Positioning method, map creation method, positioning system and device

Abstract

The present application discloses a positioning method, a map creation method, a positioning system and a device, wherein the positioning method includes: collecting posture information and an environmental image; using the posture information to obtain partial data from map data; and performing positioning based on the environmental image and the partial data to reduce the amount of map data loaded during the positioning process, quickly calculate the precise posture, and improve positioning efficiency.

Description

Technical Field

[0001] The present application belongs to the field of visual positioning technology, and in particular relates to a positioning method, a map creation method, a positioning system and a device. Background Art

[0002] Visual positioning is an integral part of autonomous driving systems, and map data is the cornerstone of positioning. Existing positioning solutions based on visual sensors typically require loading all map data at once for positioning, resulting in large amounts of loaded map data and low positioning efficiency. Summary of the Invention

[0003] The embodiments of the present application provide an implementation solution that is different from the prior art and is suitable for visual positioning scenarios.

[0004] Specifically, in one embodiment of the present application, a positioning method is provided, which includes: obtaining position information and an environment image; using the position information to obtain partial data from map data; and performing positioning based on the environment image and the partial data.

[0005] In another embodiment of the present application, a map creation method is provided. The method includes: obtaining mapping data of a target space; creating a map based on the mapping data to obtain a mapping result; and processing the mapping result to obtain map data that can be partially loaded, so that in a positioning event, partial data can be obtained from the map data based on pose information and positioning can be completed based on the partial data.

[0006] In another embodiment of the present application, a positioning system is provided. The positioning system includes an autonomous mobile device and a server device; wherein:

[0007] The autonomous mobile device is used to collect position information and environmental images and send a map data acquisition request to the server device;

[0008] The server device is configured to, after receiving the map data acquisition request, acquire partial data from the map data according to the position information collected by the autonomous mobile device; and send the partial data to the autonomous mobile device;

[0009] The autonomous mobile device is further configured to perform positioning based on the environmental image and the partial data.

[0010] In another embodiment of the present application, an autonomous mobile device is provided. The autonomous mobile device includes: a sensor component, a memory, and a processor; wherein:

[0011] The sensor assembly is used to collect posture information and environmental images;

[0012] The memory is used to store programs;

[0013] The processor is coupled to the memory and is configured to execute the program stored in the memory to:

[0014] Acquiring the posture information and environment image;

[0015] Using the posture information, obtaining partial data from map data;

[0016] Positioning is performed based on the environment image and the partial data.

[0017] The embodiments of the present application provide a new solution that is different from the existing technology. After collecting posture information and environmental images, the posture information is used to obtain only partial data from the map data; positioning is performed based on the environmental image and the partial data. During the positioning process, the amount of loaded map data is reduced, the accurate posture can be calculated quickly, and the positioning efficiency is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following is a brief introduction to the drawings required for the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0019] Figure 1a A flowchart of a positioning method provided in one embodiment of the present application;

[0020] Figure 1b A schematic diagram illustrating a scenario of a positioning method provided in an embodiment of the present application;

[0021] Figure 1c A schematic diagram illustrating the principle of a positioning method provided in one embodiment of the present application;

[0022] Figure 1d A schematic diagram of a map data creation process provided in an embodiment of the present application;

[0023] Figure 2 A flowchart of a map creation method provided in another embodiment of the present application;

[0024] Figure 3a A schematic diagram of the principle structure of a positioning system provided in one embodiment of the present application;

[0025] Figure 3b A schematic diagram of the scenario structure of a positioning system provided in one embodiment of the present application;

[0026] Figure 4 A schematic structural diagram of a positioning device provided in one embodiment of the present application;

[0027] Figure 5 A schematic diagram of the structure of a map creation device provided in one embodiment of the present application;

[0028] Figure 6 A schematic diagram of the principle structure of an autonomous mobile device provided in one embodiment of the present application;

[0029] Figure 7 A schematic diagram of the principle structure of a server device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0030] First, some nouns or terms that appear in the description of the embodiments of the present application are subject to the following interpretations:

[0031] Co-viewing relationship: If two images observe the same feature point (i.e., the same feature point is contained in the two images), then the two images have a co-viewing relationship; or if two feature points are observed by the same image (i.e., the two feature points appear in the same image), then the two feature points have a co-viewing relationship.

[0032] Feature points: In image processing, feature points are points where the grayscale value of an image changes dramatically, or points with significant curvature on an image edge (i.e., the intersection of two edges). Image feature points play a crucial role in feature point-based image matching algorithms. They reflect the essential characteristics of an image and can identify target objects within it. Matching these features enables image matching.

[0033] To make the purpose, technical solutions, and advantages of the embodiments of this application more clear, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0034] The terms used in the embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the present application. The singular forms of "a", "said" and "the" used in the present application are also intended to include plural forms, unless the context clearly indicates other meanings, and "multiple" generally includes at least two, but does not exclude the situation of including at least one. It should be understood that the descriptions of "first", "second" and the like herein are used to distinguish different elements, devices, etc., do not represent a sequential order, and do not limit "first" and "second" to be different types. Depending on the context, the words "if", "if" as used herein can be interpreted as "at the time of" or "when" or "in response to determining" or "in response to monitoring". Similarly, depending on the context, the phrases "if it is determined" or "if monitoring (statement condition or event)" can be interpreted as "when determining" or "in response to determining" or "when monitoring (statement condition or event)" or "in response to monitoring (statement condition or event)".

[0035] It should also be noted that the terms "include," "comprises," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a product or system comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such product or system. In the absence of further limitations, an element defined by the phrase "comprises a..." does not exclude the presence of other identical elements in the product or system comprising the element.

[0036] Currently, autonomous driving systems generally use visual positioning based on schemes such as orb-slam and VINS. The applicant has discovered through research that existing visual positioning techniques typically require loading all map data at once for positioning, significantly increasing the computing resource consumption of autonomous mobile devices. Furthermore, due to excessive computational effort and high computational complexity, autonomous mobile devices struggle to output accurate positioning results in real time, which in turn affects their stability. To this end, this application provides the following embodiments to implement an efficient map data scheduling solution, thereby improving positioning accuracy and efficiency.

[0037] The technical solutions provided by the embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0038] Figure 1a This is a flowchart of a positioning method provided by an exemplary embodiment of the present application. The execution subject of this method can be an autonomous mobile device or a server device communicating with the autonomous mobile device, etc., and this embodiment does not specifically limit this. Specifically, the positioning method can include at least the following steps:

[0039] 101. Obtaining posture information and environment images;

[0040] 102. Using the posture information, obtain partial data from map data;

[0041] 103. Perform positioning according to the environment image and the partial data.

[0042] Specifically, see Figure 1b As shown, autonomous mobile devices, such as: unmanned vehicles 310, drones 310, robots, etc. The above-mentioned posture information can be sensor information collected by various sensors on the autonomous mobile device, such as position information and posture information collected by inertial navigation systems, positioning systems (such as GPS or Beidou positioning systems), etc. Among them, the position information is the positioning data collected by the positioning system. The posture information includes inertial information sensed by the inertial navigation system, etc. The environmental image can be collected by an image sensor provided in the positioning device, wherein the image sensor may include a monocular camera, a binocular camera, an RGBD camera, etc. The environmental image can be a single image collected by the image sensor, or it can be a frame image in the environmental video collected by the image sensor.

[0043] In the above 102, the map data is obtained in advance through mapping. The map data contains regional information of multiple areas and regional map data corresponding to the multiple areas. Accordingly, step 102 "using the posture information to obtain partial data from the map data" may include:

[0044] 1021. Obtaining area information of the multiple areas in the map data;

[0045] 1022. Determine, based on the region information of the plurality of regions, a target region to which the posture information belongs in the plurality of regions;

[0046] 1023. Obtain regional map data corresponding to the target area in the map data.

[0047] In the above step 1021, the multiple regions in the map data may be manually divided or may be obtained by processing the map data based on the region division rules by the execution subject or mapping device of this embodiment. The region information of a region includes the region range information of the region and the identifier of at least one image associated with the region (i.e., an image can be collected within the region).

[0048] In the above 1022, the target area corresponding to the area within the position information can be determined based on the position information (such as GPS positioning information or Beidou positioning information) in the posture information and the area range information of each area in the map data. For example, if the position information is longitude and latitude information, the area corresponding to the area within the longitude and latitude information is the target area.

[0049] In step 1023 above, the map data is segmented by region. Therefore, once the target region is determined, regional map data corresponding to the target region can be obtained from the map data. Regional map data includes multiple positioning elements and the coordinate values ​​corresponding to each positioning element. Positioning elements include images, feature points, and the like.

[0050] In the aforementioned step 103, "performing positioning based on the environmental image and the partial data" may specifically include determining the position and pose information of the autonomous mobile device in the map coordinate system based on the environmental image, multiple positioning elements included in the partial data, and the coordinate values ​​corresponding to each positioning element. More specifically, a transformation matrix between the environmental image and some of the multiple positioning elements is determined through image feature matching; and then, based on the transformation matrix, the position and pose information of the autonomous mobile device in the map coordinate system is calculated.

[0051] It should be noted that in this embodiment, the step of "determining the position and posture information of the autonomous mobile device in the map coordinate system based on the environmental image, the multiple positioning elements included in the partial data, and the coordinate values ​​corresponding to each positioning element" can be implemented using a feature point-based visual positioning method. This document does not specifically limit the content of the feature point-based visual positioning method.

[0052] In the technical solution provided by this embodiment, after obtaining the posture information and the environment image, the posture information is used to obtain only partial data from the map data; positioning is performed based on the environment image and the partial data. During the positioning process, the amount of loaded map data is reduced, the accurate posture can be quickly calculated, and the positioning efficiency is improved.

[0053] In some optional embodiments of the present application, map data may be represented in multiple levels, each level being used to store different content, so as to facilitate the selection of information of the corresponding level. For example, the map data in the above embodiment contains a map topology connectivity layer. The map topology connectivity layer stores multiple topological nodes, the identifier of at least one image associated with the topological node, and the connection relationship between the topological nodes, which can be seen in Figure 1b The example shown. A topological node in the topological connectivity layer of this map corresponds to one of the aforementioned multiple areas. The node information of a topological node includes the area range information of the area corresponding to the topological node. The two areas corresponding to two topological nodes that are connected are adjacent; the identifier of at least one image associated with a topological node is the area information of the area corresponding to the topological node. More specifically, the at least one image associated with a topological node refers to an image that can be collected by a sensor of an autonomous mobile device in the area where the topological node is located.

[0054] Accordingly, in the aforementioned step 1022, “determining, based on the regional information of the multiple regions, the target region to which the posture information belongs in the multiple regions” may specifically be:

[0055] Based on the node information of multiple topological nodes in the map topological connectivity layer, the target topological node to which the posture information belongs is calculated.

[0056] Because the node information of a certain node is the area range information of the node, as described above, the topological node corresponding to the area range where the position information in the posture information (such as GPS positioning information or Beidou positioning information) is located is the target topological node to which it belongs.

[0057] As can be seen, in this embodiment, the aforementioned multiple regions are represented using topological nodes, and the connectivity between two regions sharing a common boundary segment is represented by edges between these nodes. A topological tree is used, and the identifier of at least one image that can be captured by each topological node is attached to each topological node on the tree, resulting in a map topological connectivity layer. Essentially, this map topological connectivity layer can be viewed as index information, facilitating the search for index information corresponding to associated data in the map data within the map topological connectivity layer based on the position information collected by multiple sensors of the autonomous mobile device.

[0058] Further, see Figure 1b and 1c As shown, the map data provided in this embodiment may also include an information association layer and a spatial element layer. The spatial element layer stores a plurality of positioning elements and coordinate information corresponding to the positioning elements. Positioning elements can be images or feature points. The information association layer stores association information between images, association information between images and feature points, and association information between feature points. Accordingly, in step 1023 mentioned above, "obtaining regional map data corresponding to the target area in the map data" may at least include:

[0059] 10231. Obtain, from the map topology connectivity layer, an identifier of at least one image associated with the target topological node;

[0060] 10232. Based on the identifier of at least one image associated with the target topological node, obtain, from the information association layer, an identifier of at least one positioning element associated with the identifier of the at least one image; wherein the positioning element is an image or a feature point;

[0061] 10233. Acquire, from the spatial element layer, the at least one positioning element and coordinate information corresponding to the at least one positioning element according to the identifier of the at least one positioning element;

[0062] The regional map data corresponding to the target area includes: the at least one positioning element and coordinate information corresponding to the at least one positioning element.

[0063] The above 10232 can be combined with Figure 1b The understanding shown in the figure, that is, "based on the identifier of at least one image associated with the target topological node, obtaining from the information association layer the identifier of at least one positioning element associated with the identifier of the at least one image" can be specifically:

[0064] Based on the identifier of at least one image associated with the target topological node, loading association information corresponding to the identifier of the at least one image from the information association layer;

[0065] The identifier of the at least one positioning element is extracted from the association information.

[0066] like Figure 1b In the example shown, the information association layer uses cluster groups to represent image-to-image association information, image-to-feature point association information, and feature point-to-feature point association relationships. Specifically, associated images and feature points are stored as a single information association item in the information association layer. To reduce the amount of data loaded into the information association layer, the information association items in the information association layer only store the identifiers of associated images and feature points, without storing specific information about the images and feature points.

[0067] After determining the target topological node through the map topology connectivity layer, such as Figure 1b and 1c As shown, based on the identifier of at least one image associated with the target topological node, the identifier of at least one positioning element associated with the identifier of the at least one image can be obtained from the information association layer.

[0068] It should be noted here that the positioning element associated with the image can be an image or a feature point.

[0069] It can be seen that in this embodiment, the association information between images, the association information between images and feature points, and the association information between feature points are stored in the information association layer. After finding the identifier of at least one image through the first-level index of the map topology connectivity layer, the identifier of at least one positioning element that has an association relationship with the identifier of the at least one image is found through the second-level index of the information association layer. Then, based on the identifier of the at least one positioning element, the at least one positioning element used for positioning and the coordinate information corresponding to the at least one positioning element are obtained from the spatial element layer. In other words, this embodiment further processes the existing map data to establish indexes such as the map topology connectivity layer and the information association layer, so as to find the positioning elements that can be accurately located and the coordinate information corresponding to the positioning elements, thereby improving the accuracy and real-time performance of positioning.

[0070] The following describes the process of creating map data. That is, the method provided in this embodiment also includes a map data creation step. Figure 1d As shown, the map data creation steps are as follows:

[0071] 104. Obtain mapping data;

[0072] 105. Create a map based on the mapping data to obtain a mapping result;

[0073] 106. Process the mapping result to obtain the map data that can be locally loaded.

[0074] The mapping data in step 104 may be collected by sensors of the autonomous mobile device, such as an image sensor, an inertial navigation system, a wheel speed meter, a GPS positioning system, or a BeiDou positioning system, during movement in the target space.

[0075] In the above 105, the collected mapping data can be used to construct a visual positioning map based on feature points. The map construction process is not specifically limited in this embodiment, and can be implemented by referring to related content in the prior art (such as Simultaneous Localization and Mapping (SLAM) technology).

[0076] See also Figure 1d As shown, the above 106 "processing the mapping result to obtain the map data that can be locally loaded" may include:

[0077] 1061. Acquire a topology map, wherein the topology map includes node information of multiple topology nodes and connection relationships between the topology nodes;

[0078] 1062. Based on the topological map, identify images included in the mapping result to determine at least one image associated with a topological node in the topological map.

[0079] 1063. Store the topological map and the identifier of at least one image associated with a topological node in the topological map to obtain a map topology connectivity layer in the map data.

[0080] It should be noted here that: Figure 1c ,avoid Figure 1c The steps in the figure are simplified because it is too complex.

[0081] In step 1061 above, the topology map may be manually configured by the user based on the mapping results. For example, the user may divide the mapping results into regions through an interactive interface and then assign corresponding topological node identifiers to each region. Alternatively, the topology map may be generated based on the mapping results using preset region division rules. However, this embodiment does not specifically limit the region division rules.

[0082] In step 1062 above, once the topology map is generated, each topology node is associated with node information (e.g., area range). The images included in the map result are associated with coordinate information; therefore, based on the coordinate information of each image, a corresponding image identifier can be attached to each topology node in the topology map. If the coordinate information of an image falls within the area represented by the node information of a topology node, then the image is considered to be an observable image within that topology node.

[0083] In a specific implementation, one or more images may be associated with a topological node. When there are multiple images associated with a topological node, multiple images observable within the topological node that do not have a co-viewing relationship or have an unsatisfactory co-viewing relationship (i.e., there are few common feature points between the two images) may be associated with the topological node. Therefore, the identification of images included in the mapping results in step 1062 may include: identifying the area within which the image's corresponding coordinate information is located, and identifying the co-viewing relationship between multiple images within the same area.

[0084] Continue to see Figure 1d The above-mentioned step 106 of “processing the mapping result to obtain the map data that can be locally loaded” may further include:

[0085] 1064. Extracting images and feature points from the mapping result to construct a spatial element layer;

[0086] 1065. Extracting common view information from the mapping result to cluster the images and feature points included in the mapping result to obtain a clustering result.

[0087] 1066. Construct an information association layer based on the clustering results.

[0088] The above-mentioned 1065, co-view information includes co-view relationships between images, feature points, and images. The above-mentioned clustering process is to cluster images and feature points with co-view relationships to obtain multiple cluster groups, which facilitates subsequent partial loading rather than full loading.

[0089] The clustering result in step 1066 includes multiple cluster groups, each of which includes multiple positioning elements clustered together. The positioning elements can be images or feature points. The identifiers corresponding to the multiple positioning elements clustered together are stored as association information items in the information association layer.

[0090] After obtaining the map topology connectivity layer, information association layer and spatial element layer, these three layers of data can be saved in blocks so that the corresponding layer data can be loaded at different stages of the positioning process. Figure 1c As shown in the figure, in the initial positioning stage, the map topology connectivity layer is loaded first, and the target topology node is determined using the pose information; in the middle positioning stage, the local association information in the information association layer is loaded, and the identifier of at least one positioning element is determined based on the local association information; in the positioning data acquisition stage, the local positioning element information in the spatial element layer is loaded, that is, at least one positioning element and its coordinate information. Figure 1c As shown, positioning is performed based on the environment image, the at least one positioning element, and the coordinate information of the positioning element, to obtain the position information of the autonomous mobile device in the map coordinate system. This shows that the embodiment of the present application divides the map data into different levels and retrieves the corresponding level data according to the positioning progress, reducing the resource consumption when loading and scheduling the full map, and achieving visual real-time positioning with low consumption.

[0091] Furthermore, it should be noted that autonomous mobile devices typically move continuously within space. For example, when an unmanned vehicle is traveling on a particular street, the environmental image captured during this current positioning may share a common view relationship with the environmental image captured during the previous positioning, unless the environmental image is no longer shared with the environmental image captured during a turn or U-turn. If the environmental image captured during this current positioning shares a common view relationship with the environmental image captured during the previous positioning, the partial data obtained from the map data in step 102 in this embodiment may include partial data acquired during the previous positioning, namely, at least one positioning element and the corresponding coordinate information of the positioning element acquired during the previous positioning. In this embodiment, whether the autonomous mobile device has experienced a change in its travel posture can be determined based on the inertial information collected by the autonomous mobile device's inertial navigation system during the current positioning and the inertial data collected during the previous positioning. The autonomous mobile device will transmit posture changes when turning or turning. When traveling straight, the autonomous mobile device's posture generally remains unchanged.

[0092] In order to obtain part of the data used for positioning at the next positioning, the method provided in this embodiment may further include the following steps:

[0093] 107. Save the partial data obtained by this positioning;

[0094] 108. When the next positioning event occurs, retrieve the partial data to perform positioning in combination with the partial data.

[0095] The above 107 stores the part of the data obtained by this positioning for easy retrieval.

[0096] In combination with the above content, it can be seen that in step 108, the next positioning can be combined with part of the data used in the current positioning, which requires the following conditions to be met: the posture of the autonomous mobile device does not change much during the two consecutive positioning processes (that is, within the allowable range); in other words, the images collected by the autonomous mobile device during the two consecutive positioning processes have a common view relationship.

[0097] In another feasible technical solution, the method provided in this embodiment may further include the following steps:

[0098] 109. Count some of the data obtained from multiple positioning events;

[0099] 110. Based on the common view relationship, divide part of the data obtained from the multiple positioning events into different categories;

[0100] 111. Count the probabilities of consecutive frames appearing in the images included in the partial data obtained in the multiple positioning events.

[0101] The information such as the usage of the partial data in the map data obtained in step 109 in each positioning event, the different categories divided in step 110, and the probability of consecutive frames occurring can all be used as a basis for determining whether to combine the partial data used in the current positioning in the next positioning event. Accordingly, step 108 of "retrieve the partial data when the next positioning event occurs, and combine the partial data for positioning" includes:

[0102] When the next positioning event occurs, obtaining the usage of the partial data in the map data obtained in each positioning event, dividing the partial data obtained in the multiple positioning events into different categories, and calculating the probability of consecutive frames appearing in the images contained in the partial data obtained in the multiple positioning events, and determining whether to load the partial data used in the current positioning;

[0103] If the determination is yes, then the partial data used in this positioning is loaded;

[0104] If the determination is negative, partial data is obtained from the map data based on the posture information collected in the next positioning event.

[0105] See also Figure 1c As shown, the statistical information obtained through the above steps 109 to 111 is used as a basis for determining whether to load the same positioning element and the coordinate information of the positioning element as the current positioning in the next positioning event.

[0106] Further, such as Figure 1c As shown, the process also adds a step of determining whether positioning is completed. When positioning is completed (such as parking), positioning is completed; if not completed, the steps mentioned in this embodiment are continued to perform positioning.

[0107] Figure 2 A flowchart of a map creation method provided by an exemplary embodiment of the present application is provided. The method can be applied to a creation device and can include at least the following steps:

[0108] 201. Obtaining mapping data of the target space;

[0109] 202. Create a map based on the mapping data to obtain a mapping result;

[0110] 203. Process the mapping result to obtain map data that can be partially loaded, so that in a positioning event, partial data can be obtained from the map data based on the posture information, and positioning can be completed based on the partial data.

[0111] In one feasible technical solution, the above step 203 of "processing the mapping result to obtain map data that can be locally loaded" includes:

[0112] 2031. Acquire a topological map, wherein the topological map includes node information of multiple topological nodes and connection relationships between the topological nodes;

[0113] 2032. Based on the topological map, identify images included in the mapping result to determine at least one image associated with a topological node in the topological map;

[0114] 2033. Store the topological map and the identifier of at least one image associated with a topological node in the topological map to obtain a map topology connectivity layer in the map data.

[0115] The above-mentioned 2031 "obtaining a topology map" can be implemented by any of the following steps:

[0116] In response to a user-triggered region division operation for a target space, the system configures corresponding topological node identifiers and corresponding node information for the divided regions based on the region division result of the user and the connectivity relationships between the multiple regions, and establishes edges between two topological nodes with connectivity relationships;

[0117] According to the regional division rules, the target space is divided into regions; based on the regional division results and the connectivity relationship between multiple regions, corresponding topological node identifiers and corresponding node information are configured for the divided regions, and edges are established between two topological nodes with connectivity relationships.

[0118] Furthermore, the above step 203 of “processing the mapping result to obtain map data that can be locally loaded” further includes:

[0119] 2034. Extracting images and feature points from the mapping result to construct a spatial element layer;

[0120] 2035. Extracting common view information from the mapping result to cluster the images and feature points included in the mapping result to obtain a clustering result;

[0121] 2036. Construct an information association layer based on the clustering results.

[0122] Specifically, the clustering result includes at least two cluster groups. Accordingly, step 2036 "constructing an information association layer based on the clustering result" includes:

[0123] Acquire multiple positioning elements within a cluster group, wherein the positioning elements are images or feature points;

[0124] The identifiers of multiple positioning elements in a cluster group are stored as an associated information item in the information association layer.

[0125] For more detailed information about each step in this embodiment, please refer to the corresponding content above and will not be repeated here.

[0126] Figure 3a and Figure 3b This is a schematic diagram of the positioning system provided in this application. The positioning system includes: an autonomous mobile device 31 and a server device 32; wherein:

[0127] The autonomous mobile device 31 is used to collect position information and environmental images and send a positioning request to the server device 32;

[0128] The server device 32 is configured to, after receiving the positioning request, obtain partial data from map data using the position information collected by the autonomous mobile device; and locate the autonomous mobile device 31 based on the environmental image and the partial data;

[0129] The autonomous mobile device 31 is used to receive the positioning result fed back by the server device 32 .

[0130] Furthermore, the positioning system provided in this embodiment may also include a collection device. The collection device may be the same device as the autonomous mobile device 31, or may be a different device. Specifically,

[0131] The acquisition device is used to acquire mapping data and send the mapping data to the server device;

[0132] The server device 32 is further configured to create a map based on the mapping data to obtain a mapping result; process the mapping result to obtain the map data that can be locally loaded, so that in a positioning event, partial data can be obtained from the map data based on the posture information, and positioning can be completed based on the partial data.

[0133] Accordingly, the aforementioned map topology connectivity layer, information association layer, and spatial element layer can all be stored in the server device 32, so that the server device 32 can load data layer by layer and locally after receiving the positioning request sent by the autonomous mobile device 31 to locate the autonomous mobile device 31.

[0134] The server device can be a server cluster, a single server, a virtual server, etc.

[0135] The server device described in this embodiment can also implement the corresponding functions in the above method embodiment. For details, please refer to the above content and will not be repeated here.

[0136] This application also provides a positioning system, the structure of which is the same as the above Figure 3a and 3b In the above embodiment, the autonomous mobile device completely relies on the server device for positioning, and only collects data locally. The difference of the embodiment of the present application is that the autonomous mobile device in this embodiment has a certain data processing capability locally, and only obtains the corresponding data from the server device, and then performs positioning based on the obtained data. That is,

[0137] The autonomous mobile device is used to collect position information and environmental images and send a map data acquisition request to the server device;

[0138] The server device is configured to, after receiving the map data acquisition request, acquire partial data from the map data according to the position information collected by the autonomous mobile device; and send the partial data to the autonomous mobile device;

[0139] The autonomous mobile device is further configured to perform positioning based on the environmental image and the partial data.

[0140] Similarly, the aforementioned map topology connectivity layer, information association layer, and spatial element layer can all be stored in the server device 32, so that after receiving the map data acquisition request sent by the autonomous mobile device 31, the server device 32 can load data layer by layer and locally, so as to feedback a small amount of partial data that can ensure positioning accuracy to the autonomous mobile device 31.

[0141] The server device can be a server cluster, a single server, a virtual server, etc.

[0142] The server device described in this embodiment can also implement the corresponding functions in the above method embodiment. For details, please refer to the above content and will not be repeated here.

[0143] Figure 4 This is a schematic diagram of a positioning device according to an exemplary embodiment of the present application. The device includes an acquisition module 41 and a positioning module 42. Acquisition module 41 is configured to acquire position information and an environmental image, and to utilize the position information to retrieve partial data from map data. Positioning module 42 is configured to perform positioning based on the environmental image and the partial data.

[0144] Furthermore, the map data contains regional information of multiple regions and regional map data corresponding to the multiple regions; and the acquisition module 41, when used to obtain partial data from the map data using the posture information, is specifically used to:

[0145] Acquire regional information of the multiple regions in the map data; determine, based on the regional information of the multiple regions, a target region to which the posture information belongs in the multiple regions; and acquire regional map data corresponding to the target region in the map data.

[0146] Optionally, the map data includes a map topology connectivity layer, which stores node information of multiple topological nodes, an identifier of at least one image associated with the topological node, and connection relationships between the topological nodes. Specifically, a topological node corresponds to one of the multiple regions; the node information of a topological node includes region range information of the region corresponding to the topological node; two regions corresponding to two topological nodes that are connected are adjacent; and the identifier of at least one image associated with a topological node is the region information of the region corresponding to the topological node.

[0147] Furthermore, when the acquisition module 41 is used to determine the target area to which the posture information belongs in the multiple areas based on the area information of the multiple areas, it is specifically used to:

[0148] Based on the node information of multiple topological nodes in the map topological connectivity layer, the target topological node to which the posture information belongs is calculated.

[0149] Optionally, the map data further includes an information association layer and a spatial element layer, wherein the spatial element layer stores a plurality of positioning elements and coordinate information corresponding to the positioning elements, the positioning elements being images or feature points; and the information association layer stores association information between images, association information between images and feature points, and association information between feature points. Accordingly, when the acquisition module acquires the regional map data corresponding to the target area in the map data, it is specifically configured to:

[0150] Obtaining, from the map topology connectivity layer, an identifier of at least one image associated with the target topology node;

[0151] Based on the identifier of at least one image associated with the target topological node, obtaining, from the information association layer, an identifier of at least one positioning element associated with the identifier of the at least one image; wherein the positioning element is an image or a feature point;

[0152] Acquire, from the spatial element layer, the at least one positioning element and coordinate information corresponding to the at least one positioning element according to the identifier of the at least one positioning element;

[0153] The regional map data corresponding to the target area includes: the at least one positioning element and coordinate information corresponding to the at least one positioning element.

[0154] Furthermore, when the positioning module 42 is used to perform positioning according to the environmental image and the partial data, it is specifically used to perform positioning based on the environmental image, at least one positioning element and coordinate information of the at least one positioning element.

[0155] Furthermore, the above-mentioned device also includes a creation module and a processing module. The creation module is used to obtain mapping data, create a map based on the mapping data, and obtain a mapping result. The processing module is used to process the mapping result to obtain the map data.

[0156] Furthermore, when the processing module processes the mapping result to obtain the map data, it is specifically used to:

[0157] Acquire a topological map, wherein the topological map includes node information of a plurality of topological nodes and connection relationships between the topological nodes;

[0158] Based on the topological map, identifying images included in the mapping result to determine at least one image associated with a topological node in the topological map;

[0159] The topological map and the identifier of at least one image associated with the topological nodes in the topological map are stored to obtain a map topological connectivity layer in the map data.

[0160] Furthermore, when the processing module processes the mapping result to obtain the map data, it is specifically used to:

[0161] Extracting images and feature points from the mapping results to construct a spatial element layer;

[0162] Extracting common view information from the mapping result to cluster the images and feature points contained in the mapping result to obtain a clustering result;

[0163] According to the clustering results, an information association layer is constructed.

[0164] Furthermore, the device provided in this embodiment further includes a storage module and a retrieval module. The storage module is configured to store the partial data acquired during the current positioning process. The retrieval module is configured to retrieve the partial data when the next positioning event occurs, so as to combine the partial data for positioning.

[0165] Furthermore, the apparatus provided in this embodiment further includes a statistical module. The statistical module is configured to count partial data acquired during multiple positioning events; classify the partial data acquired during the multiple positioning events into different categories based on a common view relationship; and count the probability of consecutive frames appearing in the images included in the partial data acquired during the multiple positioning events.

[0166] It should be noted here that: the positioning device provided in the above embodiment can implement the technical solutions described in the above method embodiments. The specific implementation principles of the above modules or units can be found in the corresponding contents of the above method embodiments, which will not be repeated here.

[0167] Figure 5 A schematic structural diagram of a map creation device provided by an exemplary embodiment of the present application.

[0168] The device includes an acquisition module 51, a creation module 52, and a processing module 53. The acquisition module 51 is configured to acquire mapping data of the target space. The creation module 52 is configured to create a map based on the mapping data and obtain a mapping result. The processing module 53 is configured to process the mapping result to obtain map data that can be partially loaded. This allows, during a positioning event, partial data to be obtained from the map data based on pose information and positioning to be completed based on this partial data.

[0169] Furthermore, when the processing module 53 processes the mapping result to obtain map data that can be locally loaded, it is specifically used to:

[0170] Acquire a topological map, wherein the topological map includes node information of a plurality of topological nodes and connection relationships between the topological nodes;

[0171] Based on the topological map, identifying images included in the mapping result to determine at least one image associated with a topological node in the topological map;

[0172] The topological map and the identifier of at least one image associated with the topological nodes in the topological map are stored to obtain a map topological connectivity layer in the map data.

[0173] Furthermore, the processing module 53 may obtain the topology map by any of the following methods:

[0174] In response to a user-triggered region division operation for a target space, the system configures corresponding topological node identifiers and corresponding node information for the divided regions based on the region division result of the user and the connectivity relationships between the multiple regions, and establishes edges between two topological nodes with connectivity relationships;

[0175] According to the regional division rules, the target space is divided into regions; based on the regional division results and the connectivity relationship between multiple regions, corresponding topological node identifiers and corresponding node information are configured for the divided regions, and edges are established between two topological nodes with connectivity relationships.

[0176] Furthermore, when the processing module 53 processes the mapping result to obtain map data that can be locally loaded, it is specifically used to:

[0177] Extracting images and feature points from the mapping results to construct a spatial element layer;

[0178] Extracting common view information from the mapping result to cluster the images and feature points contained in the mapping result to obtain a clustering result;

[0179] According to the clustering results, an information association layer is constructed.

[0180] Furthermore, the clustering result includes at least two cluster groups. Accordingly, when constructing the information association layer according to the clustering result, the processing module 53 is specifically used to:

[0181] A plurality of positioning elements in a cluster group is obtained, wherein the positioning elements are images or feature points; and identifiers of the plurality of positioning elements in a cluster group are stored as an associated information item in the information association layer.

[0182] It should be noted here that: the positioning device provided in the above embodiment can implement the technical solutions described in the above method embodiments. The specific implementation principles of the above modules or units can be found in the corresponding contents of the above method embodiments, which will not be repeated here.

[0183] Figure 6 The structure diagram of the autonomous mobile device provided by an embodiment of the present application is shown. As shown in the figure, the autonomous mobile device includes: a sensor component 67, a memory 61 and a processor 62; wherein,

[0184] The sensor assembly 67 is used to collect posture information and environmental images;

[0185] The memory 61 is used to store programs;

[0186] The processor 62 is coupled to the memory and is configured to execute the program stored in the memory to:

[0187] Acquiring the posture information and environment image;

[0188] Using the posture information, obtaining partial data from map data;

[0189] Positioning is performed based on the environment image and the partial data.

[0190] The memory 61 can be configured to store various other data to support operations on the autonomous mobile device. Examples of such data include instructions for any application or method operating on the autonomous mobile device. The memory 61 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.

[0191] When executing the program in the memory 61 , the processor 62 may implement other functions in addition to the above functions. For details, please refer to the description of the previous embodiments.

[0192] Further, if Figure 6 As shown, the autonomous mobile device further includes: a travel component 68, a communication component 63, a display 64, a power component 65, an audio component 66 and other components. Figure 6 Only some components are shown schematically, which does not mean that the autonomous mobile device only includes Figure 6 Components shown.

[0193] Another embodiment of the present application provides an autonomous mobile device, the structure of which is similar to the above Figure 6 Similarly. Autonomous mobile devices include: sensor components, memory and processors; among them,

[0194] The sensor assembly is used to collect mapping data during movement in the target space;

[0195] The memory is used to store programs;

[0196] The processor is coupled to the memory and is configured to execute the program stored in the memory to:

[0197] Acquiring the mapping data;

[0198] Creating a map based on the mapping data to obtain a mapping result;

[0199] The mapping result is processed to obtain map data that can be partially loaded, so that in a positioning event, partial data can be obtained from the map data based on the posture information, and positioning can be completed based on the partial data.

[0200] The aforementioned memory may be configured to store various other data to support operations on the autonomous mobile device. Examples of such data include instructions for any application or method operating on the autonomous mobile device. The memory may be implemented by any type of volatile or non-volatile memory 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.

[0201] When executing the program in the memory, the processor can realize other functions in addition to the above functions. For details, please refer to the description of the above embodiments.

[0202] Figure 7 FIG. 1 shows a schematic diagram of the structure of a server device provided in an embodiment of the present application. Figure 7 As shown, the server device includes a communication component 73, a memory 71 and a processor 72, wherein:

[0203] The memory 71 is used to store programs;

[0204] The processor 72 is coupled to the memory 71 and is configured to execute the program stored in the memory 71 to:

[0205] receiving, by the communication component, a positioning request sent by an autonomous mobile device;

[0206] Based on the positioning request, obtaining the position information and environment image collected by the autonomous mobile device;

[0207] Obtaining partial data from map data using the position information collected by the autonomous mobile device;

[0208] The autonomous mobile device is positioned according to the environment image and the partial data.

[0209] The memory 71 can be configured to store various other data to support operations on the server device. Examples of such data include instructions for any application or method operating on the server device. 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.

[0210] When executing the program in the memory, the processor can realize other functions in addition to the above functions. For details, please refer to the description of the above embodiments.

[0211] Further, if Figure 7 As shown, the server device also includes: a communication component 73, a display 74, a power component 75, an audio component 76 and other components. Figure 7 Only some components are shown schematically, which does not mean that the server device only includes Figure 7 Components shown.

[0212] The present application also provides a server device, the structure of which is similar to the above Figure 7 The server device includes a communication component, a memory and a processor, wherein:

[0213] The memory is used to store programs;

[0214] The processor is coupled to the memory and is configured to execute the program stored in the memory to:

[0215] receiving, through the communication component, mapping data collected by the autonomous mobile device during movement within the target space;

[0216] Creating a map based on the mapping data to obtain a mapping result;

[0217] The mapping result is processed to obtain the map data that can be partially loaded, so that in a positioning event, partial data can be obtained from the map data based on the posture information, and positioning can be completed based on the partial data.

[0218] When executing the program in the memory, the processor can realize other functions in addition to the above functions. For details, please refer to the description of the above embodiments.

[0219] Accordingly, an embodiment of the present application further provides a computer-readable storage medium storing a computer program, which, when executed by a computer, can implement the steps or functions of the methods provided in the above embodiments.

[0220] The technical solutions provided in the embodiments of this application are described below in conjunction with specific application scenarios.

[0221] Application Scenario 1

[0222] The supermarket service robot may be provided with a display screen for users to trigger operation instructions for checking whether a product is in stock and finding the location of the product. The display screen stores the categories to which multiple products in the supermarket belong, the shelves on which they are placed, a supermarket map, and the location of the shelf in the supermarket map. If the supermarket service robot obtains an instruction to find a target product triggered by the user through the display screen of the supermarket service robot, it may capture an image of the shelf at the current location and determine the current posture through an inertial navigation system and a positioning system; determine the shelf area to which the current posture belongs; load the positioning resource corresponding to the shelf area (the shelf area is a sub-area of ​​the total area of ​​the supermarket map) from the internal memory, wherein the positioning resource includes multiple images to be analyzed; use the captured shelf image to analyze the positioning resource, determine the position information corresponding to the target image with the highest similarity to the shelf image in the positioning resource, and use this position information as the position information of the supermarket service robot. Then, based on this position information and the position of the target product stored in the robot, the robot plans its path to the target product, so as to provide the user with a guidance service or a path prompt service.

[0223] Application Scenario 2

[0224] When the drone flies to a block, it takes a picture of the block at its current position and determines the current GPS positioning information through the inertial navigation system and positioning system (such as GPS or Beidou positioning system); then the GPS positioning information is transmitted to the cloud server through the transmission module of the drone. After receiving the GPS positioning information, the cloud server determines the block range information corresponding to the received GPS positioning information based on the map information stored in itself, and feeds back multiple images and feature point information corresponding to the block range information to the drone. The drone performs positioning based on the block picture it takes and the multiple images and feature point information received from the cloud server.

[0225] In addition to the map creation, positioning, and navigation functions mentioned in the above embodiments, autonomous vehicles (or self-moving devices) also involve other algorithm modules. These algorithm modules vary depending on the type of autonomous vehicle. For example, different algorithm modules are involved for logistics vehicles, public service vehicles, medical service vehicles, and terminal service vehicles. The following examples illustrate the algorithm modules for these four types of autonomous vehicles:

[0226] Among them, logistics vehicles refer to vehicles used in logistics scenarios, such as logistics vehicles with automatic sorting functions, logistics vehicles with refrigeration and insulation functions, and logistics vehicles with measurement functions. These logistics vehicles will involve different algorithm modules. For example, a logistics vehicle can be equipped with an automated sorting device, which can automatically remove the goods and transport, sort, and store them after the logistics vehicle arrives at the destination. This involves an algorithm module for cargo sorting, which mainly implements logical control such as cargo removal, transportation, sorting, and storage.

[0227] For another example, for cold chain logistics scenarios, logistics vehicles can also be equipped with refrigeration and insulation devices, which can refrigerate or insulate the transported fruits, vegetables, aquatic products, frozen foods, and other perishable foods, so that they are in a suitable temperature environment, solving the problem of long-distance transportation of perishable foods. This involves an algorithm module for refrigeration and insulation control, which is mainly used to dynamically and adaptively calculate the appropriate temperature for cold meals or insulation based on information such as the nature of the food (or item), perishability, transportation time, current season, and climate, and automatically adjust the refrigeration and insulation device according to the appropriate temperature. In this way, when the vehicle transports different foods or items, the transportation personnel do not need to manually adjust the temperature, freeing the transportation personnel from the tedious temperature control and improving the efficiency of refrigerated and insulated transportation.

[0228] For example, in most logistics scenarios, charges are based on the volume and / or weight of the package. However, the number of logistics packages is very large. Simply relying on couriers to measure the volume and / or weight of the packages is very inefficient and has high labor costs. Therefore, in some logistics vehicles, measuring devices are added to automatically measure the volume and / or weight of logistics packages and calculate the fees for logistics packages. This involves an algorithm module for logistics package measurement, which is mainly used to identify the type of logistics package and determine the measurement method of the logistics package, such as volume measurement or weight measurement or a combination of volume and weight measurement. It can also complete the volume and / or weight measurement according to the determined measurement method, and complete the fee calculation based on the measurement results.

[0229] Public service vehicles refer to vehicles that provide certain public services, such as fire trucks, de-icing trucks, sprinkler trucks, snowplows, garbage disposal vehicles, traffic control vehicles, etc. These public service vehicles involve different algorithm modules.

[0230] For example, for an autonomous fire truck, its main task is to carry out reasonable fire-fighting tasks at the fire scene. This involves an algorithm module for fire-fighting tasks. The algorithm module must at least implement logic such as fire condition identification, fire-fighting plan planning, and automatic control of fire-fighting equipment.

[0231] For example, the main task of a de-icing vehicle is to clear ice and snow from the road surface, which involves a de-icing algorithm module. This algorithm module must at least be able to identify the ice and snow conditions on the road surface, formulate a de-icing plan based on the ice and snow conditions, such as which sections of the road require de-icing and which sections do not, whether to use salting and the amount of salt to spread, etc., as well as the logic for automatic control of the de-icing device when the de-icing plan is determined.

[0232] Among them, medical service vehicles refer to self-driving vehicles that can provide one or more medical services. Such vehicles can provide medical services such as disinfection, temperature measurement, medication, and isolation. This involves algorithm modules that provide various self-service medical services. These algorithm modules mainly realize the identification of disinfection needs and the control of disinfection devices so that the disinfection devices can disinfect patients, or identify the patient's position and control the temperature measuring device to automatically approach the patient's forehead and other positions to measure the patient's temperature, or are used to realize the judgment of the disease, give a prescription based on the judgment result, and need to realize the identification of drugs / drug containers, as well as the control of the drug-taking robot so that it can grab drugs for patients according to the prescription, etc.

[0233] Among them, terminal service vehicles refer to self-service autonomous driving vehicles that can replace some terminal devices to provide certain convenient services to users. For example, these vehicles can provide users with printing, attendance, scanning, unlocking, payment, retail and other services.

[0234] For example, in some application scenarios, users often need to go to a specific location to print or scan documents, which is time-consuming and labor-intensive. Therefore, a terminal service vehicle that can provide users with printing / scanning services has emerged. These service vehicles can be interconnected with the user's terminal device. The user issues a print instruction through the terminal device, and the service vehicle responds to the print instruction, automatically prints the document required by the user, and can automatically deliver the printed document to the user's location. The user does not need to queue at the printer, which can greatly improve printing efficiency. Alternatively, it can respond to the scanning instruction issued by the user through the terminal device and move to the user's location. The user places the document to be scanned on the scanning tool of the service vehicle to complete the scanning, without having to queue at the printer / scanner, saving time and effort. This involves an algorithm module that provides printing / scanning services. The algorithm module at least needs to identify the connection with the user's terminal device, the response to the print / scan instruction, the positioning of the user's location, and the travel control.

[0235] For example, with the development of new retail businesses, more and more e-commerce companies are using self-service vending machines to deliver goods to office buildings and public areas. However, these vending machines are stationary and immovable, requiring users to visit them to purchase their desired items, making them relatively inconvenient. Consequently, self-driving vehicles have emerged to provide retail services. These vehicles can carry goods and move autonomously, offering a corresponding self-service shopping app or portal. Users can use their mobile phones or other devices to place orders with the self-driving vehicles through the app or portal. The order includes the product name, quantity, and user location. After receiving the order, the vehicle can determine whether the requested item is available and whether the quantity is sufficient. If the requested item is available and sufficient, it can automatically move to the user's location with the item and deliver it to the user, further improving shopping convenience and saving time, allowing users to focus on more important tasks. This involves the algorithm modules that provide retail services, which primarily implement logic for responding to user order requests, processing orders, maintaining product information, locating the user, and managing payments.

[0236] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0237] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, or of course, by hardware. Based on this understanding, the essence of the above technical solution or the part that contributes to the existing technology can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or certain parts of the embodiments.

[0238] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A positioning method, characterized in that: include: Obtain pose information and environment images; Using the posture information, obtaining regional map data of the target area to which the posture information belongs from map data; Performing positioning according to the environmental image and the regional map data; The map data includes a map topology connectivity layer, wherein the map topology connectivity layer stores node information of a plurality of topological nodes, an identifier of at least one image associated with the topological node, and connection relationships between the topological nodes; One topological node corresponds to one region; two topological nodes that are connected correspond to two adjacent regions. The map data further includes an information association layer and a spatial element layer; the spatial element layer stores a plurality of positioning elements and coordinate information corresponding to the positioning elements, the positioning elements being images or feature points; the information association layer stores association information between images, association information between images and feature points, and association information between feature points; and obtaining regional map data of the target area to which the posture information belongs from the map data, including: Acquire, from the map topology connectivity layer, an identifier of at least one target image associated with a target topology node corresponding to the target area; Acquiring, from the information association layer, an identifier of at least one positioning element associated with the identifier of the at least one target image; Acquire, from the spatial element layer, the at least one positioning element and coordinate information corresponding to the at least one positioning element according to the identifier of the at least one positioning element; The regional map data corresponding to the target area includes: the at least one positioning element and coordinate information corresponding to the at least one positioning element.

2. The method according to claim 1, characterized in that Using the posture information, determining the target area in the map data includes: Acquiring area information of multiple areas in the map data; Based on the region information of the multiple regions, a target region to which the posture information belongs is determined among the multiple regions.

3. The method according to claim 2, characterized in that The node information of a topological node includes the area range information of the area corresponding to the topological node; The identifier of at least one image associated with a topological node is the region information of the region corresponding to the topological node.

4. The method according to claim 3, characterized in that Determining, based on the region information of the plurality of regions, a target region to which the posture information belongs in the plurality of regions, includes: Based on the node information of multiple topological nodes in the map topological connectivity layer, the target topological node to which the posture information belongs is calculated.

5. The method according to any one of claims 1 to 4, characterized in that Also includes: Obtain mapping data; Creating a map based on the mapping data to obtain a mapping result; The mapping result is processed to obtain the map data that can be locally loaded.

6. The method according to claim 5, characterized in that Processing the mapping result to obtain the map data includes: Acquire a topological map, wherein the topological map includes node information of a plurality of topological nodes and connection relationships between the topological nodes; Based on the topological map, identifying images included in the mapping result to determine at least one image associated with a topological node in the topological map; The topological map and the identifier of at least one image associated with the topological nodes in the topological map are stored to obtain a map topological connectivity layer in the map data.

7. The method according to claim 6, characterized in that Processing the mapping result to obtain the map data also includes: Extracting images and feature points from the mapping results to construct a spatial element layer; Extracting common view information from the mapping result to cluster the images and feature points contained in the mapping result to obtain a clustering result; According to the clustering results, an information association layer is constructed.

8. The method according to claim 1, characterized in that Also includes: Saving the regional map data obtained by this positioning; When the next positioning event occurs, the area map data is retrieved to perform positioning in combination with the area map data.

9. A map creation method, characterized in that: include: Obtain mapping data of the target space; Creating a map based on the mapping data to obtain a mapping result; Processing the mapping result to obtain map data that can be partially loaded, so that in a positioning event, partial data can be obtained from the map data based on the pose information, and positioning can be completed based on the partial data; The map data includes a map topology connectivity layer, wherein the map topology connectivity layer stores node information of a plurality of topological nodes, an identifier of at least one image associated with the topological node, and connection relationships between the topological nodes; One topological node corresponds to one region; two topological nodes that are connected correspond to two adjacent regions. The map data also includes an information association layer and a spatial element layer; the spatial element layer stores multiple positioning elements and coordinate information corresponding to the positioning elements, and the positioning elements are images or feature points; the information association layer stores association information between images, association information between images and feature points, and association information between feature points.

10. A positioning system, characterized in that: Including autonomous mobile devices and server devices; among which: The autonomous mobile device is used to collect position information and environmental images and send a map data acquisition request to the server device; The server device is configured to, upon receiving the map data acquisition request, acquire, from the map data, regional map data of the target area to which the posture information belongs, based on the posture information collected by the autonomous mobile device; and send the regional map data to the autonomous mobile device; The autonomous mobile device is further configured to perform positioning based on the environmental image and the regional map data; The map data includes a map topology connectivity layer, wherein the map topology connectivity layer stores node information of a plurality of topological nodes, an identifier of at least one image associated with the topological node, and connection relationships between the topological nodes; One topological node corresponds to one region; two topological nodes that are connected correspond to two adjacent regions. The map data further includes an information association layer and a spatial element layer; the spatial element layer stores a plurality of positioning elements and coordinate information corresponding to the positioning elements, the positioning elements being images or feature points; the information association layer stores association information between images, association information between images and feature points, and association information between feature points; and obtaining regional map data of the target area to which the posture information belongs from the map data, including: Acquire, from the map topology connectivity layer, an identifier of at least one target image associated with a target topology node corresponding to the target area; Acquiring, from the information association layer, an identifier of at least one positioning element associated with the identifier of the at least one target image; Acquire, from the spatial element layer, the at least one positioning element and coordinate information corresponding to the at least one positioning element according to the identifier of the at least one positioning element; The regional map data corresponding to the target area includes: the at least one positioning element and coordinate information corresponding to the at least one positioning element.

11. An autonomous mobile device, characterized in that include: Sensor components, memory and processor; wherein, The sensor assembly is used to collect posture information and environmental images; The memory is used to store programs; The processor is coupled to the memory and is configured to execute the program stored in the memory to: Acquiring the posture information and environment image; Using the posture information, obtaining regional map data of the target area to which the posture information belongs from map data; Performing positioning according to the environmental image and the regional map data; The map data includes a map topology connectivity layer, wherein the map topology connectivity layer stores node information of a plurality of topological nodes, an identifier of at least one image associated with the topological node, and connection relationships between the topological nodes; One topological node corresponds to one region; two topological nodes that are connected correspond to two adjacent regions. The map data further includes an information association layer and a spatial element layer; the spatial element layer stores a plurality of positioning elements and coordinate information corresponding to the positioning elements, the positioning elements being images or feature points; the information association layer stores association information between images, association information between images and feature points, and association information between feature points; and obtaining regional map data of the target area to which the posture information belongs from the map data, including: Acquire, from the map topology connectivity layer, an identifier of at least one target image associated with a target topology node corresponding to the target area; Acquiring, from the information association layer, an identifier of at least one positioning element associated with the identifier of the at least one target image; Acquire, from the spatial element layer, the at least one positioning element and coordinate information corresponding to the at least one positioning element according to the identifier of the at least one positioning element; The regional map data corresponding to the target area includes: the at least one positioning element and coordinate information corresponding to the at least one positioning element.

Images