CN112632211A - Semantic information processing method and equipment for mobile robot - Google Patents
Semantic information processing method and equipment for mobile robot Download PDFInfo
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- 230000010365 information processing Effects 0.000 title claims abstract description 16
- 238000003672 processing method Methods 0.000 title claims description 10
- 238000000034 method Methods 0.000 claims abstract description 44
- 230000015654 memory Effects 0.000 claims description 17
- 238000004590 computer program Methods 0.000 claims description 14
- 238000013507 mapping Methods 0.000 claims description 9
- 238000012217 deletion Methods 0.000 claims description 6
- 230000037430 deletion Effects 0.000 claims description 6
- 230000009191 jumping Effects 0.000 claims description 6
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- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/29—Geographical information databases
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
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- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2455—Query execution
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
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- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
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Abstract
The application aims to provide a semantic information processing scheme for a mobile robot. According to the scheme, a user request is received and analyzed through a system of a mobile robot, semantic information corresponding to the user request is obtained, corresponding semantic information adding/inquiring/deleting operations are executed based on the semantic information according to the type of the user request, and then the semantic information and map data are correlated and adjusted. Compared with the prior art, the method and the device have the advantages that semantic information can be marked in the process of sensing the environment by the mobile robot, the method and the device are more convenient, and mismatching cannot occur. The method and the device can optimize the flow of adding the semantic information, reduce the workload of field operation and maintenance personnel, and enhance the accuracy of the semantic information.
Description
Technical Field
The application relates to the technical field of information, in particular to a semantic information processing technology for a mobile robot.
Background
The map is the basis for the mobile robot to locate and navigate. In practical application scenarios, there is a need to add additional semantic information to the map. At present, the process of adding semantic information into a map is as follows: firstly, the robot senses the whole environment to obtain a scene map, and then semantic information is marked on the map. However, the flow is redundant, and the semantic information can be added after the whole map is obtained. The whole map can be adjusted in the process of sensing the environment by the mobile robot. For example, when the mobile robot finds that the map loops back, the whole map is optimized, and the correctness of the map is ensured. Thus, the location of the semantic information added in the process of perceiving the environment may be mismatched with the map, resulting in inaccurate semantic information location.
Disclosure of Invention
An object of the application is to provide a semantic information processing method and device for a mobile robot.
According to an aspect of the present application, there is provided a semantic information processing method for a mobile robot, wherein the method includes:
receiving and analyzing a user request, and acquiring semantic information corresponding to the user request;
executing corresponding operation based on the semantic information according to the type of the user request;
and associating and adjusting the semantic information and the map data.
According to another aspect of the present application, there is also provided a semantic information processing apparatus for a mobile robot, wherein the apparatus includes:
the user request receiving module is used for receiving and analyzing a user request and obtaining semantic information corresponding to the user request;
the semantic information execution module is used for executing corresponding operation based on the semantic information according to the type of the user request;
and the association and adjustment module is used for associating and adjusting the semantic information and the map data.
According to yet another aspect of the application, there is also provided a computing device, wherein the device comprises a memory for storing computer program instructions and a processor for executing the computer program instructions, wherein the computer program instructions, when executed by the processor, trigger the device to perform the semantic information processing method for a mobile robot.
According to yet another aspect of the present application, there is also provided a computer readable medium having stored thereon computer program instructions executable by a processor to implement the semantic information processing method for a mobile robot.
According to the scheme provided by the application, a user request is received and analyzed through a system of the mobile robot, semantic information corresponding to the user request is obtained, then corresponding semantic information adding/inquiring/deleting operations are executed based on the semantic information according to the type of the user request, and then the semantic information and map data are correlated and adjusted. Compared with the prior art, the method and the device have the advantages that semantic information can be marked in the process of sensing the environment by the mobile robot, the method and the device are more convenient, and mismatching cannot occur. The method and the device can optimize the flow of adding the semantic information, reduce the workload of field operation and maintenance personnel, and enhance the accuracy of the semantic information.
Drawings
Other features, objects and advantages of the present application will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, made with reference to the accompanying drawings in which:
fig. 1 is a flowchart of a semantic information processing method for a mobile robot according to an embodiment of the present application;
FIG. 2 is a flow chart of adding semantic information according to an embodiment of the present application;
FIG. 3 is a flow chart of querying semantic information according to an embodiment of the present application;
FIG. 4 is a flow diagram of deleting semantic information according to an embodiment of the present application;
FIG. 5 is a flow chart of associating and adjusting semantic information and map data according to an embodiment of the present application;
fig. 6 is a schematic diagram of a semantic information processing device for a mobile robot according to an embodiment of the application.
The same or similar reference numbers in the drawings identify the same or similar elements.
Detailed Description
The present application is described in further detail below with reference to the attached figures.
In a typical configuration of the present application, the terminal, the device serving the network, and the trusted party each include one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, which include both non-transitory and non-transitory, removable and non-removable media, may implement the information storage by any method or technology. The information may be computer readable instructions, data structures, program means, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device.
The embodiment of the application provides a semantic information processing method for a mobile robot, which can mark semantic information in the process of sensing the environment by the mobile robot, is more convenient and quick and does not generate mismatching. The embodiment of the application overcomes the defect of redundant map semantic information adding process existing in the prior art, optimizes the semantic information adding process, reduces the workload of field operation and maintenance personnel, and enhances the accuracy of the semantic information.
In a practical scenario, the device performing the method may be a user equipment, a network device, or a device formed by integrating the user equipment and the network device through a network. The user equipment includes, but is not limited to, a terminal device such as a mobile robot, a smart phone, a tablet computer, a Personal Computer (PC), and the like, and the network device includes, but is not limited to, a network host, a single network server, multiple network server sets, or a computer set based on cloud computing. Here, the Cloud is made up of a large number of hosts or web servers based on Cloud Computing (Cloud Computing), which is a type of distributed Computing, one virtual computer consisting of a collection of loosely coupled computers.
Fig. 1 is a flowchart of a semantic information processing method for a mobile robot according to an embodiment of the present application, and the method includes step S101, step S102, and step S103.
Step S101, receiving and analyzing a user request, and obtaining semantic information corresponding to the user request.
For example, the user request is received by a system of mobile robots (mobile agents), and the semantic information M is obtained by the system parsing the user request.
In some embodiments, the type of user request comprises at least any one of: adding corresponding semantic information; inquiring corresponding semantic information; and deleting the corresponding semantic information.
And step S102, executing corresponding operation based on the semantic information according to the type of the user request.
In some embodiments, as shown in fig. 2, the step S102 includes: if the type of the user request comprises adding corresponding semantic information, firstly judging whether the mobile robot is in the process of sensing the environment. If the mobile robot is in the process of sensing the environment, marking the semantic information M as a to-be-associated state, adding the semantic information to a to-be-associated device, and waiting for subsequent processing; otherwise, the semantic information and the map data are not associated. Then, trying to add the semantic information to a semantic database, and judging whether the semantic information is repeated; if the semantic information is not repeated, the semantic information is successfully added to the semantic database; if the semantic information is repeated, the adding fails.
In some embodiments, as shown in fig. 3, the step S102 includes: if the type of the user request comprises semantic information corresponding to query, trying to query the semantic information M from a semantic database. If the semantic information M exists in the semantic database, inquiring the mapping relation between the semantic information M and a map, recalculating the position of the semantic information, and returning to the new position of the semantic information; and if the semantic information M does not exist in the semantic database, returning a result of query failure.
In some embodiments, as shown in fig. 4, the step S102 includes: and if the type of the user request comprises deleting corresponding semantic information, firstly inquiring the semantic information M from a semantic database. If the semantic information M exists in the semantic database, deleting the semantic information M, deleting the mapping relation between the semantic information M and the map, and returning a result of successful deletion (for example, a user can be notified that the semantic information M is deleted through a UI); and if the semantic information does not exist in the semantic database, returning a deletion failure result.
And step S103, associating and adjusting the semantic information and the map data.
In some embodiments, as shown in fig. 5, the step S103 includes: if the mobile robot detects that a loop occurs in the process of sensing the environment, calculating a new position of a map key frame, and updating semantic information stored in a device to be associated; traversing the semantic information in the device to be associated, if the semantic information in the device to be associated is empty, ending the operation, and exiting the step S103; and if the semantic information in the device to be associated is not null, setting the semantic information index to be 0.
In some embodiments, as shown in fig. 5, the step S103 further includes: and (c) if the semantic information in the device to be associated is not empty, setting the semantic information index as 0, and skipping to the step (a). Step (a), trying to take out a semantic information from the device to be associated according to the semantic information index, if no corresponding semantic information index exists in the device to be associated, ending the operation, and exiting the step S103; otherwise, jumping to step (b). Step (b), extracting corresponding semantic information M in the device to be associated according to the semantic information index, and inquiring a key frame F which is closest to the semantic information M in a map; if the key frame F meeting the condition exists in the map, skipping to the step (c); otherwise, adding semantic information index and jumping to the step (a). And (c) associating the semantic information M with the key frame F, calculating the mapping relation between the semantic information M and the key frame F, removing the semantic information M from the device to be associated, and skipping to the step (a).
Fig. 6 is a schematic diagram of a semantic information processing apparatus for a mobile robot according to an embodiment of the present application, which includes a user request receiving module 601, a semantic information executing module 602, and an associating and adjusting module 603.
The user request receiving module 601 receives and analyzes a user request to obtain semantic information corresponding to the user request.
For example, the user request is received by a system of mobile robots (mobile agents), and the semantic information M is obtained by the system parsing the user request.
In some embodiments, the type of user request comprises at least any one of: adding corresponding semantic information; inquiring corresponding semantic information; and deleting the corresponding semantic information.
And a semantic information executing module 602, configured to execute a corresponding operation based on the semantic information according to the type of the user request.
In some embodiments, as shown in fig. 2, the semantic information execution module 602 is configured to: if the type of the user request comprises adding corresponding semantic information, firstly judging whether the mobile robot is in the process of sensing the environment. If the mobile robot is in the process of sensing the environment, marking the semantic information M as a to-be-associated state, adding the semantic information to a to-be-associated device, and waiting for subsequent processing; otherwise, the semantic information and the map data are not associated. Then, trying to add the semantic information to a semantic database, and judging whether the semantic information is repeated; if the semantic information is not repeated, the semantic information is successfully added to the semantic database; if the semantic information is repeated, the adding fails.
In some embodiments, as shown in fig. 3, the semantic information execution module 602 is configured to: if the type of the user request comprises semantic information corresponding to query, trying to query the semantic information M from a semantic database. If the semantic information M exists in the semantic database, inquiring the mapping relation between the semantic information M and a map, recalculating the position of the semantic information, and returning to the new position of the semantic information; and if the semantic information M does not exist in the semantic database, returning a result of query failure.
In some embodiments, as shown in fig. 4, the semantic information execution module 602 is configured to: and if the type of the user request comprises deleting corresponding semantic information, firstly inquiring the semantic information M from a semantic database. If the semantic information M exists in the semantic database, deleting the semantic information M, deleting the mapping relation between the semantic information M and the map, and returning a result of successful deletion (for example, a user can be notified that the semantic information M is deleted through a UI); and if the semantic information does not exist in the semantic database, returning a deletion failure result.
An association and adjustment module 603 associates and adjusts the semantic information and map data.
In some embodiments, as shown in fig. 5, the association and adjustment module 603 is configured to: if the mobile robot detects that a loop occurs in the process of sensing the environment, calculating a new position of a map key frame, and updating semantic information stored in a device to be associated; traversing the semantic information in the device to be associated, and if the semantic information in the device to be associated is empty, ending the operation; and if the semantic information in the device to be associated is not null, setting the semantic information index to be 0.
In some embodiments, as shown in fig. 5, the association and adjustment module 603 is further configured to: and (c) if the semantic information in the device to be associated is not empty, setting the semantic information index as 0, and skipping to the step (a). Step (a), trying to take out a semantic information from the device to be associated according to the semantic information index, and if the device to be associated does not have a corresponding semantic information index, ending the operation; otherwise, jumping to step (b). Step (b), extracting corresponding semantic information M in the device to be associated according to the semantic information index, and inquiring a key frame F which is closest to the semantic information M in a map; if the key frame F meeting the condition exists in the map, skipping to the step (c); otherwise, adding semantic information index and jumping to the step (a). And (c) associating the semantic information M with the key frame F, calculating the mapping relation between the semantic information M and the key frame F, removing the semantic information M from the device to be associated, and skipping to the step (a).
To sum up, this application embodiment can be at mobile robot perception environment in-process mark semantic information, and is more convenient, and can not appear the mismatch. The embodiment of the application overcomes the defect of redundant map semantic information adding process existing in the prior art, optimizes the semantic information adding process, reduces the workload of field operation and maintenance personnel, and enhances the accuracy of the semantic information.
In addition, some of the present application may be implemented as a computer program product, such as computer program instructions, which when executed by a computer, may invoke or provide methods and/or techniques in accordance with the present application through the operation of the computer. Program instructions which invoke the methods of the present application may be stored on a fixed or removable recording medium and/or transmitted via a data stream on a broadcast or other signal-bearing medium and/or stored within a working memory of a computer device operating in accordance with the program instructions. Herein, some embodiments of the present application provide a computing device comprising a memory for storing computer program instructions and a processor for executing the computer program instructions, wherein the computer program instructions, when executed by the processor, trigger the device to perform the methods and/or aspects of the embodiments of the present application as described above.
Furthermore, some embodiments of the present application also provide a computer readable medium, on which computer program instructions are stored, the computer readable instructions being executable by a processor to implement the methods and/or aspects of the foregoing embodiments of the present application.
It should be noted that the present application may be implemented in software and/or a combination of software and hardware, for example, implemented using Application Specific Integrated Circuits (ASICs), general purpose computers or any other similar hardware devices. In some embodiments, the software programs of the present application may be executed by a processor to implement the steps or functions described above. Likewise, the software programs (including associated data structures) of the present application may be stored in a computer readable recording medium, such as RAM memory, magnetic or optical drive or diskette and the like. Additionally, some of the steps or functions of the present application may be implemented in hardware, for example, as circuitry that cooperates with the processor to perform various steps or functions.
It will be evident to those skilled in the art that the present application is not limited to the details of the foregoing illustrative embodiments, and that the present application may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned. Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or means recited in the apparatus claims may also be implemented by one unit or means in software or hardware. The terms first, second, etc. are used to denote names, but not any particular order.
Claims (10)
1. A semantic information processing method for a mobile robot, wherein the method comprises:
receiving and analyzing a user request, and acquiring semantic information corresponding to the user request;
executing corresponding operation based on the semantic information according to the type of the user request;
and associating and adjusting the semantic information and the map data.
2. The method of claim 1, wherein the type of user request comprises at least any one of:
adding corresponding semantic information;
inquiring corresponding semantic information;
and deleting the corresponding semantic information.
3. The method of claim 2, wherein performing a corresponding operation based on the semantic information according to the type of the user request comprises:
if the type of the user request comprises adding corresponding semantic information, judging whether the mobile robot is in the process of sensing the environment;
if the mobile robot is in the process of sensing the environment, marking the semantic information as a to-be-associated state, and adding the semantic information to a to-be-associated device; otherwise, not associating the semantic information with the map data;
adding the semantic information to a semantic database, and judging whether the semantic information is repeated; if the semantic information is not repeated, the semantic information is successfully added to the semantic database; if the semantic information is repeated, the adding fails.
4. The method of claim 2, wherein performing a corresponding operation based on the semantic information according to the type of the user request comprises:
if the type of the user request comprises semantic information corresponding to query, querying the semantic information from a semantic database;
if the semantic information exists in the semantic database, inquiring the mapping relation between the semantic information and a map, recalculating the position of the semantic information, and returning to the new position of the semantic information;
and if the semantic information does not exist in the semantic database, returning a result of query failure.
5. The method of claim 2, wherein performing a corresponding operation based on the semantic information according to the type of the user request comprises:
if the type of the user request comprises deleting corresponding semantic information, inquiring the semantic information from a semantic database;
if the semantic information exists in the semantic database, deleting the semantic information, deleting the mapping relation between the semantic information and the map, and returning a result of successful deletion;
and if the semantic information does not exist in the semantic database, returning a deletion failure result.
6. The method of any of claims 1-5, wherein associating and adjusting the semantic information and map data comprises:
if the mobile robot detects that a loop occurs in the process of sensing the environment, calculating a new position of a map key frame, and updating semantic information stored in a device to be associated;
traversing the semantic information in the device to be associated, and if the semantic information in the device to be associated is empty, ending the operation;
and if the semantic information in the device to be associated is not null, setting the semantic information index to be 0.
7. The method of claim 6, wherein setting the semantic information index to 0 if the semantic information in the device to be associated is not empty comprises:
if the semantic information in the device to be associated is not empty, setting the semantic information index as 0, and skipping to the step (a);
(a) trying to take out a semantic information from the device to be associated according to the semantic information index, and if the corresponding semantic information index does not exist in the device to be associated, ending the operation; otherwise, jumping to the step (b);
(b) extracting corresponding semantic information M in the device to be associated according to the semantic information index, and inquiring a key frame F which is closest to the semantic information M in a map; if the key frame F meeting the condition exists in the map, skipping to the step (c); otherwise, adding semantic information index, and jumping to the step (a);
(c) and (b) associating the semantic information M with the key frame F, calculating the mapping relation between the semantic information M and the key frame F, removing the semantic information M from the device to be associated, and skipping to the step (a).
8. A semantic information processing apparatus for a mobile robot, wherein the apparatus comprises:
the user request receiving module is used for receiving and analyzing a user request and obtaining semantic information corresponding to the user request;
the semantic information execution module is used for executing corresponding operation based on the semantic information according to the type of the user request;
and the association and adjustment module is used for associating and adjusting the semantic information and the map data.
9. A computing device, wherein the device comprises a memory for storing computer program instructions and a processor for executing the computer program instructions, wherein the computer program instructions, when executed by the processor, trigger the device to perform the method of any of claims 1 to 7.
10. A computer readable medium having stored thereon computer program instructions executable by a processor to implement the method of any one of claims 1 to 7.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| WO2023005379A1 (en) * | 2021-07-27 | 2023-02-02 | 追觅创新科技(苏州)有限公司 | Method and apparatus for saving semantic map, storage medium, and electronic device |
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| CN109872392A (en) * | 2019-02-19 | 2019-06-11 | 北京百度网讯科技有限公司 | Man-machine interaction method and device based on high-precision map |
| CN111242994A (en) * | 2019-12-31 | 2020-06-05 | 深圳优地科技有限公司 | Semantic map construction method and device, robot and storage medium |
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| CN109872392A (en) * | 2019-02-19 | 2019-06-11 | 北京百度网讯科技有限公司 | Man-machine interaction method and device based on high-precision map |
| CN111242994A (en) * | 2019-12-31 | 2020-06-05 | 深圳优地科技有限公司 | Semantic map construction method and device, robot and storage medium |
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| WO2023005379A1 (en) * | 2021-07-27 | 2023-02-02 | 追觅创新科技(苏州)有限公司 | Method and apparatus for saving semantic map, storage medium, and electronic device |
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