A path planning method, device and equipment

Abstract

The embodiments of this specification provide a path planning method, device and equipment, which can be applied to the field of indoor navigation technology, including: obtaining an indoor distribution map of a target building; processing the indoor distribution map using a panoramic segmentation image model to obtain a navigation map; wherein the panoramic segmentation image model is used to obtain an image that can be used by a preset navigation algorithm; and based on the navigation map, using a preset navigation algorithm to perform path planning. Using the embodiments of this specification can save costs while improving navigation efficiency and accuracy.

Description

Technical Field

[0001] The present application relates to the field of indoor navigation technology, and in particular to a path planning method, device and equipment. Background Art

[0002] With the large-scale popularization of outdoor navigation applications, indoor navigation is becoming more and more important.

[0003] In the prior art, indoor navigation solutions usually include: map editor, theme editor, 3D map engine, positioning module, map development platform, etc. Since the solution includes many modules, and except for the positioning module, other modules are to solve the problem of map data and navigation (for example, using a map editor to manually draw roads, rooms, etc.), in the process of realizing indoor navigation, not only a lot of manpower and material resources are needed, but also the navigation efficiency and accuracy are reduced.

[0004] Therefore, the industry is in urgent need of a technical solution that can solve the above technical problems. Summary of the invention

[0005] The embodiments of this specification provide a path planning method, device and equipment, which can save costs while improving navigation efficiency and accuracy.

[0006] A path planning method comprises: obtaining an indoor distribution map of a target building; processing the indoor distribution map using a panoramic segmentation image model to obtain a navigation map; wherein the panoramic segmentation image model is used to obtain an image that can be used by a preset navigation algorithm; and performing path planning based on the navigation map using a preset navigation algorithm.

[0007] In some embodiments, the indoor distribution map is at least one of the following: a top view, a CAD drawing.

[0008] In some embodiments, the panoramic segmentation image model is determined by: obtaining an indoor distribution map of a building; performing panoramic segmentation on the indoor distribution map to obtain a panoramic segmentation result; based on the panoramic segmentation result, annotating the instances included in the indoor distribution map; and using the annotated indoor distribution map to train a preset neural network model to obtain a panoramic segmentation image model.

[0009] In some embodiments, the panoptic segmentation result includes category information and instance information in the indoor distribution map.

[0010] In some embodiments, the preset navigation algorithm includes at least one of the following: A-Star algorithm, Dijkstra algorithm.

[0011] In some embodiments, it also includes: obtaining a path planning result; the path planning result includes a preset number of reference trajectory points; and navigating based on the path planning result.

[0012] In some embodiments, it also includes: obtaining multiple trajectory points of the target user; when each trajectory point is not within the corresponding preset range, issuing a deviation prompt message; wherein the preset range corresponding to each trajectory point is determined according to the reference trajectory point in the path planning result.

[0013] In some embodiments, the preset range corresponding to each trajectory point is determined by: calculating the distance between the target trajectory point and each reference trajectory point in the path planning result; determining the target reference trajectory point based on the distance between the target trajectory point and each reference trajectory point; wherein the target reference trajectory point represents the reference trajectory point closest to the target trajectory point; based on the coordinate information of the target reference trajectory point, determining the preset range corresponding to the target trajectory point.

[0014] In some embodiments, after issuing the deviation prompt information, it also includes: obtaining multiple track points of the target user; when each track point is not within the corresponding preset range, re-planning the path based on the multiple track points of the target user and the navigation map using a preset navigation algorithm.

[0015] In some embodiments, the navigation map is a plan view, which includes a first area and a second area; the first area represents a position corresponding to an obstacle, and the second area represents a passable position, and the passable position is used for path planning.

[0016] A path planning device comprises: an acquisition module for acquiring an indoor distribution map of a target building; a processing module for processing the indoor distribution map using a panoramic segmentation image model to obtain a navigation map; wherein the panoramic segmentation image model is used to obtain an image that can be used by a preset navigation algorithm; and a planning module for performing path planning based on the navigation map using a preset navigation algorithm.

[0017] In some embodiments, the panoramic segmentation image model includes: an acquisition unit for acquiring an indoor distribution map of a building; a segmentation unit for performing panoramic segmentation on the indoor distribution map to obtain a panoramic segmentation result; a labeling unit for labeling instances included in the indoor distribution map based on the panoramic segmentation result; and a training unit for training a preset neural network model using the labeled indoor distribution map to obtain a panoramic segmentation image model.

[0018] A path planning device includes at least one processor and a memory storing computer executable instructions, wherein when the processor executes the instructions, the steps of any method embodiment in the embodiments of this specification are implemented.

[0019] A computer-readable storage medium stores computer instructions, which, when executed, implement the steps of any method embodiment in the embodiments of this specification.

[0020] A computer program product includes a computer program, and when the computer program is executed by a processor, the steps of any method embodiment in the embodiments of this specification are implemented.

[0021] This specification provides a path planning method, device and equipment. In some embodiments, an indoor distribution map of a target building can be obtained, and the indoor distribution map can be processed using a panoramic segmentation image model to obtain a navigation map, wherein the panoramic segmentation image model is used to obtain an image that can be used by a preset navigation algorithm; based on the navigation map, a preset navigation algorithm can also be used for path planning. By using the embodiments of this specification, costs can be saved while improving navigation efficiency and accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The drawings described herein are used to provide a further understanding of this specification, constitute a part of this specification, and do not constitute a limitation of this specification. In the drawings:

[0023] Figure 1 It is a flowchart of an embodiment of a path planning method provided in this specification;

[0024] Figure 2 is a flowchart of an embodiment of determining a panoramic segmentation image model provided in this specification;

[0025] Figure 3 It is a navigation chart provided in this manual;

[0026] Figure 4 This is a practical application scenario diagram of a path planning provided in this manual;

[0027] Figure 5 It is a schematic diagram of the module structure of an embodiment of a path planning device provided in this specification;

[0028] Figure 6 This is a hardware structure block diagram of an embodiment of a path planning server provided in this specification. DETAILED DESCRIPTION

[0029] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments in this specification, not all of the embodiments. Based on one or more embodiments in this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the embodiments of this specification.

[0030] The following is an example of a specific application scenario to illustrate the implementation plan of this specification. Specifically, Figure 1 It is a flowchart of an embodiment of a path planning method provided in this specification. Although this specification provides the method operation steps or device structure shown in the following embodiments or drawings, the method or device may include more or fewer operation steps or module units after partial combination based on routine or no creative labor.

[0031] An implementation scheme provided in this specification can be applied to a client or a server, etc. The client can include a terminal device, such as a smart phone, a tablet computer, etc. The server can include a single computer device, or a server cluster composed of multiple servers, or a server structure of a distributed system, etc.

[0032] It should be noted that the acquisition, storage, use, and processing of data in the technical solution of this application are in compliance with the relevant provisions of national laws and regulations. The following embodiments are illustrative examples of application to a server, and do not limit the technical solutions in other application scenarios that can be expanded based on this specification. Figure 1 As shown, in an embodiment of a path planning method provided in this specification, the method may include the following steps.

[0033] S10: Obtain an indoor distribution map of the target building.

[0034] The target building may be a building that requires indoor navigation, such as a shopping mall, an office floor, etc.

[0035] In some embodiments, the indoor distribution map may be at least one of the following: a top view, a CAD map. The indoor distribution map may be a color map. The top view may be understood as a view obtained by orthographic projection from the top of the building downward. The CAD map refers to a CAD construction drawing, which is a drawing produced by AutoCAD software to produce the overall layout of the project, the external shape, internal layout, structural structure, internal and external decoration, material methods, equipment, construction, etc. of the building.

[0036] In some embodiments, one or more obstacles may be included in the indoor distribution map.

[0037] In some embodiments, the server may obtain the indoor distribution map of the target building from a preset organization. The preset organization stores relevant information of the target building, such as building identification, construction time, indoor distribution map, etc. The preset organization may be a service organization provided by the developer of the target building, or a third-party organization that communicates with the service organization provided by the developer of the target building, which is not limited in this specification.

[0038] S12: Processing the indoor distribution map using a panoramic segmentation image model to obtain a navigation map; wherein the panoramic segmentation image model is used to obtain an image that can be used by a preset navigation algorithm.

[0039] In the embodiment of the present specification, after obtaining the indoor distribution map of the target building, the indoor distribution map can be processed using a panoramic segmentation image model to obtain a navigation map that can be used by a preset navigation algorithm. The panoramic segmentation image model can be used to convert the indoor distribution map of the building into a navigation map. The navigation map can be a map used by the navigation algorithm.

[0040] In some embodiments, the panoptic segmentation image model can be obtained by pre-training a neural network model using an indoor distribution map. Figure 2 As shown, an embodiment of determining a panoramic segmentation image model provided by this specification may include the following steps.

[0041] S120: Acquire an indoor distribution map of the building;

[0042] S122: Performing panoptic segmentation on the indoor distribution map to obtain a panoptic segmentation result;

[0043] S124: Based on the panoptic segmentation result, annotate the instances included in the indoor distribution map;

[0044] S126: Using the labeled indoor distribution map to train a preset neural network model to obtain a panoramic segmentation image model.

[0045] Among them, panoptic segmentation can detect all objects in the image and distinguish different instances in the same category. It can be understood as a combination of semantic segmentation and instance segmentation. Semantic segmentation can classify the pixels in the image, so as to clearly segment the categories. Instance segmentation is a combination of object detection and semantic segmentation. It can detect objects in the image and label each pixel. Object detection can detect what category is contained in the image and mark it with a box. Panoptic segmentation results can include category information and instance information in the indoor distribution map.

[0046] For example, in some implementation scenarios, a large number of floor CAD drawings can be obtained, and then each floor CAD drawing can be panorama segmented to determine the categories and instances included in the CAD drawing. Furthermore, the instances in each floor CAD drawing can be labeled based on the category to which each instance in the CAD drawing belongs. Finally, the labeled floor CAD drawing can be used as a sample image to train the preset neural network model to obtain a panorama segmentation image model. Among them, the instances in the floor CAD drawing can be rooms, corridors, toilets, etc. The preset neural network model can be a convolutional neural network, a recursive neural network, a self-developed network, etc.

[0047] In some implementation scenarios, after the instances in each floor CAD drawing are marked, manual corrections can also be made.

[0048] In some embodiments, after the panoptic segmentation image model is obtained, the indoor distribution map of the target building may be input into the panoptic segmentation image model for conversion, thereby obtaining a navigation map.

[0049] In some embodiments, the navigation map is a plan view, which includes a first area and a second area; the first area represents a position corresponding to an obstacle, and the second area represents a passable position, and the passable position is used for path planning.

[0050] like Figure 3 The figure shows a navigation map provided in this specification. The black borders represent the impassable boundaries (i.e., obstacles), and the other blank areas represent the passable areas. The coordinates in the navigation map correspond to the real longitude and latitude in the indoor distribution map.

[0051] S14: Based on the navigation map, a preset navigation algorithm is used to perform path planning.

[0052] In the embodiments of the present specification, after obtaining the navigation map, a path planning may be performed based on the navigation map using a preset navigation algorithm to determine the best path.

[0053] In some embodiments, the preset navigation algorithm may include at least one of the following: A-Star algorithm, Dijkstra algorithm. Among them, the A-Star algorithm, also known as the A* algorithm, is the most effective method for solving the shortest path in a static road network. The formula is expressed as: f(n) = g(n) + h(n), where f(n) is the evaluation function of node n from the initial point to the target point, g(n) is the actual cost from the initial node to the n node in the state space, and h(n) is the estimated cost of the best path from n to the target node. The Dijkstra algorithm, also known as the Dijkstra algorithm, is a shortest path algorithm from one vertex to the remaining vertices, and solves the shortest path problem in a weighted graph. The main feature of the Dijkstra algorithm is that it starts from the starting point and adopts the strategy of a greedy algorithm, each time traversing to the adjacent nodes of the vertex that is closest to the starting point and has not been visited, until it expands to the end point.

[0054] Of course, the above is only an exemplary explanation, and the preset navigation algorithm is not limited to the above examples. Technical personnel in the relevant field may make other changes under the inspiration of the technical essence of this application, but as long as the functions and effects achieved are the same or similar to those of this application, they should be covered within the scope of protection of this application.

[0055] In some embodiments, after using a preset navigation algorithm to perform path planning, a path planning result can also be obtained, and navigation can be performed based on the path planning result. The path planning result may include a preset number of reference track points. Each reference track point has corresponding coordinate information. All reference track points can be connected to determine the best path. Figure 3 The line in the blank area represents the optimal path, which is obtained by connecting multiple reference trajectory points. The coordinate information corresponding to the multiple reference trajectory points are: (2, 5), (3, 6), (3, 7), (4, 8), (5, 9), (6, 10), (7, 11), (8, 12), (9, 13), (9, 14), (9, 15), (9, 16), (10, 16), (11, 16), (12, 16), (13, 16), (14, 16), (15, 16), (16, 16), (17 , 16), (18, 16), (19, 16), (20, 16), (21, 16), (22, 15), (23, 14), (24, 14), (25, 14), (26, 14), (27, 14), (28, 14), (29, 14), (30, 14), (31, 14), (32, 15), (33, 16), (34, 17), (35, 18), (36, 19), (37, 20), (38, 21), (39, 22), (40, 22). The preset number can be set according to the actual scenario, such as 10, 20, etc., and this manual does not limit this.

[0056] In some implementation scenarios, the reference trajectory points in the path planning results may be all the trajectory points included in the path, or may be trajectory points obtained by sampling all the trajectory points on the path, such as arranging all the trajectory points on the path and then sampling every 3 trajectory points to obtain the reference trajectory points in the path planning results.

[0057] like Figure 4 As shown in the figure, it is a practical application scenario diagram of a path planning provided by this specification. Among them, the lower figure is an indoor distribution map of a certain floor (which is a color map), and the upper figure is a navigation map obtained by processing the indoor distribution map using a panoramic segmentation image model (which is a black and white map). Further, according to the navigation map, a preset navigation algorithm can be used to perform path planning to obtain a path planning result, such as Figure 4 Middle line.

[0058] In some embodiments, based on the path planning results, during the navigation process, multiple track points of the target user can also be obtained, and the multiple track points of the target user can be matched with the reference track points in the path planning results to determine whether the target user is moving along the planned path.

[0059] In some implementation scenarios, matching multiple trajectory points of the target user with reference trajectory points in the path planning result may include: determining a preset range corresponding to each trajectory point, and determining whether each trajectory point is within the corresponding preset range.

[0060] In some implementation scenarios, the preset range corresponding to each trajectory point can be determined based on the reference trajectory points in the path planning results.

[0061] Specifically, for example, in some implementation scenarios, the preset range corresponding to each trajectory point can be determined by: calculating the distance between the target trajectory point and each reference trajectory point in the path planning result; determining the target reference trajectory point according to the distance between the target trajectory point and each reference trajectory point; wherein the target reference trajectory point represents the reference trajectory point closest to the target trajectory point; and determining the preset range corresponding to the target trajectory point based on the coordinate information of the target reference trajectory point. The target trajectory point can be any one of the multiple trajectory points of the target user.

[0062] In some implementation scenarios, determining a preset range corresponding to the target trajectory point based on the coordinate information of the target reference trajectory point may include: acquiring the coordinate information of the target reference trajectory point, and expanding on the basis of the coordinate information to determine the preset range corresponding to the target trajectory point.

[0063] Specifically, for example, after determining the target reference trajectory point, the coordinate information (x, y) of the target reference trajectory point can be obtained, and then the distance a is extended forward and backward based on the horizontal coordinate x, and the distance b is extended up and down based on the vertical coordinate y, so as to determine that the range corresponding to the horizontal coordinate of the target trajectory point is (xa, x+a), and the range corresponding to the vertical coordinate is (yb, y+b). Among them, the extension distances of the horizontal coordinate and the vertical coordinate can be the same or different, and can be set according to the actual scene, and this manual does not limit this.

[0064] In some implementation scenarios, multiple track points of the target user are obtained, and when it is confirmed that each track point is not within the corresponding preset range, a deviation prompt message can be issued. The deviation prompt message can be issued by voice, pop-up window, text message, etc.

[0065] In some embodiments, after issuing the deviation prompt information, it can also include: obtaining multiple track points of the target user; when each track point is not within the corresponding preset range, re-planning the path based on the multiple track points of the target user and the navigation map using a preset navigation algorithm.

[0066] Specifically, for example, after sending the deviation prompt information to the user, multiple track points of the user can be obtained, and the multiple track points can be matched with the reference track points in the path planning result, so as to determine whether the user has returned to the planned path based on the deviation prompt information. It should be noted that the above-mentioned method of matching multiple track points with the reference track points in the path planning result is similar to the above-mentioned method, and can be referenced to each other, and will not be described in detail.

[0067] If each track point is not within the corresponding preset range, it means that the user has not returned to the planned path. At this time, the server can re-plan the path based on the target user's multiple track points and navigation map using a preset navigation algorithm to ensure that the user can accurately reach the destination.

[0068] Of course, the above is only an exemplary description, and the embodiments of this specification are not limited to the above examples. Under the inspiration of the technical essence of this application, technicians in the relevant field may also make other changes, but as long as the functions and effects achieved are the same or similar to those of this application, they should be included in the protection scope of this application. In addition, the "first", "second", etc. involved in the above are only for distinguishing different results and have no actual meaning.

[0069] In this specification, each embodiment of the above method is described in a progressive manner, and the same or similar parts between the embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments. For relevant parts, refer to the partial description of the method embodiment.

[0070] From the above description, it can be seen that the embodiment of the present application can obtain the indoor distribution map of the target building, and use the panoramic segmentation image model to process the indoor distribution map to obtain a navigation map, wherein the panoramic segmentation image model is used to obtain an image that can be used by a preset navigation algorithm; and based on the navigation map, a preset navigation algorithm can be used for path planning. By using the embodiment of this specification, it is possible to save costs while improving navigation efficiency and accuracy.

[0071] Based on the above-mentioned path planning method, one or more embodiments of this specification also provide a path planning device. The device may include a system (including a distributed system), software (application), module, component, server, client, etc. using the method described in the embodiment of this specification and a device combined with necessary implementation hardware. Based on the same innovative concept, the device in one or more embodiments provided in the embodiment of this specification is as described in the following embodiments. Since the implementation scheme and method of the device to solve the problem are similar, the implementation of the specific device in the embodiment of this specification can refer to the implementation of the aforementioned method, and the repetitions will not be repeated. As used below, the term "unit" or "module" can implement a combination of software and / or hardware for a predetermined function. Although the device described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceived.

[0072] Specifically, Figure 5 is a schematic diagram of a module structure of an embodiment of a path planning device provided in this specification, such as Figure 5 As shown, a path planning device provided in this specification may include: an acquisition module 210 , a processing module 212 , and a planning module 214 .

[0073] The acquisition module 210 may be used to acquire an indoor distribution map of a target building;

[0074] The processing module 212 may be used to process the indoor distribution map using a panoramic segmentation image model to obtain a navigation map; wherein the panoramic segmentation image model is used to obtain an image that can be used by a preset navigation algorithm;

[0075] The planning module 214 may be configured to perform path planning based on the navigation map using a preset navigation algorithm.

[0076] In some embodiments, the indoor distribution map may be at least one of the following: a top view, a CAD view.

[0077] In some embodiments, one or more obstacles may be included in the indoor distribution map.

[0078] In some embodiments, the server may obtain the indoor distribution map of the target building from a preset organization. The preset organization stores relevant information of the target building, such as building identification, construction time, indoor distribution map, etc. The preset organization may be a service organization provided by the developer of the target building, or a third-party organization that communicates with the service organization provided by the developer of the target building, which is not limited in this specification.

[0079] In some embodiments, the panoptic segmentation image model may include:

[0080] An acquisition unit, which can be used to acquire an indoor distribution map of a building;

[0081] A segmentation unit may be used to perform panoramic segmentation on the indoor distribution map to obtain a panoramic segmentation result;

[0082] A labeling unit, which can be used to label the instances included in the indoor distribution map based on the panoptic segmentation result;

[0083] The training unit can be used to train a preset neural network model using the labeled indoor distribution map to obtain a panoramic segmentation image model.

[0084] In some embodiments, the panoptic segmentation result includes category information and instance information in the indoor distribution map.

[0085] In some embodiments, after the panoptic segmentation image model is obtained, the indoor distribution map of the target building may be input into the panoptic segmentation image model for conversion, thereby obtaining a navigation map.

[0086] In some embodiments, the navigation map is a plan view, which includes a first area and a second area; the first area represents a position corresponding to an obstacle, and the second area represents a passable position, and the passable position is used for path planning.

[0087] In some embodiments, the preset navigation algorithm may include at least one of the following: A-Star algorithm, Dijkstra algorithm.

[0088] In some embodiments, the method may further include: obtaining a path planning result; the path planning result includes a preset number of reference trajectory points; and navigating based on the path planning result.

[0089] In some embodiments, it may also include: obtaining multiple trajectory points of the target user; issuing a deviation prompt message when each trajectory point is not within the corresponding preset range; wherein the preset range corresponding to each trajectory point is determined based on the reference trajectory point in the path planning result.

[0090] Specifically, for example, in some implementation scenarios, based on the path planning results, during the navigation process, multiple trajectory points of the target user can be obtained, and the multiple trajectory points of the target user can be matched with the reference trajectory points in the path planning results to determine whether the target user is moving along the planned path.

[0091] In some implementation scenarios, matching multiple trajectory points of the target user with reference trajectory points in the path planning result may include: determining a preset range corresponding to each trajectory point, and determining whether each trajectory point is within the corresponding preset range.

[0092] In some implementation scenarios, the preset range corresponding to each trajectory point can be determined based on the reference trajectory points in the path planning results.

[0093] Specifically, for example, in some implementation scenarios, the preset range corresponding to each trajectory point can be determined by: calculating the distance between the target trajectory point and each reference trajectory point in the path planning result; determining the target reference trajectory point according to the distance between the target trajectory point and each reference trajectory point; wherein the target reference trajectory point represents the reference trajectory point closest to the target trajectory point; and determining the preset range corresponding to the target trajectory point based on the coordinate information of the target reference trajectory point. The target trajectory point can be any one of the multiple trajectory points of the target user.

[0094] In some implementation scenarios, determining a preset range corresponding to the target trajectory point based on the coordinate information of the target reference trajectory point may include: acquiring the coordinate information of the target reference trajectory point, and expanding on the basis of the coordinate information to determine the preset range corresponding to the target trajectory point.

[0095] In some implementation scenarios, multiple track points of the target user are obtained, and when it is confirmed that each track point is not within the corresponding preset range, a deviation prompt message can be issued. The deviation prompt message can be issued by voice, pop-up window, text message, etc.

[0096] In some embodiments, after issuing the deviation prompt information, it can also include: obtaining multiple track points of the target user; when each track point is not within the corresponding preset range, re-planning the path based on the multiple track points of the target user and the navigation map using a preset navigation algorithm.

[0097] Specifically, for example, in some implementation scenarios, after sending a deviation prompt message to the user, multiple track points of the user can be obtained, and the multiple track points can be matched with the reference track points in the path planning result, so as to determine whether the user has returned to the planned path based on the deviation prompt message. If each track point is not within the corresponding preset range, it can be said that the user has not returned to the planned path. At this time, the server can re-plan the path based on the multiple track points and navigation map of the target user using the preset navigation algorithm to ensure that the user can accurately reach the destination.

[0098] In the embodiments of this specification, a panoramic segmentation image model is used to quickly convert an indoor distribution map into a navigation map required by a navigation algorithm, which can not only greatly reduce labor costs but also improve navigation efficiency and accuracy.

[0099] It should be noted that the above-mentioned device may also include other implementation modes according to the description of the method embodiment. The specific implementation mode may refer to the description of the relevant method embodiment, which will not be described one by one here.

[0100] This specification also provides an embodiment of a path planning device, including a processor and a memory for storing processor executable instructions, wherein the instructions can implement the steps of the above method embodiment when executed by the processor. For example, the following steps may be included: obtaining an indoor distribution map of a target building; processing the indoor distribution map using a panoramic segmentation image model to obtain a navigation map; wherein the panoramic segmentation image model is used to obtain an image that can be used by a preset navigation algorithm; and based on the navigation map, performing path planning using a preset navigation algorithm.

[0101] The embodiments of the present specification also provide a computer program product, comprising a computer program, which can implement the following steps when executed by a processor: obtaining an indoor distribution map of a target building; processing the indoor distribution map using a panoramic segmentation image model to obtain a navigation map; wherein the panoramic segmentation image model is used to obtain an image that can be used by a preset navigation algorithm; and based on the navigation map, performing path planning using a preset navigation algorithm.

[0102] It should be noted that the above-mentioned device and computer program product may also include other implementation modes according to the description of the method or apparatus embodiment. The specific implementation modes may refer to the description of the relevant method embodiment, which will not be described one by one here.

[0103] The method embodiments provided in this specification can be executed in a mobile terminal, a computer terminal, a server or a similar computing device. Taking running on a server as an example, Figure 61 is a hardware structure block diagram of an embodiment of a path planning server provided in this specification. The server may be a path planning device or a path planning apparatus in the above embodiment. Figure 6 As shown, the server 10 may include one or more (only one is shown in the figure) processors 100 (the processor 100 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 200 for storing data, and a transmission module 300 for communication functions. A person skilled in the art can understand that Figure 6 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 6 More or fewer components shown in the figure may also include other processing hardware, such as a database or multi-level cache, GPU, or other hardware with Figure 6 Different configurations are shown.

[0104] The memory 200 can be used to store software programs and modules of application software, such as program instructions / modules corresponding to the path planning method in the embodiment of this specification. The processor 100 executes various functional applications and data processing by running the software programs and modules stored in the memory 200. The memory 200 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 200 may further include a memory remotely arranged relative to the processor 100, and these remote memories may be connected to the computer terminal 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.

[0105] The transmission module 300 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of a computer terminal. In one example, the transmission module 300 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission module 300 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0106] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0107] The methods or devices described in the above embodiments provided in this specification can implement business logic through computer programs and record them on storage media, and the storage media can be read and executed by computers to achieve the effects of the solutions described in the embodiments of this specification. The storage medium may include a physical device for storing information, which is usually to digitize the information and then store it in a medium using electrical, magnetic or optical methods. The storage medium may include: devices that use electrical energy to store information, such as various types of memories, such as RAM, ROM, etc.; devices that use magnetic energy to store information, such as hard disks, floppy disks, magnetic tapes, magnetic core memories, bubble memories, and U disks; devices that use optical methods to store information, such as CDs or DVDs. Of course, there are other types of readable storage media, such as quantum memories, graphene memories, and so on.

[0108] The above-mentioned path planning method or device embodiment provided in this specification can be implemented by a processor in a computer executing corresponding program instructions, such as using the C++ language of the Windows operating system to implement it on a PC, on a Linux system, or other implementations such as using Android or iOS system programming languages ​​to implement it on a smart terminal, as well as based on the processing logic of a quantum computer.

[0109] It should be noted that the devices, equipment, and systems described in the specification may also include other implementation methods according to the description of the relevant method embodiments. The specific implementation methods can refer to the description of the corresponding method embodiments, which will not be described one by one here.

[0110] Each embodiment in this application is described in a progressive manner, and the same or similar parts between the embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the hardware + program embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0111] For the convenience of description, the above devices are described in various modules according to their functions. Of course, when implementing one or more of the present specification, the functions of some modules can be implemented in the same or more software and / or hardware, and the modules that implement the same function can also be implemented by a combination of multiple sub-modules or sub-units.

[0112] The present invention is described with reference to the flowcharts and / or block diagrams of the methods, devices, equipment, and systems according to the embodiments of the present invention. It should be understood that it can be implemented by computer program instructions, and these computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device for implementing the specified function. These computer program instructions can also be stored in a computer-readable memory that can guide the computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device, and the instruction device is implemented in the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0113] Those skilled in the art will appreciate that one or more embodiments of the present specification may be provided as a method, system or computer program product. Therefore, one or more embodiments of the present specification may be provided in the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware.

[0114] The above is only an example of one or more embodiments of this specification and is not intended to limit one or more embodiments of this specification. For those skilled in the art, one or more embodiments of this specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of this application should be included in the scope of the claims.

Claims

1. A path planning method, characterized in that: The method comprises: Obtain the indoor distribution map of the target building; Processing the indoor distribution map using a panoramic segmentation image model to obtain a navigation map; wherein the panoramic segmentation image model is used to obtain an image that can be used by a preset navigation algorithm; Based on the navigation graph, a preset navigation algorithm is used to perform path planning; Among them, the indoor distribution map of the target building is a plurality of floor CAD drawings. Accordingly, the use of the panoramic segmentation image model to process the indoor distribution map includes: determining the categories and instances included in the CAD drawing, labeling the instances in each floor CAD drawing based on the category to which each instance in the CAD drawing belongs, using the labeled floor CAD drawings as sample images, and training a preset neural network model to obtain a panoramic segmentation image model, wherein the instances in the floor CAD drawings are rooms, corridors, and toilets.

2. The method according to claim 1, characterized in that The preset navigation algorithm includes at least one of the following: A-Star algorithm, Dijkstra algorithm.

3. The method according to claim 1, characterized in that Also includes: Get path planning results; The path planning result includes a preset number of reference trajectory points; Navigation is performed based on the path planning result.

4. The method according to claim 3, characterized in that Also includes: Obtain multiple trajectory points of the target user; When each trajectory point is not within the corresponding preset range, a deviation prompt message is issued; wherein the preset range corresponding to each trajectory point is determined according to the reference trajectory point in the path planning result.

5. The method according to claim 4, characterized in that The preset range corresponding to each track point is determined in the following way: Calculate the distance between the target trajectory point and each reference trajectory point in the path planning result; Determine a target reference trajectory point according to the distance between the target trajectory point and each reference trajectory point; wherein the target reference trajectory point represents the reference trajectory point closest to the target trajectory point; Based on the coordinate information of the target reference trajectory point, a preset range corresponding to the target trajectory point is determined.

6. The method according to claim 4, characterized in that After the deviation prompt message is issued, it also includes: Obtain multiple trajectory points of the target user; In the case that each track point is not within the corresponding preset range, the path planning is re-performed using a preset navigation algorithm based on multiple track points of the target user and the navigation map.

7. The method according to claim 1, characterized in that The navigation map is a plan view, which includes a first area and a second area; the first area represents the position corresponding to the obstacle, and the second area represents the passable position, and the passable position is used for path planning.

8. A path planning device, characterized in that: include: An acquisition module, used to acquire an indoor distribution map of a target building; A processing module, used to process the indoor distribution map using a panoramic segmentation image model to obtain a navigation map; wherein the panoramic segmentation image model is used to obtain an image that can be used by a preset navigation algorithm; A planning module, used for performing path planning based on the navigation map using a preset navigation algorithm; Among them, the indoor distribution map of the target building is a plurality of floor CAD drawings. Accordingly, the use of the panoramic segmentation image model to process the indoor distribution map includes: determining the categories and instances included in the CAD drawing, labeling the instances in each floor CAD drawing based on the category to which each instance in the CAD drawing belongs, using the labeled floor CAD drawings as sample images, and training a preset neural network model to obtain a panoramic segmentation image model, wherein the instances in the floor CAD drawings are rooms, corridors, and toilets.

9. A path planning device, characterized in that: The method comprises at least one processor and a memory storing computer executable instructions, wherein the processor implements the steps of the method according to any one of claims 1 to 7 when executing the instructions.

10. A computer-readable storage medium, characterized in that: Computer instructions are stored thereon, and when the instructions are executed by a processor, the steps of the method described in any one of claims 1 to 7 are implemented.

11. A computer program product, characterized in that The invention comprises a computer program, which implements the steps of the method according to any one of claims 1 to 7 when being executed by a processor.

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