Narrow road area passing method, self-moving device and storage medium

Abstract

The invention provides a narrow road area passing method, a self-moving device and a storage medium, the method is applied to the self-moving device, and the method comprises the following steps: when it is detected that the self-moving device moves to a narrow road area, generating a first local map according to a global static map and point cloud data acquired in real time, the precision of the first local map is higher than that of the global static map; and according to the first local map, controlling the self-moving device to pass in the narrow road area. According to the technical scheme, the possibility that the self-moving equipment passes through the narrow road area can be improved.

Description

Technical Field

[0001] The present application relates to the field of device navigation, and in particular to a method for navigating through a narrow passage area, a self-mobile device and a storage medium. Background Art

[0002] Autonomous devices, such as mobile robots, are robots that can move autonomously and perform various tasks. They are usually equipped with components such as sensors, controllers, drives, and power supplies, and can autonomously navigate in different environments, sense the surrounding environment, avoid obstacles, and complete various tasks such as transporting items, patrolling, cleaning, etc.

[0003] Mobile robots usually need to move in complex environments. In particular, due to the placement of some temporary obstacles, there will be some areas with narrow passage width. When the mobile robot moves to these areas, if the mobile robot has poor driving ability, it will wait in place or take a detour, which is time-consuming. Summary of the invention

[0004] The present application provides a narrow passage area passage method, a self-moving device and a storage medium, aiming to improve the possibility of a mobile robot passing through an area with a narrow width.

[0005] In a first aspect, a method for passing through a narrow road area is provided, which is applied to a self-moving device, and the method comprises:

[0006] When it is detected that the mobile device moves to a narrow road area, a first local map is generated according to the global static map and the point cloud data acquired in real time, wherein the accuracy of the first local map is higher than that of the global static map;

[0007] The self-moving device is controlled to pass through the narrow road area according to the first local map.

[0008] In this technical solution, when it is detected that a mobile device moves to a narrow passage area, a first local map is generated according to the global static map and point cloud data acquired in real time, and according to the first local map, the mobile device is controlled to pass through the narrow passage area. Since the accuracy of the first local map is higher than that of the global static map, and the first local map is generated by the global static map and point cloud data acquired in real time, the accuracy of the first local map is relatively high. Controlling the mobile device to pass through the narrow passage area according to the first local map can increase the possibility of the mobile device passing through the narrow passage area.

[0009] In combination with the first aspect, in a possible implementation, generating a first local map based on a global static map and point cloud data acquired in real time includes: determining the accuracy of the first local map based on the minimum width of the current position of the self-mobile device, wherein the accuracy of the first local map is negatively correlated with the minimum width; generating the first local map based on the accuracy of the first local map, the global static map, and point cloud data acquired in real time. Determining the accuracy of the first local map based on the minimum width of the current position of the self-mobile device can make the accuracy of the first local map reasonable, thereby increasing the possibility of the self-mobile device passing through a narrow area.

[0010] In combination with the first aspect, in a possible implementation, the accuracy of the first local map is determined according to the minimum width of the current position of the self-moving device, including: determining the minimum width of the current position of the self-moving device according to the position of the self-moving device in the global static map; and determining the accuracy of the first local map corresponding to the minimum width according to the minimum width and a preset association relationship. By determining the minimum width of the current position of the self-moving device according to the position of the self-moving device in the global static map, and determining the accuracy of the first local area corresponding to the minimum width according to the minimum width and a preset association relationship, a suitable accuracy can be determined for the first local map, thereby increasing the possibility of the self-moving device passing through a narrow area.

[0011] In combination with the first aspect, in a possible implementation manner, the method further includes: setting the preset association relationship according to the size of the self-moving device.

[0012] In combination with the first aspect, in a possible implementation, the first local map is generated according to the accuracy of the first local map, the global static map and the point cloud data acquired in real time, including: generating a first map according to the accuracy of the first local map, the global static map and the point cloud data acquired in real time; determining a traversable area according to the first map; if a first minimum width of the traversable area is greater than or equal to a first preset width, determining that the self-mobile device can pass through the narrow road area, and using the first map as the first local map; if the first minimum width of the traversable area is less than the first preset width and greater than or equal to a second preset width, updating the first map according to the global static map and the point cloud data acquired in real time to obtain a second map, wherein the accuracy of the second map is greater than the accuracy of the first map; and using the second map as the first local map. By comparing the minimum width of the passable area in the local map with the first preset width, when the minimum width of the passable area in the local map is greater than the preset width, the local map is determined as the first local map, and when the minimum width of the passable area in the local map is less than the first preset width and greater than the second preset width, the accuracy of the local map is updated, and the appropriate accuracy can be further adjusted and determined, thereby further improving the possibility of a mobile device passing through a narrow area.

[0013] In combination with the first aspect, in a possible implementation, the method further includes: if the first minimum width of the passable area is less than the second preset width, determining that the self-moving device cannot pass through the narrow road area, and controlling the self-moving device to re-plan a path to travel around the narrow road area. When the minimum width of the passable area in the local map is less than the second preset width, determining to re-plan a path to travel around the narrow road area can avoid the self-moving device being unable to pass through the narrow road area.

[0014] In combination with the first aspect, in a possible implementation method, when it is detected that the self-mobile device moves to a narrow road area, before generating a first local map based on the global static map and the point cloud data acquired in real time, it also includes: obtaining the positioning position of the self-mobile device, and determining whether the positioning position is located in the narrow road area, wherein the narrow road area is pre-planned in the global static map; if the positioning position is located in the narrow road area, it is determined that the self-mobile device has moved to the narrow road area; if the positioning position is not located in the narrow road area, it is determined that the self-mobile device has not moved to the narrow road area.

[0015] In combination with the first aspect, in a possible implementation, after the self-moving device is controlled to pass through the narrow road area according to the first local map, the method further includes: after the self-moving device passes through the narrow road area, generating a second local map according to the global static map and point cloud data acquired in real time, wherein the accuracy of the second local map is less than the accuracy of the first local map and greater than the accuracy of the global static map; and controlling the self-moving device to travel according to the second local map. After the self-moving device passes through the narrow road area, generating a local map with lower accuracy to control the self-moving device to travel can increase the coverage area of ​​the local map and avoid negative impact on the navigation planning of the self-moving device.

[0016] In a second aspect, a narrow passage area passage device is provided, which is applied to a self-moving device, and the device comprises:

[0017] A local map generation module, when detecting that the mobile device moves to a narrow road area, generates a first local map according to the global static map and the point cloud data acquired in real time, wherein the accuracy of the first local map is higher than that of the global static map;

[0018] The passage control module is used to control the self-moving device to pass through the narrow road area according to the first local map.

[0019] In a third aspect, a self-moving device is provided, comprising a memory, a sensor and one or more processors, wherein the memory and the sensor are connected to the one or more processors, the sensor is used to acquire point cloud data in real time, and the one or more processors are used to execute one or more computer programs stored in the memory, and when the one or more processors execute the one or more computer programs, the self-moving device implements the narrow road area passage method of the first aspect mentioned above.

[0020] In a fourth aspect, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the processor executes the narrow lane area passage method of the first aspect.

[0021] The present application can achieve the following technical effects: since the accuracy of the first local map is higher than that of the global static map, and the first local map is generated by the global static map and point cloud data acquired in real time, the first local map has high accuracy, and the passage of a mobile device in a narrow passage area is controlled according to the first local map, which can increase the possibility of the mobile device passing through the narrow passage area. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the description of the embodiments of the present application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0023] Figure 1 A schematic diagram of a flow chart of a method for passing through a narrow passage area provided in an embodiment of the present application;

[0024] Figure 2 A schematic diagram of a flow chart of another method for passing through a narrow passage area provided in an embodiment of the present application;

[0025] Figure 3 It is a schematic structural diagram of a narrow passage area passage device provided in an embodiment of the present application;

[0026] Figure 4 It is a structural schematic diagram of a self-moving device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0027] In order to make the purpose, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present application.

[0028] It should be noted that, if there is no conflict, the various features in the embodiments of the present application can be combined with each other, all within the scope of protection of the present application. In addition, although the functional module division is performed in the device schematic diagram and the logical order is shown in the flow chart, in some cases, the steps shown or described can be performed in a sequence different from the module division in the device or the flow chart. Furthermore, the words "first", "second", "third", etc. used in this application do not limit the data and execution order, but only distinguish the same items or similar items with basically the same functions and effects.

[0029] The technical solution of the present application is applicable to the navigation scenarios of self-moving devices, and in particular to the navigation scenarios of self-moving devices in narrow lane areas. A self-moving device refers to a device that can move autonomously to complete a task, and the self-moving device includes but is not limited to cleaning robots, transport robots, unmanned transport vehicles and other equipment. A narrow lane area refers to an area that may be judged as impassable by a self-moving device due to its narrow width. The actual width of the narrow lane area is usually slightly larger than the actual width of the self-moving device. As for the specific width of the area defined as a narrow lane area, it can be set according to the specific situation of the self-moving device, and this application does not impose any restrictions.

[0030] In the navigation scenario of a self-moving device, the self-moving device is usually located and navigated based on a global static map and a local map. The global static map is a large-scale representation of the entire environment, usually with low resolution and details. It contains the overall structure and characteristics of the environment in which the self-moving device is located, such as rooms, corridors, obstacles, etc. The local map is a detailed representation of the self-moving device in a smaller range near the current location. It has a higher resolution and more precise details and is used for accurate navigation and obstacle avoidance of the self-moving device in the local environment. The global static map and the local map play different roles in robot navigation. The global static map provides an overview of the overall environment and helps the self-moving device plan a general path and goal. The local map is used to perceive and respond to the specific situation around the self-moving device in real time, ensuring that the robot can move safely and accurately. The local map is updated based on real-time sensor data to reflect the dynamic changes around the self-moving device.

[0031] Both the global static map and the local map can be represented in the form of a grid map. The local map uses a grid to represent the passability of the environment and the distribution of obstacles. The local map includes multiple grids, each of which is a square.

[0032] See also Figure 1 , Figure 1 A schematic diagram of a flow chart of a narrow passage area passage method provided in an embodiment of the present application, such as Figure 1 As shown, the method comprises the following steps:

[0033] S101, when it is detected that the mobile device moves to a narrow road area, a first local map is generated according to the global static map and point cloud data acquired in real time.

[0034] Here, the narrow passage area refers to an area with a narrow passage width.

[0035] In a feasible implementation, it is possible to detect whether the self-moving device has moved to a narrow road area based on a global static map. Specifically, the positioning position of the self-moving device can be obtained to determine whether the positioning position of the self-moving device is located in a narrow road area, and the narrow road area is pre-planned in the global static map; if the positioning position of the self-moving device is located in a narrow road area, it is determined that the self-moving device has moved to the narrow road area; if the positioning position of the self-moving device is not located in the narrow road area, it is determined that the self-moving device has not moved to the narrow road area. Among them, the narrow road area may refer to an area in the global static map where the distance between obstacles on both sides is less than a preset distance, and the positioning position of the self-moving device here refers to the position of the self-moving device in the global static map. The preset distance can be set based on the size of the self-moving device.

[0036] Here, generating the first local map according to the global static map and the point cloud data acquired in real time means mapping obstacles in the global static map and obstacles corresponding to the point cloud data acquired in real time to the local map to form obstacles in the local map, thereby obtaining the first local map.

[0037] Among them, the point cloud data is a data set formed by the sensor of the self-mobile device scanning the environment in real time; the point cloud data can reflect the real-time position of obstacles around the self-mobile device, and the point cloud data includes multiple scanning points. The meaning of the scanning point will be different depending on the sensor that scans the scanning point. For example, if the scanning point is obtained by scanning a laser radar, then the scanning point refers to the laser position point formed by the laser radar scanning the environment; if the scanning point is obtained by scanning a depth camera, then the scanning point refers to the pixel point formed by the depth camera scanning the environment. The point cloud data can be obtained by scanning the environment around the self-mobile device in real time by the sensor of the self-mobile device. The point cloud data may include the laser radar data obtained by the laser radar scanning the environment in real time, and / or the depth image data obtained by the depth camera scanning the environment in real time.

[0038] The accuracy of the generated first local map is higher than that of the global static map. For example, if the accuracy of the global static map is 5cm*5cm, that is, the grid side length on the global static map is 5cm, then the accuracy of the first local map can be 2cm*2cm, 1cm*1cm, etc. That is, the grid side length on the first local map is 2cm, 1cm, etc.

[0039] The first local map can be generated through the following steps A1-A2:

[0040] A1. Determine the accuracy of the first local map according to the minimum width of the current position of the mobile device.

[0041] Here, the minimum width of the current position of the mobile device refers to the minimum width of the current position of the mobile device in the global static map. The accuracy of the first local map is negatively correlated with the minimum width of the current position of the mobile device, that is, the larger the minimum width of the current position of the mobile device, the smaller the accuracy of the first local map; the smaller the minimum width of the current position of the mobile device, the higher the accuracy of the first local map.

[0042] Specifically, step A1, determining the accuracy of the first local map according to the minimum width of the current position of the mobile device, can be performed through the following steps A11-A12:

[0043] A11. Determine the minimum width of the current position of the mobile device according to the position of the mobile device in the global static map.

[0044] In a feasible implementation, the moving path from the current position of the mobile device can be determined on the global static map to obtain the current moving path; multiple groups of symmetrical position points on the current moving path are obtained, each group of symmetrical position points includes two position points symmetrical on both sides of the moving path; the distance between two position points in each group of symmetrical position points is calculated to obtain multiple distances; the minimum distance among the multiple distances is determined as the minimum width of the current position of the mobile device.

[0045] A12. Determine the accuracy of the first local map corresponding to the minimum width of the current location of the mobile device based on the minimum width of the current location of the mobile device and the preset association relationship.

[0046] Here, the preset association relationship is the association relationship between the preset minimum width and the accuracy of the local map. The accuracy of the local map corresponding to the minimum width in the preset association relationship should be the accuracy that enables the self-moving device to pass through the narrow area corresponding to the minimum width, that is, for any minimum width (hereinafter referred to as the target minimum width), the minimum conversion width obtained by converting the target minimum width to the local map of the target accuracy is greater than the maximum device width of the self-moving device in the local map of the target accuracy. The target accuracy is the accuracy corresponding to the target minimum width. The calculation formula for the minimum conversion width is: k0 = k min -2×L1, k0 represents the minimum conversion width, k min represents the minimum width, L1 represents the diagonal length of the grid in the local map of the target accuracy, and the maximum device width is calculated as follows: kd max =n max ×L2,kd max Indicates the maximum device width, n max It represents the maximum number of grids that a mobile device may occupy in a local map of target accuracy, and L2 represents the side length of a grid in a local map of target accuracy.

[0047] Among them, the preset association relationship can be set according to the size of the self-moving device.

[0048] Take the actual width of the self-moving device as 52 cm as an example. On a local map with an accuracy of 2.5 cm*2.5 cm, the self-moving device occupies a maximum of 22 grids, and the maximum device width of the self-moving device is 22*2.5 cm=55 cm; on a local map with an accuracy of 2 cm*2 cm, the self-moving device occupies a maximum of 27 grids, and the maximum device width of the self-moving device is 27*2 cm=54 cm; on a local map with an accuracy of 1.9 cm*1.9 cm, the self-moving device occupies a maximum of 28 grids, and the maximum device width of the self-moving device is 28*1.9 cm=53.2 cm; on a local map with an accuracy of 1.8 cm*1.8 cm, the self-moving device occupies a maximum of 29 grids, and the maximum device width of the self-moving device is 29*1.8 cm=52.2 cm; and so on, the maximum device width of the self-moving device in local maps of different accuracy can be determined. For the minimum width of 60cm, the minimum conversion width corresponding to 60cm in the local map with an accuracy of 2.5cm*2.5cm is 60cm-2*(2.5cm*1.414)=52.93cm, which is smaller than the maximum device width of 55cm of the self-equipped device, and the accuracy of 2.5cm*2.5cm does not meet the requirements; the minimum conversion width corresponding to 60cm in the local map with an accuracy of 2cm*2cm is 60-2*(2*1.414)=54.344cm, which is larger than the maximum device width of 54cm of the self-equipped device, and the accuracy of 2cm*2cm meets the requirements, so the correspondence between the minimum width of 60cm and the accuracy of 2cm*2cm can be established. For a minimum width of 59 cm, the minimum conversion width corresponding to 59 cm in a local map with an accuracy of 2.5 cm*2.5 cm is 59 cm-2*(2.5 cm*1.414)=51.93 cm, which is less than the maximum device width of 55 cm of the self-equipped device, and the accuracy of 2.5 cm*2.5 cm does not meet the requirement; the minimum conversion width corresponding to 59 cm in a local map with an accuracy of 2 cm*2 cm is 59-2*(2*1.414)=53.344 cm, which is less than the maximum device width of 54 cm of the self-equipped device, and the accuracy of 2 cm*2 cm does not meet the requirement; the minimum conversion width corresponding to 59 cm in a local map with an accuracy of 1.9 cm*1.9 cm is 59-2*(1.9 cm*1.414)=53.63 cm, which is greater than the maximum device width of 53.2 cm of the self-equipped device, and the accuracy of 1.9 cm*1.9 cm meets the requirement, so a corresponding relationship between the minimum width of 59 cm and the accuracy of 1.9 cm*1.9 cm can be established.For the minimum width of 58cm, the minimum conversion width of 58cm in the local map with an accuracy of 2.5cm*2.5cm is 59cm-2*(2.5cm*1.414)=50.93cm, which is less than the maximum device width of 55cm for the self-equipped device, and the accuracy of 2.5cm*2.5cm does not meet the requirements; the minimum conversion width of 58cm in the local map with an accuracy of 2cm*2cm is 58-2*(2*1.414)=52.344cm, which is less than the maximum device width of 54cm for the self-equipped device, and the accuracy of 2cm*2cm does not meet the requirements; the minimum conversion width of 58cm in the local map with an accuracy of 1.9cm is 58-2*(2*1.414)=52.344cm, which is less than the maximum device width of 54cm for the self-equipped device, and the accuracy of 2cm*2cm does not meet the requirements; *1.9cm in the local map, the corresponding minimum conversion width is 58-2*(1.9cm*1.414)=52.63cm, which is less than the maximum device width of 53.2cm of the self-equipped device, and 1.9cm*1.9cm does not meet the requirements; 58cm in the local map with an accuracy of 1.8cm*1.8cm corresponds to a minimum conversion width of 58-2*(1.8cm*1.414)=52.91cm, which is greater than the device width of 52.2cm of the self-equipped device, and 1.8cm*1.8cm meets the requirements, so the association relationship between the minimum width of 58cm and the accuracy of 1.8cm*1.8cm can be established. By analogy, different association relationships between minimum widths and accuracies can be established, thereby obtaining a preset association relationship.

[0049] For example, the preset association relationship may be as shown in Table 1:

[0050] Minimum Width Accuracy 60cm 2cm*2cm 59cm 1.9cm*1.9cm 58cm 1.8cm*1.8cm … …

[0051] Table 1

[0052] It can be understood that in order to allow the self-moving device to have a stronger traffic capability, the accuracy of the first local map may be smaller than the association accuracy in the preset association relationship.

[0053] In the above steps A11-A12, the minimum width of the current position of the mobile device is determined according to the position of the mobile device in the global static map, and the accuracy of the first local area corresponding to the minimum width is determined according to the minimum width and the preset association relationship. The appropriate accuracy can be determined for the first local map, thereby improving the possibility of the mobile device passing through the narrow area.

[0054] A2. Generate a first local map according to the accuracy of the first local map, the global static map and the point cloud data acquired in real time.

[0055] The first local map can be generated through the following steps A21-A24:

[0056] A21. Generate a first map according to the accuracy of the first local map, the global static map, and the point cloud data acquired in real time.

[0057] Among them, the accuracy of the local map can be set according to the accuracy of the first local map. For example, if the accuracy of the first local map is 2cm*2cm, the accuracy of the local map can be set to 2cm*2cm. Then, the local map area corresponding to the local map can be determined on the global static map, and obstacles located in the local map area on the global static map can be mapped to the local map to form obstacles, and each scanning point in the point cloud data can be mapped to the local map to form obstacles, thereby generating a first map.

[0058] A22. Determine the passable area based on the first map.

[0059] Here, the traversable area refers to an area without obstacles in the first map.

[0060] A23. If the first minimum width of the passable area is greater than or equal to the first preset width, determine a narrow road area passable by the self-mobile device, and use the first map as the first local map.

[0061] Here, the first preset width may refer to the device width of the mobile device in the first map, and the first minimum width refers to the minimum width of the passable area in the first map.

[0062] If the first minimum width of the passable area is greater than or equal to the first preset width, it means that the accuracy of the first map is sufficient, and the mobile device can pass through the passable area based on the first map.

[0063] A23. If the first minimum width of the passable area is less than the first preset width and greater than or equal to the second preset width, the first map is updated according to the global static map and the point cloud data acquired in real time to obtain the second map.

[0064] Here, the second preset width may refer to the real width of the self-moving device. If the first minimum width of the passable area is less than the first preset width and greater than or equal to the second preset width, it means that the accuracy of the first map is insufficient, and the self-moving device cannot pass through the passable area based on the first map. However, the width of the passable area is greater than the real width of the self-moving device, and improving the map accuracy can help the self-moving device pass through the narrow area.

[0065] The accuracy of the second map is greater than that of the first map. Updating the first map according to the global static map and the point cloud data acquired in real time to obtain the second map means that after increasing the accuracy of the first map, obstacles in the global static map and obstacles corresponding to the point cloud data are mapped to the first map with increased accuracy, thereby obtaining the second map.

[0066] A24. Use the second map as the first local map.

[0067] In the above steps A21-A24, by comparing the minimum width of the passable area in the local map with the first preset width, when the minimum width of the passable area in the local map is greater than the preset width, the local map is determined as the first local map, and when the minimum width of the passable area in the local map is less than the first preset width and greater than the second preset width, the accuracy of the local map is updated, and the appropriate accuracy can be further adjusted and determined, thereby further improving the possibility of the mobile device passing through the narrow area.

[0068] In some possible cases, if the first minimum width of the passable area is less than the second preset width, it means that the actual width of the narrow road area is less than the actual width of the self-moving device, and the self-moving device is determined to be impassable in the narrow road area, and the self-moving device is controlled to re-plan the path to travel around the narrow road area. When the minimum width of the passable area in the local map is less than the second preset width, it is determined to re-plan the path to travel around the narrow road area, which can avoid the self-moving device being unable to pass through the narrow road area.

[0069] In the above steps A1-A2, the accuracy of the first local map is determined according to the minimum width of the current position of the self-moving device, so that the accuracy of the first local map can be reasonable, thereby increasing the possibility of the self-moving device passing through the narrow road area.

[0070] S102: Control the mobile device to pass through a narrow road area according to the first local map.

[0071] Here, controlling the self-moving device to pass through the narrow road area according to the first local map means determining the self-moving device and the passable area according to the first local map, and controlling the self-moving device to pass through the passable area in the first local map. The passable area in the first local map is essentially the presentation of the narrow road area in the first local map.

[0072] In the above Figure 1 In the corresponding technical solution, when it is detected that the self-moving device moves to a narrow road area, a first local map is generated according to the global static map and the point cloud data obtained in real time, and according to the first local map, the self-moving device is controlled to pass through the narrow road area. Since the accuracy of the first local map is higher than that of the global static map, and the first local map is generated by the global static map and the point cloud data obtained in real time, the accuracy of the first local map is relatively high. Controlling the self-moving device to pass through the narrow road area according to the first local map can increase the possibility of the self-moving device passing through the narrow road area.

[0073] See also Figure 2 , Figure 2 A schematic diagram of a flow chart of another narrow passage area passage method provided in an embodiment of the present application, such as Figure 2 As shown, the method comprises the following steps:

[0074] S201, when it is detected that the mobile device moves to a narrow road area, a first local map is generated according to the global static map and point cloud data acquired in real time.

[0075] S202: Control the mobile device to pass through a narrow road area according to the first local map.

[0076] Here, regarding the specific implementation of steps S201 to S202, reference may be made to the aforementioned steps S101 to S102, which will not be repeated here.

[0077] S203, after the mobile device passes through the narrow road area, a second local map is generated according to the global static map and the point cloud data acquired in real time.

[0078] Here, the principle of detecting whether the self-moving device passes through the narrow passage area is similar to the principle of detecting whether the self-moving device moves to the narrow passage area in the aforementioned step S101. If the positioning position of the self-moving device is not located in the narrow passage area, it is determined that the self-moving device passes through the narrow passage area.

[0079] The accuracy of the second local map is less than that of the first local map, and greater than that of the global static map. The principle of generating the second local map according to the global static map and the point cloud data acquired in real time can refer to the principle of generating the first local map according to the global static map and the point cloud data acquired in real time in the aforementioned step S101, which will not be repeated here.

[0080] S204: Control the mobile device to travel according to the second local map.

[0081] In the above Figure 2 In the corresponding technical solution, when the self-moving device passes through a narrow road area, the local map is switched to a local map with lower accuracy, and the self-moving device is controlled to travel according to the local map with lower accuracy, which can increase the coverage area of ​​the local map and avoid negative impact on the navigation planning of the self-moving device.

[0082] The method of the present application is introduced above, and the device of the present application is introduced below.

[0083] See also Figure 3 , Figure 3 Schematic diagram of a narrow passage area passage device provided in an embodiment of the present application, which is applied to self-moving equipment. Figure 3 As shown, the narrow passage area passage device 30 includes:

[0084] The local map generation module 301 generates a first local map according to the global static map and the point cloud data acquired in real time when the mobile device is detected to move to the narrow road area, wherein the accuracy of the first local map is higher than that of the global static map;

[0085] The passage control module 302 is configured to control the self-moving device to pass through the narrow road area according to the first local map. The self-moving device is configured to control the self-moving device to pass through the narrow road area according to the first local map.

[0086] In a possible design, the local map generation module 301 is specifically used to: determine the accuracy of the first local map based on the minimum width of the current position of the mobile device, wherein the accuracy of the first local map is negatively correlated with the minimum width; generate the first local map based on the accuracy of the first local map, the global static map and the point cloud data acquired in real time.

[0087] In a possible design, the local map generation module 301 is specifically used to: determine the minimum width of the current position of the self-mobile device according to the position of the self-mobile device in the global static map; determine the accuracy of the first local map corresponding to the minimum width according to the minimum width and a preset association relationship.

[0088] In a possible design, the local map generation module is further used to set the preset association relationship according to the size of the self-moving device.

[0089] In a possible design, the local map generation module 301 is specifically used to: generate a first map according to the accuracy of the first local map, the global static map and the point cloud data acquired in real time; determine a passable area according to the first map; if the first minimum width of the passable area is greater than or equal to the first preset width, determine that the self-mobile device can pass through the narrow road area, and use the first map as the first local map; if the first minimum width of the passable area is less than the first preset width and greater than or equal to the second preset width, update the first map according to the global static map and the point cloud data acquired in real time to obtain a second map, wherein the accuracy of the second map is greater than the accuracy of the first map; and use the second map as the first local map.

[0090] In a possible design, the passage control module 302 is also used to: if the first minimum width of the passable area is less than the second preset width, determine that the self-moving device cannot pass through the narrow road area, and control the self-moving device to re-plan the path to travel around the narrow road area.

[0091] In a possible design, the local map generation module 301 is further used to: obtain the positioning position of the self-mobile device, and determine whether the positioning position is located in the narrow road area, wherein the narrow road area is pre-planned in the global static map; if the positioning position is located in the narrow road area, it is determined that the self-mobile device has moved to the narrow road area; if the positioning position is not located in the narrow road area, it is determined that the self-mobile device has not moved to the narrow road area.

[0092] In a possible design, the local map generation module 301 is also used to generate a second local map according to the global static map and point cloud data acquired in real time after the self-moving device passes through the narrow road area, wherein the accuracy of the second local map is less than the accuracy of the first local map and greater than the accuracy of the global static map; the traffic control module 302 is also used to control the driving of the self-moving device according to the second local map.

[0093] It should be noted that Figure 3 For the contents not mentioned in the corresponding embodiments, please refer to the description of the aforementioned method embodiments, which will not be repeated here.

[0094] The above-mentioned device, when detecting that the self-moving device moves to a narrow passage area, generates a first local map according to the global static map and the point cloud data acquired in real time, and controls the self-moving device to pass through the narrow passage area according to the first local map. Since the accuracy of the first local map is higher than that of the global static map, and the first local map is generated by the global static map and the point cloud data acquired in real time, the accuracy of the first local map is relatively high. Controlling the self-moving device to pass through the narrow passage area according to the first local map can increase the possibility of the self-moving device passing through the narrow passage area.

[0095] See also Figure 4 , Figure 4 4 is a schematic diagram of a self-moving device provided in an embodiment of the present application, wherein the self-moving device 40 includes a processor 401, a memory 402, and a sensor 403. The memory 402 and the sensor 403 are connected to the processor 401, for example, through a bus.

[0096] The processor 401 is configured to support the self-mobile device 40 to perform the corresponding functions of the method in the above method embodiment. The processor 401 can be a central processing unit (CPU), a network processor (NP), a hardware chip or any combination thereof. The above hardware chip can be an application specific integrated circuit (ASIC), a programmable logic device (PLD) or a combination thereof. The above PLD can be a complex programmable logic device (CPLD), a field-programmable gate array (FPGA), a generic array logic (GAL) or any combination thereof.

[0097] The sensor 403 may include a laser radar and / or a depth camera and other sensor devices. The sensor 403 is used to obtain point cloud data in real time.

[0098] The memory 402 is used to store program codes, etc. The memory 402 may include a volatile memory (VM), such as a random access memory (RAM); the memory 402 may also include a non-volatile memory (NVM), such as a read-only memory (ROM), a flash memory, a hard disk drive (HDD) or a solid-state drive (SSD); the memory 402 may also include a combination of the above-mentioned types of memories.

[0099] The memory 402 can be used to store non-volatile software programs, non-volatile computer executable programs and modules, such as program instructions / modules corresponding to the narrow lane area passage method in the embodiment of the present application. The processor executes various functional applications and data processing of the charging diagnosis method by running the non-volatile software programs, instructions and modules stored in the memory, that is, realizes the functions of the narrow lane area passage method provided by the above method embodiment.

[0100] The memory 402 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and application programs required for at least one function. The data storage area may store data created according to the use of the narrow lane area access device, etc. In some embodiments, the memory may include a memory remotely arranged relative to the processor, and these remote memories may be connected to the narrow lane area access device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0101] The one or more modules are stored in the memory, and when executed by the one or more processors, the narrow road area passage method in any of the above-mentioned method embodiments is executed, for example, the method steps described in the above-mentioned method embodiments are executed to realize the functions of the modules described in the above-mentioned device embodiments.

[0102] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a computer, the computer executes the method described in the above embodiment.

[0103] A person skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0104] The above disclosure is only the preferred embodiment of the present application, which certainly cannot be used to limit the scope of rights of the present application. Therefore, equivalent changes made according to the claims of the present application are still within the scope covered by the present application.

Claims

1. A method for passing through a narrow road area, characterized in that: Applied to a self-equipped device, the method comprises: When it is detected that the mobile device moves to a narrow road area, a first local map is generated according to the global static map and the point cloud data acquired in real time, wherein the accuracy of the first local map is higher than that of the global static map; The self-moving device is controlled to pass through the narrow road area according to the first local map.

2. The method according to claim 1, characterized in that Generate a first local map based on the global static map and the point cloud data acquired in real time, including: Determining the accuracy of the first local map according to the minimum width of the current position of the mobile device, wherein the accuracy of the first local map is negatively correlated with the minimum width; The first local map is generated according to the accuracy of the first local map, the global static map and the point cloud data acquired in real time.

3. The method according to claim 2, characterized in that Determining the accuracy of the first local map according to the minimum width of the current position of the mobile device includes: Determine the minimum width of the current position of the self-mobile device according to the position of the self-mobile device in the global static map; According to the minimum width and the preset association relationship, the accuracy of the first local map corresponding to the minimum width is determined.

4. The method according to claim 3, characterized in that The method further comprises: The preset association relationship is set according to the size of the self-moving device.

5. The method according to claim 2, characterized in that: Generating the first local map according to the accuracy of the first local map, the global static map and the point cloud data acquired in real time, including: Generate a first map according to the accuracy of the first local map, the global static map and the point cloud data acquired in real time; Determining a traversable area according to the first map; If the first minimum width of the passable area is greater than or equal to the first preset width, it is determined that the self-moving device can pass through the narrow road area, and the first map is used as the first local map; If the first minimum width of the passable area is less than the first preset width and greater than or equal to the second preset width, updating the first map according to the global static map and the point cloud data acquired in real time to obtain a second map, wherein the accuracy of the second map is greater than the accuracy of the first map; The second map is used as the first local map.

6. The method according to claim 5, characterized in that The method further comprises: If the first minimum width of the passable area is smaller than the second preset width, it is determined that the self-moving device cannot pass through the narrow road area, and the self-moving device is controlled to re-plan a path to travel around the narrow road area.

7. The method according to claim 1, characterized in that When it is detected that the mobile device moves to the narrow road area, before generating the first local map according to the global static map and the point cloud data acquired in real time, the method further includes: Obtaining the positioning position of the self-moving device, and determining whether the positioning position is located in the narrow road area, wherein the narrow road area is pre-planned in the global static map; If the positioning position is located in the narrow passage area, determining that the mobile device moves to the narrow passage area; If the positioning position is not located in the narrow passage area, it is determined that the self-moving device has not moved to the narrow passage area.

8. The method according to claim 1, characterized in that After controlling the self-moving device to pass through the narrow road area according to the first local map, the method further includes: After the mobile device passes through the narrow road area, a second local map is generated according to the global static map and the point cloud data acquired in real time, wherein the accuracy of the second local map is less than the accuracy of the first local map and greater than the accuracy of the global static map; The self-moving device is controlled to travel according to the second local map.

9. A self-propelled device, characterized in that: The device comprises a memory, a sensor and a processor, wherein the memory and the sensor are connected to the processor, the sensor is used to acquire point cloud data in real time, and the processor is used to execute one or more computer programs stored in the memory, and when the processor executes the one or more computer programs, the self-mobile device implements the method according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the processor is caused to perform the method according to any one of claims 1 to 8.

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