CN116263598A - Relocation method, device and storage medium of mobile device - Google Patents

Abstract

The present application belongs to the technical field of artificial intelligence, and specifically relates to a relocation method, device and storage medium of a self-mobile device. The method includes: responding to an instruction to relocate the self-mobile device in the working area, acquiring current environment information collected from the mobile device based on the current location; identifying the current environment information, and obtaining an area identifier corresponding to the current environment information Information; determine the local area map indicated by the area identification information in the area map; obtain the template environment information corresponding to at least one map position in the local area map; match the current environment information with the template environment information to determine the self-mobile device in the local area location on the map. It can solve the cumbersome problem of relocating from the mobile device. By identifying the area identification information when relocation is required from the mobile device, and using the local area map indicated by the area identification information to perform relocation, the efficiency of relocation can be improved.

Description

Relocation method, device and storage medium of mobile device

technical field

The present application belongs to the technical field of artificial intelligence, and specifically relates to a relocation method, device and storage medium of a self-mobile device.

Background technique

At present, self-mobile devices can realize autonomous positioning and navigation by means of simultaneous localization and mapping (Simultaneous Localization and Mapping, SLAM) technology. However, in the process of SLAM, it may be hijacked, such as being moved, suspended or dragged in a large area. At this time, an uncontrollable drift error will occur in the positioning of the self-mobile device, and repositioning is required.

The traditional relocation method includes: searching for the original starting position from the hijacked position of the self-mobile device, thereby completing the relocation of the self-mobile device.

However, the traditional relocation method is too cumbersome, which leads to the problem of low relocation efficiency.

Contents of the invention

The present application provides a relocation method, device and storage medium of a self-mobile device, which can solve the problem of low relocation efficiency of the self-mobile device due to cumbersome relocation methods. This application provides the following technical solutions:

In a first aspect, a method for relocating a self-mobile device is provided, the method comprising: in response to an instruction for relocating the self-mobile device in a working area, acquiring the current location of the self-mobile device Collected current environmental information;

Identifying the current environment information to obtain area identification information corresponding to the current environment information;

determining the partial area map indicated by the area identification information in the area map of the working area;

Acquiring template environment information corresponding to at least one map position in the local area map;

Matching the current environment information with the template environment information to determine the location of the ego mobile device in the local area map.

Optionally, the identifying the current environment information to obtain the area identification information corresponding to the current environment information includes:

Inputting the current environment information into a pre-trained area recognition model to obtain the area identification information; the area identification model is obtained by training a preset neural network model using training data; the training data includes sample environment information and the The region label corresponding to the sample environment information.

Optionally, the training data further includes the classification label of the first obstacle corresponding to the sample environment information, and the classification label of the first obstacle is used for joint training of the neural network model in combination with the area label , to obtain the area identification model; correspondingly, the inputting the current environment information into the pre-trained area identification model to obtain the area identification information includes: inputting the current environment information into the area identification model to obtain The classification result of the first obstacle corresponding to the current environment information and the area identification information;

or,

The training data also includes the first characteristic information of the first obstacle; correspondingly, the inputting the current environment information into the pre-trained area recognition model to obtain the area identification information includes: inputting the current Inputting the environment information and the first characteristic information into the area identification model to obtain the area identification model;

Wherein, the first obstacle is used to indicate area identification information.

Optionally, the identifying the current environment information to obtain the area identification information corresponding to the current environment information further includes:

acquiring second characteristic information of a first obstacle, where the first obstacle is used to indicate area identification information;

matching the current environment information with the second feature information;

If the current environment information includes information matching the second feature information, determine that the area identification information corresponding to the current environment information is the area identification information indicated by the first obstacle.

Optionally, the second characteristic information includes contour information of the first obstacle; the second characteristic information further includes size information and/or distance information of the first obstacle.

Optionally, the area identification information is the position coordinates of the first obstacle in the area map; determining the local area map indicated by the area identification information in the area map of the working area includes:

In the area map, a local area map of a preset shape and a preset size is determined based on the area identification information;

or,

In the region map that has undergone region division, the local region map to which the region identification information belongs is determined, and the region map is pre-divided into a plurality of partial region maps.

Optionally, determining the partial area map indicated by the area identification information in the area map of the working area includes:

Based on the correspondence between the area identification information and the local area map, the local area map corresponding to the area identification information is determined.

Optionally, matching the current environment information with the template environment information to determine the location of the mobile device includes:

inputting the current environment information and the template environment information into a pre-trained relocation neural network to obtain the position; the relocation neural network is used to determine whether the current environment information matches the template environment information, and The location corresponding to the matched template environment information is determined as the location.

Optionally, the template environment information includes characteristic information of the second obstacle.

The second aspect provides an electronic device, the device includes a processor and a memory; a program is stored in the memory, and the program is loaded and executed by the processor to realize the mobile device described in the first aspect The relocation method.

A third aspect provides a computer-readable storage medium, where a program is stored in the storage medium, and when the program is executed by a processor, the program is used to implement the self-mobile device relocation method provided in the first aspect.

The beneficial effects of the present application are: by responding to the instruction of relocating the self-mobile device in the working area, the current environment information collected from the mobile device based on the current location is acquired, the current environment information is identified, and the current environment information is obtained Corresponding area identification information, determine the local area map indicated by the area identification information in the area map of the working area, obtain the template environment information corresponding to at least one map position in the local area map, match the current environment information with the template environment information, and Determine the location from the mobile device in the local area map. The problem of low relocation efficiency caused by cumbersome relocation methods of the self-mobile device can be solved. By identifying the area identification information when the mobile device needs to be relocated, and using the local area map indicated by the area identification information to relocate the current location of the mobile device. At this time, the self-mobile device does not need to search for a certain position in the entire working area, but can realize relocation by using a local area map, which can improve the efficiency of relocation. At the same time, the self-mobile device does not need to move to the original starting position, but only needs to match the current environment information of the current location with the template environment information of the local area map, which can save resources of the self-mobile device and further improve relocation. s efficiency.

In addition, the neural network model is jointly trained by using the classification label and the area label of the first obstacle to obtain an area recognition model, and the trained area identification model will be more accurate, which can improve the accuracy of area identification information identification.

In addition, when identifying the current environment information, the area identification model can compare the current environment information with the first feature information to determine whether there is a first obstacle. Since the first obstacle can indicate the area identification information, it can Reduce the computational difficulty of the network model and save computing resources from mobile devices.

Description of drawings

FIG. 1 is a schematic structural diagram of a mobile device provided by an embodiment of the present application;

FIG. 2 is a flow chart of a relocation method from a mobile device provided by an embodiment of the present application;

FIG. 3 is a block diagram of an apparatus for relocating self-mobile equipment provided by an embodiment of the present application;

Fig. 4 is a block diagram of an electronic device provided by an embodiment of the present application.

Detailed ways

The technical solutions of the present application will be clearly and completely described below in conjunction with the accompanying drawings. Apparently, the described embodiments are some of the embodiments of the present application, not all of them. Hereinafter, the present application will be described in detail with reference to the drawings and embodiments. It should be noted that, in the case of no conflict, the embodiments in the present application and the features in the embodiments can be combined with each other.

It should be noted that the terms "first" and "second" in the description and claims of the present application and the above drawings are used to distinguish similar objects, but not necessarily used to describe a specific sequence or sequence.

In the application, unless stated to the contrary, the orientation words used such as "upper, lower, top, bottom" are generally used for the directions shown in the drawings, or for the parts themselves in the vertical, vertical Or in the direction of gravity; similarly, for the convenience of understanding and description, "inner and outer" refer to the inner and outer relative to the outline of each component itself, but the above orientation words are not used to limit the present application.

As shown in Figure 1, it is a schematic structural diagram of a self-moving device provided by an embodiment of the present application. The self-moving device can be a self-moving device such as a sweeping robot and a floor washing robot. This embodiment does not make any reference to the device type of the self-moving device limited. It can be seen from FIG. 1 that the mobile device at least includes: a driving component 110 , a moving component 120 , a controller 130 and a first sensor 140 .

The driving component 110 is connected with the moving component 120 and is used to drive the moving component 120 to run, so as to drive the mobile device to move.

The driving component 110 is connected with the controller 130 and is used to drive the moving component 120 to run in response to an instruction issued by the controller 130 .

Optionally, the driving assembly 110 may be implemented as a DC motor, a servo motor, a stepping motor, etc., and this embodiment does not limit the implementation of the driving assembly 110 .

The first sensor 140 is used to collect current environment information. Optionally, the first sensor 140 may be a camera equipped with a color system (Red Green Blue, RGB) detection function, an infrared sensor, or a laser radar sensor, etc. The type of the first sensor 140 is not limited in this embodiment.

Optionally, the first sensor 140 may be installed on the casing of the self-mobile device, and used to collect the environment where the self-mobile device is located. The collection range of the first sensor 140 includes, but is not limited to: the area directly in front of, obliquely above, and/or obliquely below the direction of travel of the mobile device; and/or the area on the left side of the direction of travel of the self-mobile device; The area on the right side of the traveling direction of the device; and/or the rear side area of the traveling direction of the self-mobile device, etc., this embodiment does not limit the collection range of the first sensor 140 .

In addition, the number of the first sensor 140 can be one or at least two. In the case where the number of the first sensor 140 is at least two, the types of different first sensors 140 are the same or different. This embodiment does not apply to the first sensor 140. The number and implementation methods are limited.

The first sensor 140 is connected with the controller 130 to send the collected current environment information to the controller 130 .

The controller 130 is used to relocate the mobile device. Optionally, the controller 130 may be implemented as a single-chip microcomputer or a processor, and this embodiment does not limit the implementation manner of the controller 130 .

In this embodiment, the controller 130 is configured to: respond to an instruction to relocate the mobile device in the working area, obtain the current environment information collected from the mobile device based on the current location; identify the current environment information, and obtain The regional identification information corresponding to the current environmental information; determine the local area map indicated by the regional identification information in the regional map of the working area; obtain the template environmental information corresponding to at least one map position in the local area map; compare the current environmental information with the template environmental information Match to determine the location from the mobile device in the local area map.

Optionally, the instruction for relocating the mobile device within the working area is generated by the mobile device based on the sensing data of the second sensor. At this time, the mobile device is also provided with a second sensor 150 , which is connected to the controller 130 and used to send sensing data to the controller 130 . Correspondingly, after the controller 130 receives the sensing data, it determines whether the self-mobile device is hijacked based on the sensing data; Positioning instructions; if it is determined that the self-mobile device has not been hijacked, the step of whether the self-mobile device is hijacked is performed again until the self-mobile device finishes working.

Wherein, being hijacked refers to an abnormal movement of the mobile device, and the abnormal movement refers to a movement that does not occur independently of the mobile device, and the abnormal movement cannot be sensed by the mobile device. Therefore, when the self-mobile device is hijacked, it cannot locate or accurately locate its own position. For example, when the self-mobile device is moved, suspended in the air during the movement, or dragged in a large area, etc., it is the case that the self-mobile device is hijacked.

Correspondingly, disengagement hijacking refers to the end of abnormal movement of the self-mobile device, such as: returning to the ground after being moved, or when the self-mobile device stops being dragged after being dragged, it is self-moving The device is out of the hijacked situation.

Schematically, the sensing data includes, but is not limited to: height data, displacement data, and/or angle data, etc. from the mobile device. Correspondingly, the second sensor 150 includes, but is not limited to, a gyroscope, a displacement sensor, or an image collector etc., this embodiment does not limit the implementation manner of the second sensor 150 . At this time, determining whether the mobile device is hijacked based on the sensing data includes: comparing the change of the sensing data with the template change in the hijacked state; If the changes match, it is determined that the self-mobile device is hijacked; if the change of the sensing data does not match the template change in the hijacked state, it is determined that the self-mobile device is not hijacked. Alternatively, determining whether the self-mobile device is hijacked based on the sensing data includes: determining whether the variation value of the sensing data is within the variation range of the hijacked state; if so, determining that the self-mobile device is hijacked; The mobile device was not hijacked.

Schematically, the sensing data includes but not limited to: contact data, etc. Correspondingly, the second sensor 150 includes but not limited to a pressure sensor, or a contact sensor, etc., and this embodiment does not limit the implementation of the second sensor 150 . At this time, determining whether the mobile device is hijacked based on the sensing data includes: determining whether the sensing data indicates that there is an object approaching the mobile device; if so, determining that the mobile device is hijacked; if not, determining that the mobile device is not hijacked hijack.

Alternatively, the command to relocate the mobile device within the working area is sent from a control device communicatively connected to the mobile device. Wherein, the control device may be a mobile phone, a remote controller, or a wearable device, etc., and this embodiment does not limit the type of the control device.

Alternatively, the instruction to relocate the mobile device within the working area is generated when the mobile device receives a trigger operation acting on the repositioning control. At this time, a relocation control is also provided on the self-mobile device, and the relocation control may be a physical button or a virtual control displayed through a touch screen. This embodiment does not limit the implementation of the relocation control.

The above manner of obtaining the relocation instruction is only illustrative. In actual implementation, the manner of obtaining the relocation instruction from the mobile device may also be other manners, which will not be listed here in this embodiment.

It should be added that, in actual implementation, the self-moving device may also include other components, such as a power supply component, a shock absorption component, etc., which will not be listed here in this embodiment.

In the traditional relocation method, the original starting position is searched from the hijacked position of the mobile device. The search method is usually: the self-mobile device moves randomly in the working area, and receives the infrared signal emitted by the original starting position (such as the charging stand) through the infrared receiving device. When the infrared signal is received by the infrared receiving device, move to the position of the infrared signal to move to the original starting position, and read the map position of the original starting position stored in the area map of the work area to complete the reset position. However, the traditional relocation method cannot relocate the self-mobile device when it is free from hijacking. It needs to search the original starting position in the entire working area, and the efficiency of relocation is low. However, in this embodiment, the current location of the mobile device is relocated by identifying the area identification information when the mobile device needs to be relocated, and using the local area map indicated by the area identification information. At this time, the self-mobile device does not need to search for a certain position in the entire working area, but can realize relocation by using a local area map, which can improve the efficiency of relocation. At the same time, the self-mobile device does not need to move to the original starting position, but only needs to match the current environment information of the current location with the template environment information of the local area map, which can save resources of the self-mobile device and further improve relocation. s efficiency.

The method for relocating the self-mobile device provided by this application will be described in detail below.

A method for relocating a self-mobile device provided in this embodiment is shown in FIG. 2 . In this embodiment, the method is used in the controller 130 shown in FIG. 1 as an example for illustration. The method includes at least the following steps:

Step 201 , in response to an instruction to relocate the mobile device within the working area, acquire current environment information collected based on the current location of the mobile device.

The instruction for relocating the self-mobile device is generated by the self-mobile device based on the sensing data of the second sensor; or, it is sent by a control device connected to the self-mobile device in communication; It is generated when a trigger operation is performed on the positioning control, and this embodiment does not limit the acquisition method of the instruction for relocating from the mobile device.

In an example, the current environment information is obtained from the current environment where the mobile device is located. The current environment information may be image data and/or point cloud data, and the current environment information may be three-dimensional data or two-dimensional data, and this embodiment does not limit the implementation manner of the current environment information.

Optionally, the current environment information may be collected by the controller controlling the first sensor when the mobile device obtains a relocation instruction; or, it is collected continuously after the first sensor is powered on. The timing of collection is limited.

Step 202, identifying the current environment information to obtain area identification information corresponding to the current environment information.

The area identification information is used to uniquely indicate a certain local area in the working area.

Optionally, the area identification information is the position coordinates of the first obstacle in the area map. Wherein, the first obstacle is used to indicate area identification information. Specifically, the first obstacle refers to an obstacle that can indicate the attribute of the local area. For example: the first obstacle is a dining table, and the attribute of the local area indicated by the dining table is a restaurant; another example: the first obstacle is a toilet, and the attribute of the local area indicated by the toilet is a bathroom; another example: the first obstacle is a bed, the bed The attribute of the indicated local area is a bedroom, and this embodiment does not list all implementations of the first obstacle here.

Alternatively, the area identification information is an area identification of the local area, and the area identification may be an attribute or a label of the local area. For example: in the case that the area identification is an attribute, the area identification information is a restaurant, a bathroom, and/or a bedroom. For example: in the case that the area identification is a label, the label 1, 2, 3, etc. are pre-set for each local area map in the area identification information area map.

In this embodiment, the attributes of the local area include restaurants, bathrooms, and bedrooms as examples for illustration. In actual implementation, the attribute division method of the local area can also be in other ways, for example: divide the attributes of the local area into: office area , coffee break area, etc., this embodiment does not limit the attribute division method of the local area.

The way to identify the current environmental information and obtain the area identification information corresponding to the current environmental information includes but is not limited to at least one of the following:

The first identification method is to input the current environment data into the pre-trained area identification model to obtain area identification information. Wherein, the region recognition model is obtained by using training data to train a preset neural network model.

Optionally, the realization of training data includes but not limited to the following situations:

In the first case, the training data only includes the sample environment information and the region labels corresponding to the sample environment information.

Correspondingly, the training process of the area recognition model includes: inputting the sample environment information into the preset first neural network model to obtain the first training result; inputting the first training result and the area label corresponding to the sample environment information into the first loss function, Obtaining a first loss result; training the first neural network model based on the first loss result to reduce the difference between the first training result and the corresponding region label until the neural network model converges to obtain a region recognition model.

Wherein, in the case where the area label is the position coordinate label of the first obstacle, the area identification information is the position coordinates of the first obstacle; when the area label is the area attribute of the first obstacle, the area identification information is the The area attribute of an obstacle.

Inputting the current environment information into the pre-trained area recognition model to obtain area identification information includes: inputting the current environment information into the pre-trained area identification model to obtain the area identification information corresponding to the current environment information.

Wherein, the first neural network model can be convolutional neural network (Convolutional Neural Networks, CNN), recursive neural network (Recursive Neural Network, RNN), feedforward neural network (Feedforward Neural Network, FNN), this embodiment does not apply to the first neural network The implementation of the network model is limited.

In the second case, the training data not only includes the sample environment information and the area label corresponding to the sample environment information, but also includes the classification label of the first obstacle corresponding to the sample information, wherein the classification label of the first obstacle is used to combine the area label pair The neural network model is jointly trained to obtain a region recognition model.

Wherein, the classification label of the first obstacle is the attribute label of the first obstacle.

Correspondingly, the training process of the area recognition model includes: inputting the sample environment information into the preset second neural network model to obtain the second training result, the second training result includes the area prediction result and the classification prediction result; the second training result , the region label and the classification label are input into the second loss function to obtain the second loss result; the second neural network model is trained based on the second loss result to reduce the difference between the second training result and the corresponding region label and classification label value until the second neural network model converges to obtain the region recognition model.

Wherein, the second neural network model includes two network branches, one of which is used to calculate the region prediction result, and the other branch is used to calculate the classification result.

Since during the training process, the training of the second neural network model will end when the results of the two network branches are close to the true value, therefore, the trained area identification model will be more accurate, which can improve the accuracy of area identification information identification .

Correspondingly, inputting the current environment information into the pre-trained area recognition model to obtain area identification information includes: inputting the current environment information into the area identification model to obtain the classification result and area identification information of the first obstacle corresponding to the current environment information.

In the third case, the training data not only includes the sample environment information and the region label corresponding to the sample environment information, but also includes first feature information of the first obstacle corresponding to the sample environment information.

Optionally, the first feature information may be contour information of the first obstacle, or a feature vector of the first obstacle, and this embodiment does not limit the implementation manner of the first feature information.

Correspondingly, the training process of the region recognition model includes: inputting the sample environment information and the first feature information into the preset third neural network model to obtain the third training result; inputting the third training result and the region label into the third loss function , to obtain the third loss result; based on the third loss result, the third neural network model is trained to reduce the difference between the third training result and the corresponding region label, until the third neural network model converges, and the region recognition model is obtained .

The third neural network model is used to compare the sample environment information with the first feature information to determine whether the sample environment information matches the first feature information, thereby determining whether there is a first obstacle in the sample environment information, due to the first Obstacles can indicate area identification information, therefore, the calculation difficulty of the network model can be reduced, and the calculation resources of mobile devices can be saved.

Correspondingly, inputting the current environment information into the pre-trained area recognition model to obtain the area identification information includes: inputting the current environment information and the first feature information of the first obstacle into the area identification model to obtain the area identification information.

Wherein, the first characteristic information of the first obstacle is pre-stored in the self-mobile device.

The second identification method is to obtain the second feature information of the first obstacle; match the acquired current environment information with the second feature information; if the current environment information includes information matching the second feature information, Then it is determined that the area identification information corresponding to the current environment information is the area identification information indicated by the first obstacle.

Optionally, the second feature information is the same as or different from the first feature information. The second feature information may be the outline information of the first obstacle, the size information and/or distance information of the first obstacle, or the information that can be used to describe the characteristics of the first obstacle. This embodiment does not make any contribution to the second feature information. limited.

For example: the first obstacle is a dining table, and the second feature information is the shape and size of the dining table. If the current environment information includes information matching the shape and size of the dining table, it is determined that the area identification information corresponding to the current environment information is the area identification information indicated by the dining table, such as a restaurant.

Step 203, determine the partial area map indicated by the area identification information in the area map of the working area.

In this embodiment, the local area map may change based on changes in area identification information.

In an example, the area identification information is the position coordinates of the first obstacle in the area map. Correspondingly, the manner of determining the partial area map indicated by the area identification information in the area of the working area includes but not limited to at least one of the following:

The first type: In the area map, a local area map with a preset shape and a preset size is determined based on the area identification information.

Wherein, the preset shape and preset size are pre-stored in the mobile device. The preset shape may be a circle, a rectangle, or an irregular shape, and this embodiment does not limit the implementation of the preset shape.

Schematically, determining a local area map with a preset shape and a preset size based on the area identification information includes: taking the position coordinates of the first obstacle as the centroid of the local area map to generate a local area with a preset shape and a preset size map.

For example, the first obstacle is the charging stand, and the area identification information is the location coordinates of the charging stand. The default shape is a circle, and the default size is a radius of 2 meters. At this time, it is determined that the local area map is a circular area on the area map with the location coordinates of the charging stand as the center and a radius of 2 meters.

In other embodiments, the position coordinates of the first obstacle may also be located at the edge of the local area map or other positions. This embodiment does not limit the method of determining the local area map with a preset shape and a preset size based on the area identification information. .

The second type: in the area map that has been segmented, determine the local area map to which the area identification information belongs, and the area map is pre-divided into a plurality of local area maps.

Among them, the way of dividing the regional map into multiple local regional maps includes but not limited to: dividing the regional map according to attributes, or dividing the regional map according to the preset segmentation size. In actual implementation, the division method of the regional map can also be Other manners are not listed here in this embodiment.

For example: Divide the area map into a bedroom area map and a bathroom area map. When the area identification information is the location coordinates of the charging stand, the charging stand is located in the bedroom area, and the local area map to which the area identification information belongs is the bedroom area map.

In another example, the corresponding relationship between the area identification information and the local area map is stored in the mobile device. At this time, determining the local area map indicated by the area identification information in the area map of the working area includes: determining the local area map corresponding to the area identification information based on the correspondence between the area identification information and the local area map.

For example, the area identification information is the label of each local area map. At this time, after the mobile device obtains the label, the local area map corresponding to the label is found from the correspondence.

Step 204, acquiring template environment information corresponding to at least one map position in the local area map.

In this embodiment, the local area map includes template environment information corresponding to multiple map locations, and the multiple template environment information all have location coordinates corresponding to the map locations. The controller obtains the template environment information corresponding to at least one map position.

Step 205, matching the current environment information with the template environment information to determine the location of the ego mobile device in the local area map.

Wherein, the template environment information includes characteristic information of the second obstacle.

The manner of matching the current environment information with the template environment information includes but not limited to at least one of the following:

The first is to input the current environment information and the template environment information into the pre-trained relocation neural network to obtain the position of the self-mobile device in the local area map. The relocation neural network is used to determine whether the current environment information matches the template environment information, and determine the position corresponding to the matched template environment information as the position of the self-mobile device in the local area map.

Optionally, the template environment information includes characteristic information of the second obstacle. The second obstacle may be any obstacle in the working area, such as a table, a chair, a carpet, a wall, etc., and this embodiment does not limit the type of the second obstacle.

The feature information of the second obstacle may be the shape, size, or feature vector of the second obstacle, and this embodiment does not limit the implementation manner of the feature information of the second obstacle.

Correspondingly, the training process of the relocation neural network includes: inputting the sample environment information and each template environment information into the preset neural network model to obtain the similarity result; comparing the similarity result with the real similarity result, and based on the comparison result Train the neural network model to obtain a relocation neural network.

Match the current environment information with the template environment information to determine the position of the self-mobile device in the local area map, including: input the current environment information and the template environment information into the pre-trained relocation neural network to obtain multiple similarity results, The map position corresponding to the environmental template information ranked first in the similarity result is determined as the position of the self-mobile device in the local area map.

The second method is to calculate the similarity between the current environment information and each template environment information; the map position corresponding to the environment template information with the greatest similarity is determined as the position of the self-mobile device in the local area map.

To sum up, the method for relocating the self-mobile device provided in this embodiment obtains the current environment information collected by the self-mobile device based on the current location by responding to an instruction to relocate the self-mobile device in the work area, Identify the current environmental information, obtain the area identification information corresponding to the current environmental information, determine the local area map indicated by the area identification information in the area map of the working area, obtain the template environment information corresponding to at least one map position in the local area map, and set The current environment information is matched with the template environment information to determine the location of the mobile device in the local area map. The problem of low relocation efficiency caused by cumbersome relocation methods of the self-mobile device can be solved. By identifying the area identification information when the mobile device needs to be relocated, and using the local area map indicated by the area identification information to relocate the current location of the mobile device. At this time, the self-mobile device does not need to search for a certain position in the entire working area, but can realize relocation by using a local area map, which can improve the efficiency of relocation. At the same time, the self-mobile device does not need to move to the original starting position, but only needs to match the current environment information of the current location with the template environment information of the local area map, which can save resources of the self-mobile device and further improve relocation. s efficiency.

In addition, the neural network model is jointly trained by using the classification label and the area label of the first obstacle to obtain an area recognition model, and the trained area identification model will be more accurate, which can improve the accuracy of area identification information identification.

In addition, when identifying the current environment information, the area identification model can compare the current environment information with the first feature information to determine whether there is a first obstacle. Since the first obstacle can indicate the area identification information, it can Reduce the computational difficulty of the network model and save computing resources from mobile devices.

FIG. 3 is a block diagram of an apparatus for relocating self-mobile equipment provided by an embodiment of the present application. In this embodiment, the application of the apparatus to the self-mobile equipment shown in FIG. 1 is used as an example for illustration. The device at least includes the following modules: a first acquisition module 310 , an information identification module 320 , a map determination module 330 , a second acquisition module 340 and a relocation module 350 .

The first acquiring module 310 is configured to acquire current environment information collected from the mobile device based on the current location in response to an instruction to relocate the mobile device within the working area.

The information identification module 320 is configured to identify the current environment information, and obtain area identification information corresponding to the current environment information.

The map determination module 330 is configured to determine a partial area map indicated by the area identification information in the area map of the working area.

The second acquiring module 340 is configured to acquire template environment information corresponding to at least one map position in the local area map.

The relocation module 350 is configured to match the current environment information with the template environment information, so as to determine the location of the ego mobile device in the local area map.

Relevant details refer to the above-mentioned examples.

It should be noted that when the self-mobile device relocation device provided in the above-mentioned embodiments performs relocation, it only uses the division of the above-mentioned functional modules as an example. In practical applications, the above-mentioned functions can be assigned by different The completion of the functional modules means that the internal structure of the self-mobile device relocation device is divided into different functional modules, so as to complete all or part of the functions described above. In addition, the apparatus for relocating self-mobile equipment provided by the above-mentioned embodiments and the embodiment of the method for relocating self-mobile equipment belong to the same concept, and its specific implementation process is detailed in the method embodiment, and will not be repeated here.

This embodiment provides an electronic device, as shown in FIG. 4 , the electronic device may be the self-moving device in FIG. 1 . The electronic device includes at least a processor 401 and a memory 402 .

The processor 401 may include one or more processing cores, such as a 4-core processor, an 8-core processor, and the like. The processor 401 can adopt at least one hardware form in DSP (Digital Signal Processing, digital signal processing), FPGA (Field-Programmable Gate Array, field programmable gate array), PLA (Programmable Logic Array, programmable logic array) accomplish. The processor 401 may also include a main processor and a coprocessor, the main processor is a processor for processing data in the wake-up state, and is also called a CPU (Central Processing Unit, central processing unit); Low-power processor for processing data in standby state. In some embodiments, the processor 401 may be integrated with a GPU (Graphics Processing Unit, image processor), and the GPU is used for rendering and drawing the content to be displayed on the display screen. In some embodiments, the processor 401 may further include an AI (Artificial Intelligence, artificial intelligence) processor, where the AI processor is configured to process computing operations related to machine learning.

Memory 402 may include one or more computer-readable storage media, which may be non-transitory. The memory 402 may also include high-speed random access memory and non-volatile memory, such as one or more magnetic disk storage devices and flash memory storage devices. In some embodiments, the non-transitory computer-readable storage medium in the memory 402 is used to store at least one instruction, and the at least one instruction is used to be executed by the processor 401 to realize the motor brake provided by the method embodiment of the present application. method.

In some embodiments, the electronic device may optionally further include: a peripheral device interface and at least one peripheral device. The processor 401, the memory 402, and the peripheral device interface may be connected through a bus or a signal line. Each peripheral device can be connected with the peripheral device interface through a bus, a signal line or a circuit board. Schematically, peripheral devices include but are not limited to: radio frequency circuits, touch screens, audio circuits, and power supplies.

Of course, the electronic device may also include fewer or more components, which is not limited in this embodiment.

Optionally, the present application also provides a computer-readable storage medium, in which a program is stored, and the program is loaded and executed by a processor to implement the self-mobile device relocation method in the foregoing method embodiments.

The technical features of the above-mentioned embodiments can be combined arbitrarily. To make the description concise, all possible combinations of the technical features in the above-mentioned embodiments are not described. However, as long as there is no contradiction in the combination of these technical features, should be considered as within the scope of this specification.

The above-mentioned embodiments only represent several implementation modes of the present application, and the description thereof is relatively specific and detailed, but it should not be construed as limiting the scope of the patent for the invention. It should be noted that those skilled in the art can make several modifications and improvements without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the scope of protection of the patent application should be based on the appended claims.

Claims (11)

1. A method of relocation from a mobile device, the method comprising:

responding to an instruction for repositioning the self-mobile equipment in a working area, and acquiring current environment information acquired by the self-mobile equipment based on the current position;

identifying the current environment information to obtain region identification information corresponding to the current environment information;

Determining a local area map indicated by the area identification information in an area map of the working area;

acquiring template environment information corresponding to at least one map position in the local area map;

and matching the current environment information with the template environment information to determine the position of the self-mobile device in the local area map.

2. The method of claim 1, wherein the identifying the current environmental information to obtain the area identification information corresponding to the current environmental information includes:

inputting the current environment information into a pre-trained region identification model to obtain the region identification information; the region identification model is obtained by training a preset neural network model by using training data; the training data comprises sample environment information and area labels corresponding to the sample environment information.

3. The method of claim 2, wherein the step of determining the position of the substrate comprises,

the training data further comprises a classification label of a first obstacle corresponding to the sample environment information, wherein the classification label of the first obstacle is used for carrying out combined training on the neural network model by combining the regional label to obtain the regional identification model; correspondingly, the step of inputting the current environment information into a pre-trained area identification model to obtain the area identification information comprises the following steps: inputting the current environment information into the area identification model to obtain a classification result of a first obstacle corresponding to the current environment information and the area identification information;

Or,

the training data further includes first characteristic information of the first obstacle; correspondingly, the step of inputting the current environment information into a pre-trained area identification model to obtain the area identification information comprises the following steps: inputting the current environment information and the first characteristic information into the region identification model to obtain the region identification model;

wherein the first obstacle is used for indicating area identification information.

4. The method of claim 1, wherein the identifying the current environmental information to obtain the area identification information corresponding to the current environmental information further comprises:

acquiring second characteristic information of a first obstacle, wherein the first obstacle is used for indicating area identification information;

matching the current environment information with the second characteristic information;

and determining that the area identification information corresponding to the current environment information is the area identification information indicated by the first obstacle when the current environment information comprises information matched with the second characteristic information.

5. The method according to claim 3 or 4, wherein the second characteristic information comprises profile information of the first obstacle; the second characteristic information further comprises size information and/or distance information of the first obstacle.

6. The method of claim 1, wherein the area identification information is a location coordinate of a first obstacle in the area map; the determining the local area map indicated by the area identification information in the area map of the working area comprises the following steps:

determining a local area map with a preset shape and a preset size based on the area identification information in the area map;

or,

in the area map subjected to the area division, a local area map to which the area identification information belongs is determined, and the area map is divided into a plurality of local area maps in advance.

7. The method of claim 1, wherein determining a local area map indicated by the area identification information in an area map of the work area comprises:

and determining the local area map corresponding to the area identification information based on the corresponding relation between the area identification information and the local area map.

8. The method of claim 1, wherein matching the current environmental information with the template environmental information to determine the location of the self-mobile device comprises:

inputting the current environment information and the template environment information into a pre-trained repositioning neural network to obtain the position; the repositioning neural network is used for determining whether the current environment information and the template environment information are matched or not, and determining the position corresponding to the matched template environment information as the position.

9. The method of claim 8, wherein the template environmental information includes characteristic information of a second obstacle.

10. An electronic device comprising a processor and a memory; stored in the memory is a program that is loaded and executed by the processor to implement the relocation method of a self-mobile device according to any one of claims 1 to 9.

11. A computer readable storage medium, characterized in that the storage medium has stored therein a program which, when executed by a processor, is adapted to carry out a relocation method of a self-mobile device according to any of claims 1 to 9.

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