Multi-robot cooperative positioning method and device, equipment and storage medium

Abstract

The embodiment of the application provides a kind of multi-robot cooperative positioning method and device, equipment and storage medium, belong to robot positioning technical field.The method comprises: obtaining autonomous positioning information;Scan external marker, obtain observation data, and obtain the current position information of external marker;Wherein, external marker includes at least one of the following: second robot, road sign, landmark;According to observation data, current position information and autonomous positioning information, pose estimation is carried out to obtain update positioning information and the positioning estimation information of external marker;Update positioning information and positioning estimation information are sent to central server, to make central server obtain the positioning estimation information of at least one second robot to first robot to obtain positioning reference information, and according to positioning reference information and update positioning information, fusion calculation is carried out to obtain target positioning information;Receive target positioning information from central server.The embodiment of the application can improve the positioning accuracy of robot.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of robot positioning technology, and particularly relates to a multi-robot cooperative positioning method and device, equipment and a storage medium. BACKGROUND

[0002] The positioning of a robot is the basis for tasks such as mapping and navigation, and therefore the positioning of a robot affects the accuracy of navigation. Traditional positioning methods mainly use GPS positioning, but GPS positioning cannot be applied in an indoor environment, and therefore the positioning of a robot in an indoor environment has become a current research hotspot.

[0003] In related technologies, multi-robot cooperative positioning is mainly divided into autonomous positioning of a single robot and cooperative positioning of multiple robots. Autonomous positioning of a single robot has poor effect, and the relative positioning method of multiple robots, for example, a distance measurement method, calculates the current pose of a robot by recording the distance moved by a wheel and the initial pose of the robot, needs to know the initial pose of the robot, and has a large cumulative error. Therefore, how to improve the positioning accuracy of a robot has become a technical problem to be solved. SUMMARY

[0004] The main purpose of the embodiments of the present application is to provide a multi-robot cooperative positioning method and device, equipment and a storage medium, which improve the positioning accuracy of a robot.

[0005] To achieve the above purpose, a first aspect of the embodiments of the present application provides a multi-robot cooperative positioning method applied to a first robot, wherein the first robot is in communication connection with at least one second robot and a center server, and the method comprises the following steps.

[0006] obtaining autonomous positioning information;

[0007] scanning an external marker to obtain observation data and current position information of the external marker, wherein the external marker comprises at least one of the following: a second robot, a road sign and a landmark;

[0008] performing pose estimation according to the observation data, the current position information and the autonomous positioning information to obtain updated positioning information and positioning estimation information of the external marker;

[0009] sending the updated positioning information and the positioning estimation information to the center server, so that the center server obtains positioning reference information of at least one second robot to the first robot, and performs fusion calculation according to the positioning reference information and the updated positioning information to obtain target positioning information;

[0010] receiving the target positioning information from the center server.

[0011] In some embodiments, the first robot and the second robot are provided with preset sensors, and the autonomous positioning information is obtained by:

[0012] obtaining perception data of the preset sensors; wherein the preset sensors include at least one of the following: an odometer, a vision sensor, an inertial sensor, an electromagnetic induction sensor, and a laser scanner;

[0013] performing positioning analysis according to the perception data to obtain the autonomous positioning information.

[0014] In some embodiments, the observation data includes distance data and relative orientation information; the external identifier is scanned to obtain observation data, and current position information of the external identifier is obtained, including:

[0015] scanning the distance data and the relative orientation information of the external identifier;

[0016] scanning identification information of the external identifier, and performing position information matching from a preset position database of the central server according to the identification information to obtain the current position information of the external identifier.

[0017] In some embodiments, the pose estimation is performed according to the observation data, the current position information, and the autonomous positioning information to obtain updated positioning information and positioning estimation information of the external identifier, including:

[0018] inputting the distance data, the relative orientation information, the current position information, and the autonomous positioning information into a preset extended Kalman filter model to perform pose estimation, to obtain the updated positioning information and the positioning estimation information of the external identifier; wherein the extended Kalman filter model is as follows:

[0019]

[0020]

[0021] ∑ i,t+1 -1 =∑ i,t -1 +H i -1 R i,t -1 H i

[0022]

[0023] wherein, is the updated positioning information or the positioning estimation information, v i,tis zero-mean Gaussian noise, whose covariance matrix is ​​R iω,t , observation data z at time t i,t The corresponding observation noise v i,t =[v ij,t ] j∈{1,…,N} , h iλ is an observation model, and the observation model is used to calculate the current position information and observation data of the external marker, For autonomous positioning information.

[0024] In some embodiments, sending the updated positioning information to a central server so that the central server obtains at least one second robot's positioning estimation information of the first robot to obtain positioning reference information, and performing a fusion calculation based on the at least one positioning reference information and the updated positioning information to obtain target positioning information includes:

[0025] The updated positioning information is sent to the central server, so that the central server obtains the positioning estimation information of the first robot by at least one second robot to obtain the positioning reference information, and inputs the at least one positioning reference information and the updated positioning information into a preset inverse covariance cross model for fusion calculation to obtain the target positioning information; wherein the inverse covariance cross model is as follows:

[0026]

[0027]

[0028]

[0029]

[0030] in, To update location information, is the positioning reference information, c1 and c2 are the estimation error variances after fusion, and ω is the fusion coefficient.

[0031] In some embodiments, the method is applied to a central server, the central server being communicatively connected to at least one first robot and at least one second robot, and includes:

[0032] receiving updated location information and location estimate information of at least one of the first robots;

[0033] receiving updated location information and location estimate information of at least one of the second robots;

[0034] filtering the positioning estimation information of the first robot from the positioning estimation information of the second robot as positioning reference information, and performing fusion calculation according to the updated positioning information of the first robot and the positioning reference information to obtain target positioning information of the first robot;

[0035] filtering the positioning estimation information of the second robot from the positioning estimation information of the first robot as positioning reference information, and performing fusion calculation according to the updated positioning information of the second robot and the positioning reference information to obtain target positioning information of the second robot.

[0036] To achieve the above object, a second aspect of the embodiment of the present application provides a multi-robot cooperative positioning method, applied to a controller of a first robot, the first robot being in communication connection with at least one second robot and a center server, and the controller comprising:

[0037] an information acquisition module, configured to acquire autonomous positioning information;

[0038] a scanning module, configured to scan an external identifier to obtain observation data and acquire current position information of the external identifier, wherein the external identifier comprises at least one of the following: a second robot, a road sign and a landmark;

[0039] a positioning information estimation module, configured to perform pose estimation according to the observation data, the current position information and the autonomous positioning information to obtain updated positioning information and positioning estimation information of the external identifier;

[0040] an information sending module, configured to send the updated positioning information to the center server, so that the center server acquires positioning estimation information of the first robot from at least one second robot as positioning reference information, and performs fusion calculation according to the positioning reference information and the updated positioning information to obtain target positioning information;

[0041] an information receiving module, configured to receive the target positioning information from the center server.

[0042] To achieve the above object, a third aspect of the embodiment of the present application provides a multi-robot cooperative positioning device, comprising:

[0043] applied to a center server, the center server being in communication connection with at least one first robot and at least one second robot, and the center server comprising:

[0044] a first receiving module, configured to receive updated positioning information and positioning estimation information of at least one first robot;

[0045] The second receiving module is configured to receive updated positioning information and positioning estimation information of at least one second robot;

[0046] The first pose estimation module is configured to filter the positioning estimation information of the first robot from the positioning estimation information of the second robot as positioning reference information, and perform pose estimation according to the updated positioning information of the first robot and the positioning reference information to obtain target positioning information of the first robot;

[0047] The second pose estimation module is configured to filter the positioning estimation information of the second robot from the positioning estimation information of the first robot as positioning reference information, and perform pose estimation according to the updated positioning information of the second robot and the positioning reference information to obtain target positioning information of the second robot.

[0048] To achieve the above object, a fourth aspect of the embodiment of the present application provides an electronic device, which comprises a memory and a processor, the memory stores a computer program, and the processor implements the method of the first aspect when executing the computer program.

[0049] To achieve the above object, a fifth aspect of the embodiment of the present application provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the method of the first aspect.

[0050] The multi-robot cooperative positioning method and device, equipment and storage medium provided by the present application, through the self-positioning of the robot to obtain the self-positioning information, and the perception of the observation data of other external markers, the position estimation of the robot is updated according to the observation data of the external markers, the current position information and the self-positioning information, and finally the updated positioning information is sent to the center server, so that the center server fuses the target position information according to the updated positioning information and the position estimation information of other robots. Therefore, through the self-positioning, observation updating and cooperative updating of each robot, the cooperative positioning of the robot is realized, and the positioning accuracy of the robot is greatly improved. BRIEF DESCRIPTION OF DRAWINGS

[0051] Figure 1 is the system framework diagram of the multi-robot cooperative positioning method provided by the embodiment of the present application;

[0052] Figure 2 is the flowchart of the multi-robot cooperative positioning method provided by the embodiment of the present application;

[0053] Figure 3 is Figure 2 the flowchart of step S201 in

[0054] Figure 4 is Figure 2 a flowchart of step S202 in

[0055] Figure 5 is Figure 2 a flowchart of step S203 in

[0056] Figure 6 is Figure 2 a flowchart of step S204 in

[0057] Figure 7 is a flowchart of a multi-robot cooperative positioning method provided by an embodiment of the present application;

[0058] Figure 8 is a scene schematic diagram of a multi-robot cooperative positioning method provided by an embodiment of the present application;

[0059] Figure 9 is an effect comparison diagram of a multi-robot cooperative positioning method provided by an embodiment of the present application and an existing method;

[0060] Figure 10 is an effect comparison diagram of a multi-robot cooperative positioning method provided by an embodiment of the present application and an existing method;

[0061] Figure 11 is a structural schematic diagram of a multi-robot cooperative positioning device provided by an embodiment of the present application;

[0062] Figure 12 is a structural schematic diagram of a multi-robot cooperative positioning device provided by an embodiment of the present application;

[0063] Figure 13 is a hardware structural schematic diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0064] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application, and are not used to limit the present application.

[0065] It should be noted that although the functional modules are divided in the device schematic diagram, and the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a manner different from the module division in the device or the order in the flowchart. The terms "first", "second", etc. in the specification and claims and the above-described drawings are used to distinguish similar objects, and do not necessarily describe a specific order or sequence.

[0066] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of this application.

[0067] First, the terms involved in the present application are analyzed:

[0068] Artificial intelligence (AI): is a new technical science that studies, develops theories, methods, technologies and application systems for simulating, extending and expanding human intelligence; robot positioning is a branch of computer science, which attempts to understand the essence of intelligence and produce a new intelligent machine that can react in a similar way to human intelligence. The research in this field includes robots, language recognition, image recognition, natural language processing and expert systems. Robot positioning can simulate the information process of human consciousness and thinking. Robot positioning is also a theory, method, technology and application system that uses digital computers or digital computer controlled machines to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0069] Extended Kalman Filter (EKF): an extended form of standard Kalman filter in nonlinear situation, EKF algorithm is to perform Taylor expansion on nonlinear function, omit high order term, and retain the first order term of expansion term, to realize the linearization of nonlinear function, and finally calculate the state estimation value and variance estimation value of the system by Kalman filter algorithm to filter the signal. Extended Kalman filter is a kind of nonlinear Kalman filter, which is used to estimate mean and covariance, and is widely used in nonlinear robot state estimation, GPS and navigation.

[0070] Covariance intersection: in Kalman filter, when the covariance between two state variables is not determined, the algorithm for merging the estimated values is called covariance intersection. Information items a and b are known, and information item c is to be fused. The mean / covariance of a and b is known, but the cross correlation is unknown. Covariance intersection can update the mean / covariance of c as where ω is calculated to minimize a certain norm (such as logdet or trace).

[0071] The positioning problem of the robot is the basis for tasks such as mapping and navigation, and multi-robot cooperative positioning has great application value in intelligent logistics, vehicle networks, and robot cooperation. The traditional CPS positioning method cannot be applied in an indoor environment due to the influence of buildings, and the positioning effect of a single robot is often crossed. Therefore, multi-robot cooperative positioning in an indoor environment has great research and application value.

[0072] Multi-robot system positioning can be divided into autonomous positioning of a single robot and cooperation of multiple robots. There are many mature methods for autonomous positioning of a single robot. Among them, the relative positioning method such as odometry calculates the current pose of the robot by recording the distance moved by the wheels and the initial pose of the robot. This method needs to know the initial pose of the robot and has a large cumulative error. The absolute positioning method such as positioning based on GPS and wireless signals generally measures the distance from the robot to each signal transmitting end and calculates the absolute position of the robot using a three-edge positioning algorithm. This method does not need to know the initial position of the robot, but cannot estimate the attitude of the robot and the positioning result is not continuous. The cooperative positioning method based on optimization such as maximum likelihood and maximum a posteriori method models the multi-robot cooperative positioning problem as a nonlinear least squares problem and then solves it offline. The cooperative positioning method based on EKF, such as covariance intersection, regards the cooperative positioning of multiple robots as the fusion of state estimation of autonomous positioning of the robot and state estimation given by mutual perception. The covariance intersection algorithm can fuse multiple state estimations with unknown correlations and can give a positioning result with lower error.

[0073] In summary, the relative positioning method of the related art needs to know the initial pose of the robot, and the cumulative error will cause a large deviation in the positioning result. The absolute positioning method based on wireless signals generally cannot obtain the attitude of the robot and the positioning result is not continuous. The cooperative positioning method based on optimization has a great burden on the communication between robots. Although the covariance intersection algorithm has high computational efficiency, the fusion accuracy is not high.

[0074] Therefore, the embodiments of the present application provide a multi-robot cooperative positioning method and device, equipment and a storage medium, which aims to obtain autonomous positioning information by first autonomously positioning the robot, perceive observation data of other external markers, update the position estimation of the robot according to the observation data of the external markers, the current position information and the autonomous positioning information, and finally send the updated positioning information to the center server, so that the center server fuses the updated positioning information and the position estimation information of the robot by other people to obtain target position information. Therefore, through autonomous positioning, observation updating and cooperative updating of each robot, the cooperative positioning of the robot is realized, and the positioning accuracy of the robot is greatly improved.

[0075] The multi-robot cooperative positioning method and device, equipment and storage medium provided by the embodiments of the present application are described in detail through the following embodiments. First, the multi-robot cooperative positioning method in the embodiments of the present application is described.

[0076] The embodiments of the present application can acquire and process related data based on robot positioning technology. Among them, robot positioning (Artificial Intelligence, AI) is a theory, method, technology and application system that uses a digital computer or a machine controlled by a digital computer to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0077] The basic technology of robot positioning generally includes technologies such as sensors, special robot positioning chips, cloud computing, distributed storage, big data processing technology, operation / interaction system, mechatronics, etc. The robot positioning software technology mainly includes computer vision technology, robot technology, biometric technology, speech processing technology, natural language processing technology, and machine learning / deep learning, etc.

[0078] The multi-robot cooperative positioning method provided by the embodiments of the present application relates to the field of robot positioning technology. The multi-robot cooperative positioning method provided by the embodiments of the present application can be applied in a terminal, can be applied in a server, and can also be software running in a terminal or a server. In some embodiments, the terminal can be a smart phone, a tablet computer, a notebook computer, a desktop computer, etc.; the server can be configured as a standalone physical server, can be configured as a server cluster or a distributed system composed of multiple physical servers, can also be configured as a cloud server providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and basic cloud computing services such as big data and robot positioning platform; the software can be an application that implements the multi-robot cooperative positioning method, etc., but is not limited to the above forms.

[0079] The application is operable in a multitude of generic or specific computer system environments or configurations. Examples include personal computers, server computers, handheld or laptop devices, tablet devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like. The application can be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, and the like, that perform particular tasks or implement particular abstract data types. The application can also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules can be located in local and remote computer storage media including memory storage devices.

[0080] Reference is made to Figure 1 , Figure 1 is a system framework diagram of a multi-robot cooperative positioning system, the multi-robot cooperative positioning system comprising a first robot, a plurality of second robots, and a central server, the first robot, the plurality of second robots, and the central server being communicatively connected, the communication mode comprising at least one of the following: WIFI, Bluetooth, GPRS.

[0081] The application takes the robot whose positioning fusion is completed by the central server as the first robot, and takes the robot observing the first robot as the second robot. When the positioning fusion of the central server is completed, if the first robot has positioning estimation information of other robots, the first robot is defined as the second robot, and the second robot performing the positioning fusion is defined as the first robot.

[0082] Figure 2 is an optional flowchart of a multi-robot cooperative positioning method provided by an embodiment of the application, and the multi-robot cooperative positioning method is applied to a first robot, the first robot being communicatively connected to a second robot and a central server. Figure 2 The method in can include but is not limited to comprising steps S201 to S206.

[0083] Step S201, acquiring autonomous positioning information;

[0084] Step S202, scanning an external identifier to obtain observation data and acquire current position information of the external identifier; wherein the external identifier comprises at least one of the following: a second robot, a road sign, and a landmark.

[0085] Step S203, performing pose estimation according to the observation data, the current position information, and the autonomous positioning information to obtain updated positioning information and positioning estimation information of the external identifier.

[0086] Step S204, sending the updated positioning information to the center server, so that the center server obtains the positioning reference information of the first robot by the positioning estimation information of the at least one second robot, and performs fusion calculation according to the positioning reference information and the updated positioning information to obtain the target positioning information;

[0087] Step S205, receiving the target positioning information from the center server.

[0088] The steps S201 to S205 shown in the embodiments of the present application, the first robot obtains the autonomous positioning information by autonomous positioning, then scans the external identifier to obtain the observation data, and performs pose estimation according to the observation data, the current position information and the autonomous positioning information to obtain the updated positioning information and the positioning estimation information of the external identifier, so as to realize the positioning update of the first robot and predict the positioning estimation information of the external identifier, and then send the updated positioning information and the positioning estimation information to the center server, so that the center server takes the positioning estimation information of the first robot by the second robot as the positioning reference information, and performs fusion calculation on the positioning reference information and the updated positioning information to obtain the target positioning information. Therefore, the autonomous positioning is firstly performed, then the positioning is updated according to the observation data, and finally the fusion calculation is performed on the estimated positioning of the first robot by the multiple second robots and the updated positioning by the center server, so as to realize the cooperative positioning of the multiple robots and improve the positioning accuracy of the robot.

[0089] It should be noted that the accuracy of the traditional GPS positioning is 10 meters, but the robot needs to position its own position with an accuracy much higher than 10 meters, especially the indoor positioning of the robot, which requires higher positioning accuracy, especially the transport type robot applied in the processing factory. If the transport type robot moves deviates, it will affect the processing operation, so the positioning accuracy of the robot is required to be higher. The present application improves the positioning accuracy of the robot by updating the positioning result of the robot after the autonomous positioning result of the robot, and then obtaining the target positioning result by the center server according to the updated positioning result and the positioning result of the robot by the other robots.

[0090] Please refer to Figure 3 In some embodiments, the first robot and the second robot are provided with a preset sensor, and step S201 can include but is not limited to steps S301 to S302:

[0091] Step S301, obtaining the perception data of the preset sensor; wherein the preset sensor includes at least one of the following: odometer, vision sensor, inertial sensor, electromagnetic induction sensor, laser scanner;

[0092] Step S302, performing positioning analysis according to the perception data to obtain autonomous positioning information.

[0093] The steps S301 to S302 are described in detail as follows:

[0094] The first robot and the second robot are autonomous mobile robots, and a basic function of the autonomous mobile robots is that the autonomous mobile robots can automatically determine their own positions in an environment, and the positioning manner of the robots depends on the sensors used. Generally, a commonly used positioning sensor of a robot is a preset sensor on the first robot and the second robot. In step S301, the preset sensor includes at least one of an odometer, a vision sensor, an inertial sensor, an electromagnetic induction sensor, and a laser scanner. The vision sensor can be a camera, the electromagnetic induction sensor can be an ultrasonic wave, and the laser scanner can be a 2D laser radar or an infrared ray. Therefore, autonomous positioning is performed by using the preset sensor configured on the first robot to obtain perception data. When the preset sensor is an odometer, the perception data is odometer data. Therefore, the current perception data of the first robot is obtained by setting the preset sensor to realize autonomous positioning of the robot.

[0095] In step S302 of some embodiments, positioning analysis is performed according to the perception data. When the perception data is odometer data, autonomous positioning information is obtained by analyzing the perception data by using an effective pose estimation algorithm.

[0096] Specifically, the process of autonomous positioning of the first robot according to the perception data is as follows:

[0097] The multi-robot cooperative positioning system includes a multi-robot system of the first robot and N second robots, and the pose of the first robot numbered i at time t can be expressed as p i,t = θ i,t , q i,t T ], where θ i,t is an attitude angle of the first robot in a world coordinate system, q i,t = x i,t , y i,t ] T is a position in the world coordinate system. The positioning problem of the multi-mobile robot system can be described as solving formula (1):

[0098] s i,t = [ q 1,t T ,…, p i,t T ,…, q N,t T ] T (1)

[0099] In the autonomous positioning stage, if the perception data is odometry data, the first robot updates motion according to its own odometry data, and the update formula is shown as formula (2):

[0100]

[0101] The advantages of steps S301 to S302 of the embodiment of the present application are that the first robot perceives to obtain perception data by using a preset sensor carried by itself, and autonomous positioning is completed according to the perception data, so that the autonomous positioning is simple.

[0102] It should be noted that the autonomous positioning method in the embodiment adopts an odometry method, and in other embodiments, the autonomous positioning method can obtain perception data by scanning the outside through a 2D laser radar, and after processing the perception data, the autonomous positioning information of the first robot can be easily obtained by using a feature descriptor.

[0103] Please refer to Figure 4 In some embodiments, step S202 can include but is not limited to steps S401 to S402:

[0104] Step S401, scanning distance data and relative orientation information of the external identifier;

[0105] Step S402, scanning the identification information of the external identifier, and matching the position information from the preset position database of the center server according to the identification information to obtain the current position information of the external identifier.

[0106] The observation data includes distance data and relative orientation information, in step S401, the distance data and relative orientation data of the external identifier are scanned by a preset perception sensor, and the perception sensor can be a laser radar or a camera or other visual perception sensor. The distance data and relative orientation information of the external identifier can be recognized by the laser radar or the camera. The external identifier includes a second robot, a road sign and a landmark, the landmark is a landmark building, and the landmark building, for example, a skyscraper, a church, a temple, a statue, a lighthouse, a bridge, etc. The road sign is a traffic sign. Whether it is a second robot or a landmark, a road sign has the latest current position information. The current position information of the landmark and the road sign is fixed, and the current position information of the second robot is the latest position information.

[0107] The current position information of the landmark or the road sign is preset in advance, and the current position information of the second robot is acquired from the second robot. Therefore, the position database is preset in advance in the central server, the central server stores the current position information of the landmark or the road sign in the position database in advance, and the central server also stores the updated positioning information in the position database after receiving the updated positioning information of the second robot. If the central server does not complete the fusion of the position information of the second robot, the updated positioning information is used as the position information of the second robot. If the central server completes the fusion of the position information of the second robot, the central server determines the target position information of the second robot as the current position information of the second robot. Therefore, in step S402, the external identifier is scanned to obtain the identification information, and the position information in the position database is filtered according to the identification information, so as to filter out the position information matched with the identification information as the current position information of the external identifier.

[0108] For example, if the identification information of the second robot scanned is A3, and the position information matched with the identification information A3 extracted from the position database is (05, 12, 43), the position information (05, 12, 43) is used as the current position information.

[0109] Please refer to Figure 5 In some embodiments, step S203 can include but is not limited to step S501.

[0110] In step S501, the distance data, the relative position information, the current position information and the autonomous positioning information are input into a preset extended Kalman filter model for pose estimation to obtain the updated positioning information and the positioning estimation information of the external identifier. The extended Kalman filter model is shown in the following formulas (3) to (6):

[0111]

[0112]

[0113] Σ i,t+1 -1 =Σ i,t -1 +H i T R i,t -1 H i (5)

[0114]

[0115] wherein, is the updated positioning information or the positioning estimation information, v i,t is a zero-mean Gaussian noise, and the covariance matrix thereof is R iω,t, t time observation data z i,t Corresponding observation noise v i,t = [v ij,t ] j∈{1,…,N} , h iλ is an observation model, and the observation model is used to calculate the current position information of the external marker and the observation data to obtain the observation data, is the autonomous positioning information.

[0116] In step S501 of some embodiments, by inputting the updated positioning information and the positioning estimation information of the second robot into an extended Kalman filter model, and the extended Kalman filter model has a wider range of application and higher state estimation accuracy, it can process systems with any update frequency. Therefore, the distance data, the relative position information, the current position information, and the autonomous positioning information are estimated by the extended Kalman filter model to estimate the updated positioning information of the first robot. Therefore, after the first robot completes autonomous positioning, the distance data, the relative position information, the current position information, and the autonomous positioning information of the observed external marker are used to estimate the pose information of the first robot as the updated positioning information, and the positioning estimation information of the observed external marker is also estimated. As can be seen from formula (3), since the observation data of the first robot to the external marker is not necessarily accurate, the observation noise of the first robot needs to be calculated, and the observation data is obtained based on the observation noise and the autonomous positioning information, and then the observation data, the autonomous positioning information, and the current position information are estimated by formula (4) to obtain the updated positioning information. Similarly, formula (4) can also calculate the positioning estimation information of the external marker.

[0117] The advantage of step S501 of the present application is that the distance data, the relative position information, the current position information, and the autonomous positioning information are estimated by the extended Kalman filter model to estimate the updated positioning information of the first robot and the positioning estimation information of the external marker. Therefore, the position information of the external marker and the distance and relative position between the external marker are used to update the positioning information of the first robot, so that the positioning of the first robot is more accurate.

[0118] Please refer to Figure 6 In some embodiments, step S204 can include but is not limited to step S601:

[0119] Step S601, the updated positioning information is sent to the center server, so that the center server obtains the positioning estimation information of at least one second robot to the first robot to obtain the positioning reference information, and inputs at least one positioning reference information and the updated positioning information into a preset inverse covariance intersection model for fusion calculation to obtain the target positioning information; wherein the inverse covariance intersection model is as follows formula (7) to (10):

[0120]

[0121]

[0122]

[0123]

[0124] wherein, for updating the positioning information, for the positioning reference information, c1, c2 are the fused estimated error variances, and ω is the fusion coefficient.

[0125] If the first robot observes the second robot, the first robot uploads the positioning estimation information of the second robot and its own updated positioning information, so the second robot also uploads the positioning estimation information of the first robot and its own updated positioning information to the center server. In step S601 of some embodiments, the center server receives the updated positioning information and the positioning estimation information of the first robot and the second robot, and takes the positioning estimation information of the first robot by the second robot as the positioning reference information, and then calculates the target positioning information by fusing the updated positioning information and the positioning reference information through the inverse covariance intersection model. Since the center server receives the positioning estimation information of multiple second robots, the positioning estimation information related to the first robot is selected from the positioning estimation information of the second robots as the positioning reference information, and there are multiple positioning reference information, so the multiple positioning reference information and the updated positioning information are fused to obtain the target positioning information.

[0126] It should be noted that formulas (7) to (9) are all calculated to obtain the estimated error variances of the updated positioning information and the positioning reference information in the fusion process, also known as weight coefficients, so as to fuse multiple positioning reference information and updated positioning information to obtain more accurate target positioning information. The positioning operation of the first robot is the same as that of the second robot, so each robot is positioned by the multi-robot cooperative positioning method to realize the positioning cooperation of multiple robots and improve the positioning accuracy.

[0127] In step S601 of the embodiment, the center server takes the positioning estimation information of the first robot by the multiple second robots as the positioning reference information, and fuses the multiple positioning reference information and the updated positioning information to obtain the target positioning information through the inverse covariance intersection model, so that the positioning of the first robot is more accurate.

[0128] Please refer to Figure 7The embodiment of the application further provides a multi-robot cooperative positioning method applied to a center server, the center server being in communication connection with at least one first robot and at least one second robot, and the multi-robot cooperative positioning method can include but is not limited to steps S701 to S704:

[0129] Step S701: receiving updated positioning information and positioning estimation information of the first robot;

[0130] Step S702: receiving updated positioning information and positioning estimation information of the at least one second robot;

[0131] Step S703: screening out fusion calculation information of the first robot from the positioning estimation information of the second robot as positioning reference information, and performing pose estimation according to the updated positioning information and the positioning reference information of the first robot to obtain target positioning information of the first robot;

[0132] Step S704: screening out positioning estimation information of the second robot from the positioning estimation information of the first robot as positioning reference information, and performing fusion calculation according to the updated positioning information and the positioning reference information of the second robot to obtain target positioning information of the second robot.

[0133] In the steps S701 to S704 shown in the embodiment of the application, the updated positioning information and the positioning estimation information of a plurality of first robots and a plurality of second robots are respectively received, the positioning estimation information related to the first robot is screened out from the positioning estimation information as positioning reference information, then the positioning reference information and the updated positioning information are fused to obtain the target positioning information of the first robot; at the same time, the positioning estimation information of the second robot by the first robot is taken as the positioning reference information, and the positioning reference information and the updated positioning information are fused to obtain the target positioning information of the second robot. Therefore, the center server fuses the updated positioning information of the robot and the positioning estimation information of other robots to obtain more accurate target positioning information, so that the positioning of each robot is more accurate.

[0134] In step S701 of some embodiments, the center server receives the updated positioning information and the positioning estimation information of the first robot, and before uploading the updated positioning information and the positioning estimation information, the first robot needs to first obtain autonomous positioning information by autonomous positioning, and obtain observation data by observing external markers, and perform pose estimation according to the observation data, the autonomous positioning information and the current position information of the external markers to obtain the updated positioning information and the positioning estimation information of the external markers, and upload the positioning estimation information and the updated positioning information of the external markers as the second robot to the center server, so that the center server receives the positioning information of the first robot which has been updated, so that the positioning of the first robot is more accurate.

[0135] In step S702 of some embodiments, the central server receives the updated positioning information and positioning estimation information of the second robot, and before uploading the updated positioning information and positioning estimation information, the second robot needs to first autonomously locate to obtain autonomous positioning information, and obtain observation data by observing external markers, and perform posture estimation based on the observation data, autonomous positioning information and the current position information of the external marker to obtain updated positioning information and positioning estimation information of the external marker, and upload the positioning estimation information and updated positioning information of the first robot with the external marker to the central server, so that the central server receives the updated positioning information of the second robot, making the positioning of the second robot more accurate.

[0136] In step S703 of some embodiments, the updated positioning information of the first robot and the positioning reference information are automatically fused and calculated by the central server to obtain more accurate target positioning information, thereby improving the positioning accuracy of the first robot.

[0137] In step S704 of some embodiments, the updated positioning information of the second robot and the positioning reference information are automatically fused and calculated by the central server to obtain more accurate target positioning information, thereby improving the positioning accuracy of the second robot.

[0138] like Figure 8 As shown, the embodiment of the present application is applied to the collaborative positioning operation of two robots A and B. Robot A updates the movement and observes the landmarks and the robot B. A,B Perform observation updates to obtain s containing updated positioning information and positioning estimation information A,t , robot B also gets s B,t , s A, and s B,t Both contain the location estimation information of robot A. Robots A and B communicate with the central server at fixed intervals to estimate their state. A, and s B,t The two state estimates are sent to the central server, which uses inverse covariance cross-correlation to fuse the two state estimates to obtain target positioning information to improve the robot's positioning accuracy.

[0139] The above steps are used to realize the positioning of the robot, thereby improving the positioning accuracy of the robot. The specific effects are described as follows:

[0140] The MRCLAM dataset is established by the University of Toronto Institute of Aerospace Studies, and is widely used in multi-robot cooperative positioning, multi-robot cooperation, etc. The data set is collected using high-precision measuring equipment, and contains complete odometer data, observation data and real position information of five robots at each time. The average positioning accuracy of each positioning method on the MRCLAM dataset is shown in Table 1.

[0141]

[0142]

[0143] Table 1: MRCLAM average positioning accuracy [m]

[0144] As can be seen from Table 1, the multi-robot cooperative positioning method of the present application improves the average positioning accuracy of the multi-robot system, wherein the cooperative positioning based on covariance intersection reduces the average error by 2.14% compared with the autonomous positioning of each robot, and the method based on inverse covariance intersection reduces the positioning error by 3.21%, which verifies the advantage of inverse covariance intersection in state fusion compared with covariance intersection.

[0145] In the simulation experiment, we let three robots move in the indoor simulation environment built, record the real trajectory of each robot movement, autonomous positioning result and the multi-robot cooperative positioning trajectory of the present application as shown in Figure 9 The first dashed line segment LX1 is the real motion trajectory of the robot, the second dashed line segment LX2 is the autonomous positioning result of each robot, and the third solid line segment LX3 is the cooperative positioning trajectory of the present application. It can be seen that the autonomous positioning result of the three robots has a certain deviation from the real trajectory of each robot, and the mutual observation between different robots gives a more accurate position estimation, and after fusion by inverse covariance intersection, the deviation of autonomous positioning is corrected, and better positioning effect is obtained. The average positioning error of robot A is reduced by 54.01%, the average positioning error of robot B is reduced by 27.70%, the average positioning error of robot A is reduced by 5.38%, and the average positioning error of the whole robot system is reduced from 0.6865m to 0.4483m, and the average positioning error is reduced by 34.7%. Figure 10 The experimental results of two robots are given, wherein the average positioning error of the autonomous positioning of the two robots is 0.5477m, the average positioning error of the cooperative positioning is 0.3940m, and the average positioning error is reduced by 28.06% after introducing the cooperative positioning of the present application. Within a certain range, the accuracy of cooperative positioning is generally positively correlated with the number of robots participating in cooperation.

[0146] Therefore, through Table 1, Figure 9 , Figure 10It can be known that the multi-robot cooperative positioning method provided in the application can better realize the positioning of multiple robots in an indoor environment, and greatly improves the positioning accuracy of each robot.

[0147] Please refer to Figure 11 The embodiment of the application further provides a multi-robot cooperative positioning device, which is applied to a controller of a first robot, the first robot is in communication connection with at least one second robot and a center server, and the above-mentioned multi-robot cooperative positioning method can be realized. The device comprises:

[0148] an information acquisition module, configured to acquire autonomous positioning information;

[0149] a scanning module, configured to scan an external identifier to obtain observation data and acquire current position information of the external identifier; wherein the external identifier comprises at least one of the following: a second robot, a road sign and a landmark;

[0150] a positioning information estimation module, configured to perform pose estimation according to the observation data, the current position information and the autonomous positioning information to obtain updated positioning information and positioning estimation information of the external identifier;

[0151] an information sending module, configured to send the updated positioning information to the center server, so that the center server acquires positioning reference information of the first robot from the positioning estimation information of the at least one second robot, and performs fusion calculation according to the positioning reference information and the updated positioning information to obtain target positioning information;

[0152] an information receiving module, configured to receive the target positioning information from the center server.

[0153] The specific implementation of the multi-robot cooperative positioning device is basically the same as that of the above-mentioned multi-robot cooperative positioning method, and will not be repeated here.

[0154] Please refer to Figure 12 The embodiment of the application further provides a multi-robot cooperative positioning device, which can realize the above-mentioned multi-robot cooperative positioning method. The device is applied to a center server, the center server is in communication connection with at least one first robot and at least one second robot, and the center server comprises:

[0155] a first receiving module, configured to receive updated positioning information and positioning estimation information of the first robot;

[0156] a second receiving module, configured to receive updated positioning information and positioning estimation information of the at least one second robot;

[0157] The first pose estimation module is configured to filter the positioning estimation information of the first robot from the positioning estimation information of the second robot as positioning reference information, and perform pose estimation according to the updated positioning information of the first robot and the positioning reference information to obtain the target positioning information of the first robot.

[0158] The second pose estimation module is configured to filter the positioning estimation information of the second robot from the positioning estimation information of the first robot as positioning reference information, and perform pose estimation according to the updated positioning information of the second robot and the positioning reference information to obtain the target positioning information of the second robot.

[0159] The specific implementation of the multi-robot cooperative positioning device is basically the same as the specific embodiments of the multi-robot cooperative positioning method described above, and will not be repeated here.

[0160] The embodiment of the present application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor implements the multi-robot cooperative positioning method when executing the computer program. The electronic device can be any intelligent terminal, such as a tablet computer or a vehicle-mounted computer.

[0161] Please refer to Figure 13 , Figure 13 The hardware structure of the electronic device of another embodiment is illustrated, which includes:

[0162] The processor 1301 can be implemented in the form of a general-purpose CPU (Central Processing Unit), a microprocessor, an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits, and is used to execute related programs to implement the technical solutions provided by the embodiments of the present application.

[0163] The memory 1302 can be implemented in the form of a ROM (ReadOnly Memory), a static storage device, a dynamic storage device, or a RAM (Random Access Memory). The memory 1302 can store an operating system and other application programs. When the technical solutions provided by the embodiments of the present application are implemented by software or firmware, the related program codes are stored in the memory 1302 and are called and executed by the processor 1301 to implement the multi-robot cooperative positioning method of the embodiments of the present application.

[0164] The input / output interface 1303 is used to realize information input and output.

[0165] The communication interface 1304 is configured to realize the communication interaction between the device and other devices, and can realize the communication through a wired manner (for example, a USB, a network cable, or the like) or a wireless manner (for example, a mobile network, WIFI, Bluetooth, or the like).

[0166] The bus 1305 is configured to transmit information between various components (for example, the processor 1301, the memory 1302, the input / output interface 1303, and the communication interface 1304) of the device.

[0167] The processor 1301, the memory 1302, the input / output interface 1303, and the communication interface 1304 are connected to each other through the bus 1305 to realize the communication connection between the device.

[0168] The computer readable storage medium provided in the embodiment of the present application stores a computer program, and the computer program is executed by the processor to realize the multi-robot cooperative positioning method.

[0169] The memory is a non-transitory computer readable storage medium, and can be used to store a non-transitory software program and a non-transitory computer executable program. In addition, the memory can include a high-speed random access memory, and can also include a non-transitory memory, for example, at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state memory device. In some embodiments, the memory can optionally include a memory remotely arranged relative to the processor, and the remote memory can be connected to the processor through a network. Examples of the network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0170] The multi-robot cooperative positioning method and device, the equipment and the storage medium provided by the embodiment of the present application are characterized in that the robot first autonomously positions to obtain autonomous positioning information, and perceives observation data of other external markers, so as to update the position estimation of the robot according to the observation data of the external markers, the current position information and the autonomous positioning information, and finally sends the updated positioning information to the center server, so that the center server fuses the target position information according to the updated positioning information and the position estimation information of the robot by other people. Therefore, through autonomous positioning, observation updating and cooperative updating of each robot, the cooperative positioning of the robot is realized, and the positioning accuracy of the robot is greatly improved.

[0171] The embodiments described in the embodiments of the present application are used to more clearly illustrate the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided by the embodiments of the present application. Those skilled in the art can know that, with the evolution of technology and the appearance of new application scenarios, the technical solutions provided by the embodiments of the present application are also applicable to similar technical problems.

[0172] Those skilled in the art can understand that the technical solutions shown in the figures do not constitute a limitation to the embodiments of the present application, and can include more or fewer steps than the figures, or combine certain steps, or different steps.

[0173] The device embodiments described above are merely illustrative, wherein the units described as separate components can or can not be physically separated, that is, can be located in one place, or can be distributed to multiple network units. Part or all of the modules can be selected according to actual needs to achieve the purpose of the embodiments.

[0174] Those skilled in the art can understand that all or some steps in the above disclosed method, functional modules / units in the system and device can be implemented as software, firmware, hardware and their appropriate combinations.

[0175] The terms "first", "second", "third", "fourth" and the like in the description of the present application and the above-mentioned figures (if any) are used to distinguish similar objects, and do not necessarily indicate a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not necessarily limit to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0176] It should be understood that in the present application, "at least one" means one or more, and "multiple" means two or more. "And / or" is used to describe the association between the associated objects, which means that there can be three relationships, for example, "A and / or B" can mean that there are three cases of only A, only B and A and B at the same time, where A and B can be singular or plural. The character " / " generally represents that the associated objects before and after are in an "or" relationship. "At least one of the following" or similar expressions means any combination of these items, including any combination of single or multiple items. For example, at least one of a, b or c can mean a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0177] In several embodiments provided in the present application, it should be understood that the disclosed apparatus and method can be implemented by other manners. For example, the apparatus embodiments described above are merely illustrative, for example, the division of the above units is merely a logical function division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units or components shown or discussed can be indirect coupling or communication connection through some interfaces, apparatuses or units, and can be electrical, mechanical or other forms.

[0178] 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, i.e. they can be located in one place or distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0179] In addition, the functional units in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0180] If the integrated unit is realized 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 solutions of the present application essentially or the part of the prior art that makes a contribution or the whole or part of the technical solutions can be embodied in the form of a software product, which is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method of each embodiment of the present application. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various program storage media.

[0181] The preferred embodiments of the embodiments of the present application are described above with reference to the accompanying drawings, but this does not limit the scope of the embodiments of the present application. Any modifications, equivalent replacements and improvements made by those skilled in the art without departing from the scope and essence of the embodiments of the present application shall be within the scope of the embodiments of the present application.

Claims

1. A multi-robot collaborative positioning method, characterized in that: Applied to a first robot, the first robot being communicatively connected to at least one second robot and a central server, the method comprising: Obtain autonomous positioning information; Scanning an external marker to obtain observation data and acquire current location information of the external marker; wherein the external marker includes at least one of the following: a second robot, a road sign, and a landmark; Performing pose estimation based on the observation data, the current position information, and the autonomous positioning information to obtain updated positioning information and positioning estimation information of the external marker; Sending the updated positioning information and the positioning estimation information to a central server, so that the central server obtains positioning estimation information of at least one second robot relative to the first robot to obtain positioning reference information, and performing a fusion calculation based on the positioning reference information and the updated positioning information to obtain target positioning information; Receiving the target positioning information from the central server; The sending of the updated positioning information to the central server so that the central server obtains positioning estimation information of at least one second robot relative to the first robot to obtain positioning reference information, and performing fusion calculation based on the at least one positioning reference information and the updated positioning information to obtain target positioning information includes: The updated positioning information is sent to the central server, so that the central server obtains the positioning estimation information of the first robot by at least one second robot to obtain the positioning reference information, and inputs the at least one positioning reference information and the updated positioning information into a preset inverse covariance cross model for fusion calculation to obtain the target positioning information; wherein the inverse covariance cross model is as follows: in, To update location information, To locate reference information, 、 is the estimated error variance after fusion, is the fusion coefficient.

2. The method according to claim 1, characterized in that The first robot and the second robot are provided with preset sensors, and the autonomous positioning information is obtained: Acquire perception data from a preset sensor; wherein the preset sensor includes at least one of the following: an odometer, a visual sensor, an inertial sensor, an electromagnetic induction sensor, and a laser scanner; Positioning analysis is performed based on the perception data to obtain the autonomous positioning information.

3. The method according to claim 1, characterized in that The observation data includes: distance data and relative orientation information; the scanning of the external marker to obtain the observation data and acquire the current position information of the external marker includes: Scanning the distance data and the relative orientation information to the external marker; The identification information of the external identifier is scanned, and location information is matched with a preset location database of the central server according to the identification information to obtain the current location information of the external identifier.

4. The method according to claim 3, characterized in that The performing pose estimation based on the observation data, the current position information, and the autonomous positioning information to obtain updated positioning information and positioning estimation information of the external marker includes: The distance data, the relative orientation information, the current position information, and the autonomous positioning information are input into a preset extended Kalman filter model for pose estimation to obtain the updated positioning information and the positioning estimation information of the external marker; wherein the extended Kalman filter model is as follows: in, To update location information or location estimate information, is zero-mean Gaussian noise, and its covariance matrix is , Observation data at all times The corresponding observation noise , is an observation model, and the observation model is used to calculate the current position information and observation data of the external marker, For autonomous positioning information.

5. A multi-robot collaborative positioning method, characterized in that: Applied to a central server, the central server being communicatively connected to at least one first robot and at least one second robot, the method comprising: receiving updated positioning information and positioning estimate information of the first robot; receiving updated location information and location estimate information of at least one of the second robots; Filtering the estimated positioning information of the first robot from the estimated positioning information of the second robot as positioning reference information, and performing a fusion calculation based on the updated positioning information of the first robot and the positioning reference information to obtain target positioning information of the first robot; Filtering the positioning estimation information of the second robot from the positioning estimation information of the first robot as positioning reference information, and performing a fusion calculation based on the updated positioning information of the second robot and the positioning reference information to obtain target positioning information of the second robot; The performing a fusion calculation based on the updated positioning information of the second robot and the positioning reference information to obtain the target positioning information of the second robot includes: Inputting at least one of the positioning reference information and the updated positioning information into a preset inverse covariance cross model for fusion calculation to obtain the target positioning information; wherein the inverse covariance cross model is as follows: in, To update the location information, To locate reference information, 、 is the estimated error variance after fusion, is the fusion coefficient.

6. A multi-robot collaborative positioning method, characterized in that: A controller applied to a first robot, the controller executing the multi-robot collaborative positioning method according to any one of claims 1 to 4, the first robot being communicatively connected to at least one second robot and a central server, the controller comprising: Information acquisition module, used to obtain autonomous positioning information; a scanning module, configured to scan an external marker, obtain observation data, and acquire current location information of the external marker; wherein the external marker includes at least one of the following: a second robot, a road sign, and a landmark; A positioning information estimation module is used to perform pose estimation based on the observation data, the current position information and the autonomous positioning information to obtain updated positioning information and positioning estimation information of the external marker; an information sending module, configured to send the updated positioning information to a central server, so that the central server obtains positioning estimation information of at least one second robot relative to the first robot to obtain positioning reference information, and performs a fusion calculation based on the positioning reference information and the updated positioning information to obtain target positioning information; The information receiving module is used to receive the target positioning information from the central server.

7. A multi-robot collaborative positioning device, characterized in that: The method is applied to a central server, the central server executing the multi-robot collaborative positioning method according to claim 5, the central server being communicatively connected to at least one first robot and at least one second robot, and the central server comprising: a first receiving module, configured to receive updated positioning information and positioning estimation information of the first robot; a second receiving module, configured to receive updated positioning information and positioning estimation information of at least one of the second robots; a first pose estimation module, configured to filter the positioning estimation information of the first robot from the positioning estimation information of the second robot as positioning reference information, and perform pose estimation based on the updated positioning information of the first robot and the positioning reference information to obtain target positioning information of the first robot; A second pose estimation module is used to filter out the positioning estimation information of the second robot from the positioning estimation information of the first robot as positioning reference information, and perform pose estimation based on the updated positioning information and the positioning reference information of the second robot to obtain the target positioning information of the second robot.

8. An electronic device, characterized in that: The electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the multi-robot collaborative positioning method according to any one of claims 1 to 5 when executing the computer program.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the multi-robot collaborative positioning method according to any one of claims 1 to 5 is implemented.

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