WO2023005379A1

WO2023005379A1 - Method and apparatus for saving semantic map, storage medium, and electronic device

Info

Publication number: WO2023005379A1
Application number: PCT/CN2022/094640
Authority: WO
Inventors: 曹蒙
Original assignee: 追觅创新科技(苏州)有限公司
Priority date: 2021-07-27
Filing date: 2022-05-24
Publication date: 2023-02-02
Also published as: CN113656418B; CN113656418A

Abstract

Embodiments of the present disclosure provide a method and apparatus for saving a semantic map, a storage medium, and an electronic device. The method for saving a semantic map comprises: acquiring a first semantic map constructed by a mobile robot after executing a current target event; when there is a second semantic map, calculating a similarity score between the second semantic map and the first semantic map, wherein the second semantic map is a historical semantic map saved by the mobile robot; and when the similarity score is greater than a first preset threshold, updating the second semantic map saved in the mobile robot into the first semantic map. The technical solution above is used to solve the problem in the prior art that a semantic map saved by a mobile robot may be the wrong map.

Description

Semantic map preservation method and device, storage medium, electronic device

This disclosure claims the priority of the following patent application: a Chinese patent application submitted to the China Patent Office on July 27, 2021, with the application number 202110853400.7, and the title of the invention is "Semantic Map Preservation Method and Device, Storage Medium, and Electronic Device"; The entire contents of the aforementioned patent applications are incorporated by reference in this disclosure.

technical field

The present disclosure relates to the communication field, and in particular, to a method and device for storing a semantic map, a storage medium, and an electronic device.

Background technique

With the advancement of science and technology and the development of artificial intelligence, intelligent robots have also been applied to various fields. The positioning and map construction of mobile robots are hot issues in the field of mobile robots.

The existing laser displacement sensor (Laser Distance Sensor, referred to as LDS) type mobile robot uses LDS radar information for positioning and mapping. Robots are prone to positioning errors, or positioning errors may occur after the mobile robot is lifted and then put down. This positioning error will cause the mobile robot to build a wrong map and then save a wrong map. When the follow-up mobile robot cleans according to the wrong map, confusion occurs. For example, the mobile robot needs to go to room A to clean, but due to positioning and map errors, it goes to room B. When an error occurs in the map, the user must manually delete it. The error message will always exist,

Aiming at the problem that the semantic map saved by the mobile robot may be the wrong map in the related technology, no effective technical solution has been proposed yet.

Therefore, it is necessary to improve the related technology to overcome the above-mentioned defects in the related technology.

Contents of the invention

Embodiments of the present disclosure provide a method and device for saving a semantic map, a storage medium, and an electronic device, so as to at least solve the problem that the semantic map saved by a mobile robot may be a wrong map.

According to an embodiment of the present disclosure, there is provided a method for saving a semantic map, which includes: obtaining the first semantic map constructed by the mobile robot after executing the current target event; if there is a second semantic map, calculating the A similarity score between the second semantic map and the first semantic map, wherein the second semantic map is a historical semantic map saved by the mobile robot; when the similarity score is greater than a first preset threshold , updating the second semantic map stored in the mobile robot to the first semantic map.

In an exemplary embodiment, if there is a second semantic map, before calculating the similarity score between the second semantic map and the first semantic map, the method further includes: executing During the process of the last target event, construct the second semantic map, wherein the second semantic map includes: object type information, object coordinate information, and object semantic weight information; After a target event, obtaining the second semantic map constructed by the mobile robot, and/or obtaining the first semantic map constructed by the mobile robot after executing the current target event, includes: when the mobile robot executes the current During the target event, the first semantic map is constructed, wherein the first semantic map includes: object type information, object coordinate information, and object semantic weight information; after the current target event is executed, Obtain the first semantic map constructed by the mobile robot.

In an exemplary embodiment, if there is a second semantic map, calculating the similarity score between the second semantic map and the first semantic map includes: traversing the second semantic map and the first semantic map A semantic map, to obtain multiple target similarity scores corresponding to multiple identical coordinate information in the second semantic map and the first semantic map; obtain the second target similarity scores through the multiple target similarity scores A similarity score between the semantic map and the first semantic map.

In an exemplary embodiment, traversing the second semantic map and the first semantic map to obtain a plurality of coordinate information corresponding to the same coordinate information in the second semantic map and the first semantic map The target similarity score includes: for any coordinate information in the plurality of identical coordinate information, obtaining the first category information corresponding to the any coordinate information in the second semantic map, and the any The second category information corresponding to the coordinate information in the first semantic map; if the first category information is the same as the second category information, based on the first semantic weight corresponding to the first category information, Or the second semantic weight corresponding to the second type of information determines the target similarity score of any coordinate information.

In an exemplary embodiment, the first category information corresponding to the any coordinate information in the second semantic map, and the second category information corresponding to the any coordinate information in the first semantic map are acquired After information, the method further includes: in a case where the first category information is different from the second category information, determining that the target similarity score of any coordinate information is smaller than a second preset threshold.

In an exemplary embodiment, after matching the second semantic map with the first semantic map to obtain a similarity score between the second semantic map and the first semantic map, the method further The method includes: when the similarity score is less than a first preset threshold, storing the second semantic map in the mobile robot, and prohibiting storing the first semantic map in the mobile robot.

In an exemplary embodiment, the method further includes: saving the first semantic map if the second semantic map does not exist.

According to another embodiment of the present disclosure, a storage device for a semantic map is provided, the device includes: an acquisition module, configured to acquire the first semantic map constructed by the mobile robot after executing the current target event; a calculation module, using In the case that there is a second semantic map, calculate the similarity score between the second semantic map and the first semantic map, wherein the second semantic map is a historical semantic map saved by the mobile robot; save A module configured to update the second semantic map stored in the mobile robot to the first semantic map when the similarity score is greater than a first preset threshold.

According to yet another embodiment of the present disclosure, there is also provided a computer-readable storage medium, where a computer program is stored in the computer-readable storage medium, wherein the computer program is configured to perform the above-mentioned saving of the semantic map when running method.

According to yet another embodiment of the present disclosure, there is also provided an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to perform any of the above Steps in the method examples.

Through the present disclosure, the first semantic map constructed by the mobile robot after executing the current target event is obtained; if there is a second semantic map, the similarity score between the second semantic map and the first semantic map is calculated, Wherein, the second semantic map is a historical semantic map saved by the mobile robot; when the similarity score is greater than a first preset threshold, update the second semantic map saved in the mobile robot For the first semantic map, that is, by comparing the second semantic map and the size relationship between the similarity score of the first semantic map constructed by the current target event and the first preset threshold, when the similarity score is greater than In the case of the first preset threshold, it is determined that the first semantic map is the correct map, and then the first semantic map is saved. Through the above technical solution, the problem in the related art that the semantic map saved by the mobile robot may be a wrong map is solved. .

The present disclosure has the following beneficial effects: based on the semantic recognition function of the AI of the mobile robot, by mapping the semantic information together with the location information of the semantic onto the semantic map, by comparing the second semantic information constructed by the mobile robot after executing the last target event Map, and the first semantic map constructed by the mobile robot after executing the current target event, to confirm whether there is a problem with the first semantic map, and whether to save the first semantic map, to solve the problem that the map saved by the mobile robot may be a wrong map, Furthermore, in the case of map errors, abnormal behavior of the mobile robot and other problems, the robustness of the long-term operation of the mobile robot is improved, and the influence of the mobile robot's map construction and overlay is avoided.

Description of drawings

The drawings described here are used to provide a further understanding of the present disclosure, and constitute a part of the present disclosure. The schematic embodiments of the present disclosure and their descriptions are used to explain the present disclosure, and do not constitute improper limitations to the present disclosure. In the attached picture:

1 is a block diagram of the hardware structure of a mobile robot according to a method for storing a semantic map according to an embodiment of the present disclosure;

FIG. 2 is a flow chart (1) of a method for saving a semantic map according to an embodiment of the present disclosure;

FIG. 3 is a flow chart (2) of a method for saving a semantic map according to an embodiment of the present disclosure;

Fig. 4 is a structural block diagram of an apparatus for saving a semantic map according to an embodiment of the present disclosure.

Detailed ways

Hereinafter, the present disclosure will be described in detail with reference to the accompanying drawings and embodiments. It should be noted that, in the case of no conflict, the embodiments in the present disclosure and the features in the embodiments can be combined with each other.

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

The method embodiments provided by the embodiments of the present disclosure may be executed in a mobile robot or a similar computing device. Taking running on a mobile robot as an example, FIG. 1 is a block diagram of a hardware structure of a mobile robot according to a method for saving a semantic map according to an embodiment of the present disclosure. As shown in Figure 1, the mobile robot can include one or more (only one is shown in Figure 1) processor 102 (the processor 102 can include but not limited to processing devices such as microprocessor MCU or programmable logic device FPGA, etc.) and a memory 104 for storing data. In an exemplary embodiment, the above-mentioned mobile robot may also include a transmission device 106 and an input and output device 108 for communication functions. Those skilled in the art can understand that the structure shown in FIG. 1 is only for illustration, and it does not limit the structure of the above-mentioned mobile robot. For example, the mobile robot may also include more or fewer components than those shown in FIG. 1 , or have a different configuration that is functionally equivalent to or more functionally than that shown in FIG. 1 .

The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the method for saving the semantic map of the mobile robot in the embodiment of the present disclosure, and the processor 102 runs the computer program stored in the memory 104. program, so as to execute various functional applications and data processing, that is, to realize the above-mentioned method. The memory 104 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, the memory 104 may further include memory located remotely from the processor 102, and these remote memories may be connected to the mobile robot through a network. Examples of the aforementioned networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

The transmission device 106 is used to receive or transmit data via a network. A specific example of the above-mentioned network may include a wireless network provided by a mobile robot's communication provider. In one example, the transmission device 106 includes a network interface controller (NIC for short), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (Radio Frequency, referred to as RF) module, which is used to communicate with the Internet in a wireless manner.

In this embodiment, a method for saving a semantic map is provided, which is applied to a mobile robot. FIG. 2 is a flow chart (1) of a method for saving a semantic map according to an embodiment of the present disclosure. The process includes the following steps:

Step S202: Obtain the first semantic map constructed by the mobile robot after executing the current target event;

Step S204: If there is a second semantic map, calculate the similarity score between the second semantic map and the first semantic map, wherein the second semantic map is a historical semantic map saved by the mobile robot ;

It should be noted that the target event can be understood as: cleaning work and returning to the target area, the second semantic map is the semantic map constructed when the mobile robot completed the cleaning work last time and returned to the target area; the first semantic map is the mobile robot The semantic map constructed when the cleaning work is completed and returned to the target area, wherein the semantic map is a map including the type information of the object, the coordinate information of the object, and the semantic weight information of the object.

Step S206: When the similarity score is greater than a first preset threshold, update the second semantic map stored in the mobile robot to the first semantic map.

Through the embodiment of the present disclosure, the first semantic map constructed by the mobile robot after executing the current target event is obtained; if there is a second semantic map, the similarity between the second semantic map and the first semantic map is calculated score, wherein the second semantic map is the historical semantic map saved by the mobile robot; when the similarity score is greater than the first preset threshold, the second semantic map saved in the mobile robot The map is updated to the first semantic map, that is, by comparing the second semantic map and the size relationship between the similarity score of the first semantic map constructed by the current target event and the preset threshold, when the similarity score is greater than In the case of the first preset threshold, it is determined that the first semantic map is the correct map, and then the first semantic map is saved. Through the above technical solution, the problem in the related art that the semantic map saved by the mobile robot may be a wrong map is solved. .

In an exemplary embodiment, obtaining the second semantic map stored by the mobile robot includes: constructing the second semantic map during the execution of the last target event by the mobile robot, wherein the second The semantic map includes: the type information of the object, the coordinate information of the object, and the semantic weight information of the object; after the execution of the last target event, obtain the second semantic map constructed by the mobile robot, and/or obtain the mobile robot After the robot executes the first semantic map constructed by the current target event, it includes: constructing the first semantic map during the execution of the current target event by the mobile robot, wherein the first semantic map includes: The type information of the object, the coordinate information of the object, and the semantic weight information of the object; after the execution of the current target event, the first semantic map constructed by the mobile robot is obtained.

That is to say, the second semantic map is constructed during the execution of the last target event by the mobile robot, and the first semantic map is constructed during the execution of the current target event by the mobile robot, wherein, Both the second semantic map and the first semantic map include: the type information of the object, the coordinate information of the object, and the semantic weight information of the object. After the mobile robot executes the target event, the second semantic map and the first semantic map are obtained, wherein , the semantic weight information of the object is used to indicate the weight in the process of matching the second semantic map with the first semantic map. The semantic weight information of the object can be preset by the user or dynamically given by the mobile robot according to the actual situation. It should be noted that this disclosure is not limited in the embodiments of the present disclosure.

Further, the specific implementation of building the second semantic map is as follows: during the execution of the last target event, the mobile robot monitors the target objects within the visual range of the mobile robot in real time, and acquires the first target within the visual range The first target image of the object, input the first target image of the first target object acquired by the mobile robot into the artificial intelligence detection model, and obtain the first image information corresponding to the first target image, wherein the first image The information includes: the first category information of the first target object, the first semantic weight information of the first target object; and according to the relative positional relationship between the mobile robot and the first target object and The coordinate information determines the first coordinate information of the first target object; saves the first type information, the first semantic weight information, and the first coordinate information to the second semantic map, and traverses the travel area of the mobile robot , to obtain the second semantic map corresponding to all traveling areas.

Further, the specific implementation of building the first semantic map is as follows: during the process of executing the current target event, the mobile robot monitors the target objects within the visible range of the mobile robot in real time, and acquires the information of the second target object within the visible range. The second target image, inputting the second target image of the second target object acquired by the mobile robot into the artificial intelligence detection model to obtain the second image information corresponding to the second target image, wherein the second image information includes : the second category information of the second target object, the second semantic weight information of the second target object; and according to the relative positional relationship between the mobile robot and the second target object and the coordinate information of the mobile robot in the traveling area Determining the second coordinate information of the second target object; saving the second type information, the second semantic weight information, and the second coordinate information to the first semantic map, traversing the travel area of the mobile robot, and The first semantic map corresponding to all traveling areas is obtained.

In an exemplary embodiment, the second semantic map is matched with the first semantic map to obtain a similarity score between the second semantic map and the first semantic map, and the specific method is as follows: traversing The second semantic map and the first semantic map are used to obtain multiple target similarity scores corresponding to multiple identical coordinate information in the second semantic map and the first semantic map; through the multiple target similarity scores to obtain the similarity scores between the second semantic map and the first semantic map.

It can be understood as determining the same coordinate information of the second semantic map and the first semantic map, and determining whether the type of the target object under the same coordinate information is the same, and determining the corresponding target similarity score, the obtained multiple The target similarity scores are added to determine the similarity score.

In an exemplary embodiment, for any coordinate information among the plurality of identical coordinate information, the first category information corresponding to the any coordinate information in the second semantic map is acquired, and the any A coordinate information corresponding to the second category information in the first semantic map; if the first category information is the same as the second category information, based on the first semantic weight corresponding to the first category information , or the second semantic weight corresponding to the second category information determines the target similarity score of any coordinate information; if the first category information is different from the second category information, determine the The target similarity score of any coordinate information is less than a second preset threshold.

In other words, compare whether the first type information and the second type information corresponding to the same coordinate information are the same, and if the first type information and the second type information are the same, according to the first semantic weight corresponding to the first type information Or the second semantic weight corresponding to the second type of information is used as the target similarity score corresponding to the same coordinate information. When the first type of information is different from the second type of information, the target similarity score may be zero, It can also be set as a negative value according to the actual situation, where there is corresponding first type information in the second semantic map, and if there is no corresponding second type information in the first semantic map, determine the first The category information is different from the second category information.

Further, the corresponding relationship between semantic weights and target similarity scores may also be preset, and if the first category information and the second category information are the same, the corresponding target similarity scores are determined according to the semantic weights.

For example, the correspondence between the semantic weight and the target similarity score is preset to be 1:10, and for any coordinate information in the plurality of identical coordinate information, obtain the coordinate information in the second semantic The first type of information corresponding to the map, and the second type of information corresponding to any coordinate information in the first semantic map; when the first type of information is the same as the second type of information, Determine that the semantic weight corresponding to the first type of information is 0.2, that is, the target similarity score of multiple identical coordinate information is 2; when the corresponding relationship between the semantic weight and the target similarity score is set to 1:1 in advance, determine the second The semantic weight corresponding to one type of information is 0.2, and the target similarity score corresponding to the same coordinate information is 0.2. It should be noted that the above values are only for better understanding of this embodiment, and are not limited in this embodiment of the present disclosure.

In an exemplary embodiment, after matching the second semantic map with the first semantic map to obtain a similarity score between the second semantic map and the first semantic map, after the similarity If the degree score is less than the first preset threshold, the second semantic map is saved in the mobile robot, and the first semantic map is prohibited from being saved in the mobile robot.

Match the second semantic map with the first semantic map to obtain the similarity score of the two semantic maps. If the similarity score is less than the first preset threshold, it is considered that the first semantic map has overlapped, and the first semantic map is not saved. Map operation, recovering the second semantic map, and continuing to save the second semantic map, wherein an overlay is used to indicate that an error occurred during the construction of the first semantic map.

In an exemplary embodiment, if the second semantic map does not exist, the first semantic map is saved.

Before matching the second semantic map with the first semantic map, determine whether the second semantic map is saved in the mobile robot, and the second semantic map is not saved in the mobile robot, that is, the second semantic map is not obtained. In the case of a semantic map, the first semantic map is directly saved.

In order to better understand the storage method of the above-mentioned semantic map, the above-mentioned technical solutions are explained below in conjunction with optional embodiments, but are not intended to limit the technical solutions of the embodiments of the present disclosure.

Fig. 3 is a flow chart (2) of a method for saving a semantic map according to an embodiment of the present disclosure, as shown in Fig. 3 , the specific steps are as follows:

Step S301: start;

Step S302: The mobile robot starts cleaning and AI detects objects;

It should be noted that AI recognizes the object to obtain the type information of the object, the coordinate information of the object, and the semantic weight information of the object;

Step S303: Map the object to the first semantic map through coordinate transformation;

Specifically, the camera is used to monitor objects within the visible range of the machine in real time, and when a target object is detected, the first position is determined according to the relative positional relationship between the mobile robot and the target object and the coordinate information of the mobile robot in the travel area. First coordinate information of a target object, and map the coordinate information of the target object to the first semantic map, wherein the first semantic map includes: the type of the target object, the coordinate information of the target object and the semantic weight of the target object, this The semantic weight represents the weight of the comparison between the second semantic map and the first semantic map. The semantic weight can be set in advance, and different objects correspond to different weights. For example, objects that are not easy to move such as beds, TV cabinets, and sofas have high weights; Objects that are not easy to move in a short period of time, such as tables, chairs, and floor fans, are given lower weights, while dynamic objects such as people and shoes are not given weights.

Step S304: detecting an instruction to save the first semantic map;

When the mobile robot finishes cleaning and returns to the charging pile, it receives an instruction to save the first semantic map, and executes the operation of saving the semantic map according to the instruction.

Step S305: Determine whether the second semantic map is saved in the mobile robot, if the second semantic map is saved in the mobile robot, execute step S307, and if the second semantic map is not saved in the mobile robot, execute step S306;

Step S306: saving the first semantic map and the corresponding environment map;

Step S307: Determine the size relationship between the similarity score between the second semantic map and the first semantic map and a preset threshold (equivalent to the first preset threshold in the above embodiment);

Step S308: if the size relationship indicates that the similarity score is greater than a preset threshold, save the first semantic map and the corresponding environment map;

Step S309: if the size relationship indicates that the similarity score is smaller than a preset threshold, save the second semantic map and the corresponding environment map.

In the above embodiment, the semantic information within the visible range of the mobile robot is obtained, and different types of objects detected by AI are used to assign different weights to the semantic information on the semantic map, that is, different semantic information has different semantic weights; Compare the two semantic maps to get the similarity scores of the two semantic maps. When the similarity score is greater than the preset threshold, it is confirmed that the updated map does not overlap and can be saved normally. Specifically, detect whether the second semantic map is saved in the mobile robot, if the second semantic map is not saved, then save the first semantic map and its corresponding environment map; if save the second semantic map, then compare the second semantic map and The first semantic map, get the similarity score of the two semantic maps, if the similarity score is greater than the preset threshold, it is considered that there is no overlapping map, then save the first semantic map and the corresponding environment map, if the similarity score is less than the preset threshold, it is considered that the first semantic map constructed by the mobile robot has overlapped, the operation of saving the map is not performed, and the second semantic map is restored. Through this embodiment, the map saved by the mobile robot may be a wrong map, and then the mobile robot behaves abnormally when the map is wrong, which improves the robustness of the mobile robot for a long time, and avoids the problem of the mobile robot. The impact of building and overlaying maps.

Through the description of the above embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by means of software plus a necessary general-purpose hardware platform, and of course also by hardware, but in many cases the former is better implementation. Based on such an understanding, the technical solution of the present disclosure can be embodied in the form of a software product in essence or the part that contributes to the prior art, and the computer software product is stored in a storage medium (such as ROM/RAM, disk, CD) contains several instructions to enable a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in various embodiments of the present disclosure.

In this embodiment, a storage device for a semantic map is also provided, and the device is used to implement the above embodiments and preferred implementation modes, and what has been explained will not be repeated. As used below, the term "module" may be a combination of software and/or hardware that realizes a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementations in hardware, or a combination of software and hardware are also possible and contemplated.

Fig. 4 is a structural block diagram of a storage device for a semantic map according to an embodiment of the present disclosure, as shown in Fig. 4 , including:

An acquisition module 42, configured to acquire the first semantic map constructed by the mobile robot after executing the current target event;

A calculation module 44, configured to calculate a similarity score between the second semantic map and the first semantic map if there is a second semantic map, wherein the second semantic map is saved by the mobile robot historical semantic map;

A saving module 46, configured to update the second semantic map saved in the mobile robot to the first semantic map when the similarity score is greater than a first preset threshold.

In an exemplary embodiment, the above apparatus further includes: a construction module, configured to construct the second semantic map during the execution of the last target event by the mobile robot, wherein the second semantic map Including: the type information of the object, the coordinate information of the object, and the semantic weight information of the object; after the execution of the last target event, obtain the second semantic map constructed by the mobile robot, and/or obtain the mobile robot at Executing the first semantic map constructed by the current target event includes: constructing the first semantic map during the execution of the current target event by the mobile robot, wherein the first semantic map includes: category information, object coordinate information, and object semantic weight information; after executing the current target event, obtain the first semantic map constructed by the mobile robot.

In an exemplary embodiment, the matching module is further configured to traverse the second semantic map and the first semantic map to obtain multiple identical coordinates in the second semantic map and the first semantic map A plurality of target similarity scores corresponding to the information; the similarity scores between the second semantic map and the first semantic map are obtained through the multiple target similarity scores.

In an exemplary embodiment, the matching module is further configured to, for any coordinate information in the plurality of identical coordinate information, obtain the first category corresponding to the any coordinate information in the second semantic map information, and the second category information corresponding to any coordinate information in the first semantic map; if the first category information and the second category information are the same, based on the first category information The corresponding first semantic weight, or the second semantic weight corresponding to the second category information determines the target similarity score of any coordinate information;

In an exemplary embodiment, the matching module is further configured to determine that the target similarity score of any coordinate information is smaller than the second preset if the first category information is different from the second category information. Set the threshold.

In an exemplary embodiment, the saving module is further configured to match the second semantic map with the first semantic map to obtain a similarity score between the second semantic map and the first semantic map Afterwards, if the similarity score is less than a first preset threshold, the second semantic map is saved in the mobile robot, and the first semantic map is prohibited from being saved in the mobile robot.

In an exemplary embodiment, the saving module is further configured to, before matching the second semantic map with the first semantic map, if the second semantic map is not obtained, directly The first semantic map is saved in the mobile robot.

An embodiment of the present disclosure also provides a storage medium, the storage medium includes a stored program, wherein the above-mentioned program executes any one of the above-mentioned methods when running.

Optionally, in this embodiment, the above-mentioned storage medium may be configured to store program codes for performing the following steps:

S1, acquiring the first semantic map constructed by the mobile robot after executing the current target event;

S2. If there is a second semantic map, calculate a similarity score between the second semantic map and the first semantic map, where the second semantic map is a historical semantic map saved by the mobile robot;

S3. In a case where the similarity score is greater than a first preset threshold, update the second semantic map stored in the mobile robot to the first semantic map.

Embodiments of the present disclosure also provide an electronic device, including a memory and a processor, where a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

Optionally, the above-mentioned electronic device may further include a transmission device and an input-output device, wherein the transmission device is connected to the above-mentioned processor, and the input-output device is connected to the above-mentioned processor.

Optionally, in this embodiment, the above-mentioned processor may be configured to execute the following steps through a computer program:

S3. When the similarity score is greater than a first preset threshold, update the second semantic map stored in the mobile robot to the first semantic map.

Optionally, in this embodiment, the above-mentioned storage medium may include but not limited to: U disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), Various media that can store program codes such as removable hard disks, magnetic disks, or optical disks.

Optionally, for specific examples in this embodiment, reference may be made to the examples described in the foregoing embodiments and optional implementation manners, and details are not repeated in this embodiment.

Obviously, those skilled in the art should understand that each module or each step of the above-mentioned disclosure can be realized by a general-purpose computing device, and they can be concentrated on a single computing device, or distributed in a network composed of multiple computing devices Alternatively, they may be implemented in program code executable by a computing device so that they may be stored in a storage device to be executed by a computing device, and in some cases in an order different from that shown here The steps shown or described are carried out, or they are separately fabricated into individual integrated circuit modules, or multiple modules or steps among them are fabricated into a single integrated circuit module for implementation. As such, the present disclosure is not limited to any specific combination of hardware and software.

The above descriptions are only preferred embodiments of the present disclosure, and are not intended to limit the present disclosure. For those skilled in the art, the present disclosure may have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

A method for preserving a semantic map, characterized in that: comprising:

Obtain the first semantic map constructed by the mobile robot after executing the current target event;

If there is a second semantic map, calculate the similarity score between the second semantic map and the first semantic map, where the second semantic map is a historical semantic map saved by the mobile robot;

If the similarity score is greater than a first preset threshold, the second semantic map stored in the mobile robot is updated to the first semantic map.
The method for preserving a semantic map according to claim 1, wherein, if there is a second semantic map, before calculating the similarity score between the second semantic map and the first semantic map, the method Also includes:

During the process of the mobile robot executing the last target event of the current target event, the second semantic map is constructed, wherein the second semantic map includes: object type information, object coordinate information, object Semantic weight information;

After executing the last target event, obtain the second semantic map constructed by the mobile robot, and/or

Obtaining the first semantic map constructed by the mobile robot after executing the current target event, including:

During the execution of the current target event by the mobile robot, the first semantic map is constructed, wherein the first semantic map includes: object type information, object coordinate information, and object semantic weight information;

After the execution of the current target event, the first semantic map constructed by the mobile robot is obtained.
The method for preserving a semantic map according to claim 2, wherein, if there is a second semantic map, calculating the similarity score between the second semantic map and the first semantic map includes:

Traversing the second semantic map and the first semantic map to obtain a plurality of target similarity scores corresponding to a plurality of identical coordinate information in the second semantic map and the first semantic map;

A similarity score between the second semantic map and the first semantic map is obtained through the plurality of target similarity scores.
The method for saving a semantic map according to claim 3, characterized in that: traversing the second semantic map and the first semantic map to obtain a plurality of the second semantic map and the first semantic map Multiple target similarity scores corresponding to the same coordinate information, including:

For any coordinate information in the plurality of identical coordinate information, obtain the first category information corresponding to the any coordinate information in the second semantic map, and the any coordinate information in the first The second type of information corresponding to the semantic map;

If the first category information is the same as the second category information, determine the any A target similarity score for coordinate information.
The method for saving a semantic map according to claim 4, characterized in that: obtaining the first category information corresponding to the any coordinate information in the second semantic map, and the any coordinate information in the second semantic map After the corresponding second category information in a semantic map, the method also includes:

If the first category information is different from the second category information, it is determined that the target similarity score of any coordinate information is smaller than a second preset threshold.
The preservation method of the semantic map according to claim 1, wherein the method further comprises:

If the similarity score is less than a first preset threshold, the second semantic map is saved in the mobile robot, and the first semantic map is prohibited from being saved in the mobile robot.
The preservation method of the semantic map according to claim 1, wherein the method further comprises:

If the second semantic map does not exist, save the first semantic map.
A storage device for a semantic map, characterized in that the device includes:

An acquisition module, configured to acquire the first semantic map constructed by the mobile robot after executing the current target event;

A calculation module, configured to calculate a similarity score between the second semantic map and the first semantic map if there is a second semantic map, wherein the second semantic map is a history saved by the mobile robot semantic map;

A saving module, configured to update the second semantic map saved in the mobile robot to the first semantic map when the similarity score is greater than a first preset threshold.
A computer-readable storage medium, characterized in that the computer-readable storage medium includes a stored program, wherein the program executes the method described in any one of claims 1 to 7 when running.
An electronic device, comprising a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to execute the computer program described in any one of claims 1 to 7 through the computer program. Methods.