Method and apparatus for automatic indoor map recognition and drawing based on artificial intelligence algorithms

Abstract

This invention proposes a method and apparatus for automatic indoor map recognition and drawing based on artificial intelligence algorithms. The method includes: arranging reference positioning devices in an indoor space and determining the coordinates of key locations; acquiring the inherent signal parameters of the key location coordinates relative to each of the reference positioning devices; acquiring images at each key location coordinate based on the inherent signal parameters, and feature parameters of each key identification element in the images; and drawing an indoor map based on the key location coordinates, the images at each key location coordinate, and the feature parameters of the corresponding key identification elements in the images, according to a preset algorithm. The apparatus is used to implement the method. This invention can achieve automatic recognition and drawing of indoor maps, improving map drawing efficiency.

Description

Technical Field

[0001] This invention relates to the field of communication technology, and specifically to a method and apparatus for automatic indoor map recognition and drawing based on artificial intelligence algorithms. Background Technology

[0002] In recent years, with the development of artificial intelligence technology, people's demand for indoor navigation and location services has been increasing, making the recognition and mapping of indoor maps extremely important.

[0003] Indoor map recognition and creation typically employs CNN algorithms to classify indoor images and identify different regions, while object detection algorithms identify elements within those regions. These two methods are combined to recognize and create the indoor map. However, the sheer number of elements within these regions necessitates manual labeling of the dataset, which cannot meet the demands of practical applications. Therefore, how to automatically recognize and create indoor maps is a pressing issue that needs to be addressed. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention proposes an automatic indoor map recognition and drawing method based on artificial intelligence algorithms, comprising:

[0005] Based on the indoor space, a reference positioning device is deployed, and the coordinates of key locations are determined;

[0006] Obtain the inherent signal parameters of the key position coordinates relative to each of the aforementioned reference positioning devices;

[0007] Based on the inherent signal parameters, images at the coordinates of each key location are obtained, as well as the feature parameters of each key identifier element in the image;

[0008] Based on the coordinates of each key location, the images at each key location coordinate, and the feature parameters of the key recognition elements in the corresponding images, an indoor map is drawn according to a preset algorithm.

[0009] In one embodiment, the step of arranging a reference positioning device based on an indoor space and determining the coordinates of key locations includes:

[0010] Multiple Wi-Fi access devices are deployed in the indoor space;

[0011] Select multiple key location coordinates in the indoor space, including windows, doorways, stairwells, load-bearing walls, room corners, or the center of the room.

[0012] In one embodiment, the step of obtaining the key location coordinates relative to the inherent signal parameters of each of the reference positioning devices includes:

[0013] Control the robot to stop at various key coordinate positions;

[0014] The robot is controlled to connect to the multiple WIFI access devices in sequence and obtain the inherent signal parameters of each WIFI access device, including WIFI signal strength and request response time.

[0015] In one embodiment, the step of obtaining the image at each key location coordinate based on inherent signal parameters, and the feature parameters of each key identifier element in the image, includes:

[0016] Based on the key location coordinates, plan different intersecting travel paths, with each path passing through at least two of the key location coordinates;

[0017] The robot is controlled to move along different paths according to preset travel rules, and the changing signal parameters relative to each WIFI access device are acquired in each travel path.

[0018] When the robot moves to the key location coordinates, it acquires the key identification elements at the corresponding key location coordinates, as well as the feature parameters of each key identification element. The key identification elements include doors, windows, stairs, or furniture.

[0019] Images of the robot acquiring coordinates at key locations.

[0020] In one embodiment, the step of drawing an indoor map based on the coordinates of each key location, the image at each key location coordinate, and the feature parameters of the key identification elements in the corresponding image, according to a preset algorithm, includes:

[0021] Based on the coordinates of each key location, the images at each key location coordinate, and the feature parameters of the key identification elements in the corresponding images, the apartment size information in the corresponding images is determined according to a preset algorithm. The feature parameters include size information or pixel information.

[0022] Based on the preset travel rules and the changing signal parameters relative to each WIFI access device when the robot moves along different travel paths, the apartment layout elements are determined. The apartment layout elements include doors, windows, stairs, ordinary walls, load-bearing walls or floors.

[0023] Based on the apartment size information and apartment features, draw an interior map.

[0024] In one embodiment, after the step of drawing an indoor map based on the coordinates of each key location, the key identifier elements at each key location coordinate, and the corresponding feature parameters according to a preset algorithm, the method further includes:

[0025] In response to a touch operation based on the indoor map, a key identification element corresponding to the touch operation and an editing dialog box for the feature parameters of the key identification element are pushed.

[0026] The present invention also provides an indoor map automatic recognition and drawing device based on artificial intelligence algorithms, comprising:

[0027] The module is used to determine the coordinates of key locations by arranging reference positioning equipment based on the indoor space.

[0028] The first acquisition module is used to acquire the inherent signal parameters of the key position coordinates relative to each of the reference positioning devices;

[0029] The second acquisition module is used to acquire the image at the coordinates of each key location based on the inherent signal parameters, as well as the feature parameters of each key identifier element in the image;

[0030] The processing module is used to draw indoor maps based on the coordinates of each key location, the images at each key location coordinate, and the feature parameters of the key recognition elements in the corresponding images, according to a preset algorithm.

[0031] In one embodiment, the determining module is specifically used for:

[0032] Multiple Wi-Fi access devices are deployed in the indoor space;

[0033] Select multiple key location coordinates in the indoor space, including windows, doorways, stairwells, load-bearing walls, room corners, or the center of the room;

[0034] The first acquisition module is specifically used for:

[0035] Control the robot to stop at various key coordinate positions;

[0036] The robot is controlled to connect to the multiple WIFI access devices in sequence and obtain the inherent signal parameters of each WIFI access device, including WIFI signal strength and request response time.

[0037] The second acquisition module is specifically used for:

[0038] Based on the key location coordinates, plan different intersecting travel paths, with each path passing through at least two of the key location coordinates;

[0039] The robot is controlled to move along different paths according to preset travel rules, and the changing signal parameters relative to each WIFI access device are acquired in each travel path.

[0040] When the robot moves to the key location coordinates, it acquires the key identification elements at the corresponding key location coordinates, as well as the feature parameters of each key identification element. The key identification elements include doors, windows, stairs, or furniture.

[0041] Images of the robot acquiring coordinates at key locations;

[0042] The processing module is specifically used for:

[0043] Based on the coordinates of each key location, the images at each key location coordinate, and the feature parameters of the key identification elements in the corresponding images, the apartment size information in the corresponding images is determined according to a preset algorithm. The feature parameters include size information or pixel information.

[0044] Based on the preset travel rules and the changing signal parameters relative to each WIFI access device when the robot moves along different travel paths, the apartment layout elements are determined. The apartment layout elements include doors, windows, stairs, ordinary walls, load-bearing walls or floors.

[0045] Based on the apartment size information and apartment features, draw an interior map.

[0046] The present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described method for automatic indoor map recognition and drawing based on artificial intelligence algorithms.

[0047] The present invention also provides a computer storage medium storing a computer program that, when executed by a processor, implements the above-described method for automatic indoor map recognition and drawing based on artificial intelligence algorithms.

[0048] This invention, in its embodiments, involves deploying reference positioning devices within an indoor space and determining key location coordinates; acquiring the inherent signal parameters of these key location coordinates relative to each reference positioning device; obtaining images of each key location coordinate based on these inherent signal parameters, as well as feature parameters of key identifying elements within those images; and drawing an indoor map based on the key location coordinates, the images at those coordinates, and the feature parameters of the corresponding key identifying elements, according to a preset algorithm. This allows for targeted determination of the placement of reference positioning devices and key location coordinates based on different indoor spaces, improving the utilization rate of the reference positioning devices and reducing the number of key location coordinates, indirectly reducing the required number of key identifying elements. Furthermore, this invention's method of drawing indoor maps based on key location coordinates, the images at those coordinates, and the feature parameters of the corresponding key identifying elements, according to a preset algorithm, enables automatic recognition and drawing of indoor maps, improving map drawing efficiency. Attached Figure Description

[0049] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0050] Figure 1 This is a flowchart of the automatic indoor map recognition and drawing method based on artificial intelligence algorithm according to the first embodiment of the present invention;

[0051] Figure 2 This is a detailed flowchart of S11 in the first embodiment of the present invention;

[0052] Figure 3 This is a detailed flowchart of S12 in the first embodiment of the present invention;

[0053] Figure 4 This is a detailed flowchart of S13 in the first embodiment of the present invention;

[0054] Figure 5 This is a detailed flowchart of S14 in the first embodiment of the present invention;

[0055] Figure 6 This is a structural block diagram of the indoor map automatic recognition and drawing device based on artificial intelligence algorithm according to the third embodiment of the present invention;

[0056] Figure 7 This is a schematic diagram of the internal structure of a computer according to another embodiment of the present invention. Detailed Implementation

[0057] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Well-known modules, units, and their connections, links, communications, or operations are not shown or described in detail. Furthermore, the described features, architectures, or functions can be combined in any way in one or more embodiments. Those skilled in the art should understand that the various embodiments described below are for illustrative purposes only and are not intended to limit the scope of protection of the present invention. It is also readily understood that the modules, units, or processing methods in the various embodiments described herein and shown in the accompanying drawings can be combined and designed in various different configurations. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0058] First embodiment:

[0059] Please refer to Figures 1 to 5As shown in the figure, this invention discloses an automatic indoor map recognition and drawing method based on artificial intelligence algorithms, including:

[0060] S11, based on the indoor space, a reference positioning device is set up, and the coordinates of key locations are determined.

[0061] The indoor spaces referred to in this embodiment include, but are not limited to, residences, shopping malls, office buildings, and factories. The number of reference positioning devices is one or more. In a planar indoor space, multiple reference positioning devices can be distributed on the same floor of the indoor space. In a three-dimensional indoor space (such as a multi-story shopping mall or factory), multiple reference positioning devices can be distributed on multiple floors of the indoor space.

[0062] As a preferred option and not a limitation, the reference positioning device in this embodiment is a WIFI access device. Please refer to [reference needed]. Figure 2 As shown, this step S11 includes S111-S112, wherein:

[0063] S111 involves deploying multiple WIFI access devices in an indoor space.

[0064] The Wi-Fi access device in this step can be a modem, router, hub, access point (AP), or mesh networking device with Wi-Fi capability. Typically, the Wi-Fi access devices are evenly distributed throughout the indoor space to ensure varying signal strength and response times from the same indoor coordinates to each device, facilitating precise robot positioning.

[0065] S112, Select multiple key location coordinates in the indoor space, including windows, doorways, stairwells, load-bearing walls, room corners, or the center of the room.

[0066] The selection rules for key location coordinates are usually the center, corners, and areas with large signal variation in an independent space, in order to facilitate the accurate determination of the apartment layout elements later.

[0067] S12, Obtain the inherent signal parameters of the key position coordinates relative to each of the reference positioning devices.

[0068] As an example and not a limitation, this step is an execution step before the actual generation of the indoor map, used to obtain the inherent signal parameters of the key location coordinates relative to each of the aforementioned reference positioning devices in advance.

[0069] As a preferred option and not a limitation, in this embodiment, please refer to... Figure 3 As shown, this step S12 includes S121-S122, wherein:

[0070] S121, control the robot to stay at each key coordinate position.

[0071] The robot referred to in this embodiment typically includes a tracked robot, a humanoid robot, or a robot dog. When the robot is stationary at each key coordinate position, the robot's network communication equipment is at a fixed height.

[0072] S122, control the robot to connect to the multiple WIFI access devices in sequence, and obtain the inherent signal parameters of each WIFI access device, including WIFI signal strength and request response time.

[0073] This step is used to enable the robot to learn the inherent signal parameters of each WIFI access device at each key location coordinate, so that the robot can find and accurately locate each key location coordinate on its own.

[0074] As an improvement rather than a limitation to this step, the robot in this embodiment may include multiple network communication devices, each of which is matched with a corresponding WIFI access device in the indoor space, and the inherent signal parameters are collected, sorted and compared by the processor to improve the robot's positioning efficiency.

[0075] S13, based on the inherent signal parameters, obtain the images at the coordinates of each key location, as well as the feature parameters of each key identifier element in the image.

[0076] As an example and not a limitation, this step represents the execution steps in actual indoor map generation. When the robot reaches the coordinates of each key location, it acquires images of those locations and obtains the feature parameters of each key identifier element in the image. These key identifier elements can be specified in advance by the user or automatically selected by the robot after training using a convolutional neural network; this embodiment does not impose any limitations. The feature parameters are used for comparison to derive the dimensional information related to the apartment layout in the image.

[0077] Please refer to this as a preferred option rather than a limitation. Figure 4 As shown, this step S13 includes S131-S134, wherein:

[0078] S131, Based on the key location coordinates, plan different intersecting travel paths, with each path passing through at least two of the key location coordinates.

[0079] The purpose of intersecting the travel paths is to ensure that at least two travel paths in different directions appear at the same key location coordinates, so that the subsequent generation data of unit size information and unit elements can be mutually verified and fine-tuned, thereby improving the reliability of indoor map drawing.

[0080] S132, control the robot to move along the different travel paths according to the preset travel rules, and obtain the change signal parameters relative to each WIFI access device in each travel path.

[0081] The preset movement rules in this embodiment include, but are not limited to, controlling the robot's movement with preset movement speed, angular velocity, and attitude. During the robot's movement along various paths, different signal parameters will be generated due to variations in the area of ​​openings (spaces formed by windows, doors, or floor openings), wall areas, or floor areas between different paths and various Wi-Fi access devices. By calculating the start time and duration of these changing parameters and comparing them with other changing signal parameters along the current movement path, the corresponding area of ​​openings (spaces formed by windows or doors), wall areas, or floor areas along that path can be calculated, facilitating the subsequent determination of apartment layout elements.

[0082] S133, when the robot moves to the key position coordinates, it acquires the key identification elements at the corresponding key position coordinates, as well as the feature parameters of each key identification element. The key identification elements include doors, windows, stairs, or furniture.

[0083] S134, Images obtained by controlling the robot to acquire the coordinates of each key position.

[0084] In this embodiment, images acquired by the robot along the same travel path are usually sequentially numbered to generate an image set, which facilitates the subsequent drawing of indoor maps.

[0085] S14. Based on the coordinates of each key location, the images at each key location coordinate, and the feature parameters of the key recognition elements in the corresponding images, draw an indoor map according to a preset algorithm.

[0086] Typically, indoor maps can be created using mapping algorithms, combined with trained convolutional neural networks, to generate corresponding two-dimensional or three-dimensional maps by filling in the base map and eliminating self-intersections.

[0087] Please refer to this as a preferred option rather than a limitation. Figure 5 As shown, this step S14 includes S141-S143, wherein:

[0088] S141, based on the coordinates of each key location, the images at each key location coordinate, and the feature parameters of the key identification elements in the corresponding images, the apartment size information in the corresponding images is determined according to a preset algorithm, wherein the feature parameters include size information or pixel information.

[0089] When a robot carries a laser rangefinder, the feature parameters are typically size information. In this embodiment, the feature parameters include both size and pixel information. As an example and not a limitation, the size information of the apartment in the corresponding image can be determined by the proportion of the feature parameters to other elements (such as walls, windows, and doors) in the apartment layout.

[0090] S142, based on the preset travel rules and the changing signal parameters relative to each WIFI access device when the robot moves along different travel paths, determine the apartment type elements, which include doors, windows, stairs, ordinary walls, load-bearing walls or floors.

[0091] In this embodiment, when generating apartment layout elements, the apartment size information determined in step S141 can be referenced to further improve the reliability of apartment layout elements and sizes.

[0092] S143, Draw an interior map based on the apartment size information and apartment features.

[0093] Steps S141-S143 generate floor plan dimensions and floor plan elements sequentially, making the indoor map drawing more accurate and the determination of load-bearing walls, stairs, and floors more reliable, thus improving the accuracy of the indoor map drawing.

[0094] As an improvement to, and not a limitation of, this embodiment, step S14 may further include:

[0095] In response to a touch operation based on the indoor map, a key identification element corresponding to the touch operation and an editing dialog box for the feature parameters of the key identification element are pushed.

[0096] As a preferred option rather than a limitation, the push content also includes notes on key identification elements. In this embodiment, editable key identification element feature parameters can help improve the drawing accuracy of indoor maps.

[0097] This invention, in its embodiments, involves deploying reference positioning devices within an indoor space and determining key location coordinates; acquiring the inherent signal parameters of these key location coordinates relative to each reference positioning device; obtaining images of each key location coordinate based on these inherent signal parameters, as well as feature parameters of key identifying elements within those images; and drawing an indoor map based on the key location coordinates, the images at those coordinates, and the feature parameters of the corresponding key identifying elements, according to a preset algorithm. This allows for targeted determination of the placement of reference positioning devices and key location coordinates based on different indoor spaces, improving the utilization rate of the reference positioning devices and reducing the number of key location coordinates, indirectly reducing the required number of key identifying elements. Furthermore, this invention's method of drawing indoor maps based on key location coordinates, the images at those coordinates, and the feature parameters of the corresponding key identifying elements, according to a preset algorithm, enables automatic recognition and drawing of indoor maps, improving map drawing efficiency.

[0098] Second embodiment:

[0099] Please refer to Figure 6As shown, the present invention also provides an indoor map automatic recognition and drawing device 100 based on artificial intelligence algorithms, including a determining module 110, a first acquisition module 120, a second acquisition module 130, and a processing module 140, wherein:

[0100] The determination module 110, connected to the first acquisition module 120, is used to deploy a reference positioning device based on the indoor space and determine the coordinates of key locations.

[0101] The first acquisition module 120 is connected to the second acquisition module 130 and is used to acquire the inherent signal parameters of the key position coordinates relative to each of the reference positioning devices.

[0102] The second acquisition module 130 is connected to the processing module 140 and is used to acquire the image at the coordinates of each key location based on the inherent signal parameters, as well as the feature parameters of each key identifier element in the image.

[0103] The processing module 140 is used to draw an indoor map based on the coordinates of each key location, the image at each key location coordinate, and the feature parameters of the key recognition elements in the corresponding image, according to a preset algorithm.

[0104] As a preferred option rather than a limitation, module 110 is specifically used for:

[0105] Multiple Wi-Fi access devices are deployed in the indoor space;

[0106] Select multiple key location coordinates in the indoor space, including windows, doorways, stairwells, load-bearing walls, room corners, or the center of the room.

[0107] The first acquisition module 120 is specifically used for:

[0108] Control the robot to stop at various key coordinate positions;

[0109] The robot is controlled to connect to the multiple WIFI access devices in sequence and obtain the inherent signal parameters of each WIFI access device, including WIFI signal strength and request response time.

[0110] The second acquisition module 130 is specifically used for:

[0111] Based on the key location coordinates, plan different intersecting travel paths, with each path passing through at least two of the key location coordinates;

[0112] The robot is controlled to move along different paths according to preset travel rules, and the changing signal parameters relative to each WIFI access device are acquired in each travel path.

[0113] When the robot moves to the key location coordinates, it acquires the key identification elements at the corresponding key location coordinates, as well as the feature parameters of each key identification element. The key identification elements include doors, windows, stairs, or furniture.

[0114] Images of the robot acquiring coordinates at key locations.

[0115] Processing module 140 is specifically used for:

[0116] Based on the coordinates of each key location, the images at each key location coordinate, and the feature parameters of the key identification elements in the corresponding images, the apartment size information in the corresponding images is determined according to a preset algorithm. The feature parameters include size information or pixel information.

[0117] Based on the preset travel rules and the changing signal parameters relative to each WIFI access device when the robot moves along different travel paths, the apartment layout elements are determined. The apartment layout elements include doors, windows, stairs, ordinary walls, load-bearing walls or floors.

[0118] Based on the apartment size information and apartment features, draw an interior map.

[0119] The modules in this embodiment are the same as the corresponding steps in the first embodiment described above, and will not be repeated here.

[0120] This invention, in its embodiments, involves deploying reference positioning devices within an indoor space and determining key location coordinates; acquiring the inherent signal parameters of these key location coordinates relative to each reference positioning device; obtaining images of each key location coordinate based on these inherent signal parameters, as well as feature parameters of key identifying elements within those images; and drawing an indoor map based on the key location coordinates, the images at those coordinates, and the feature parameters of the corresponding key identifying elements, according to a preset algorithm. This allows for targeted determination of the placement of reference positioning devices and key location coordinates based on different indoor spaces, improving the utilization rate of the reference positioning devices and reducing the number of key location coordinates, indirectly reducing the required number of key identifying elements. Furthermore, this invention's method of drawing indoor maps based on key location coordinates, the images at those coordinates, and the feature parameters of the corresponding key identifying elements, according to a preset algorithm, enables automatic recognition and drawing of indoor maps, improving map drawing efficiency.

[0121] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0122] This invention also provides a computer storage medium storing a computer program that, when executed by a processor, implements the indoor map automatic recognition and drawing method based on artificial intelligence algorithms as described in the above embodiments.

[0123] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the automatic indoor map recognition and drawing methods based on artificial intelligence algorithms described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0124] Alternatively, if the integrated units of this invention are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this invention, or the parts that contribute to related technologies, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, terminal, or network device, etc.) to execute all or part of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, RAM, ROM, magnetic disks, or optical disks.

[0125] Corresponding to the computer storage medium described above, one embodiment also provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the indoor map automatic recognition and drawing method based on artificial intelligence algorithms as described in the above embodiments.

[0126] This computer device can be a terminal, and its internal structure diagram can be as follows: Figure 7 As shown, the computer device includes a processor, memory, network interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements an automatic indoor map recognition and drawing method based on artificial intelligence algorithms. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0127] This invention, in its embodiments, involves deploying reference positioning devices within an indoor space and determining key location coordinates; acquiring the inherent signal parameters of these key location coordinates relative to each reference positioning device; obtaining images of each key location coordinate based on these inherent signal parameters, as well as feature parameters of key identifying elements within those images; and drawing an indoor map based on the key location coordinates, the images at those coordinates, and the feature parameters of the corresponding key identifying elements, according to a preset algorithm. This allows for targeted determination of the placement of reference positioning devices and key location coordinates based on different indoor spaces, improving the utilization rate of the reference positioning devices and reducing the number of key location coordinates, indirectly reducing the required number of key identifying elements. Furthermore, this invention's method of drawing indoor maps based on key location coordinates, the images at those coordinates, and the feature parameters of the corresponding key identifying elements, according to a preset algorithm, enables automatic recognition and drawing of indoor maps, improving map drawing efficiency.

[0128] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0129] The above embodiments merely illustrate several implementation methods of the present invention, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this invention patent should be determined by the appended claims.

Claims

1. A method for automatic indoor map recognition and drawing based on artificial intelligence algorithms, characterized in that, include: Based on the indoor space, a reference positioning device is deployed, and the coordinates of key locations are determined; Obtain the inherent signal parameters of the key position coordinates relative to each of the aforementioned reference positioning devices; Based on the inherent signal parameters, images at the coordinates of each key location are obtained, as well as the feature parameters of each key identifier element in the image; Based on the coordinates of each key location, the images at each key location coordinate, and the feature parameters of the key recognition elements in the corresponding images, an indoor map is drawn according to a preset algorithm.

2. The method as described in claim 1, characterized in that, The step of arranging reference positioning equipment in an indoor space and determining the coordinates of key locations includes: Multiple Wi-Fi access devices are deployed in the indoor space; Select multiple key location coordinates in the indoor space, including windows, doorways, stairwells, load-bearing walls, room corners, or the center of the room.

3. The method as described in claim 2, characterized in that, The step of obtaining the key location coordinates relative to the inherent signal parameters of each of the reference positioning devices includes: Control the robot to stop at various key coordinate positions; The robot is controlled to connect to the multiple WIFI access devices in sequence and obtain the inherent signal parameters of each WIFI access device, including WIFI signal strength and request response time.

4. The method as described in claim 3, characterized in that, The step of obtaining the image at each key location coordinate based on the inherent signal parameters, and the feature parameters of each key identifier element in the image, includes: Based on the key location coordinates, plan different intersecting travel paths, with each path passing through at least two of the key location coordinates; The robot is controlled to move along different paths according to preset travel rules, and the changing signal parameters relative to each WIFI access device are acquired in each travel path. When the robot moves to the key location coordinates, it acquires the key identification elements at the corresponding key location coordinates, as well as the feature parameters of each key identification element. The key identification elements include doors, windows, stairs, or furniture. Images of the robot acquiring coordinates at key locations.

5. The method as described in claim 4, characterized in that, The step of drawing an indoor map based on the coordinates of each key location, the images at each key location coordinate, and the feature parameters of the key recognition elements in the corresponding images, according to a preset algorithm, includes: Based on the coordinates of each key location, the images at each key location coordinate, and the feature parameters of the key identification elements in the corresponding images, the apartment size information in the corresponding images is determined according to a preset algorithm. The feature parameters include size information or pixel information. Based on the preset travel rules and the changing signal parameters relative to each WIFI access device when the robot moves along different travel paths, the apartment layout elements are determined. The apartment layout elements include doors, windows, stairs, ordinary walls, load-bearing walls or floors. Based on the apartment size information and apartment features, draw an interior map.

6. The method as described in claim 1, characterized in that, Following the step of drawing an indoor map based on the coordinates of each key location, the key identifier elements at each key location coordinate, and the corresponding feature parameters according to a preset algorithm, the method further includes: In response to a touch operation based on the indoor map, a key identification element corresponding to the touch operation and an editing dialog box for the feature parameters of the key identification element are pushed.

7. An indoor map automatic recognition and drawing device based on artificial intelligence algorithms, characterized in that, include: The module is used to determine the coordinates of key locations by arranging reference positioning equipment based on the indoor space. The first acquisition module is used to acquire the inherent signal parameters of the key position coordinates relative to each of the reference positioning devices; The second acquisition module is used to acquire the image at the coordinates of each key location based on the inherent signal parameters, as well as the feature parameters of each key identifier element in the image; The processing module is used to draw indoor maps based on the coordinates of each key location, the images at each key location coordinate, and the feature parameters of the key recognition elements in the corresponding images, according to a preset algorithm.

8. The apparatus as claimed in claim 7, characterized in that, The module is specifically used for: Multiple Wi-Fi access devices are deployed in the indoor space; Select multiple key location coordinates in the indoor space, including windows, doorways, stairwells, load-bearing walls, room corners, or the center of the room; The first acquisition module is specifically used for: Control the robot to stop at various key coordinate positions; The robot is controlled to connect to the multiple WIFI access devices in sequence and obtain the inherent signal parameters of each WIFI access device, including WIFI signal strength and request response time. The second acquisition module is specifically used for: Based on the key location coordinates, plan different intersecting travel paths, with each path passing through at least two of the key location coordinates; The robot is controlled to move along different paths according to preset travel rules, and the changing signal parameters relative to each WIFI access device are acquired in each travel path. When the robot moves to the key location coordinates, it acquires the key identification elements at the corresponding key location coordinates, as well as the feature parameters of each key identification element. The key identification elements include doors, windows, stairs, or furniture. Images of the robot acquiring coordinates at key locations; The processing module is specifically used for: Based on the coordinates of each key location, the images at each key location coordinate, and the feature parameters of the key identification elements in the corresponding images, the apartment size information in the corresponding images is determined according to a preset algorithm. The feature parameters include size information or pixel information. Based on the preset travel rules and the changing signal parameters relative to each WIFI access device when the robot moves along different travel paths, the apartment layout elements are determined. The apartment layout elements include doors, windows, stairs, ordinary walls, load-bearing walls or floors. Based on the apartment size information and apartment features, draw an interior map.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the indoor map automatic recognition and drawing method based on artificial intelligence algorithm as described in any one of claims 1 to 6.

10. A computer storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the automatic indoor map recognition and drawing method based on artificial intelligence algorithms as described in any one of claims 1 to 6.

Images