A robot positioning method and related apparatus

Abstract

Embodiments of the present application provide a robot positioning method and related equipment, a first confidence higher target map is determined through feature information of a target robot at a current position, a map where the robot is currently located can be determined, current position information of the target robot and a second confidence thereof are determined based on the determined target map through a particle filtering method, whether to acquire historical position information is judged based on the first confidence and the second confidence, and then a target matching position where a distance between the target matching position and a position corresponding to the current position information in the historical position information is the smallest can be determined, in a case where a distance between the target matching position and the position corresponding to the current position information is greater than a preset threshold, it can be considered that the target robot is currently at a new position, and the current position information is stored, so that the accuracy, objectivity and practicality of the robot positioning information can be improved.

Description

Technical Field

[0001] The present invention relates to the field of robot control technology, and in particular to a robot positioning method and related equipment. Background Art

[0002] With the advancement of robotic manufacturing technology and the expansion of its application areas, the demand for robot intelligence is increasing. Currently, most robots use AMCL (adaptive Monte Carlo Localization) technology for positioning. This technology uses a particle filter to estimate the robot's position and, based on the evaluation results and known map information, determines the robot's current location. This technology is suitable for local positioning problems on large-scale maps.

[0003] However, current robot positioning methods cannot automatically determine the map the robot is currently in, especially when the robot undergoes a large-scale displacement. The map conversion cannot be detected in time, resulting in the inability to synchronously update the map information for matching positioning. The robot continues to position based on the wrong map, which in turn leads to reduced accuracy of positioning information and difficulty in timely locating the time of fault occurrence, further reducing the practicality of the data. Summary of the Invention

[0004] The present invention provides a robot positioning method and related equipment to solve the problem that the current robot positioning method uses known maps for matching positioning, cannot automatically determine the map the robot is currently in, and cannot detect map conversion, which leads to reduced accuracy of the robot's positioning information, difficulty in timely locating the time of fault occurrence, and further reduced practicality of the data.

[0005] In a first aspect, the present invention provides a robot positioning method, comprising:

[0006] Obtain feature information of the target robot at its current location;

[0007] Acquire a target map and a first confidence level corresponding thereto based on the feature information, wherein the first confidence level is determined based on a degree of matching between the feature information in the candidate map and the feature information at the current location, and the target map is a candidate map having a higher first confidence level;

[0008] Obtaining current position information and a second confidence level corresponding thereto, wherein the current position information is determined based on a particle filtering method, and the second confidence level is a confidence level corresponding to the current position information determined using the particle filtering method;

[0009] Determining, based on the first confidence level and the second confidence level, a target matching position having a minimum distance between the historical position information of the target robot and the position corresponding to the current position information;

[0010] In a case where the distance between the target matching position and the position corresponding to the current position information is greater than a preset threshold, the current position information is stored.

[0011] Optionally, the robot positioning method further includes:

[0012] When the distance is less than or equal to the preset threshold, obtaining a target matching sample set of the target matching position;

[0013] When the number of samples in the target matching sample set is less than a first preset number of samples, the current location information is added to the target matching sample set.

[0014] Optionally, the robot positioning method further includes:

[0015] When the number of samples is greater than or equal to the first preset number of samples, determining a first sample set and a second sample set, wherein the first sample set includes a second preset number of target matching samples stored earlier and continuously, and the second sample set includes a third preset number of target matching samples stored later and continuously;

[0016] Obtaining a confidence difference of the second confidence level corresponding to the target matching sample in the first sample set and / or the second sample set;

[0017] When the confidence difference is greater than or equal to a preset confidence difference, the current position information is stopped from being added to the target matching sample set.

[0018] Optionally, the robot positioning method further includes:

[0019] When the confidence difference is less than a preset confidence difference, obtaining a confidence variance of the second confidence corresponding to the target matching sample in the second sample set;

[0020] Determining a credibility level of a second confidence level of the current location information based on the confidence level variance;

[0021] When the confidence level is greater than or equal to the preset confidence threshold, obtaining a matching condition between the first confidence level of the target map and the first confidence level corresponding to the target matching sample in the first sample set;

[0022] If the target map where the target robot is located is not matched to the first confidence level, stop adding the current position information to the target matching sample set.

[0023] Optionally, the robot positioning method further includes:

[0024] When the first confidence level of the target map where the target robot is located is matched, a distribution of characteristic objects at the location of the target is obtained;

[0025] According to the distribution of the characteristic objects, determining whether the target robot is currently in a featureless area;

[0026] When the target robot is in the featureless area, stop adding the current position information to the target matching sample set.

[0027] Optionally, the robot positioning method further includes:

[0028] When the target robot is not in the featureless area, the target map where the target robot is located, the first confidence level, the current position information, and the second confidence level are stored in the target matching sample set of the target matching position in the historical position information of the target robot.

[0029] Optionally, before the step of determining, based on the first confidence level and the second confidence level, a target matching position having the smallest distance between the historical position information of the target robot and the position corresponding to the current position information, the method further includes:

[0030] Obtaining the number of location information in the historical location information;

[0031] When the amount of position information is zero, the target map where the target robot is located, the first confidence level, the current position information, and the second confidence level are stored in the historical position information.

[0032] In a second aspect, the present invention further provides a robot positioning device, characterized in that it includes:

[0033] Feature acquisition module, used to obtain feature information of the target robot at its current location;

[0034] a map matching module, configured to obtain a target map and a first confidence level corresponding thereto based on the feature information, wherein the first confidence level is determined based on a degree of matching between the feature information in the candidate map and the feature information at the current location, the target map being the candidate map having the higher first confidence level;

[0035] a position matching module, configured to obtain current position information and a second confidence level corresponding thereto, wherein the current position information is determined based on a particle filtering method, and the second confidence level is the confidence level corresponding to the current position information determined using the particle filtering method;

[0036] a determination module, configured to determine, based on the first confidence level and the second confidence level, a target matching position having a minimum distance between the position in the historical position information of the target robot and the position corresponding to the current position information;

[0037] The storage module is configured to store the current location information when a distance between the target matching location and a location corresponding to the current location information is greater than a preset threshold.

[0038] In a third aspect, the present invention further provides an electronic device comprising a memory and a processor, wherein the processor is configured to implement the steps of the robot positioning method as described in any one of the first aspects above when executing a computer program stored in the memory.

[0039] In a fourth aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the robot positioning method as described in any one of the first aspects above.

[0040] As can be seen from the above technical solutions, the embodiment of the present application provides a robot positioning method and related equipment, including: obtaining feature information of a target robot at its current location; obtaining a target map and its corresponding first confidence level based on the feature information, wherein the first confidence level is determined based on the matching degree between the feature information in a candidate map and the feature information at the current location, and the target map is a candidate map with a higher first confidence level; obtaining current location information and its corresponding second confidence level, wherein the current location information is determined based on a particle filter method, and the second confidence level is the confidence level corresponding to the current location information determined using the particle filter method; determining a target matching position in the target robot's historical location information with the minimum distance from the position corresponding to the current location information based on the first confidence level and the second confidence level; and storing the current location information when the distance between the target matching position and the position corresponding to the current location information is greater than a preset threshold. Current robot positioning methods cannot automatically determine the map the robot is currently in and cannot detect map changes, which results in reduced accuracy of the robot's positioning information and makes it difficult to locate the time of occurrence of a fault in a timely manner, further reducing the practicality of the data. The embodiment of the present application determines a target map with a high first confidence level through the feature information of the target robot at the current position, and can determine the map where the robot is currently located. The current position information of the target robot and its second confidence level are determined based on the determined target map through the particle filtering method. Based on the above-mentioned first confidence level and second confidence level, it is determined whether it is necessary to obtain historical position information, and then the target matching position with the minimum distance between the position corresponding to the current position information in the historical position information can be determined. When the distance between the target matching position and the position corresponding to the current position information is greater than a preset threshold, it can be considered that the target robot is currently in a new position, and the current position information is stored, thereby improving the accuracy, objectivity and practicality of the robot positioning information. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solution of the present application, the following is a brief introduction to the drawings required for use in the embodiments. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0042] Figure 1 A schematic flow chart of a robot positioning method provided in an embodiment of the present application;

[0043] Figure 2 A schematic structural diagram of a robot positioning device provided in an embodiment of the present application;

[0044] Figure 3A schematic structural diagram of an electronic device provided in an embodiment of the present application;

[0045] Figure 4 A schematic structural diagram of a computer-readable storage medium provided in an embodiment of the present application. DETAILED DESCRIPTION

[0046] The embodiments will be described in detail below, and examples thereof are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementation methods described in the following embodiments do not represent all implementation methods consistent with the present application. They are only examples of systems and methods consistent with some aspects of the present application as detailed in the claims. In the several embodiments provided in the embodiments of the present application, it should be understood that the disclosed devices and methods can also be implemented in other ways, and the device embodiments described below are merely exemplary.

[0047] like Figure 1 As shown, the embodiment of the present application provides a method for robot positioning, the execution subject of which may be a server and a controller, etc., and the method includes:

[0048] Step S110: Acquire feature information of the target robot at its current location.

[0049] For example, the feature information may include terrain information of the target robot's current location, such as turn information and slope information, or object information of the target robot's current location, such as object shape information and the positional relationship between at least two objects, or a combination of the above. Multiple pieces of feature information may be provided.

[0050] Step S120: obtaining a target map and its corresponding first confidence based on the feature information, wherein the first confidence is determined based on the matching degree between the feature information in the candidate map and the feature information of the current location, and the target map is a candidate map with a higher first confidence.

[0051] Exemplarily, due to the high flexibility of item information, the weight of the first confidence of the terrain information can be set higher than that of the item information, and the item information can be matched when there are at least two candidate maps with equal confidence. The target map can be determined in the candidate map while acquiring the feature information, and the acquisition of the feature information can be stopped when there is only one candidate map. The target map and the first confidence can also be acquired when the feature information is completed. When the target map is not matched or the highest matching degree is lower than the preset matching degree threshold, a first alarm message can be issued and data collection can be stopped to remind the management personnel that the robot may be in an unknown map or the robot information collection unit is faulty. The first alarm message can include the matching status of the terrain information and the matching status of the item information, and the reason for the low matching degree can be determined based on the above two matching conditions.

[0052] Step S130: Obtain current position information and its corresponding second confidence level, wherein the current position information is determined based on a particle filtering method, and the second confidence level is the confidence level corresponding to the current position information determined using the particle filtering method.

[0053] For example, laser data can be acquired by the target robot at its current location, and the robot's current location can be determined based on the laser data and the target map. The current location information and its corresponding second confidence level can be acquired using a particle filter based on an adaptive Monte Carlo localization method. A confidence interval and confidence level for the target robot's current location can be determined based on the distribution of particles in the target map. The current location information and the second confidence level can then be determined based on the confidence interval and confidence level.

[0054] Step S140: Determine, based on the first confidence level and the second confidence level, a target matching position having the smallest distance between the historical position information of the target robot and the position corresponding to the current position information.

[0055] Exemplarily, the target position information of the robot and its corresponding third confidence level can be predicted in combination with the target robot's running trajectory, wherein the third confidence level can be determined by the degree of matching between the feature information and the target feature information at the position corresponding to the target position information and the first confidence level. If the third confidence level is higher than the second confidence level, the historical position information of the target robot can be obtained. Otherwise, it can be assumed that the target robot may have a fault such as slipping, causing the target robot to deviate from the running trajectory, and the target position information and the third confidence level can be ignored. The distance can include Euclidean distance and / or rotation difference. If the first confidence level is greater than a first preset threshold and the second confidence level is greater than a second preset threshold, the historical position information of the target robot is obtained. Otherwise, the current position information is ignored. When the number of times the position is ignored exceeds the number of times the position is ignored threshold, a prompt message is sent to the administrator.

[0056] Step S150: When the distance between the target matching position and the position corresponding to the current position information is greater than a preset threshold, the current position information is stored.

[0057] Exemplarily, the preset threshold may be determined based on the data collection accuracy of the target robot. If the distance between the target matching position and the position corresponding to the current position information is less than or equal to the preset threshold, the current position information of the target robot may be updated to the target matching position.

[0058] By matching feature information acquired by the target robot at its current location with known candidate maps, the robot's current target map and the corresponding first confidence level can be determined. This allows the target robot to automatically determine its current target map, preventing the robot from being unable to synchronize map updates when it moves to another map or a location far from its previous location, which could affect positioning accuracy. This improves the robot's intelligence and convenience. A particle filter method is used to determine the target robot's current position information and its second confidence level based on the determined target map. The need to obtain historical position information is determined based on these first and second confidence levels, preventing situations where the predicted position differs from the actual position due to factors such as slippage during robot movement. When the distance between the target matching position and the location corresponding to the current position information is greater than a preset threshold, the target robot is considered to be in a new location and the current position information is stored. This improves the accuracy, objectivity, and practicality of the robot's positioning information, allowing for timely detection and localization of fault occurrences, further enhancing the practicality of the robot positioning method.

[0059] In a feasible implementation manner, the above robot positioning method further includes:

[0060] When the distance is less than or equal to the preset threshold, obtaining a target matching sample set at the target matching position;

[0061] When the number of samples in the target matching sample set is less than the first preset number of samples, the current location information is added to the target matching sample set.

[0062] Exemplarily, the target matching sample set is all samples of target matching positions in the historical position information of the target robot, including: historical first confidence and historical second confidence. The first preset number of samples may be negatively correlated with the first confidence and the second confidence.

[0063] When the number of samples in the target matching sample set is less than the first preset sample number, it can be considered that the target matching samples in the historical location information are too few and cannot be used as a test standard, and the probability that the current location information is the target matching location is higher. Therefore, the current location information can be added to the target matching sample set of the target matching location, and the sample size of the target matching location can be supplemented, thereby improving the integrity of the test data, facilitating the testing of other samples, and improving the test level and the accuracy and objectivity of the positioning information.

[0064] In a feasible implementation manner, the above robot positioning method further includes:

[0065] When the number of samples is greater than or equal to the first preset number of samples, determining a first sample set and a second sample set, wherein the first sample set includes the second preset number of target matching samples stored earlier and continuously, and the second sample set includes a third preset number of target matching samples stored later and continuously;

[0066] Obtaining a confidence difference of the second confidence level corresponding to the target matching sample in the first sample set and / or the second sample set;

[0067] When the confidence difference is greater than or equal to a preset confidence difference, the current location information is stopped from being added to the target matching sample set.

[0068] Exemplarily, the sum of the second preset number of samples and the third preset number of samples is less than or equal to the first preset number of samples. The confidence difference includes the confidence difference of the second confidence within the first sample set and / or the second sample set. In the case that the confidence difference of the second confidence within the first sample set and / or the second sample set is less than the first preset confidence difference, the confidence average of the second confidence of the first sample set and / or the second sample set can be obtained, and the confidence difference also includes the difference of the confidence averages. In the case that the difference of the confidence averages is greater than the second preset confidence difference, the current location information can be stopped from being added to the target matching sample set.

[0069] When the confidence difference of the second confidence levels within the first sample set and / or the second sample set is greater than or equal to the preset confidence difference, it can be considered that the numerical fluctuations of the continuously acquired second confidence levels in the sample set are large, resulting in a low degree of credibility of the sample data as a test standard. When the numerical values ​​of the second confidence levels of the first sample set and the second sample set are continuous and stable, but the numerical difference of the second confidence levels between the two sample sets is large, it can be considered that the environment in which the target robot is located may have changed during the sampling time of the second sample set, and therefore it is necessary to stop uploading the current position information. Therefore, by verifying the significant differences in the historical position information through the above method, the accuracy and objectivity of the current position information can be improved, and the accuracy and integrity of the historical position information can be protected, avoiding the input of irrelevant information that affects the subsequent test accuracy, thereby improving the accuracy and practicality of the robot positioning method.

[0070] In a feasible implementation manner, the above robot positioning method further includes:

[0071] When the confidence difference is less than a preset confidence difference, obtaining a confidence variance of the second confidence corresponding to the target matching sample in the second sample set;

[0072] Determining a credibility of a second confidence level of the current location information based on the confidence level variance;

[0073] When the credibility is greater than or equal to the preset credibility threshold, obtaining a matching condition between the first confidence of the target map and the first confidence corresponding to the target matching sample in the first sample set;

[0074] If the first confidence level of the target map where the target robot is located is not matched, stop adding the current position information to the target matching sample set.

[0075] By performing a variance test on the second confidence level in the second sample set, the data in the sample set can be fully utilized, so that it can be determined whether the sample difference in the second sample set can be regarded as a sample error, and then the credibility of the sample data can be determined. In the case of a large variance, stopping the addition of data can protect the accuracy and integrity of the historical position information, avoid the input of irrelevant information affecting the subsequent test accuracy, and thus improve the accuracy and practicality of the robot positioning method.

[0076] In a feasible implementation manner, the above robot positioning method further includes:

[0077] When the first confidence level of the target map where the target robot is located is matched, a distribution of characteristic objects at the location of the target robot is obtained;

[0078] According to the distribution of the above-mentioned characteristic objects, determine whether the above-mentioned target robot is currently in a featureless area;

[0079] When the target robot is in the featureless area, stop adding the current position information to the target matching sample set.

[0080] Exemplarily, when the target robot only detects terrain information, and the detected terrain information is featureless information, for example, the target robot detects that it is currently in a corridor and cannot determine at least one of the length and width of the corridor, or can match at least two candidate maps based on the above at least one, it can be considered that the target robot is currently in a featureless area. In the case that the target robot is currently in a featureless area, the target robot can be controlled to move based on the matching results with the candidate maps and the distribution of other feature objects in the matching area in the candidate maps until it can match a position where the feature object information of the target map can be determined. The robot is positioned at the position where the feature object information of the target map can be determined, and the positioning information of the target robot in the featureless area is determined as the above current position information based on the movement trajectory and positioning results, and the above current position information is added to the target matching sample set.

[0081] In a feasible implementation manner, the above robot positioning method further includes:

[0082] When the target robot is not in the featureless area, the target map where the target robot is located, the first confidence level, the current position information and the second confidence level are stored in the target matching sample set of the target matching position in the historical position information of the target robot.

[0083] If the target robot is not in a featureless area, the first confidence level of the target robot's current location can be considered to be highly reliable. If the second confidence level is high, the accuracy of the current location information collected by the target robot can be considered to be highly accurate. Therefore, if the distance between the location corresponding to the current location information and the target matching location is less than a preset distance, the location corresponding to the current location information can be considered to be the target matching location. By updating the current location information to the target matching sample set of the target matching location, the accuracy of the robot's positioning and the data integrity of the historical location information can be improved, thereby improving the practicality of the robot's positioning.

[0084] In a feasible embodiment, before the step of determining the target matching position having the smallest distance between the historical position information of the target robot and the position corresponding to the current position information according to the first confidence level and the second confidence level, the step further includes:

[0085] Obtain the number of location information in the above historical location information;

[0086] When the amount of the position information is zero, the target map where the target robot is located, the first confidence level, the current position information and the second confidence level are stored in the historical position information.

[0087] When the number of position information in the historical position information is zero, it means that the target robot cannot match the target matching position. Therefore, the current position information can be directly uploaded to the historical position information to avoid the inability to perform subsequent data verification, resulting in the inability to create a new position in the historical position information, affecting the data integrity of the robot positioning and the practicality of the positioning method.

[0088] like Figure 2 As shown, Figure 2 A schematic structural diagram of a robot positioning device provided in an embodiment of the present application, the device comprising:

[0089] Feature acquisition module 201, used to obtain feature information of the target robot at its current location;

[0090] a map matching module 202 configured to obtain a target map and a corresponding first confidence level based on the feature information, wherein the first confidence level is determined based on a degree of matching between the feature information in the candidate map and the feature information at the current location, and the target map is a candidate map having a higher first confidence level;

[0091] The position matching module 203 is configured to obtain current position information and a second confidence level corresponding thereto, wherein the current position information is determined based on a particle filtering method, and the second confidence level is the confidence level corresponding to the current position information determined using the particle filtering method;

[0092] A determination module 204 is configured to determine, based on the first confidence level and the second confidence level, a target matching position having a minimum distance between the position corresponding to the current position information and the historical position information of the target robot;

[0093] The storage module 205 is configured to store the current location information when the distance between the target matching location and the location corresponding to the current location information is greater than a preset threshold.

[0094] A robot positioning device 200 can realize Figure 1 To avoid repetition, the various processes implemented in the method embodiment will not be described again here.

[0095] See also Figure 3 , Figure 3 A schematic structural diagram of an electronic device provided in an embodiment of the present application.

[0096] An embodiment of the present application provides an electronic device 300, including a memory 310, a processor 320, and a computer program 311 stored in the memory 310 and executable on the processor 320. When the processor 320 executes the computer program 311, the following steps are implemented:

[0097] Obtain feature information of the target robot at its current location;

[0098] Obtaining a target map and its corresponding first confidence level based on the feature information, wherein the first confidence level is determined based on a degree of matching between the feature information in the candidate map and the feature information at the current location, and the target map is a candidate map having a higher first confidence level;

[0099] Obtaining current position information and a corresponding second confidence level thereof, wherein the current position information is determined based on a particle filtering method, and the second confidence level is the confidence level corresponding to the current position information determined using the particle filtering method;

[0100] Determining, based on the first confidence level and the second confidence level, a target matching position having a minimum distance between the historical position information of the target robot and the position corresponding to the current position information;

[0101] In a case where the distance between the target matching position and the position corresponding to the current position information is greater than a preset threshold, the current position information is stored.

[0102] In the specific implementation process, when the processor 320 executes the computer program 311, it can achieve Figure 1 Any implementation manner in the corresponding embodiments.

[0103] Since the electronic device introduced in this embodiment is a device used to implement a device in the embodiment of the present application, based on the method introduced in the embodiment of the present application, technical personnel in this field can understand the specific implementation of the electronic device of this embodiment and its various variations. Therefore, how the electronic device implements the method in the embodiment of the present application is not introduced in detail here. As long as the equipment used by technical personnel in this field to implement the method in the embodiment of the present application falls within the scope of protection to be protected by this application.

[0104] like Figure 4 As shown, Figure 4 A schematic structural diagram of a computer-readable storage medium provided in an embodiment of the present application.

[0105] This embodiment provides a computer-readable storage medium 400, on which a computer program 411 is stored. When the computer program 411 is executed by a processor, the following steps are implemented:

[0106] Obtain feature information of the target robot at its current location;

[0107] Obtaining a target map and its corresponding first confidence level based on the feature information, wherein the first confidence level is determined based on a degree of matching between the feature information in the candidate map and the feature information at the current location, and the target map is a candidate map having a higher first confidence level;

[0108] Obtaining current position information and a corresponding second confidence level thereof, wherein the current position information is determined based on a particle filtering method, and the second confidence level is the confidence level corresponding to the current position information determined using the particle filtering method;

[0109] Determining, based on the first confidence level and the second confidence level, a target matching position having a minimum distance between the historical position information of the target robot and the position corresponding to the current position information;

[0110] In a case where the distance between the target matching position and the position corresponding to the current position information is greater than a preset threshold, the current position information is stored.

[0111] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0112] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0113] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0114] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0115] The present application also provides a computer program product, which includes computer software instructions. When the computer software instructions are executed on a processing device, the processing device is caused to execute the following Figure 1 The process in the robot positioning method in the corresponding embodiment.

[0116] The above-mentioned computer program product includes one or more computer instructions. When the above-mentioned computer program instructions are loaded and executed on a computer, the above-mentioned process or function according to the embodiment of the present application is generated in whole or in part. The above-mentioned computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The above-mentioned computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the above-mentioned computer instructions may be transmitted from one website, computer, server or data center to another website, computer, server or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) method. The above-mentioned computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a server or data center that includes one or more available media integrated therein. The above-mentioned available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)).

[0117] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0118] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the above units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0119] The units described above as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0120] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0121] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the above-mentioned methods in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0122] In summary, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A robot positioning method, characterized in that: include: Obtain feature information of the target robot at its current location; Acquire a target map and a first confidence level corresponding thereto based on the feature information, wherein the first confidence level is determined based on a degree of matching between the feature information in the candidate map and the feature information at the current location, and the target map is a candidate map having a higher first confidence level; Obtaining current position information and a second confidence level corresponding thereto, wherein the current position information is determined based on a particle filtering method, and the second confidence level is a confidence level corresponding to the current position information determined using the particle filtering method; Determining, based on the first confidence level and the second confidence level, a target matching position having a minimum distance between the historical position information of the target robot and the position corresponding to the current position information; When the distance between the target matching position and the position corresponding to the current position information is greater than a preset threshold, storing the current position information; When the distance is less than or equal to the preset threshold, obtaining a target matching sample set of the target matching position; When the number of samples in the target matching sample set is less than a first preset number of samples, adding the current location information to the target matching sample set; When the number of samples is greater than or equal to the first preset number of samples, determining a first sample set and a second sample set, wherein the first sample set includes a second preset number of target matching samples stored earlier and continuously, and the second sample set includes a third preset number of target matching samples stored later and continuously; Obtaining a confidence difference of the second confidence level corresponding to the target matching sample in the first sample set and / or the second sample set; When the confidence difference is greater than or equal to a preset confidence difference, the current position information is stopped from being added to the target matching sample set.

2. The robot positioning method according to claim 1, wherein: Also includes: When the confidence difference is less than a preset confidence difference, obtaining a confidence variance of the second confidence corresponding to the target matching sample in the second sample set; Determining a credibility level of a second confidence level of the current location information based on the confidence level variance; When the confidence level is greater than or equal to a preset confidence threshold, obtaining a matching condition between the first confidence level of the target map and the first confidence level corresponding to the target matching sample in the first sample set; If the target map where the target robot is located is not matched to the first confidence level, stop adding the current position information to the target matching sample set.

3. The robot positioning method according to claim 2, wherein: Also includes: When the first confidence level of the target map where the target robot is located is matched, obtaining a distribution of characteristic objects at the location where the target robot is located; According to the distribution of the characteristic objects, determining whether the target robot is currently in a featureless area; When the target robot is in the featureless area, stop adding the current position information to the target matching sample set.

4. The robot positioning method according to claim 3, wherein: Also includes: When the target robot is not in the featureless area, the target map where the target robot is located, the first confidence level, the current position information, and the second confidence level are stored in the target matching sample set of the target matching position in the historical position information of the target robot.

5. The robot positioning method according to claim 1, wherein: Before the step of determining, based on the first confidence level and the second confidence level, a target matching position having the smallest distance between the historical position information of the target robot and the position corresponding to the current position information, the method further includes: Obtaining the number of location information in the historical location information; When the amount of position information is zero, the target map where the target robot is located, the first confidence level, the current position information, and the second confidence level are stored in the historical position information.

6. A robot positioning device, characterized in that: include: Feature acquisition module, used to obtain feature information of the target robot at its current location; a map matching module, configured to obtain a target map and a first confidence level corresponding thereto based on the feature information, wherein the first confidence level is determined based on a degree of matching between the feature information in the candidate map and the feature information at the current location, the target map being the candidate map having the higher first confidence level; a position matching module, configured to obtain current position information and a second confidence level corresponding thereto, wherein the current position information is determined based on a particle filtering method, and the second confidence level is the confidence level corresponding to the current position information determined using the particle filtering method; a determination module, configured to determine, based on the first confidence level and the second confidence level, a target matching position having a minimum distance between the position in the historical position information of the target robot and the position corresponding to the current position information; A storage module, configured to store the current location information if the distance between the target matching location and the location corresponding to the current location information is greater than a preset threshold; The robot positioning device is further configured to, when the distance is less than or equal to the preset threshold, obtain a target matching sample set of the target matching position; When the number of samples in the target matching sample set is less than a first preset number of samples, adding the current location information to the target matching sample set; When the number of samples is greater than or equal to the first preset number of samples, determining a first sample set and a second sample set, wherein the first sample set includes a second preset number of target matching samples stored earlier and continuously, and the second sample set includes a third preset number of target matching samples stored later and continuously; Obtaining a confidence difference of the second confidence level corresponding to the target matching sample in the first sample set and / or the second sample set; When the confidence difference is greater than or equal to a preset confidence difference, the current position information is stopped from being added to the target matching sample set.

7. An electronic device comprising a memory and a processor, characterized in that: The processor is configured to implement the steps of the robot positioning method according to any one of claims 1 to 5 when executing the computer program stored in the memory.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the robot positioning method according to any one of claims 1 to 5 are implemented.

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