping scene information matching the first cruise scene information can be determined as the target mapping scene information, and the map fragment associated with the target mapping scene information is used as the first map fragment to improve the processing speed of the first-level positioning. Here, the similarity between the cruising scene information and the mapping scene information may be determined based on cosine similarity, Euclidean distance, Manhattan distance, or Pearson correlation coefficient, etc., which is not limited here.

[0112] In some examples, the mapping scene information with the highest similarity score may be used as the target mapping scene information, and the map segment associated with the target mapping scene information may be used as the first map segment.

[0113] In other examples, when searching for target mapping scene information that matches the first cruise scene information, in addition to considering the similarity between the first cruise scene information and the mapping scene information, a spatiotemporal consistency check may be performed on the information matching to improve the reliability of the information matching. In these examples, searching for target mapping scene information that matches the first cruise scene information in the mapping scene information set of the target scene may include the following steps S3201 and S3203:

[0114] Step S3201: searching, in a mapping scene information set, K frames of first mapping scene information whose similarity with the first cruise scene information meets a set condition.

[0115] In this embodiment, K is an integer greater than or equal to 1. In order to improve the robustness of the primary positioning, K may be an integer greater than or equal to 2, that is, at least two first mapping scene information may be determined through step S3201 for screening the target mapping scene information.

[0116] The setting condition may be that the similarity is greater than or equal to the second setting threshold, or may be the first K frames arranged from high to low similarity, etc., which is not limited here.

[0117] Step S3202: for each frame of first mapping scene information, determine a consistency score of the first mapping scene information according to whether the first mapping scene information has an association relationship with the second mapping scene information.

[0118] The second mapping scene information is mapping scene information whose similarity with the second cruise scene information satisfies a set condition, and the second cruise scene information is cruise scene information determined based on a second cruise data set collected by the movable device in the target scene at a second time, wherein the second time is before the first time. For example, the second cruise data set may be a previous frame data set or a previous frame key data set of the first cruise data set.

[0119] When the first mapping scene information and the second mapping scene information are associated, the consistency score of the first mapping scene information is related to the consistency score of the second mapping scene information. For example, the consistency score of the first mapping scene information can be accumulated by a set increment based on the consistency score of the second mapping scene information. Here, the set increment can be simply set to a step size of "1". Of course, other compensating set increments can also be used as needed, which is not limited here.

[0120] When the first mapping scene information has no association relationship with the second mapping scene information, the consistency score of the first mapping scene information may be set to an initial value, where the initial value is, for example, “0”.

[0121] Step S3203: Select a frame of mapping scene information from among the first mapping scene information whose consistency score is greater than or equal to a set value as target mapping scene information that matches the first cruising scene information.

[0122] The set value is related to the initial value and the set increment of the above consistency score. The set value can be determined by comprehensively considering the response speed and positioning accuracy. For example, if the initial value is 0 and the set increment is 1, the set value can be 2 or 3, etc.

[0123] In step S3203, if the consistency score of only one frame of the first mapping scene information reaches the set value, then the first mapping scene information of this frame is the target mapping scene information that matches the first cruising scene information.

[0124] In step S3203, if there are more than two frames (including two frames) of the first mapping scene information whose consistency scores reach the set value, then the frame with the highest similarity to the first cruise scene information can be selected from these first mapping scene information as the target mapping scene information.

[0125] In this example, the temporal and spatial consistency of positioning is checked by checking the correlation of continuous cruise scene information with similar mapping scene information, which is beneficial to improving the reliability of positioning.

[0126] Furthermore, when determining whether the first mapping scene information and the second mapping scene information have an association relationship, it can be determined based on the first mapping scene information and the second mapping scene information themselves. For example, if the similarity between the first mapping scene information and the second mapping scene information reaches a third set threshold, it can be considered that the two have an association relationship.

[0127] When determining whether the first mapping scene information and the second mapping scene information have an association relationship, it can also be determined based on the first mapping scene information in combination with the mapping scene information of the adjacent frames of the first mapping scene information, and / or based on the second mapping scene information in combination with the mapping scene information of the adjacent frames of the second mapping scene information, that is, it can be determined by constructing an information set centered on the first mapping scene information and / or the second mapping scene information whether there is an association relationship between the first mapping scene information and the second mapping scene information, which can reduce the amount of data processing to confirm the association relationship on the one hand, and can also speed up the convergence speed and improve the positioning efficiency while improving the reliability of information matching on the other hand. Therefore, in some examples, determining whether the first mapping scene information and the second candidate mapping scene information have an association relationship can include the following steps: constructing a first mapping scene information set through the first mapping scene information and its adjacent frame mapping scene information; and, when the first mapping scene information set and the second mapping scene information set have the same mapping scene information, determining that the first mapping scene information in the first mapping scene information set and the second mapping scene information in the second mapping scene information set have an association relationship. The second mapping scene information set is constructed by the second mapping scene information and its adjacent frame mapping scene information.

[0128] In this example, the consistency score of the first mapping scene information is also the consistency score of the first mapping scene information set to which the first mapping scene information belongs, that is, the first mapping scene information in the first mapping scene information set has the same consistency score. Similarly, the consistency score of the second mapping scene information is also the consistency score of the second mapping scene information set to which the second mapping scene information belongs.

[0129] See also Figure 4 , taking the setting condition of selecting the top three mapping scene information in similarity as an example, that is, K is 3, the figure shows the cruise scene information C1, C2, and C3, which are obtained based on three consecutive key frame data sets collected after the movable device enters the target scene.

[0130] Take the cruise scene information C3 as the first cruise scene information and the cruise scene information C2 as the second cruise scene information as an example. By calculating the similarity between the cruise scene information C3 and each frame of the mapping scene information in the mapping scene information set, the first mapping scene information M2, M5, and M10 with the top three similarities are obtained, wherein the first mapping scene information M2 and the adjacent mapping scene information M1 and M3 constitute the first mapping scene information set G9, the first mapping scene information M5 and the adjacent mapping scene information M4 and M6 constitute the first mapping scene information set G10, and the first mapping scene information M10 and the adjacent mapping scene information M9 and M11 constitute the first mapping scene information set G11. Based on whether the first mapping scene information sets G9, G10, and G11 have the same mapping scene information as the second mapping scene information sets G7 and G8 corresponding to the cruise scene information C2, it can be determined that the first mapping scene information in the first mapping scene information set has an associated relationship with the candidate mapping scene information in the second mapping scene information set. For example, the second mapping scene information set G8 includes mapping scene information M2-M6. Therefore, the first mapping scene information sets G9 and G10 are associated with the second mapping scene information set G8. The consistency scores of the first mapping scene information M2 and M5 in the first mapping scene information sets G9 and G10, that is, the consistency scores of the first mapping scene information sets G9 and G10, will be determined based on the consistency score of the second mapping scene information set G8.

[0131] In addition, when constructing the first mapping scene information set through the first mapping scene information and the mapping scene information of the adjacent frames, different first mapping scene information sets with the same mapping scene information can be merged, which can expand the scope of spatiotemporal consistency verification, further accelerate the convergence speed, and is conducive to selecting the first mapping scene information with higher similarity to match the first cruise scene information while satisfying spatiotemporal consistency. In this regard, constructing the first mapping scene information set through the first mapping scene information and the mapping scene information of the adjacent frames can include the following steps: for each frame of the first mapping scene information, setting the first mapping scene information and the mapping scene information of the adjacent frames to form a first mapping scene information set; merging different first mapping scene information sets with the same mapping scene information to obtain an updated first mapping scene information set.

[0132] Continue to see Figure 4When the cruising scene information C1 is the first cruising scene information, the mapping scene information M2, M4, and M20 ranked in the top three in similarity are selected from the mapping scene information set, and three mapping scene information sets G1, G2, and G3 are constructed based on the mapping scene information M2, M4, and M20 respectively. The mapping scene information sets G1 and G2 are merged into the mapping scene information set G4. Therefore, for the cruising scene information C1, two mapping scene information sets G3 and G4 are finally obtained. Since the cruising scene information C1 is the first data set for the mobile device to enter the target scene, the consistency scores of the mapping scene information sets G3 and G4 are the initial value 0, expressed as G3-0 and G4-0.

[0133] Further, when the cruise scene information C2 becomes the first cruise scene information, the cruise scene information C1 becomes the second cruise scene information, and the mapping scene information sets G3 and G4 obtained based on the cruise scene information C1 also become the second mapping scene information sets. By calculating the similarity between the cruise scene information C2 and each frame of mapping scene information in the mapping scene information set, the mapping scene information M3, M5, and M30 with the top three similarities are obtained, wherein the mapping scene information M3 and the adjacent mapping scene information M2 and M4 constitute the mapping scene information set G5, the mapping scene information M5 and the adjacent mapping scene information M4 and M6 constitute the mapping scene information set G6, and the mapping scene information M30 and the adjacent mapping scene information M29 and M31 constitute the mapping scene information set G7, wherein the mapping scene information sets G5 and G6 have the same mapping scene information M4, therefore, the mapping scene information sets G5 and G6 can be merged to obtain updated mapping scene information sets G7 and G8. Since the mapping scene information set G8 and the mapping scene information set G4 have the same mapping scene information, the consistency score of the mapping scene information set G8 will be accumulated to "1", expressed as G8-1, and the candidate mapping scene information set G7 and the candidate mapping scene information sets G3 and G4 do not have the same mapping scene information, so the consistency score of the candidate mapping scene information set G7 is the initial value 0, expressed as G7-0. Taking the setting value as "2" as an example, for the cruise scene information C2, the first mapping scene information that makes the consistency score reach the set value cannot be matched, so the target mapping scene information that matches the cruise scene information C2 cannot be matched, that is, the first-level positioning cannot be completed based on the cruise scene information C2, and tracking needs to be continued. At this time, the mapping scene information sets G3 and G4 obtained for the cruise scene information C1 can be deleted.

[0134] After receiving the next frame of cruise data set, the cruise scene information C3 obtained based on the frame of cruise data set becomes the first cruise scene information, and the cruise scene information C2 becomes the second cruise scene information. At this time, since the first mapping scene information set G9, G10 and the second mapping scene information set G8 have the same mapping scene information, the consistency score of the first mapping scene information M2, M5 will continue to accumulate the set increment based on the consistency score of the second mapping scene information set G8, that is, the consistency score of the first mapping scene information set G9, G10 will be accumulated to "2", expressed as G9-2, G10-2, and the consistency score of the first mapping scene information set G11 is the initial value "0". Since the consistency scores of the first mapping scene information sets G9 and G10 reach the set value "2", the first mapping scene information M2 with higher similarity can be selected from the first mapping scene information M2 and M5 as the target mapping scene information matching the cruise scene information C3, and the map segment associated with the first mapping scene information M2 is further set as the first map segment associated with the cruise scene information C3 to complete the primary positioning.

[0135] In some examples, when associating the mapping scene information with the map fragments in the target scene map, the association may be based on the same source data set, that is, the associated mapping scene information and the map fragments are obtained based on the same mapping data set. That is, taking the i-th mapping scene information as an example, if the mapping scene information i is obtained based on the mapping data set i, then the map fragment associated with the mapping scene information i may be a map fragment that can be obtained based on the mapping data set i.

[0136] In other examples, in order to further improve the robustness of the method of this embodiment and improve the tolerance for detection errors, it can be set as follows: the associated mapping scene information and map fragments are obtained based on a mapping data set and multiple mapping data sets containing the mapping data set, respectively. In other words, the map fragments associated with each frame of mapping scene information are neighboringly expanded, and not only include the single-frame map fragments corresponding to the current frame mapping data set, but also include at least part of the single-frame map fragments corresponding to the previous and next adjacent frame mapping data sets. In this example, the range of neighboring expansion can be set as needed, and the map fragments associated with each frame of mapping scene information are determined according to the setting.

[0137] For example, see Figure 5, the first cruise scene information corresponds to the first cruise data set 521d, and the first mapping scene information matching the first cruise scene information corresponds to the first mapping data set 511d. In this example, the first map segment 512 is a part of the target scene map 510. The first map segment 512 not only includes the single-frame map segment that can be obtained based on the first mapping data set 511d, but also includes other map parts adjacent to the single-frame map segment in the target scene map 510. In other words, the first map segment 512 can be composed of a plurality of consecutive mapping data sets including the first mapping data set 511d, for example Figure 5 The mapping data sets 511a to 511e in the figure are obtained by splicing the obtained multiple single-frame map fragments. As mentioned above, the multiple mapping data sets here can be multiple continuous key frame data sets collected during the mapping process, which will not be repeated here.

[0138] In order to facilitate the establishment of an association relationship between the mapping scene information and the map fragments in the target scene map, when mapping, the single-frame map fragments corresponding to each mapping data set can be marked in the target scene map, wherein, since the scene data in the adjacent frame mapping data sets will have overlapping content, the single-frame map fragments corresponding to the adjacent frame mapping data sets will also overlap. In other words, the target scene map is spliced ​​from multiple single-frame map fragments. In this way, the map fragment associated with each frame of mapping scene information can be determined based on the mark in the target scene map, so that the map fragment includes the single-frame map fragment corresponding to the associated mapping scene information and at least a portion of the single-frame map fragment corresponding to the adjacent frame mapping scene information.

[0139] In another example, the first map fragment associated with the first cruise scene information is searched in the target scene map, or the first cruise scene information is used as a query vector to perform a cross-attention query in the target scene map to obtain the first map fragment associated with the first cruise scene information, etc., which is not limited here.

[0140] Step S330, obtaining a second map segment based on at least two cruise data sets; wherein the at least two cruise data sets include the first cruise data set and at least one other cruise data set whose sampling time is before the first time.

[0141] In this embodiment, the second map segment is a continuous map segment, and the at least two cruise data sets may be at least two adjacent cruise data sets, or may be at least two adjacent key data sets, so as to reduce redundant data processing and improve the effectiveness of data processing.

[0142] When positioning is performed during cruising, since the first time is the current time, the sampling time of the other cruise data sets in at least two cruise data sets is before the first time.

[0143] In this embodiment, relative to the single-frame map fragment corresponding to the first cruise data set, the second map fragment is expanded adjacently, and includes not only the single-frame map fragment corresponding to the first cruise data set, but also at least part of the single-frame map fragment corresponding to the adjacent frame cruise data set. This is conducive to improving the tolerance capability for detection errors, improving the robustness of the method of this embodiment, and thus obtaining a more accurate secondary positioning result.

[0144] Continue to see Figure 5 , the second map segment 522 can be all or part of the local map 510 obtained based on all cruise data sets collected by the mobile device in the target scene up to the current time. The second map segment 522 not only includes the single-frame map segment that can be obtained based on the first cruise data set 521d, but also includes other map parts adjacent to the single-frame map segment. For example, the second map segment 522 can be spliced ​​by multiple single-frame map segments obtained from the cruise data sets 521b to 521d.

[0145] Step S340: determining a correspondence between map elements in the first map segment and map elements in the second map segment.

[0146] In this embodiment, semantic matching may be performed between the first map segment and the second map segment to determine a corresponding relationship between map elements in the first map segment and map elements in the second map segment.

[0147] When semantic matching is performed on two map segments, it can be mainly performed based on map elements used to represent iconic scene elements. Taking an underground parking garage as an example, the iconic scene elements are parking spaces and pillars. Therefore, based on map elements with semantic categories of parking spaces and pillars in the map segments, semantic matching can be performed on the first map segment and the second map segment, thereby determining the corresponding relationship between map elements such as parking spaces and pillars in the two map segments.

[0148] Figure 6 A schematic diagram of a parking garage map is shown. Figure 6For the first map segment 512 and the second map segment 522, by performing semantic matching on the first map segment 512 and the second map segment 522, the correspondence between map elements such as parking spaces and pillars in the first map segment 512 and the second map segment 522 can be determined. For example, the parking space 512a in the first map segment 512 corresponds to the parking space 522a in the second map segment 522, that is, the parking space 512a in the first map segment 512 and the parking space 522a in the second map segment 522 correspond to the same parking space in the parking garage, and the pillar 512b in the first map segment 512 corresponds to the pillar 522b in the second map segment 522, etc.

[0149] In this embodiment, since the first map segment comes from the target scene map, the mobile device knows the position of the map element in the first map segment in the target scene map, or in other words, the mobile device knows the position of the map element in the first map segment in the map coordinate system. In this way, after determining the correspondence between the map element in the first map segment and the map element in the second map segment in step S340, the position of the map element in the second map segment in the map coordinate system can be determined based on the correspondence, thereby achieving the secondary positioning of the vehicle in the map coordinate system, which can also be called precise positioning.

[0150] In some examples, the cruise data set includes multiple images corresponding to the same timestamp, and the multiple images are collected by different cameras carried by the movable device. Taking a vehicle as an example, the vehicle can be equipped with 6 cameras, including a front-view camera, a rear-view camera, and another 4 cameras on both sides. In this example, the first cruise scene information can be obtained based on the first image in the first cruise data set, and the second map fragment is obtained based on all images in at least two cruise data sets. The first image is, for example, an image collected by a front-view camera. In the example, the first-level positioning is performed based on the first cruise scene information obtained from the first image, which can reduce the amount of data processing and improve the processing speed; and the second map fragment is obtained based on all images, which can improve the accuracy and matching efficiency of the semantic matching of the two map fragments, so as to further improve the positioning accuracy of the secondary positioning.

[0151] In some examples, the target scene map and the second map fragment obtained during cruising can be a bird's-eye view (BEV) of the target scene from a bird's-eye view. Since the bird's-eye view can reflect the overall layout and structural characteristics of the target scene and can also clearly show the spatial relationship between different elements, this type of map is conducive to reducing the difficulty of matching and improving the accuracy of semantic matching. According to the needs of semantic matching, the bird's-eye view can be a two-dimensional view or a three-dimensional view with height information, which is not limited here.

[0152] In some examples, the target scene map and the target scene map Figure 1 The first map segment of the part can be set to: the map element represented by the vector graphics maps the scene element in the target scene. Similarly, the second map segment can also be set to: the map element represented by the vector graphics maps the scene element in the target scene. Figure 5 Taking a parking garage as an example, a parking space can be represented by a rectangular vector image depicting the outline of the parking space. In this example, the scene elements are mapped with vector graphics, and a large number of data points of the mapped scene elements do not need to be stored. When semantic matching is performed on two map fragments, matching calculations between a large number of data points are not required, which can effectively improve the matching efficiency without affecting the matching accuracy.

[0153] In the example of mapping scene elements in a target scene represented by vector graphics, determining the correspondence between map elements in a first map segment and map elements in a second map segment in step S340 may include: determining the correspondence between map elements in the first map segment and map elements in the second map segment based on the graphics of the map elements, element categories, and relative positions of different map elements in the same map segment.

[0154] In the example, as two corresponding map elements in two map segments, the graphics and element categories of the two are the same, and the relative positions of the two map elements to other map elements in the same map segment are also the same, which can ensure the accuracy and reliability of the matching in a simple matching manner. Among them, the same graphics include the same graphics shape and size, and the element category indicates the category of the scene element mapped by the graphics, for example, the element category of the two map elements is parking space or pillar.

[0155] Step S350, determining the first position of the movable device in the target scene map at the first time according to the corresponding relationship determined in step S340 and the position of the movable device in the second map segment at the first time.

[0156] In this embodiment, the position of the movable device in the second map segment at the first time can be determined based on the internal parameters of the sensor used to collect the cruise data set and the external parameters at the first time. In this way, when the position of the map element in the target scene map in the first map segment is known, the first position of the movable device in the target scene map at the first time can be determined based on the corresponding relationship between the map elements in the two map segments and the position of the movable device in the second map segment, thereby completing the precise positioning of the movable device.

[0157] According to step S310 to step S350, the method of this embodiment realizes the positioning of the movable device during cruising by matching the elements between the first map segment in the target scene map obtained by mapping and the second map segment created during cruising. Compared with the feature recognition of a single element, since the matching of map elements between two map segments is a multi-element, multi-angle matching, the method of this embodiment has a strong tolerance for errors that may be introduced in the intermediate links, and can effectively reduce the impact of environmental conditions on positioning accuracy. Moreover, even if the method of this embodiment is implemented using standard hardware such as cameras and wheel speed meters, stable positioning accuracy can be obtained, and it has strong versatility.

[0158] In addition, the method of this embodiment performs neighbor expansion on the single-frame map fragment obtained from the first cruise data set to obtain a second map fragment, and performs element matching between map fragments through the second map fragment, which further enhances the tolerance capability of the method of this embodiment, thereby effectively reducing the impact of environmental conditions and the like on positioning accuracy.

[0159] <Second Embodiment>

[0160] Figure 7 The present invention shows methods for positioning a movable device according to other embodiments. Different from the first embodiment, after obtaining the first position of the movable device in the target scene map at the first time, the present embodiment will also perform a validity check on the first position to further improve the accuracy of positioning the movable device in the target scene without GNSS signals. Figure 7 As shown, the method may include the following steps S710 to S780:

[0161] Step S710, based on a first cruise data set collected by the movable device in the target scene at a first time, obtain first cruise scene information describing scene characteristics.

[0162] Step S720: searching the target scene map for a first map segment associated with the first cruise scene information.

[0163] Step S730, obtaining a second map fragment based on at least two cruise data sets; wherein different cruise data sets correspond to different timestamps, data in the same cruise data set have the same timestamp, and the at least two cruise data sets include a first cruise data set and at least one other cruise data set whose sampling time is before the first time.

[0164] Step S740: determining a correspondence between map elements in the first map segment and map elements in the second map segment.

[0165] Step S750, determining the first position of the movable device in the target scene map at the first time based on the correspondence between the map elements in the first map segment and the map elements in the second map segment, and the position of the movable device in the second map segment at the first time.

[0166] Step S760, determining a first moving distance of the movable device in the target scene map based on the first position and the second position of the movable device in the target scene map.

[0167] In this embodiment, the second position is determined based on a third cruise data set collected by the movable device in the target scene at a third time, and the third time is before the first time.

[0168] In this embodiment, the third cruise data set may be a cruise data set that can complete secondary positioning before the first cruise data set and thus obtain the nearest neighbor of the second position. The steps of obtaining the second position based on the third cruise data set can refer to steps S310 to S350 of obtaining the first position based on the first cruise data set, which will not be repeated here.

[0169] Step S770: determining a second moving distance of the movable device based on the movement data of the first cruise data set and the movement data of the third cruise data set.

[0170] The movement data may include data output by a wheel speedometer.

[0171] In this embodiment, since the movement data reflects the movement speed and movement direction of the movable device at the corresponding time, based on the movement data of the first cruise data set and the movement data of the third cruise data set, the movement distance of the movable device in the sampling time interval of the two cruise data sets can be calculated as the second movement distance.

[0172] Step S780, comparing the first moving distance and the second moving distance, and determining the validity of the first position according to the comparison result.

[0173] When the first position is valid, it is determined to complete the initial positioning of the movable device in the target scene at the first time.

[0174] When the first position is invalid, tracking can be continued, and the next frame cruise data set is used as the first cruise data set, and the method steps of this embodiment are executed again back to step S710 until the initialization positioning is completed.

[0175] In step S780, an absolute value of a difference between the first moving distance and the second moving distance may be calculated, and the validity of the first position may be determined based on the absolute value of the difference.

[0176] For example, it can be set as follows: when the absolute value of the difference is less than or equal to a fourth set threshold, the first position is determined to be valid, and when the absolute value of the difference is greater than the fourth set threshold, the first position is determined to be invalid.

[0177] For another example, it can also be set that when the absolute value of the difference is less than or equal to the fourth set threshold, the consistency count is updated, for example, the consistency count is increased by "1", and when the updated consistency count reaches the fourth set threshold, the first position is determined to be valid, otherwise the first position is determined to be invalid, and the next positioning is performed back to step S710. In this example, the initial value of the consistency count can be set to 0. Taking the fourth set threshold as 2 as an example, when performing steps S710 to S780 for positioning twice in a row, if the absolute value of the difference between the two moving distances is less than or equal to the fourth set threshold, the consistency count is accumulated to "2", and at this time, it can be determined that the initialization positioning is completed.

[0178] In this embodiment, when steps S710 to S780 are currently executed, if the first map fragment cannot be matched in step S720, or when it is determined in step S780 that the absolute value of the difference is greater than the fourth set threshold, the consistency count can be cleared so that the consistency count can be accumulated again when steps S710 to S780 are executed next time to further improve the accuracy of positioning.

[0179] <Third Embodiment>

[0180] Figure 8 The mobile control method of a mobile device according to some embodiments is shown. The method can be implemented by a mobile device or by a server, which is not limited here. Figure 8 As shown, the mobile control method may include the following steps S810 to S870:

[0181] Step S810, obtaining first cruise scene information describing scene characteristics based on a first cruise data set collected by the movable device in the target scene at the first time.

[0182] Step S820: searching the target scene map for a first map segment associated with the first cruise scene information.

[0183] Step S830, obtaining a second map fragment based on at least two cruise data sets; wherein different cruise data sets correspond to different timestamps, data in the same cruise data set have the same timestamp, and the at least two cruise data sets include a first cruise data set and at least one other cruise data set whose collection time is before the first timestamp.

[0184] Step S840: determining a correspondence between map elements in the first map segment and map elements in the second map segment.

[0185] Step S850, determining the first position of the movable device in the target scene map at the first time based on the correspondence between the map elements in the first map segment and the map elements in the second map segment, and the position of the movable device in the second map segment at the first time.

[0186] Step S860: determining a moving path of the movable device according to the first position and the target position of the movable device in the target scene.

[0187] Based on the first position and the target position, as well as the target scene map, a moving path of the movable device in the target scene is determined.

[0188] Furthermore, since the target scene map is used to represent static scene elements in the target scene, in an embodiment, dynamic scene elements such as other vehicles in the target scene can also be determined based on the cruise data set to further combine the dynamic scene elements in the target scene to determine the moving path of the movable device in the target scene.

[0189] Step S870: Control the movable device to move based on the determined moving path.

[0190] Taking the target scene as a parking garage as an example, the target position is the target parking space. Based on the determined moving path, the mobile device can be controlled to move in the parking garage to reach the target parking space and complete parking.

[0191] <Fourth Embodiment>

[0192] This embodiment provides a chip capable of implementing the method of the embodiment of the present disclosure, such as Fig. 9 As shown, the chip 900 includes a storage unit 920 and a processing unit 910. The storage unit 920 is used to store a computer program. The processing unit 910 is configured to implement the method according to any embodiment of the present disclosure when executing the computer program stored in the storage unit.

[0193] Chip 900 may be a processor chip having data processing capabilities.

[0194] Chip 900 may be a processor chip used in an automated driving control unit (ADCU), etc.

[0195] The chip 900 may be a system on a chip (SOC), which integrates multiple processors, multiple memories, I / O interfaces, etc., to achieve a miniaturized design of the controller.

[0196] <Fifth Embodiment>

[0197] This embodiment provides a controller such as Fig.10 As shown, the controller 1000 includes a memory 1002 and a processor 1001, the memory 1002 is used to store a computer program run by the processor 1001, and the processor 1001 is configured to implement a method according to any embodiment of the present disclosure when executing the computer program stored in the memory 1002.

[0198] The controller is, for example, an intelligent driving domain controller, or a central controller of a vehicle.

[0199] <Sixth Embodiment>

[0200] This embodiment provides a mobile device, which may include the chip 900 according to the fourth embodiment or the controller 1000 according to the fifth embodiment.

[0201] In some embodiments, Fig.11 As shown, the removable device 1100 may also include a memory 1102 and a processor 1101, the memory 1102 is used to store a computer program run by the processor 1101, and the processor 1101 is configured to implement a method according to any embodiment of the present disclosure when executing the computer program stored in the memory 1102.

[0202] The movable device 1100 is, for example, a vehicle with intelligent driving function, or an intelligent robot of any form, etc., which is not limited here.

[0203] The embodiments of the present disclosure also provide a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, any method in the aforementioned embodiments of the present disclosure is implemented. Optionally, the computer-readable storage medium may be a non-transitory storage medium, but is not limited thereto, and may also be a transient storage medium.

[0204] The embodiments of the present disclosure further provide a computer program product, which may include a computer program. When the computer program is executed by a processor, any method in the aforementioned embodiments of the present disclosure may be implemented.

[0205] The above embodiments focus on explaining the differences from other embodiments, and the same or similar parts between different embodiments can be referenced to each other.

[0206] The present disclosure may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement any method in the foregoing embodiments of the present disclosure.

[0207] Computer readable storage medium can be a tangible device that can hold and store instructions used by an instruction execution device. Computer readable storage medium can be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof. More specific examples (non-exhaustive list) of computer readable storage medium include: 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), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, for example, a punch card or a convex structure in a groove on which instructions are stored, and any suitable combination thereof. The computer readable storage medium used here is not interpreted as a transient signal itself, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagated by a waveguide or other transmission medium (for example, a light pulse by an optical fiber cable), or an electrical signal transmitted by a wire.

[0208] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0209] The computer program instructions for performing the operation of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, which may include object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. Computer-readable program instructions may be executed entirely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via 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., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be personalized by utilizing the state information of the computer-readable program instructions, and the electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.

[0210] Various aspects of the present disclosure are described herein with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer-readable program instructions.

[0211] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so as to produce a machine, so that when these instructions are executed by the processor of the computer or other programmable data processing device, a device for implementing the functions / actions specified in one or more boxes in the flowchart and / or block diagram is generated. These computer-readable program instructions can also be stored in a computer-readable storage medium, and these instructions make the computer, programmable data processing device, and / or other equipment work in a specific manner, so that the computer-readable medium storing the instructions includes a manufactured product, which includes instructions for implementing various aspects of the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0212] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operating steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to implement the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0213] The flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the systems, methods and computer program products according to multiple embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of an instruction, and the module, a program segment or a part of an instruction contains one or more executable instructions for implementing the specified logical function. In some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes 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 boxes in the block diagram and / or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions. It should be noted that implementation by hardware, implementation by software, and implementation by a combination of software and hardware are all equivalent.

[0214] The embodiments of the present disclosure have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The choice of terms used herein is intended to best explain the principles of the embodiments, practical applications, or technical improvements to the technology in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein. The scope of the present disclosure is defined by the appended claims.

Claims

1. A method for positioning a movable device, characterized in that: include: Based on a first cruise data set collected by the mobile device in the target scene at the first time, first cruise scene information describing scene characteristics is obtained; Searching for a first map segment associated with the first cruising scene information in a target scene map; Obtaining a second map segment based on at least two cruise data sets; wherein different cruise data sets correspond to different timestamps, and data in the same cruise data set have the same timestamp, and the at least two cruise data sets include the first cruise data set and at least one other cruise data set collected before the first time; Determining a correspondence between map elements in the first map segment and map elements in the second map segment; According to the corresponding relationship and the position of the movable device in the second map segment at the first time, the first position of the movable device in the target scene map at the first time is determined.

2. The method according to claim 1, characterized in that The step of searching the target scene map for a first map segment associated with the first cruise scene information includes: In the mapping scene information set of the target scene, search for target mapping scene information matching the first cruise scene information; wherein the mapping scene information set includes multiple frames of mapping scene information corresponding to different timestamps, each frame of the mapping scene information is associated with a map fragment constituting the target scene map, the associated mapping scene information and the map fragment are respectively obtained based on a mapping data set and multiple mapping data sets including the mapping data set, different mapping data sets correspond to different timestamps, and data in the same mapping data set have the same timestamp; A map segment associated with the target mapping scene information is determined as the first map segment.

3. The method according to claim 1, characterized in that The cruise data set includes a plurality of images corresponding to the same timestamp, and the plurality of images are collected by different cameras carried by the movable device; The first cruise scene information is obtained based on a first image in the first cruise data set; The second map segment is obtained based on all images in the at least two cruise datasets.

4. The method according to claim 1, characterized in that: The first cruise data set includes a first image, and the first cruise scene information describing scene characteristics is obtained based on the first cruise data set collected by the movable device in the target scene at the first time, including: Image features are extracted from the first image to obtain a feature vector of the first image as the first cruise scene information.

5. The method according to claim 1, characterized in that The target scene map and the second map fragment are map elements represented by vector graphics, mapping scene elements in the target scene.

6. The method according to claim 5, characterized in that The determining the correspondence between the map elements in the first map segment and the map elements in the second map segment includes: Based on the graphics of the map elements, the element categories, and the relative positions of different map elements in the same map segment, a corresponding relationship between the map elements in the first map segment and the map elements in the second map segment is determined.

7. The method according to claim 2, characterized in that The step of searching the target mapping scene information set of the target scene for the target mapping scene information that matches the first cruise scene information includes: In the mapping scene information set, searching for first mapping scene information whose similarity with the first cruise scene information meets a set condition; Determining a consistency score of the first mapping scene information according to whether the first mapping scene information and the second mapping scene information have an association relationship; wherein the second mapping scene information is mapping scene information whose similarity with the second cruise scene information satisfies the set condition, and the collection time of the second cruise data set corresponding to the second cruise scene information is before the first time; In the first mapping scene information whose consistency score is greater than or equal to a set value, a frame of mapping scene information is selected as the target mapping scene information.

8. The method according to claim 7, characterized in that Determining whether the first mapping scene information and the second mapping scene information have an association relationship includes: Constructing a first mapping scene information set by using the first mapping scene information and mapping scene information of adjacent frames of the first mapping scene information; When the first mapping scene information set and the second mapping scene information set have the same mapping scene information, determining that the first mapping scene information in the first mapping scene information set and the second mapping scene information in the second mapping scene information set have an association relationship; The second mapping scene information set is constructed by the second mapping scene information and adjacent frame mapping scene information of the second mapping scene information.

9. The method according to claim 8, characterized in that The constructing a first mapping scene information set by using the first mapping scene information and the mapping scene information of adjacent frames of the first mapping scene information comprises: For each frame of first mapping scene information, setting the first mapping scene information and the mapping scene information of adjacent frames of the first mapping scene information to form a first mapping scene information set; Different first mapping scene information sets having the same mapping scene information are merged to obtain an updated first mapping scene information set.

10. The method according to any one of claims 1 to 9, characterized in that The cruise data set also includes movement data reflecting the movement speed of the movable device at the corresponding timestamp; after determining the first position of the movable device in the target scene map at the first time, the method further includes: Determine a first movement distance of the movable device in the target scene map based on the first position and a second position of the movable device in the target scene map; wherein the second position is determined based on a third cruise data set collected by the movable device at a third time, and the third time is before the first time; determining a second movement distance of the movable device based on the movement data of the first cruise data set and the movement data of the third cruise data set; The first moving distance is compared with the second moving distance, and the validity of the first position is determined according to the comparison result.

11. A mobile control method for a movable device, characterized in that: include: Based on a first cruise data set collected by the mobile device in the target scene at the first time, first cruise scene information describing scene characteristics is obtained; Searching for a first map segment associated with the first cruising scene information in a target scene map; Obtaining a second map segment based on at least two cruise data sets; wherein different cruise data sets correspond to different timestamps, and data in the same cruise data set have the same timestamp, and the at least two cruise data sets include the first cruise data set and at least one other cruise data set whose acquisition time is before the first timestamp; Determining a correspondence between map elements in the first map segment and map elements in the second map segment; Determine a first position of the movable device in the target scene map at the first time according to the corresponding relationship and the position of the movable device in the second map segment at the first time; determining a moving path of the movable device according to the first position and a target position of the movable device in the target scene; Based on the determined movement path, the movable device is controlled to move.

12. A chip, characterized in that: The chip comprises: the chip comprises a processing unit, and the processing unit is used to execute the method as claimed in any one of claims 1 to 11.

13. A controller, characterized in that: The invention comprises a memory and a processor, wherein the memory is used to store computer instructions, and the processor is used to call the computer instructions from the memory to execute: the method according to any one of claims 1 to 11.

14. A movable device, characterized in that: comprising the chip according to claim 12, or the controller according to claim 13; or, The removable device comprises: a memory and a processor, the memory is used to store computer instructions, and the processor is used to call the computer instructions from the memory to execute: the method according to any one of claims 1 to 11.

15. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor, the computer program implements: the method according to any one of claims 1 to 11.

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