Robot positioning method and device, robot and computer readable storage medium

Abstract

The invention relates to the technical field of robots, and provides a robot positioning method, which comprises the following steps: constructing a global map, obtaining a first position relation of a target reference object relative to the global map, obtaining a second position relation of a robot relative to the target reference object, and finally determining the position of the robot according to the first position relation and the second position relation. And determining the current position of the robot on the global map. On the basis of determining the absolute position of the target reference object on the global map, the target reference object is used as a reference of the robot relative to the odometer, and the absolute position of the robot on the global map can be determined. By adopting the mode of combining absolute positioning correction with the relative speedometer, the speedometer error accumulation of the robot in the long-distance operation process is reduced, so that the influence of environmental conditions on the positioning precision is reduced, and the positioning accuracy of the robot is improved. The invention further discloses a robot positioning device, a robot and a computer readable storage medium.

Description

Technical Field

[0001] The present application relates to the field of robot technology, for example, to a robot positioning method and device, a robot, and a computer-readable storage medium. Background Art

[0002] At present, intelligent train inspection robots need to move back and forth in the maintenance tunnel to perform train inspection tasks. In this process, the robot's position relative to the starting point of the tunnel needs to be determined in real time. However, the train maintenance tunnel is a typical "degraded" scene for robots. First, there are walls on both sides, and the walls themselves lack sufficient geometric features, making it impossible to record the robot's travel through the wall features.

[0003] There are many robot positioning methods in related technologies. For example, through a rail with a rack, the distance is converted by counting the number of gears running on the rack to complete the positioning; the robot is positioned by scanning a QR code; a magnetic sensor is used to sense whether the magnetic strip is in the center of the sensor, and the switch value of the offset result is output for the correction of the heading deviation of the chassis for positioning; the robot is positioned by judging whether it is within a certain range above the RFID tag by scanning an RFID (Radio Frequency Identification) tag; and high-precision positioning is achieved through 2D radar navigation.

[0004] In the process of implementing the embodiments of the present disclosure, it is found that there are at least the following problems in the related art:

[0005] In the train maintenance tunnel, the intelligent inspection robot needs to determine its position relative to the starting point of the tunnel in real time. The use of related technologies such as gear rack positioning, QR code positioning, magnetic stripe positioning, RFID positioning and 2D radar navigation requires on-site modification, has high requirements on environmental conditions, and the positioning accuracy is easily affected by the environment, resulting in low accuracy of robot positioning.

[0006] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present application, and therefore may include information that does not constitute the prior art known to ordinary technicians in the field. Summary of the invention

[0007] In order to provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. The summary is not an extensive review, nor is it intended to identify key / critical components or delineate the scope of protection of these embodiments, but rather serves as a prelude to the detailed description that follows.

[0008] The embodiments of the present disclosure provide a robot positioning method and device, a robot, and a computer-readable storage medium to reduce the impact of environmental conditions on positioning accuracy and improve the accuracy of robot positioning.

[0009] In some embodiments, the method includes: constructing a global map and obtaining a first position relationship of a target reference object relative to the global map; obtaining a second position relationship of the robot relative to the target reference object; and determining the current position of the robot on the global map based on the first position relationship and the second position relationship.

[0010] Optionally, obtaining a first positional relationship of the target reference object relative to the global map includes: determining the target reference object; using a lidar to scan the target reference object at multiple angles to obtain multi-perspective point cloud data; using coarse and fine registration methods to align the multi-perspective point cloud data to obtain a complete model of the target reference object; aligning the model of the target reference object to the global map to obtain a first positional relationship between the target reference object and the global map.

[0011] Optionally, determining the target reference object includes: when the robot starts to perform an inspection task, determining the front of the vehicle as the target reference object; when the robot parks, determining the target reference object according to the current parking position of the robot.

[0012] Optionally, determining the target reference object according to the current parking position of the robot includes: obtaining a current bogie corresponding to the current parking position of the robot; determining the current bogie as the target reference object; wherein each bogie corresponds to at least one parking position.

[0013] Optionally, constructing a global map includes: constructing an offline map based on a simultaneous positioning and mapping technology; and obtaining a global map by using the origin of the offline map as the origin of a world coordinate system.

[0014] Optionally, obtaining a second position relationship of the robot relative to the target reference object includes: determining the position of the target reference object as an initial position; and detecting a distance and direction of the current position of the robot relative to the initial position to obtain the second position relationship.

[0015] Optionally, the current position of the robot on the global map is determined based on the first position relationship and the second position relationship, including: determining the relative position of the robot and the target reference object based on the second position relationship; and calculating the current position of the robot on the global map based on the relative position and the first position relationship.

[0016] In some embodiments, the device includes: a processor and a memory storing program instructions, and the processor is configured to execute the above-mentioned robot positioning method when executing the above-mentioned program instructions.

[0017] In some embodiments, the robot includes: a robot body; and the above-mentioned robot positioning device is installed on the robot body.

[0018] In some embodiments, the computer-readable storage medium stores program instructions, and when the program instructions are run, the robot positioning method described above is executed.

[0019] The robot positioning method and device, robot, and computer-readable storage medium provided by the embodiments of the present disclosure can achieve the following technical effects:

[0020] Construct a global map, obtain the first position relationship of the target reference object relative to the global map, and obtain the second position relationship of the robot relative to the target reference object. Finally, determine the current position of the robot on the global map based on the first position relationship and the second position relationship. On the basis of determining the absolute position of the target reference object on the global map, use the target reference object as a reference for the robot's relative odometer to determine the absolute position of the robot on the global map. The combination of absolute positioning correction and relative odometer reduces the accumulation of odometer errors during long-distance operation of the robot, thereby reducing the impact of environmental conditions on positioning accuracy and improving the accuracy of robot positioning.

[0021] The above general description and the following description are exemplary and explanatory only and are not intended to limit the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] One or more embodiments are exemplarily described by corresponding drawings, which do not limit the embodiments. Elements with the same reference numerals in the drawings are shown as similar elements, and the drawings do not constitute a scale limitation, and wherein:

[0023] Figure 1 is a schematic diagram of a robot positioning method provided by an embodiment of the present disclosure;

[0024] Figure 2 is a schematic diagram of another robot positioning method provided by an embodiment of the present disclosure;

[0025] Figure 3 is a schematic diagram of another robot positioning method provided by an embodiment of the present disclosure;

[0026] Figure 4 is a schematic diagram of another robot positioning method provided by an embodiment of the present disclosure;

[0027] Figure 5 is a schematic diagram of a robot positioning device provided by an embodiment of the present disclosure;

[0028] Figure 6It is a schematic diagram of a robot provided by an embodiment of the present disclosure.

[0029] Reference numerals:

[0030] 800: device for chiller control; 801: processor; 802: memory; 803: communication interface; 804: bus; 900: robot. DETAILED DESCRIPTION

[0031] In order to be able to understand the features and technical contents of the embodiments of the present disclosure in more detail, the implementation of the embodiments of the present disclosure is described in detail below in conjunction with the accompanying drawings. The attached drawings are for reference only and are not used to limit the embodiments of the present disclosure. In the following technical description, for the convenience of explanation, a full understanding of the disclosed embodiments is provided through multiple details. However, one or more embodiments can still be implemented without these details. In other cases, to simplify the drawings, well-known structures and devices can be simplified for display.

[0032] The terms "first", "second", etc. in the specification and claims of the embodiments of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the terms used in this way can be interchanged where appropriate, so that the embodiments of the embodiments of the present disclosure described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions.

[0033] Unless otherwise stated, the term "plurality" means two or more.

[0034] In the embodiment of the present disclosure, the character " / " indicates that the preceding and following objects are in an "or" relationship. For example, A / B indicates: A or B.

[0035] The term "and / or" is a description of the association relationship between objects, indicating that three relationships can exist. For example, A and / or B means: A or B, or, A and B.

[0036] The term "correspondence" may refer to an association relationship or a binding relationship. The correspondence between A and B means that there is an association relationship or a binding relationship between A and B.

[0037] When the inspection robot is inspecting in tunnels or on the side of a vehicle, it needs to be positioned to obtain the robot's position relative to the map or the origin. Solutions based on gears, QR codes, magnetic strips, etc. require on-site modification or manual addition of information. Methods based on 2D / 3D lasers will enter a degraded scenario due to the long straight corridor properties of the tunnel, and long-distance operation will cause the positioning error to continue to increase.

[0038] The disclosed embodiment discloses a robot, including a construction module, an acquisition module and a determination module. The construction module is configured to construct a global map. The acquisition module is configured to acquire a first position relationship of a target reference object relative to the global map and to acquire a second position relationship of the robot relative to the target reference object. The determination module is configured to determine the current position of the robot on the global map based on the first position relationship and the second position relationship. The robot also includes a processor, which is electrically connected to the above-mentioned modules and is used to control the above-mentioned modules to perform actions.

[0039] Figures 1 to 4 is a schematic diagram of a robot positioning method provided by an embodiment of the present disclosure. Any of the following methods can be executed in the robot, or in a server or terminal device that is connected to the robot for communication. In the embodiment of the present disclosure, the robot's processor is used as the execution subject to explain the solution.

[0040] Based on the above robot structure, such as Figure 1 As shown, the embodiment of the present disclosure provides a robot positioning method, comprising:

[0041] S01: The processor constructs a global map and obtains a first position relationship of a target reference object relative to the global map.

[0042] S02: The processor obtains a second position relationship of the robot relative to the target reference object.

[0043] S03: The processor determines the current position of the robot on the global map according to the first position relationship and the second position relationship.

[0044] Among them, the first position relationship includes one or more of the coordinate origin of the global map, the coordinate relationship between the target reference object and the global map, and the spatial relationship between the target reference object and the global map. The coordinate relationship between the target reference object and the global map refers to the specific position and direction of the target reference object on the global map; the spatial relationship between the target reference object and the global map refers to the position and posture of the target reference object on the global map, that is, the rotation and translation vectors. The second position relationship includes one or more of the initial posture of the robot, the relative odometer data, and the corrected posture of the parking point. The initial posture of the robot refers to the starting position and posture of the robot relative to the target reference object or the origin of the global map, which can be obtained by aligning with the target reference object model at the beginning of the task; the relative odometer data refers to the posture change data collected by the robot in real time through the sensor during the inspection process, including speed, acceleration, angular velocity, etc.; the corrected posture of the parking point refers to the current posture of the robot relative to the bogie obtained by aligning with the target reference object model again when the robot reaches the preset parking point. Position parameters refer to coordinates in two-dimensional or three-dimensional space, such as (x, y, z), and attitude parameters describe the direction of the robot relative to the target reference, expressed as a rotation matrix or Euler angles (such as pitch, roll, yaw). The translation vector refers to the straight-line distance and direction from the robot's previous position to the current position. The rotation vector refers to the rotation change of the robot relative to the previous position.

[0045] The robot positioning method provided by the embodiment of the present disclosure is used to construct a global map, obtain a first position relationship of the target reference object relative to the global map, and obtain a second position relationship of the robot relative to the target reference object. Finally, the current position of the robot on the global map is determined based on the first position relationship and the second position relationship. On the basis of determining the absolute position of the target reference object on the global map, the target reference object is used as a reference for the robot's relative odometer to determine the absolute position of the robot on the global map. The combination of absolute positioning correction and relative odometer reduces the accumulation of odometer errors during long-distance operation of the robot, thereby reducing the impact of environmental conditions on positioning accuracy and improving the accuracy of robot positioning.

[0046] Based on the above robot structure, such as Figure 2 As shown, the embodiment of the present disclosure provides a robot positioning method, comprising:

[0047] S21, the processor builds a global map.

[0048] S22, the processor determines a target reference object.

[0049] S23, the processor uses the laser radar to scan the target reference object at multiple angles to obtain multi-view point cloud data.

[0050] S24, the processor uses a coarse registration and a fine registration method to register the multi-view point cloud data to obtain a complete model of the target reference object.

[0051] S25: The processor aligns the model of the target reference object to the global map to obtain a first positional relationship between the target reference object and the global map.

[0052] S02: The processor obtains a second position relationship of the robot relative to the target reference object.

[0053] S03: The processor determines the current position of the robot on the global map according to the first position relationship and the second position relationship.

[0054] Among them, the coarse registration includes the FPFH (Fast Point Feature Histograms) algorithm to extract the features of the point cloud and perform feature matching, and the RANSAC (Random Sample Consensus) algorithm is used to eliminate mismatches to obtain an initial rigid body transformation model. On this basis, Point-to-Plane ICP is used as the fine registration algorithm to further align the point cloud. Finally, multiple point clouds are aligned to the same point cloud to obtain a complete point cloud model of the target reference object.

[0055] The robot positioning method provided by the embodiment of the present disclosure is adopted. The processor constructs a global map and provides a structured representation of the environment as the basis for positioning and path planning. Then, the processor determines the target reference object and uses a laser radar to scan the target reference object from multiple angles to obtain multi-perspective point cloud data. The multi-angle scanning can capture comprehensive information of the target reference object and provide data for creating an accurate model. The coarse registration and fine registration methods are used to align the multi-perspective point cloud data to obtain a complete model of the target reference object. The coarse registration provides a rough alignment, while the fine registration criterion optimizes the registration result through a more accurate algorithm to ensure the accuracy of the model of the target reference object. Finally, the processor aligns the model of the target reference object to the global map to obtain the first position relationship between the target reference object and the global map. By aligning the model of the target reference object with the global map, its position in the global map, that is, the first position relationship, can be accurately determined.

[0056] Optionally, the processor determines the target reference object, including: when the robot starts to perform an inspection task, the processor determines the front of the vehicle as the target reference object; when the robot parks, the processor determines the target reference object according to the current parking position of the robot.

[0057] Among them, the robot parking is determined according to the following method. Specifically, during the movement of the robot, it is judged whether the robot has reached the parking point. If the difference between the current position of the robot and the position of the parking point is less than a preset threshold, it is determined that the robot has reached the corresponding parking point.

[0058] In this way, when the robot starts to perform the inspection task, the processor determines the front of the vehicle as the target reference object. The front of the vehicle as the starting reference object can ensure that the robot has a clear starting reference point at the beginning of the inspection, which is convenient for the robot to locate its initial position relative to the known environment, so that the robot can establish a correspondence with the global map and provide a reference for subsequent path planning and navigation. The robot will move to a new position during the inspection process, and determining a new reference object based on the current position can improve the accuracy of positioning. Therefore, when the robot parks, the processor determines the target reference object based on the current parking position of the robot, allowing the robot to dynamically update the reference object during the inspection process to maintain accurate positioning and map updates.

[0059] Optionally, the processor determines the target reference object based on the current parking position of the robot, including: the processor obtains the current bogie corresponding to the current parking position of the robot; the processor determines the current bogie as the target reference object; wherein each bogie corresponds to at least one parking position.

[0060] In this way, as part of the train structure, the position of the bogie is predetermined and unchanged, and can be used as a stable reference point. In addition, the geometric features and position information of the bogie can be captured by the robot's sensors and used for accurate point cloud registration. Therefore, the processor obtains the current bogie corresponding to the current parking position of the robot, and determines the current bogie as the target reference, allowing the robot to use the bogie as a reference for positioning, thereby improving the accuracy and reliability of positioning. Among them, each bogie corresponds to at least one parking position. In tasks such as train maintenance, specific maintenance work is usually carried out in specific parts of the train. By associating the bogie with the parking position, the robot can repeatedly stop at the correct position to perform the task, thereby providing the robot with a predefined task execution point, ensuring the orderliness and repeatability of the task.

[0061] Based on the above robot structure, such as Figure 3 As shown, the embodiment of the present disclosure provides a robot positioning method, comprising:

[0062] S31, the processor builds an offline map based on simultaneous positioning and mapping technology.

[0063] S32: The processor uses the origin of the offline map as the origin of the world coordinate system to obtain a global map.

[0064] S33: The processor obtains a first position relationship of the target reference object relative to the global map.

[0065] S02: The processor obtains a second position relationship of the robot relative to the target reference object.

[0066] S03: The processor determines the current position of the robot on the global map according to the first position relationship and the second position relationship.

[0067] Among them, SLAM (Simultaneous Localization and Mapping) technology refers to the process in which a robot starts from an unknown position in an unknown environment, perceives the environment through its own sensors (such as lidar, camera, etc.) during movement, realizes its own positioning, and builds an incremental map at the same time. In SLAM, the robot can continuously update and optimize its own position estimation and map information by perceiving the features in the surrounding environment (such as points, lines, surfaces, etc.) and combining its own motion information, thereby helping the robot to achieve autonomous navigation, environmental perception, obstacle avoidance and other functions, and improving the robot's intelligence level and adaptability.

[0068] The robot positioning method provided by the embodiment of the present disclosure is used to construct an offline map based on simultaneous positioning and mapping technology. Through SLAM technology, a map can be constructed while exploring the environment, and the structured information of the environment can be recorded, thereby generating a detailed three-dimensional or two-dimensional representation of the environment for subsequent navigation and positioning. The processor uses the origin of the offline map as the origin of the world coordinate system to obtain a global map. Selecting the origin of an offline map as the origin of the coordinate system can simplify the positioning algorithm and provide a consistent navigation reference for the robot. The global map provides a complete view of the environment, allowing the robot to better understand its workspace and optimize path planning.

[0069] Based on the above robot structure, such as Figure 4 As shown, the embodiment of the present disclosure provides a robot positioning method, comprising:

[0070] S01: The processor constructs a global map and obtains a first position relationship of a target reference object relative to the global map.

[0071] S41, the processor determines the position of the target reference object as the initial position.

[0072] S42, the processor detects the distance and direction of the robot's current position relative to the initial position to obtain a second position relationship.

[0073] S03: The processor determines the current position of the robot on the global map according to the first position relationship and the second position relationship.

[0074] Using the robot positioning method provided by the embodiment of the present disclosure, the initial position is the reference for robot positioning, which can be a specific reference object, such as a car head or a bogie, whose position has exact coordinates on the global map. Therefore, the processor determines the position of the target reference object as the initial position, providing a clear starting point for the robot, and all subsequent positioning and navigation will be based on the initial position. The processor detects the distance and direction of the robot's current position relative to the initial position to obtain a second position relationship. By measuring the distance and direction to the initial position, the robot can calculate its position on the global map, and even in an indoor environment or tunnel without a GPS signal, the robot can know how far it is from the initial position and what the direction is. Thereby establishing an accurate second position relationship between the robot's current position and the initial position.

[0075] Optionally, the processor determines the current position of the robot on the global map based on the first position relationship and the second position relationship, including: the processor determines the relative position of the robot and the target reference object based on the second position relationship; the processor calculates the current position of the robot on the global map based on the relative position and the first position relationship.

[0076] Among them, the processor calculates the current position of the robot on the global map according to the relative position and the first position relationship, including: using coordinate transformation (such as homogeneous coordinate transformation) to transform the local posture of the robot into the global coordinate system. In actual application, the origin of the global map is the reference point of the world coordinate system, denoted as O. The position of the bogie model on the global map is P (Px, Py, Pz, R, P), (Px, Py, Pz) is the position coordinate, R is the rotation matrix, P is the point cloud data of the bogie model, and the initial posture of the robot is I (Ix, Iy, Iz, RI), where (Ix, Iy, Iz) is the position coordinate of the robot relative to the bogie, and RI is the rotation matrix of the robot relative to the bogie. The robot starts the inspection task at the starting position I. During the movement of the robot, the IMU provides angular velocity and acceleration data, and the wheel speed encoder provides wheel speed data. According to the sensor data, the local posture change of the robot is calculated, denoted as △ (Δx, △ y, △ z, △ R). The robot reaches the new parking point, recorded as I', and then uses the laser radar to scan and align to obtain the new local pose I' (I'x, I'y, I'z, R{I'}). Use coordinate transformation (such as homogeneous coordinate transformation) to transform the robot's local pose into the global coordinate system: Gx = Px + R × (I'x - Ix) + △ x,Gy=Py+R×(I'y-Iy)+ △ y,Gz=Pz+R×(I'z-Iz)+△ z, GR = R × RI × R{I'} × ΔR, where G (Gx, Gy, Gz, GR) is the current position and posture of the robot on the global map.

[0077] In this way, the second position relationship is the distance and direction between the robot and the target reference object detected in real time by sensor data after the robot starts from the initial position, which can provide the robot with real-time position information relative to the target reference object. The first position relationship is predetermined, and it describes the position of the target reference object on the global map. Therefore, combined with the second position relationship, the robot can calculate its own position on the global map. By combining the relative position and the predetermined position relationship, the robot can correct the sensor error and improve the accuracy of positioning.

[0078] Combination Figure 5 As shown, the embodiment of the present disclosure provides a robot positioning device 800, including a processor (processor) 801 and a memory (memory) 802. Optionally, the device may also include a communication interface (Communication Interface) 803 and a bus 804. Among them, the processor 801, the communication interface 803, and the memory 802 can communicate with each other through the bus 804. The communication interface 803 can be used for information transmission. The processor 801 can call the logic instructions in the memory 802 to execute the robot positioning method of the above embodiment.

[0079] In addition, the logic instructions in the memory 802 described above may be implemented in the form of software functional units and when sold or used as independent products, may be stored in a computer-readable storage medium.

[0080] The memory 802 is a computer-readable storage medium that can be used to store software programs and computer executable programs, such as program instructions / modules corresponding to the method in the embodiment of the present disclosure. The processor 801 executes the function application and data processing by running the program instructions / modules stored in the memory 802, that is, the robot positioning method in the above embodiment is implemented.

[0081] The memory 802 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application required for at least one function; the data storage area may store data created according to the use of the terminal device, etc. In addition, the memory 802 may include a high-speed random access memory and may also include a non-volatile memory.

[0082] Combination Figure 6As shown, the embodiment of the present disclosure provides a robot 900, including: a robot body, and the above-mentioned robot positioning device 800. The robot positioning device 800 is installed on the robot body. The installation relationship described here is not limited to placement inside the robot, but also includes installation connections with other components of the robot, including but not limited to physical connections, electrical connections or signal transmission connections, etc. It can be understood by those skilled in the art that the robot positioning device 800 can be adapted to a feasible robot body, thereby realizing other feasible embodiments.

[0083] An embodiment of the present disclosure provides a computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions are configured to execute the above-mentioned robot positioning method.

[0084] The technical solution of the embodiment of the present disclosure can be embodied in the form of a software product, which is stored in a storage medium and includes one or more instructions for enabling a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in the embodiment of the present disclosure. The aforementioned storage medium may be a non-transient storage medium, including: 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, and other media that can store program codes.

[0085] The above description and the accompanying drawings sufficiently illustrate the embodiments of the present disclosure to enable those skilled in the art to practice them. Other embodiments may include structural, logical, electrical, process, and other changes. The embodiments represent only possible variations. Unless explicitly required, individual components and functions are optional, and the order of operations may vary. Portions and features of some embodiments may be included in or replace portions and features of other embodiments. Moreover, the terms used in this application are only used to describe the embodiments and are not used to limit the claims. As used in the description of the embodiments and the claims, the singular forms "a" and "an" shall be construed as "a", "an" and "an" unless the context clearly indicates otherwise.

[0086] (a), "an" and "the" are intended to include plural forms as well. Similarly, the term "and / or" as used in this application is intended to include any and all possible combinations of one or more of the associated listed items. In addition, when used in this application, the term "including"

[0087] "comprise" and its variations "comprises" and / or comprising refer to the presence of stated features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or groups thereof. In the absence of further restrictions, the elements defined by the sentence "comprise one..." do not exclude the presence of other identical elements in the process, method or device including the elements. In this article, each embodiment may focus on the differences from other embodiments, and the same and similar parts between the various embodiments may refer to each other. For the methods, products, etc. disclosed in the embodiments, if they correspond to the method part disclosed in the embodiments, then the relevant parts can refer to the description of the method part.

[0088] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software may depend on the specific application and design constraints of the technical solution. The technicians may use different methods for each specific application to implement the described functions, but such implementations should not be considered to exceed the scope of the embodiments of the present disclosure. The technicians may clearly understand that, for the convenience and simplicity of description, the specific working processes of the systems, devices and units described above may refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here.

[0089] In the embodiments disclosed herein, the disclosed methods and products (including but not limited to devices, equipment, etc.) can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units can be only a logical function division. There may be other division methods in actual implementation, such as multiple 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 each other shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms. The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to implement this embodiment. In addition, each functional unit in the embodiment of the present disclosure may be integrated in a processing unit, or each unit may exist physically alone, or two or more units may be integrated in one unit.

[0090] The flowchart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system, method and computer program product according to the embodiment of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of the code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. In some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. In the description corresponding to the flowchart and the block diagram in the accompanying drawings, the operations or steps corresponding to different boxes can also occur in a different order from the order disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. Each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented by a dedicated hardware-based system that performs the specified functions or actions, or may be implemented by a combination of dedicated hardware and computer instructions.

Claims

1. A robot positioning method, characterized in that: include: Constructing a global map and obtaining a first position relationship of the target reference object relative to the global map; Acquire a second position relationship of the robot relative to the target reference object; The current position of the robot on the global map is determined according to the first position relationship and the second position relationship.

2. The method according to claim 1, characterized in that Obtaining the first position relationship of the target reference object relative to the global map includes: Determine the target reference; Use laser radar to scan the target reference object at multiple angles to obtain multi-view point cloud data; Use coarse registration and fine registration methods to register multi-view point cloud data to obtain a complete model of the target reference object; The model of the target reference object is aligned to the global map to obtain a first positional relationship between the target reference object and the global map.

3. The method according to claim 2, characterized in that Determine the target reference, including: When the robot starts to perform the inspection task, it determines the front of the vehicle as the target reference; When the robot parks, the target reference object is determined based on the current parking position of the robot.

4. The method according to claim 3, characterized in that According to the current parking position of the robot, determine the target reference, including: Get the current bogie corresponding to the current parking position of the robot; Determine the current bogie as the target reference object; Each bogie corresponds to at least one parking position.

5. The method according to any one of claims 1 to 4, characterized in that: Build a global map, including: Build offline maps based on simultaneous positioning and mapping technology; The origin of the offline map is used as the origin of the world coordinate system to obtain the global map.

6. The method according to any one of claims 1 to 4, characterized in that: Obtaining a second position relationship of the robot relative to the target reference object, including: Determine the position of the target reference object as the initial position; The distance and direction of the robot's current position relative to the initial position are detected to obtain a second position relationship.

7. The method according to any one of claims 1 to 4, characterized in that: Determining a current position of the robot on the global map according to the first position relationship and the second position relationship includes: Determining the relative position of the robot and the target reference object according to the second position relationship; According to the relative position and the first position relationship, the current position of the robot on the global map is calculated.

8. A robot positioning device, comprising a processor and a memory storing program instructions, characterized in that: The processor is configured to execute the robot positioning method according to any one of claims 1 to 7 when running the program instructions.

9. A robot, characterized in that: include: Robot body; The robot positioning device according to claim 8 is installed on the robot body.

10. A computer-readable storage medium storing program instructions, characterized in that: When the program instructions are executed, the computer is used to execute the robot positioning method according to any one of claims 1 to 7.

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