A positioning method, an electronic terminal and a computer readable storage medium

Abstract

This application provides a positioning method, an electronic terminal, and a computer-readable storage medium. The positioning method includes: acquiring first point cloud data and compensating for the first point cloud data to obtain second point cloud data; matching the second point cloud data with a grid map to obtain a first matching result; and determining the pose of a target object based on the first matching result. This positioning method, by compensating for the acquired first point cloud data and then matching the compensated second point cloud data with a grid map in loop closure detection, can improve the positioning accuracy of the target object in loop closure detection.

Description

Technical Field

[0001] This application relates to the field of navigation and positioning technology, and in particular to a positioning method, an electronic terminal, and a computer-readable storage medium. Background Technology

[0002] Robot localization is the core foundation and key prerequisite for robots to achieve autonomous movement, environmental interaction, and task execution. Its performance directly determines the operational accuracy, motion safety, and scene adaptability of the target object. Loop closure detection is an important step in the robot localization process.

[0003] In loop closure detection, the collected point cloud data needs to be matched with the constructed grid map to determine the robot's pose. However, due to the ranging error of the sensors, there will be matching errors between the point cloud data and the grid map, which will affect the robot's positioning accuracy. Summary of the Invention

[0004] This application provides a positioning method, an electronic terminal, and a computer-readable storage medium to improve the positioning accuracy of target objects in loop closure detection.

[0005] To solve the above-mentioned technical problems, the first technical solution adopted in this application is: to provide a positioning method, including:

[0006] Acquire the first point cloud data and compensate for it to obtain the second point cloud data;

[0007] The second point cloud data is matched with the raster map to obtain the first matching result;

[0008] Determine the pose of the target object based on the first matching result;

[0009] The first point cloud data is compensated to obtain the second point cloud data, including:

[0010] The distance parameter representing the first point cloud data is compensated using the error compensation parameter to obtain the second point cloud data. The distance parameter represents the distance from the first point cloud data to the sensor.

[0011] In one embodiment, the method further includes:

[0012] The first point cloud data is matched with the raster map to obtain the second matching result;

[0013] Determining the pose of the target object based on the first matching result includes:

[0014] The pose of the target object is determined based on the first and second matching results.

[0015] In one embodiment, the error compensation parameters are determined based on the resolution of the raster map and / or the performance parameters of the sensor;

[0016] The sensor's performance parameters include at least one of the following: the sensor's ranging accuracy, the sensor's reflection intensity, and the sensor's error parameters.

[0017] In one embodiment, the error compensation parameters are further determined based on the error of the raster map;

[0018] The error of the raster map is determined based on the number of point cloud data frames in the local raster map and the number of point cloud data frames in the global raster map.

[0019] In one embodiment, the error compensation parameters are determined based on the resolution of the raster map and the error parameters of the sensor;

[0020] If the sensor's error parameter is less than or equal to the grid map's resolution, then the error compensation parameter is determined based on the grid map's resolution.

[0021] If the sensor's error parameters are greater than the resolution of the raster map, then there are multiple error compensation parameters, and these multiple error compensation parameters increase in increments of a preset step value.

[0022] In one embodiment, the preset step value is the resolution of the raster map.

[0023] In one embodiment, the distance parameter represented by the first point cloud data is compensated using an error compensation parameter to obtain the second point cloud data, including:

[0024] Multiple error compensation parameters are used to compensate for the distance parameters represented by the first point cloud data to obtain multiple second point cloud data.

[0025] The second point cloud data is matched with the raster map to obtain the first matching result, including:

[0026] Multiple second-point cloud data points are matched with the raster map to obtain multiple first-point matching results;

[0027] Determining the pose of the target object based on the first and second matching results includes:

[0028] The pose of the target object is obtained by weighted summation of multiple first and second matching results and calculating the average value.

[0029] To solve the above-mentioned technical problems, the second technical solution adopted in this application is: to provide an electronic terminal, which includes a memory and a processor coupled to each other, the processor being used to execute program instructions stored in the memory, and the processor being used to execute program data to implement the steps in the positioning method described above.

[0030] To solve the above-mentioned technical problems, the third technical solution adopted in this application is: to provide a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps in the positioning method described above.

[0031] The beneficial effects of this application are as follows: Unlike existing technologies, the provided positioning method includes: acquiring first point cloud data and compensating for it to obtain second point cloud data; matching the second point cloud data with a grid map to obtain a first matching result; and determining the pose of the target object based on the first matching result. This positioning method, by compensating for the acquired first point cloud data and then matching the compensated second point cloud data with a grid map in loop closure detection, can improve the positioning accuracy of the target object in loop closure detection. Attached Figure Description

[0032] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0033] Figure 1 A flowchart illustrating the first embodiment of the positioning method provided in this application;

[0034] Figure 2a This is a schematic diagram of the first point cloud data;

[0035] Figure 2b This is a schematic diagram illustrating the first point cloud data matching with a raster map in existing technology;

[0036] Figure 2c This is a schematic diagram of an embodiment for compensating the first point cloud data;

[0037] Figure 2d This is a schematic diagram of another embodiment for compensating the first point cloud data;

[0038] Figure 3 This is a schematic diagram illustrating the matching of cloud data with a raster map.

[0039] Figure 4 This is a flowchart illustrating a second embodiment of the positioning method provided in this application;

[0040] Figure 5 This is a schematic diagram of the framework of an embodiment of the electronic terminal provided in this application;

[0041] Figure 6This is a schematic diagram of a framework of an embodiment of the computer-readable storage medium provided in this application. Detailed Implementation

[0042] The embodiments of this application will now be described in detail with reference to the accompanying drawings.

[0043] Before providing a further detailed description of the embodiments of this application, the nouns and terms involved in the embodiments of this application will be explained, and the nouns and terms involved in the embodiments of this application shall be interpreted as follows.

[0044] In the following description, specific details such as particular system architectures, interfaces, and technologies are presented for illustrative purposes rather than for limiting purposes, in order to provide a thorough understanding of this application.

[0045] In this article, the term "and / or" simply describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. Additionally, the character " / " generally indicates that the preceding and following related objects have an "or" relationship. Furthermore, "more" in this article means two or more objects.

[0046] 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 embodiments of this application only and is not intended to limit this application.

[0047] Target object localization is the core foundation and key prerequisite for its autonomous movement, environmental interaction, and task execution. Its performance directly determines the target object's operational accuracy, movement safety, and scene adaptability. Loop closure detection is a crucial step in the target object localization process.

[0048] The inventors of this application have discovered that in loop closure detection, point cloud data is acquired using lidar. However, when lidar measures distance, the angle between the line connecting two adjacent lidar points and the center of the lidar is fixed. This results in the lidar point cloud being denser closer to the target object (the target object refers to the object on which the lidar is installed, such as a robot) and sparser further away from the target object. For example... Figure 2a As shown, O represents the target object. The laser point cloud A1 located below the target object O is closer to the target object O, while the laser point cloud A2 located above the target object O is farther away from the target object O. Since the angles n1 and n2 between any two adjacent laser point clouds and the center of the lidar on the target object O are the same, the laser point cloud A1 located below the target object O is denser, while the laser point cloud A2 located above the target object O is sparser.

[0049] See Figure 2bDuring loop closure detection, more laser point clouds are matched with the grid map B, and the pose of the target object O is determined based on the matching results. However, since the laser point cloud A1 located below the target object O is relatively dense, if the laser point cloud A1 located below the target object O is completely matched with the grid map B, it will cause a matching error between the laser point cloud A2 located above the target object O and the grid map B. That is, the laser point cloud A2 cannot completely fall into the grid map B, which will affect the positioning accuracy of the target object O in loop closure detection.

[0050] In view of this, this application provides a positioning method. The positioning method of this application compensates the acquired first point cloud data, and in loop closure detection, it uses the compensated second point cloud data to match with the grid map, thereby determining the pose of the target object, which can improve the positioning accuracy of the target object in loop closure detection.

[0051] To enable those skilled in the art to better understand the technical solution of this application, a positioning method provided by this application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0052] Please see Figure 1 The above is a flowchart illustrating the first embodiment of the positioning method of this application, specifically including:

[0053] Step S11: Obtain the first point cloud data and compensate for it to obtain the second point cloud data.

[0054] The first point cloud data is collected using sensors installed on the target object. The collected first point cloud data is as follows: Figure 2a Point cloud data A1 and point cloud data A2 are shown in the figure. Sensors include lidar (e.g., mechanical lidar, solid-state lidar, etc.), vision systems (e.g., depth cameras, structured light cameras, binocular cameras, etc.), etc., and are not specifically limited. This application embodiment uses lidar as an example for illustration.

[0055] Specifically, a laser pulse is emitted by a lidar sensor mounted on the target object. When the laser pulse encounters an obstacle in the environment, it is reflected. The reflected signal is received by the lidar. Based on the received reflected signal, the distance between the reflection point and the target object, the reflection angle, etc., are determined and calculated to obtain the three-dimensional spatial coordinates (x, y, z) of the reflection point, thus obtaining the first point cloud data. In essence, the reflection point is the point on the obstacle where the laser pulse is projected, and the first point cloud data represents the three-dimensional coordinates of that point, effectively indicating the location of the obstacle.

[0056] After acquiring the first point cloud data, the first point cloud data is compensated to obtain the second point cloud data. In one embodiment of this application, error compensation parameters are used to compensate for the distance parameters represented by the first point cloud data to obtain the second point cloud data. It should be noted that the reflected signal after the laser pulse is projected onto an obstacle can determine the distance between the obstacle and the target object. Based on this distance and parameters such as the reflection angle of the reflected signal, the first point cloud data can be calculated. Since the matching error between the laser point cloud and the grid map is caused by the ranging distance of the laser radar (the distance from the target object to the obstacle), this application uses error compensation parameters to compensate for the distance parameters represented by the first point cloud data to obtain the second point cloud data. It can be understood that the distance parameters represented by the first point cloud data represent the distance from the first point cloud data (i.e., the obstacle) to the sensor (i.e., the target object), such as... Figure 2b As shown in k.

[0057] In one embodiment, the distance parameter represented by the first point cloud data is denoted as... The error compensation parameter is denoted as Then the compensated distance parameter for: Based on the compensated distance parameters Determine the second point of cloud data.

[0058] In one embodiment, the error compensation parameters are determined based on the resolution of the raster map and the error parameters of the sensor.

[0059] It should be noted that the sensor's error parameter represents the maximum absolute value of the deviation between the actual measured value and the true value of the target within the sensor's nominal operating range. This includes the maximum ranging error and / or the maximum angle measurement error. In a specific embodiment of this application, the sensor's error parameter is the maximum ranging error, which ensures the maximum possible deviation when the lidar measures the target distance. A grid map discretizes continuous space into a regular grid (grid) array. The resolution of a grid map refers to the side length of the actual physical space corresponding to a single grid cell, such as... Figure 2b As shown in d.

[0060] In one embodiment, if the sensor's error parameter is less than or equal to the grid map's resolution, then the error compensation parameter is determined based on the grid map's resolution. It should be noted that if the sensor's error parameter is less than or equal to the grid map's resolution, it can be assumed that the error is highly likely caused by the grid map's resolution, and in this case, the error compensation parameter is determined based on the grid map's resolution.

[0061] In loop closure detection, matching the point cloud with the raster map involves determining whether the point cloud falls within the raster map. For example... Figure 2c and Figure 2d As shown, where, Figure 2c The resolution d1 of the raster map shown is greater than Figure 2d The resolution d2 of the raster map shown is [not specified]. When point cloud data A2 does not fall within raster map B, its distance parameter k needs to be compensated. Figure 2c In the context of raster maps, where the resolution d1 is relatively high, error compensation parameters can be set. 1 is within 0.5 raster resolution, that is... If the value is within the range of 0 to 0.5 d1, then point cloud data A2 can fall into raster map B; Figure 2d In this context, the resolution d2 of the raster map is relatively small, so error compensation parameters need to be set. 2 is within 0.5-1 raster resolution, that is... 2. If the value is within the range of 0.5d² to d², then point cloud data A2 will fall into raster map B. Based on this, the error compensation parameter is negatively correlated with the resolution of the raster map; that is, the larger the resolution of the raster map, the smaller the error compensation parameter, and vice versa.

[0062] In one embodiment of this application, if the sensor's error parameter is greater than the resolution of the raster map, then the error compensation parameters include multiple parameters, and these multiple error compensation parameters increase in increments of a preset step value. The preset step value is the resolution of the raster map.

[0063] In one embodiment, assuming the grid map resolution is 5cm and the LiDAR error parameter is 11cm, then the error compensation parameter is... There are three parameters: 5cm, 10cm, and 15cm. These three error compensation parameters increase in preset step values ​​of 5cm. In this embodiment, the preset step value is equal to the resolution of the raster map, which is 5cm. In another embodiment, the preset step value can be greater than or less than the resolution of the raster map.

[0064] In some embodiments, the calculation of the error compensation distance can be more detailed to meet the demand for higher accuracy. Based on this, the error compensation parameters are determined based on the resolution of the raster map and / or the performance parameters of the sensor, wherein the sensor performance parameters are at least one of: the sensor's ranging accuracy, the sensor's reflectivity, and the sensor's error parameters. When multiple error terms (including the resolution of the raster map and / or the performance parameters of the sensor) are used simultaneously to calculate the error compensation distance, coefficients can be added to control the contribution of different error terms to the error compensation parameters. The coefficients of different error terms can be optimized and adjusted based on testing.

[0065] Specifically, the error compensation parameters are calculated as follows:

[0066] .

[0067] in, This represents the i-th error term (including the resolution of the raster map and / or the performance parameters of the sensor). This represents the coefficient of the i error terms.

[0068] It should be noted that the sensor's ranging accuracy, reflection intensity, and error parameters can be obtained from the sensor's hardware manual.

[0069] In another embodiment of this application, the error compensation parameter can also be determined based on the error of the raster map; the error of the raster map can be determined based on the number of point cloud data frames in the local raster map and the number of point cloud data frames in the global raster map.

[0070] It's important to note that because ranging errors in point cloud data accumulate on the raster map, the magnitude of these errors also affects the calculation of error compensation parameters. Understandably, local raster map construction times are shorter, resulting in smaller accumulated errors, while global raster map construction times are longer, leading to larger accumulated errors. Therefore, when compensating for point cloud data, the accumulated error is introduced from the map construction time, thereby improving error compensation accuracy. It's also understood that longer map construction times involve more point cloud data frames, so the number of point cloud data frames can be used to characterize map construction time.

[0071] Specifically, in this embodiment, the error compensation parameter is calculated as follows:

[0072] .

[0073] Where n represents the number of point cloud data frames in the local raster map, and N represents the number of point cloud data frames in the global raster map.

[0074] Specifically, a global grid map refers to a grid map that covers the entire area traversed by the target object; a local grid map refers to a grid map of a small area currently traversed by the target object, recording environmental information of a predetermined area around the target object.

[0075] After calculating the error compensation parameters through the above process, the distance parameters represented by the first point cloud data are compensated using the error compensation parameters to obtain the second point cloud data. If there are multiple error compensation parameters, the distance parameters represented by the first point cloud data are compensated using each of the multiple error compensation parameters to obtain multiple second point cloud data.

[0076] Step S12: Match the second point cloud data with the raster map to obtain the first matching result.

[0077] The second point cloud data is matched with the raster map to obtain the first matching result. The first matching result includes the matching score and the pose of the target object.

[0078] Ideally, during matching, all second-point cloud data should fall into the raster map, such as... Figure 3 As shown, in this case, the matching score is 100%, and the pose of the target object can be further determined based on the successfully matched second point cloud data. However, in reality, most of the second point cloud data may fall into the grid map, while a very small portion of the second point cloud data may not. Assuming there are 50 second point cloud data, 48 of them fall into the grid map, and the remaining 2 do not, the matching score is 48 / 50 = 0.96. Based on the matched second point cloud data, the pose of the target object is determined, thus obtaining the first matching result.

[0079] Step S13: Determine the pose of the target object based on the first matching result.

[0080] The pose of the target object represented by the first matching result is determined as the final pose of the target object.

[0081] Understandably, if there are multiple error compensation parameters, then the distance parameter represented by the first point cloud data is compensated using these multiple error compensation parameters respectively, resulting in multiple second point cloud data. These multiple second point cloud data are then matched with the raster map to obtain multiple first matching results. The location of the target object is determined based on these multiple first matching results. Specifically, the matching score can be used as a weight to calculate a weighted average of the target object's pose, yielding the final pose of the target object. This can be represented as follows:

[0082] .

[0083] in, This represents the pose of the target object, and m represents the number of the first matching results. This represents the pose of the target object in the j-th first matching result. This represents the matching score in the j-th first matching result.

[0084] The positioning method in this embodiment compensates for the first point cloud data, which can effectively optimize the pose error generated during the point cloud matching process, so that the pose of the target object will not shift, and improve the positioning accuracy of the target object in loop closure detection.

[0085] Combination Figure 4 , Figure 4 This is a flowchart illustrating the second embodiment of the positioning method of this application. In this embodiment, steps S21 and S22 are the same as those described above. Figure 1In the first embodiment shown, steps S11 and S12 are the same, the difference being that this embodiment further includes the following after step S22:

[0086] Step S23: Match the first point cloud data with the raster map to obtain the second matching result.

[0087] The uncompensated raw first point cloud data is matched with the raster map. The pose of the target object is determined based on the successfully matched first point cloud data, and a matching score is obtained, thus obtaining the second matching result.

[0088] Step S24: Determine the pose of the target object based on the first matching result and the second matching result.

[0089] Specifically, multiple first matching results and second matching results are weighted and summed, and the average value is calculated to obtain the pose of the target object. In one embodiment, the pose of the target object in the first matching result and the pose of the target object in the second matching result are weighted and averaged based on the corresponding matching scores to determine the pose of the target object.

[0090] It should be noted that the pose of the target object obtained without compensation may also be correct. In this embodiment, the uncompensated matching result and the compensated matching result are combined to further improve the positioning accuracy.

[0091] Please see Figure 5 , Figure 5 This is a schematic diagram of a framework of an embodiment of the electronic terminal provided in this application. The electronic terminal 80 includes a memory 81 and a processor 82 coupled to each other. The processor 82 is used to execute program instructions stored in the memory 81 to implement the steps of any of the above-described positioning method embodiments. In a specific implementation scenario, the electronic terminal 80 may include, but is not limited to, a microcomputer or a server. In addition, the electronic terminal 80 may also include mobile devices such as laptops and tablets, which are not limited here.

[0092] Specifically, processor 82 controls itself and memory 81 to implement the steps of any of the above-described positioning method embodiments. Processor 82 may also be referred to as a CPU (Central Processing Unit). Processor 82 may be an integrated circuit chip with signal processing capabilities. Processor 82 may also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. A general-purpose processor may be a microprocessor or any conventional processor. Furthermore, processor 82 may be implemented using integrated circuit chips.

[0093] Please see Figure 6 , Figure 6 This is a schematic diagram of a framework of an embodiment of the computer-readable storage medium provided in this application. The computer-readable storage medium 90 stores program instructions 901 that can be executed by a processor. The program instructions 901 are used to implement the steps of any of the above-described positioning method embodiments.

[0094] In some embodiments, the functions or modules of the apparatus provided in this disclosure can be used to perform the methods described in the above method embodiments. The specific implementation can be referred to the description of the above method embodiments, and for the sake of brevity, it will not be repeated here.

[0095] The description of the various embodiments above tends to emphasize the differences between the various embodiments. The similarities or similarities between them can be referred to, and for the sake of brevity, they will not be repeated here.

[0096] In the several embodiments provided in this application, it should be understood that the disclosed methods and apparatus can be implemented in other ways. For example, the apparatus implementations described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection of devices or units may be electrical, mechanical, or other forms.

[0097] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0098] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods of various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0099] The above are merely embodiments of this application and do not limit the scope of patent protection of this application. Any equivalent structural or procedural changes made using the content of this application’s specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of this application.

Claims

1. A positioning method, characterized by, include: Acquire the first point cloud data and compensate for it to obtain the second point cloud data; The second point cloud data is matched with the raster map to obtain the first matching result; The pose of the target object is determined based on the first matching result; The process of compensating the first point cloud data to obtain the second point cloud data includes: The distance parameter representing the first point cloud data is compensated using the error compensation parameter to obtain the second point cloud data, wherein the distance parameter represents the distance from the first point cloud data to the sensor; The error compensation parameters are determined based on the resolution of the raster map and the performance parameters of the sensor.

2. The positioning method according to claim 1, characterized in that, The method further includes: The first point cloud data is matched with the raster map to obtain a second matching result; Determining the pose of the target object based on the first matching result includes: The pose of the target object is determined based on the first matching result and the second matching result.

3. The positioning method according to claim 1, characterized in that, The performance parameters of the sensor are at least one of the following: the ranging accuracy of the sensor, the reflection intensity of the sensor, and the error parameter of the sensor.

4. The positioning method of claim 1, wherein, The error compensation parameters are also determined based on the error of the raster map; The error of the raster map is determined based on the number of point cloud data frames in the local raster map and the number of point cloud data frames in the global raster map.

5. The positioning method of claim 2, wherein, The error compensation parameters are determined based on the resolution of the raster map and the error parameters of the sensor; If the error parameter of the sensor is less than or equal to the resolution of the grid map, then the error compensation parameter is determined based on the resolution of the grid map; If the error parameter of the sensor is greater than the resolution of the grid map, then the error compensation parameter includes multiple parameters, and the multiple error compensation parameters increase in increments of a preset step value.

6. The positioning method according to claim 5, characterized in that, The preset step value is the resolution of the raster map.

7. The positioning method of claim 5, wherein, The distance parameters represented by the first point cloud data are compensated using error compensation parameters to obtain the second point cloud data, including: The distance parameters represented by the first point cloud data are compensated using multiple error compensation parameters to obtain multiple second point cloud data. The second point cloud data is matched with the raster map to obtain a first matching result, including: Multiple sets of second point cloud data are matched with the grid map to obtain multiple sets of first matching results; Determining the pose of the target object based on the first matching result and the second matching result includes: The pose of the target object is obtained by weighted summation of multiple first matching results and second matching results and calculating the average value.

8. An electronic terminal, characterized in that The electronic terminal includes a memory and a processor coupled to each other. The processor is used to execute program instructions stored in the memory and to execute program data to implement the steps in the positioning method as described in any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the positioning method as described in any one of claims 1 to 7.

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