CN114838729A - Path planning method, device and equipment - Google Patents
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Abstract
The embodiment of the specification provides a path planning method, a device and equipment, which can be applied to the technical field of indoor navigation and comprise the following steps: acquiring an indoor distribution map of a target building; processing the indoor distribution map by using a panoramic segmentation image model to obtain a navigation map; the panoramic segmentation image model is used for obtaining an image which can be used by a preset navigation algorithm; and planning a path by utilizing a preset navigation algorithm based on the navigation map. By utilizing the embodiment of the specification, the navigation efficiency and the navigation accuracy can be improved while the cost is saved.
Description
Technical Field
The present application relates to the field of indoor navigation technologies, and in particular, to a method, an apparatus, and a device for path planning.
Background
With the large-scale popularization of outdoor navigation applications, indoor navigation is also becoming more and more important.
In the prior art, the schemes for indoor navigation generally include: the map editor, the theme editor, the three-dimensional map engine, the positioning module, the map development platform and the like. Because the number of modules included in the scheme is large, and besides the positioning module, other modules are all used for solving the problems of map data and navigation (for example, a map editor is used for manually drawing roads, rooms and the like), a large amount of manpower and material resources are needed to be spent in the process of realizing indoor navigation, and the navigation efficiency and accuracy are reduced.
Therefore, there is a need for a solution to the above technical problems.
Disclosure of Invention
The embodiment of the specification provides a path planning method, a path planning device and a path planning device, which can save cost and improve navigation efficiency and accuracy.
A path planning method, comprising: acquiring an indoor distribution map of a target building; processing the indoor distribution map by using a panoramic segmentation image model to obtain a navigation map; the panoramic segmentation image model is used for obtaining an image which can be used by a preset navigation algorithm; and planning a path by utilizing a preset navigation algorithm based on the navigation map.
In some embodiments, the indoor profile is at least one of: top view, CAD drawing.
In some embodiments, the panorama segmented image model is determined by: acquiring an indoor distribution map of a building; carrying out panoramic segmentation on the indoor distribution map to obtain a panoramic segmentation result; marking the instances included in the indoor distribution map based on the panoramic segmentation result; and training the preset neural network model by using the marked indoor distribution map to obtain a panoramic segmentation image model.
In some embodiments, the panorama segmentation result includes category information and instance information in an indoor distribution map.
In some embodiments, the preset navigation algorithm comprises at least one of: the a-Star algorithm, the Dijkstra algorithm.
In some embodiments, further comprising: obtaining a path planning result; the path planning result comprises a preset number of reference track points; and navigating based on the path planning result.
In some embodiments, further comprising: acquiring a plurality of track points of a target user; sending yaw prompt information under the condition that each track point is not in the corresponding preset range; and the preset range corresponding to each track point is determined according to the reference track point in the path planning result.
In some embodiments, the preset range corresponding to each trace point is determined by: calculating the distance between the target track point and each reference track point in the path planning result; determining target reference track points according to the distance between the target track points and each reference track point; the target reference track point represents a reference track point which is closest to the target track point; and determining a preset range corresponding to the target track point based on the coordinate information of the target reference track point.
In some embodiments, after sending the yaw prompting message, the method further includes: acquiring a plurality of track points of a target user; and under the condition that each track point is not in the corresponding preset range, based on the plurality of track points of the target user and the navigation map, re-planning the path by using a preset navigation algorithm.
In some embodiments, the navigation map is a plan view including 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, wherein the passable position is used for path planning.
A path planner, comprising: the acquisition module is used for acquiring an indoor distribution map of a target building; the processing module is used for processing the indoor distribution map by utilizing a panoramic segmentation image model to obtain a navigation map; the panoramic segmentation image model is used for obtaining an image which can be used by a preset navigation algorithm; and the planning module is used for planning a path by utilizing a preset navigation algorithm based on the navigation map.
In some embodiments, the panorama segmented image model comprises: an acquisition unit for acquiring an indoor distribution map of a building; the dividing unit is used for carrying out panoramic division on the indoor distribution map to obtain a panoramic division result; the marking unit is used for marking the instances included in the indoor distribution map based on the panoramic segmentation result; and the training unit is used for training the preset neural network model by using the marked indoor distribution map to obtain a panoramic segmentation image model.
A path planning apparatus comprising at least one processor and a memory storing computer-executable instructions, which when executed by the processor implement the steps of any one of the method embodiments of the present specification.
A computer readable storage medium having stored thereon computer instructions which, when executed, implement the steps of any one of the method embodiments in the present specification.
A computer program product comprising a computer program which, when executed by a processor, performs the steps of any one of the method embodiments of the present specification.
The specification provides a path planning method, a path planning device and path planning equipment. In some embodiments, an indoor distribution map of a target building may be obtained, and the indoor distribution map is processed by using a panoramic split image model to obtain a navigation map, where the panoramic split image model is used to obtain an image that can be used by a preset navigation algorithm; and planning a path by utilizing a preset navigation algorithm based on the navigation map. By adopting the embodiment of the specification, the navigation efficiency and the navigation accuracy can be improved while the cost is saved.
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The accompanying drawings, which are included to provide a further understanding of the specification, are incorporated in and constitute a part of this specification, and are not intended to limit the specification. In the drawings:
fig. 1 is a schematic flow chart diagram illustrating an embodiment of a path planning method provided in the present specification;
FIG. 2 is a schematic flow chart diagram illustrating one embodiment of determining a panorama segmented image model provided by the present specification;
FIG. 3 is a navigation chart provided herein;
fig. 4 is a diagram of a practical application scenario of a path planning provided in the present specification;
fig. 5 is a schematic block diagram of an embodiment of a path planning apparatus provided in this specification;
fig. 6 is a block diagram of a hardware structure of an embodiment of a path planning server provided in this specification.
Detailed Description
In order to make those skilled in the art better understand the technical solutions in the present specification, the technical solutions in the embodiments of the present specification will be clearly and completely described below with reference to the drawings in the embodiments of the present specification, and it is obvious that the described embodiments are only a part of the embodiments in the present specification, and not all of the embodiments. All other embodiments that can be obtained by a person skilled in the art based on one or more embodiments of the present disclosure without making any creative effort shall fall within the protection scope of the embodiments of the present disclosure.
The following describes an embodiment of the present disclosure with a specific application scenario as an example. Specifically, fig. 1 is a schematic flow chart of an embodiment of a path planning method provided in this specification. Although the present specification provides the method steps or apparatus structures as shown in the following examples or figures, more or less steps or modules may be included in the method or apparatus structures based on conventional or non-inventive efforts.
One embodiment provided by the specification can be applied to a client, a server or the like. The client may include a terminal device, such as a smart phone, a tablet computer, and the like. The server may include a single computer device, or may include a server cluster formed by a plurality of servers, or a server structure of a distributed system, and the like.
It should be noted that, in the technical solution of the present application, the acquisition, storage, use, processing, etc. of data all conform to the relevant regulations of the national laws and regulations. The following embodiments are exemplified by being applied to a server, and do not limit the technical solutions in other extensible application scenarios based on the present specification. In a specific embodiment, as shown in fig. 1, in an embodiment of a path planning method provided in this specification, the method may include the following steps.
S10: and acquiring an indoor distribution map of the target building.
The target building may be a building requiring indoor navigation, such as a mall, an office floor, and the like.
In some embodiments, the indoor profile may be at least one of: top view, CAD drawing. Wherein, the indoor distribution map can be a color map. A top view is understood to be a view which is obtained by orthographic projection from above and below the building. The CAD drawing is a CAD construction drawing, which shows a drawing created by using AutoCAD software to create an overall layout of an engineering project, an external shape, an internal layout, a structural structure, interior and exterior finishing, a material method, equipment, construction, and the like of a building.
In some embodiments, one or more obstacles may be included in the indoor profile.
In some embodiments, the server may obtain the indoor profile of the target building from a preset institution. The preset mechanism stores relevant information of the target building, such as building identification, building time, indoor distribution map and the like. The preset organization may be a service organization provided by the target building developer, or may be a third party organization communicating with the service organization provided by the target building developer, which is not limited in this specification.
S12: processing the indoor distribution map by using a panoramic segmentation image model to obtain a navigation map; the panoramic segmentation image model is used for obtaining an image which can be used by a preset navigation algorithm.
In this embodiment of the present description, after the indoor distribution map of the target building is obtained, the indoor distribution map may be processed by using the panoramic segmentation image model, so as to obtain a navigation map that can be used by the preset navigation algorithm. The panorama segmented image model may be used to convert an indoor map of a building into a navigation map. The navigation map may be a map used by a navigation algorithm.
In some embodiments, the panoramic segmentation image model may be obtained by training a neural network model with an indoor distribution map in advance. In a specific embodiment, as shown in fig. 2, an embodiment of determining a panorama segmented image model provided by the present specification may include the following steps.
S120: acquiring an indoor distribution map of a building;
s122: carrying out panoramic segmentation on the indoor distribution map to obtain a panoramic segmentation result;
s124: marking the instances included in the indoor distribution map based on the panoramic segmentation result;
s126: and training the preset neural network model by using the marked indoor distribution map to obtain a panoramic segmentation image model.
The panorama segmentation can detect all targets in the picture and distinguish different instances in the same category, which can be understood as a combination of semantic segmentation and instance segmentation. Semantic segmentation can classify pixels in the picture, so that categories can be clearly segmented. The example segmentation is the combination of target detection and semantic segmentation, so that targets in the picture can be detected and each pixel can be labeled. Object detection can detect what categories are included in the picture and is indicated by a block diagram. The panorama segmentation result may include category information and instance information in the indoor distribution map.
For example, in some implementations, a large number of floor CAD drawings may be acquired, and then each floor CAD drawing may be panned to determine the categories and instances included in the CAD drawings. Furthermore, the instance in each floor CAD drawing can be labeled based on the category of each instance in the CAD drawing, and finally the labeled floor CAD drawing can be used as a sample image to train the preset neural network model, so that the panoramic segmentation image model is obtained. Examples of the floor CAD drawings may be rooms, corridors, toilets, etc. The preset neural network model can be a convolutional neural network, a recurrent neural network, a self-developed network and the like.
In some implementation scenarios, after the instance in each floor CAD drawing is labeled, manual correction may also be performed.
In some embodiments, after obtaining the panorama segmented image model, the indoor map of the target building may be input into the panorama segmented image model for conversion, thereby obtaining the navigation map.
In some embodiments, the navigation map is a plan view including 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, wherein the passable position is used for path planning.
Fig. 3 is a navigation diagram provided in this specification. Where black boxes represent impenetrable borders (i.e., obstacles) and other blank areas represent accessible areas. And the coordinates in the navigation map correspond to the real longitude and latitude in the indoor distribution map one by one.
S14: and planning a path by utilizing a preset navigation algorithm based on the navigation map.
In the embodiment of the present description, after the navigation map is obtained, a path may be planned by using a preset navigation algorithm based on the navigation map, so as to determine an optimal path.
In some embodiments, the preset navigation algorithm may include at least one of: the a-Star algorithm, the Dijkstra algorithm. The A-Star algorithm is called as A-Star algorithm, and is the most effective method for solving the shortest path in the static road network. The formula is expressed as: (n) g (n) + h (n), where f (n) is an evaluation function of node n from the initial point to the target point, g (n) is the actual cost in state space from the initial node to n nodes, and h (n) is the estimated cost of the best path from n to the target node. The Dijkstra algorithm is also called a Dikstra algorithm, is a shortest path algorithm from one vertex to other vertexes, and solves the problem of shortest paths in a weighted graph. The dijkstra algorithm is mainly characterized in that a greedy algorithm strategy is adopted from a starting point, and adjacent nodes of vertexes which are nearest to the starting point and have not been visited are traversed each time until the nodes are expanded to a terminal point.
Of course, the above description is only exemplary, the preset navigation algorithm is not limited to the above examples, and other modifications are possible for those skilled in the art in light of the technical spirit of the present application, and all that can be achieved is intended to be covered by the scope of the present application as long as the achieved functions and effects are the same as or similar to those of the present application.
In some embodiments, after the path is planned by using a preset navigation algorithm, a path planning result may be obtained, and navigation is performed based on the path planning result. The path planning result may include a preset number of reference track points. Each reference locus point corresponds to coordinate information. All reference trace points are connected to determine the best path. As shown in fig. 3, the line in the blank area represents the optimal path, and the line is obtained by connecting a plurality of reference track points, and the coordinate information corresponding to the plurality of reference track points is: (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 an actual scene, such as 10, 20, etc., and this specification does not limit this.
In some implementation scenarios, the reference track points in the path planning result may be all track points included in the path, or may also be track points obtained by sampling all track points in the path, for example, all track points in the path are arranged, and then sampling is performed once every 3 track points at intervals, so as to obtain the reference track points in the path planning result.
Fig. 4 is a diagram of an actual application scenario of a path planning provided in this specification. The lower graph is an indoor distribution graph (which is a color graph) of a certain floor, and the upper graph is a navigation graph (which is a black-and-white graph) obtained by processing the indoor distribution graph by using a panoramic division image model. Further, a path planning may be performed according to the navigation map by using a preset navigation algorithm to obtain a path planning result, such as the line in fig. 4.
In some embodiments, based on the path planning result, during the navigation process, a plurality of track points of the target user may be further obtained, and the plurality of track points of the target user are matched with the reference track points in the path planning result, so as to determine whether the target user advances according to the planned path.
In some implementation scenarios, matching a plurality of track points of the target user with reference track points in the path planning result may include: and determining a preset range corresponding to each track point, and judging whether each track point is in the corresponding preset range.
In some implementation scenarios, the preset range corresponding to each track point may be determined according to a reference track point in the path planning result.
Specifically, for example, in some implementation scenarios, the preset range corresponding to each track point may be determined by: calculating the distance between the target track point and each reference track point in the path planning result; determining target reference track points according to the distance between the target track points and each reference track point; the target reference track point represents a reference track point which is closest to the target track point; and determining a preset range corresponding to the target track point based on the coordinate information of the target reference track point. The target track point can be any one of a plurality of track points of a target user.
In some implementation scenarios, determining the preset range corresponding to the target track point based on the coordinate information of the target reference track point may include: and acquiring coordinate information of the target reference track point, and expanding the coordinate information so as to determine a preset range corresponding to the target track point.
Specifically, for example, after the target reference track point is determined, coordinate information (x, y) of the target reference track point may be obtained, and then the distance a is expanded back and forth on the basis of the abscissa x, and the distance b is expanded up and down on the basis of the ordinate y, so that it is determined that the range corresponding to the abscissa of the target track point is (x-a, x + a), and the range corresponding to the ordinate is (y-b, y + b). The extension distances of the abscissa and the ordinate may be the same or different, and may be specifically set according to an actual scene, which is not limited in this specification.
In some implementation scenarios, a plurality of track points of a target user are obtained, and yaw prompt information can be sent out under the condition that each track point is not in a corresponding preset range. Wherein, the yaw prompting information can be sent out in the modes of voice, popup, short message and the like.
In some embodiments, after issuing the yaw prompting message, the method may further include: acquiring a plurality of track points of a target user; and under the condition that each track point is not in the corresponding preset range, based on the plurality of track points of the target user and the navigation map, re-planning the path by using a preset navigation algorithm.
Specifically, for example, after the yaw prompting information is sent to the user, the multiple trajectory points of the user may be continuously obtained, and the multiple trajectory points are matched with the reference trajectory points in the path planning result, so as to determine whether the user returns to the planned path again on the basis of the yaw prompting information. It should be noted that the above-mentioned manner of matching the plurality of track points with the reference track points in the path planning result is similar to the foregoing manner, and may refer to each other, which is not described herein again.
If each track point is not in the corresponding preset range, it can be indicated that the user does not go back to the planned path again, and at this time, the server can perform path planning again by using a preset navigation algorithm based on the plurality of track points and the navigation map of the target user, so as to ensure that the user can accurately reach the destination.
It is to be understood that the foregoing is only exemplary, and the embodiments of the present disclosure are not limited to the above examples, and other modifications may be made by those skilled in the art within the spirit of the present disclosure, and the scope of the present disclosure is intended to be covered by the claims as long as the functions and effects achieved by the embodiments are the same as or similar to the present disclosure. Moreover, the references to "first", "second", etc. above are intended merely to distinguish between different results and are not intended to be limiting.
In the present specification, each embodiment of the method is described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from other embodiments. Reference is made to the description of the method embodiments.
From the above description, it can be seen that the indoor distribution map of the target building can be obtained, the indoor distribution map is processed by using the panoramic split image model, and the navigation map is obtained, wherein the panoramic split image model is used for obtaining an image which can be used by a preset navigation algorithm; and planning a path by utilizing a preset navigation algorithm based on the navigation map. By adopting the embodiment of the specification, the navigation efficiency and the navigation accuracy can be improved while the cost is saved.
Based on the path planning method, one or more embodiments of the present specification further provide a path planning apparatus. The apparatus may include systems (including distributed systems), software (applications), modules, components, servers, clients, etc. that use the methods described in the embodiments of the present specification in conjunction with any necessary apparatus to implement the hardware. Based on the same innovative conception, embodiments of the present specification provide an apparatus as described in the following embodiments. Since the implementation scheme of the apparatus for solving the problem is similar to that of the method, the specific implementation of the apparatus in the embodiment of the present specification may refer to the implementation of the foregoing method, and repeated details are not repeated. As used hereinafter, the term "unit" or "module" may be a combination of software and/or hardware that implements a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated.
Specifically, fig. 5 is a schematic block diagram of an embodiment of a path planning apparatus provided in this specification, and as shown in fig. 5, the path planning apparatus provided in this specification may include: an acquisition module 210, a processing module 212, and a planning module 214.
An obtaining module 210, which may be configured to obtain an indoor distribution map of a target building;
a processing module 212, configured to process the indoor distribution map by using a panoramic segmented image model to obtain a navigation map; the panoramic segmentation image model is used for obtaining an image which can be used by a preset navigation algorithm;
and the planning module 214 may be configured to perform path planning by using a preset navigation algorithm based on the navigation map.
In some embodiments, the indoor profile may be at least one of: top view, CAD drawing.
In some embodiments, one or more obstacles may be included in the indoor profile.
In some embodiments, the server may obtain the indoor profile of the target building from a preset institution. The preset mechanism stores relevant information of the target building, such as building identification, building time, indoor distribution map and the like. The preset organization may be a service organization provided by the target building developer, or may be a third party organization communicating with the service organization provided by the target building developer, which is not limited in this specification.
In some embodiments, the panorama segmented image model may include:
an acquisition unit, which can be used for acquiring an indoor distribution map of a building;
the dividing unit may be configured to perform panorama division on the indoor distribution map to obtain a panorama division result;
the labeling unit may be configured to label, based on the panorama segmentation result, an instance included in the indoor distribution map;
and the training unit can be used for training the preset neural network model by using the marked indoor distribution map to obtain a panoramic segmentation image model.
In some embodiments, the panorama segmentation result includes category information and instance information in an indoor distribution map.
In some embodiments, after obtaining the panorama segmented image model, the indoor map of the target building may be input into the panorama segmented image model for conversion, thereby obtaining the navigation map.
In some embodiments, the navigation map is a plan view including 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, wherein the passable position is used for path planning.
In some embodiments, the preset navigation algorithm may include at least one of: the a-Star algorithm, the Dijkstra algorithm.
In some embodiments, it may further include: obtaining a path planning result; the path planning result comprises a preset number of reference track points; and navigating based on the path planning result.
In some embodiments, it may further include: acquiring a plurality of track points of a target user; sending yaw prompt information under the condition that each track point is not in the corresponding preset range; and the preset range corresponding to each track point is determined according to the reference track point in the path planning result.
Specifically, for example, in some implementation scenarios, based on the path planning result, during the navigation process, a plurality of track points of the target user may be obtained, and the plurality of track points of the target user are matched with the reference track points in the path planning result, so as to determine whether the target user advances according to the planned path.
In some implementation scenarios, matching a plurality of track points of the target user with reference track points in the path planning result may include: and determining a preset range corresponding to each track point, and judging whether each track point is in the corresponding preset range.
In some implementation scenarios, the preset range corresponding to each track point may be determined according to a reference track point in the path planning result.
Specifically, for example, in some implementation scenarios, the preset range corresponding to each track point may be determined by: calculating the distance between the target track point and each reference track point in the path planning result; determining target reference track points according to the distance between the target track points and each reference track point; the target reference track point represents a reference track point which is closest to the target track point; and determining a preset range corresponding to the target track point based on the coordinate information of the target reference track point. The target track point can be any one of a plurality of track points of a target user.
In some implementation scenarios, determining the preset range corresponding to the target track point based on the coordinate information of the target reference track point may include: and acquiring coordinate information of the target reference track point, and expanding the coordinate information so as to determine a preset range corresponding to the target track point.
In some implementation scenarios, a plurality of track points of a target user are obtained, and yaw prompt information can be sent out under the condition that each track point is not in a corresponding preset range. Wherein, the yaw prompting information can be sent out in the modes of voice, popup, short message and the like.
In some embodiments, after issuing the yaw prompting message, the method may further include: acquiring a plurality of track points of a target user; and under the condition that each track point is not in the corresponding preset range, based on the plurality of track points of the target user and the navigation map, re-planning the path by using a preset navigation algorithm.
Specifically, for example, in some implementation scenarios, after sending the yaw prompting information to the user, the multiple trajectory points of the user may be continuously obtained, and the multiple trajectory points are matched with the reference trajectory points in the path planning result, so as to determine whether the user returns to the planned path again on the basis of the yaw prompting information. If each track point is not in the corresponding preset range, it can be indicated that the user does not go back to the planned path again, and at this time, the server can perform path planning again by using a preset navigation algorithm based on the plurality of track points and the navigation map of the target user, so as to ensure that the user can accurately reach the destination.
In the embodiment of the description, the panoramic image segmentation model is utilized, the indoor distribution map can be quickly converted into the navigation map required by the navigation algorithm, the labor cost can be greatly reduced, and the navigation efficiency and accuracy can be improved.
It should be noted that the above-mentioned description of the apparatus according to the method embodiment may also include other embodiments, and specific implementation manners may refer to the description of the related method embodiment, which is not described herein again.
The present specification also provides an embodiment of a path planning apparatus, comprising a processor and a memory for storing processor-executable instructions, which when executed by the processor, may implement the steps of the above-described method embodiments. For example, the following steps may be included: acquiring an indoor distribution map of a target building; processing the indoor distribution map by using a panoramic segmentation image model to obtain a navigation map; the panoramic segmentation image model is used for obtaining an image which can be used by a preset navigation algorithm; and planning a path by utilizing a preset navigation algorithm based on the navigation map.
Embodiments of the present specification further provide a computer program product, which contains a computer program, and when the computer program is executed by a processor, the computer program can implement the following steps: acquiring an indoor distribution map of a target building; processing the indoor distribution map by using a panoramic segmentation image model to obtain a navigation map; the panoramic segmentation image model is used for obtaining an image which can be used by a preset navigation algorithm; and planning a path by utilizing a preset navigation algorithm based on the navigation map.
It should be noted that the description of the above-mentioned apparatus and computer program product according to the method or device embodiments may also include other embodiments. The specific implementation manner may refer to the description of the related method embodiment, and is not described in detail herein.
The method embodiments provided in the present specification may be executed in a mobile terminal, a computer terminal, a server or a similar computing device. Taking an example of the path planning server running on a server, fig. 6 is a hardware structure block diagram of an embodiment of a path planning server provided in this specification, where the server may be a path planning device or a path planning apparatus in the foregoing embodiment. As shown in fig. 6, the server 10 may include one or more (only one shown) processors 100 (the processors 100 may include, but are not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA, etc.), a memory 200 for storing data, and a transmission module 300 for communication functions. It will be understood by those skilled in the art that the structure shown in fig. 6 is only an illustration and is not intended to limit the structure of the electronic device. For example, the server 10 may also include more or fewer components than shown in FIG. 6, and may also include other processing hardware, such as a database or multi-level cache, a GPU, or have a different configuration than shown in FIG. 6, for example.
The memory 200 may 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 the present specification, and the processor 100 executes various functional applications and data processing by executing the software programs and modules stored in the memory 200. Memory 200 may include high speed random access memory and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, memory 200 may further include memory located remotely from processor 100, which may be connected to a computer terminal through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The transmission module 300 is used for receiving or transmitting data via a network. Specific examples of the network described above may include a wireless network provided by a communication provider of the computer terminal. In one example, the transmission module 300 includes a Network adapter (NIC) that 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 may be a Radio Frequency (RF) module, which is used to communicate with the internet in a wireless manner.
The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
The method or apparatus provided by the present specification and described in the foregoing embodiments may implement service logic through a computer program and record the service logic on a storage medium, where the storage medium may be read and executed by a computer, so as to implement the effect of the solution described in the embodiments of the present specification. The storage medium may include a physical device for storing information, and typically, the information is digitized and then stored using an electrical, magnetic, or optical media. The storage medium may include: devices that store information using electrical energy, such as various types of memory, e.g., RAM, ROM, etc.; devices that store information using magnetic energy, such as hard disks, floppy disks, tapes, core memories, bubble memories, and usb disks; devices that store information optically, such as CDs or DVDs. Of course, there are other ways of storing media that can be read, such as quantum memory, graphene memory, and so forth.
The embodiment of the path planning method or the apparatus provided in this specification may be implemented in a computer by executing corresponding program instructions by a processor, for example, implemented in a PC end using a c + + language of a windows operating system, implemented in a linux system, or implemented in an intelligent terminal using, for example, android and iOS system programming languages, and implemented in processing logic based on a quantum computer.
It should be noted that descriptions of the apparatus, the device, and the system described above according to the related method embodiments may also include other embodiments, and specific implementations may refer to descriptions of corresponding method embodiments, which are not described in detail herein.
The embodiments in the present application are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the hardware + program class embodiment, since it is substantially similar to the method embodiment, the description is simple, and the relevant points can be referred to the partial description of the method embodiment.
For convenience of description, the above devices are described as being divided into various modules by functions, and are described separately. Of course, when implementing one or more of the present description, the functions of some modules may be implemented in one or more software and/or hardware, or the modules implementing the same functions may be implemented by a plurality of sub-modules or sub-units, etc.
The present invention has been described with reference to flowchart illustrations and/or block diagrams of methods, apparatus, devices, systems according to embodiments of the invention. It will be understood that the implementation can be by computer program instructions which can be provided to a processor of a general purpose computer, special purpose computer, embedded processor or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified. These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
As will be appreciated by one skilled in the art, one or more embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, one or more embodiments of the present description may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects.
The above description is merely exemplary of one or more embodiments of the present disclosure and is not intended to limit the scope of one or more embodiments of the present disclosure. Various modifications and alterations to one or more embodiments described herein will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims.
Claims (15)
1. A method of path planning, the method comprising:
acquiring an indoor distribution map of a target building;
processing the indoor distribution map by using a panoramic segmentation image model to obtain a navigation map; the panoramic segmentation image model is used for obtaining an image which can be used by a preset navigation algorithm;
and planning a path by utilizing a preset navigation algorithm based on the navigation map.
2. The method of claim 1, wherein the indoor profile is at least one of: top view, CAD drawing.
3. The method of claim 1, wherein the panorama segmented image model is determined by:
acquiring an indoor distribution map of a building;
carrying out panoramic segmentation on the indoor distribution map to obtain a panoramic segmentation result;
marking the instances included in the indoor distribution map based on the panoramic segmentation result;
and training the preset neural network model by using the marked indoor distribution map to obtain a panoramic segmentation image model.
4. The method of claim 3, wherein the panorama segmentation result comprises class information and instance information in an indoor histogram.
5. The method of claim 1, wherein the preset navigation algorithm comprises at least one of: the a-Star algorithm, the Dijkstra algorithm.
6. The method of claim 1, further comprising:
obtaining a path planning result; the path planning result comprises a preset number of reference track points;
and navigating based on the path planning result.
7. The method of claim 6, further comprising:
acquiring a plurality of track points of a target user;
sending yaw prompt information under the condition that each track point is not in the corresponding preset range; and the preset range corresponding to each track point is determined according to the reference track point in the path planning result.
8. The method of claim 7, wherein the predetermined range corresponding to each track point is determined by:
calculating the distance between the target track point and each reference track point in the path planning result;
determining target reference track points according to the distance between the target track points and each reference track point; the target reference track point represents a reference track point which is closest to the target track point;
and determining a preset range corresponding to the target track point based on the coordinate information of the target reference track point.
9. The method of claim 7, wherein after sending the yaw prompting message, further comprising:
acquiring a plurality of track points of a target user;
and under the condition that each track point is not in the corresponding preset range, based on the plurality of track points of the target user and the navigation map, re-planning the path by using a preset navigation algorithm.
10. The method of claim 1, wherein the navigation map is a plan view comprising 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, wherein the passable position is used for path planning.
11. A path planning apparatus, comprising:
the acquisition module is used for acquiring an indoor distribution map of a target building;
the processing module is used for processing the indoor distribution map by utilizing a panoramic segmentation image model to obtain a navigation map; the panoramic segmentation image model is used for obtaining an image which can be used by a preset navigation algorithm;
and the planning module is used for planning a path by utilizing a preset navigation algorithm based on the navigation map.
12. The apparatus of claim 11, wherein the panorama segmentation image model comprises:
an acquisition unit for acquiring an indoor distribution map of a building;
the dividing unit is used for carrying out panoramic division on the indoor distribution map to obtain a panoramic division result;
the marking unit is used for marking the instances included in the indoor distribution map based on the panoramic segmentation result;
and the training unit is used for training the preset neural network model by using the marked indoor distribution map to obtain a panoramic segmentation image model.
13. A path planning apparatus comprising at least one processor and a memory storing computer-executable instructions which, when executed by the processor, implement the steps of the method of any one of claims 1 to 10.
14. A computer-readable storage medium having stored thereon computer instructions which, when executed by a processor, carry out the steps of the method according to any one of claims 1 to 10.
15. A computer program product comprising a computer program which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 10.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118243029A (en) * | 2024-03-05 | 2024-06-25 | 北京唯得科技有限公司 | Path navigation method, system, equipment and medium for installation site of indoor division equipment |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2005032196A (en) * | 2003-07-11 | 2005-02-03 | Japan Science & Technology Agency | System for planning path for moving robot |
| CN109472416A (en) * | 2018-11-15 | 2019-03-15 | 索奇智能科技（北京）有限公司 | Indoor path planning method and device based on automatic road network data extraction, and client |
| CN110276765A (en) * | 2019-06-21 | 2019-09-24 | 北京交通大学 | Image panorama dividing method based on multi-task learning deep neural network |
| CN110672098A (en) * | 2018-06-14 | 2020-01-10 | 百度在线网络技术（北京）有限公司 | Indoor navigation line model obtaining method, device, equipment and storage medium |
| CN111242954A (en) * | 2020-01-20 | 2020-06-05 | 浙江大学 | Panorama segmentation method with bidirectional connection and shielding processing |
| WO2020125495A1 (en) * | 2018-12-17 | 2020-06-25 | 中国科学院深圳先进技术研究院 | Panoramic segmentation method, apparatus and device |
| CN111486855A (en) * | 2020-04-28 | 2020-08-04 | 武汉科技大学 | Indoor two-dimensional semantic grid map construction method with object navigation points |
| US20210109537A1 (en) * | 2019-10-09 | 2021-04-15 | Wuhan University | Autonomous exploration framework for indoor mobile robotics using reduced approximated generalized voronoi graph |
-
2022
- 2022-04-27 CN CN202210451600.4A patent/CN114838729A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2005032196A (en) * | 2003-07-11 | 2005-02-03 | Japan Science & Technology Agency | System for planning path for moving robot |
| CN110672098A (en) * | 2018-06-14 | 2020-01-10 | 百度在线网络技术（北京）有限公司 | Indoor navigation line model obtaining method, device, equipment and storage medium |
| CN109472416A (en) * | 2018-11-15 | 2019-03-15 | 索奇智能科技（北京）有限公司 | Indoor path planning method and device based on automatic road network data extraction, and client |
| WO2020125495A1 (en) * | 2018-12-17 | 2020-06-25 | 中国科学院深圳先进技术研究院 | Panoramic segmentation method, apparatus and device |
| CN110276765A (en) * | 2019-06-21 | 2019-09-24 | 北京交通大学 | Image panorama dividing method based on multi-task learning deep neural network |
| US20210109537A1 (en) * | 2019-10-09 | 2021-04-15 | Wuhan University | Autonomous exploration framework for indoor mobile robotics using reduced approximated generalized voronoi graph |
| CN111242954A (en) * | 2020-01-20 | 2020-06-05 | 浙江大学 | Panorama segmentation method with bidirectional connection and shielding processing |
| CN111486855A (en) * | 2020-04-28 | 2020-08-04 | 武汉科技大学 | Indoor two-dimensional semantic grid map construction method with object navigation points |
Non-Patent Citations (1)
| Title |
|---|
| 徐风尧;王恒升;: "移动机器人导航中的楼道场景语义分割", 计算机应用研究, no. 06, 14 June 2017 (2017-06-14) * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118243029A (en) * | 2024-03-05 | 2024-06-25 | 北京唯得科技有限公司 | Path navigation method, system, equipment and medium for installation site of indoor division equipment |
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