Method and device for determining height measurement precision

Abstract

The invention discloses a method and device for determining height measurement precision, relates to the technical field of vehicle-mounted laser radars, objectively reflects the measurement height of a laser radar through the distance between planes, and improves the accuracy of the height measurement precision. The method comprises the steps of obtaining point cloud data corresponding to at least two reference elements in a static scene, selecting the point cloud data corresponding to the two reference elements, fitting the point cloud data corresponding to the two reference elements by using a plane fitting mode to obtain a first plane and a second plane, determining a plane distance between the first plane and the second plane, and determining the plane distance between the first plane and the second plane. And calculating the height measurement precision of the laser radar according to the plane distance.

Description

Technical Field

[0001] The present application relates to the technical field of vehicle-mounted laser radar, and in particular to a method and device for determining height measurement accuracy. Background Art

[0002] As the core device for autonomous vehicles to monitor the surrounding environment, the on-board LiDAR accurately measures parameters such as target distance and height, which is an important factor in its perception and judgment of the driving environment. Effectively determining the height measurement accuracy can largely determine the measurement performance of the entire on-board LiDAR.

[0003] Related technologies In the actual use of LiDAR for algorithm development, since LiDAR has the problem of point cloud "creep" after leaving the factory, it is difficult to measure accurately with high-precision equipment in the laboratory. As a solution after the LiDAR is installed on the vehicle, a known rectangular target plate is usually used to compare the height value of the target measured by the LiDAR with the height value of the actual target to determine the height measurement accuracy of the LiDAR. However, considering that the point cloud obtained by the LiDAR scan is discrete, and the LiDAR spot has a certain size that will cause edge blur, the radar boundary will be very uneven when using certain scanning methods that are not strictly horizontal and line scanning, resulting in a large subjectivity in the determination of the target boundary and errors, resulting in large errors in the height measurement accuracy. Summary of the invention

[0004] In view of this, the present application provides a method and device for determining height measurement accuracy, the main purpose of which is to solve the problem that the determination of target boundaries in the prior art is relatively subjective and produces errors, resulting in large errors in height measurement accuracy.

[0005] According to a first aspect of the present application, a method for determining height measurement accuracy is provided, the method comprising:

[0006] Acquire point cloud data corresponding to at least two reference elements in a static scene;

[0007] Selecting point cloud data corresponding to two reference elements, and fitting the point cloud data corresponding to the two reference elements using a plane fitting method to obtain a first plane and a second plane;

[0008] A plane distance between the first plane and the second plane is determined, and a height measurement accuracy of the laser radar is calculated according to the plane distance.

[0009] Furthermore, the step of obtaining point cloud data corresponding to at least two reference elements in the static scene specifically includes:

[0010] Pre-setting at least two horizontally parallel reference elements in a static scene, and obtaining original point cloud data obtained by scanning the laser radar in the static scene;

[0011] Point cloud data corresponding to at least two reference elements are acquired from the original point cloud data.

[0012] Further, the reference element includes at least a plane and / or a line. When the reference element includes a plane, acquiring point cloud data corresponding to at least two reference elements from the original point cloud data specifically includes:

[0013] Using the spatial features of the plane, acquiring point cloud data corresponding to at least two planes from the original point cloud data;

[0014] When the reference element includes a line, acquiring point cloud data corresponding to at least two reference elements from the original point cloud data specifically includes:

[0015] The original point cloud data is divided by utilizing the reflectivity difference between the lines and other elements in the static scene, and point cloud data corresponding to at least two lines are obtained from the original point cloud data.

[0016] Furthermore, after acquiring the point cloud data corresponding to at least two reference elements in the static scene, the method further includes:

[0017] The coordinate points in the point cloud data that do not meet the plane condition are eliminated by utilizing the difference between different coordinate points in the point cloud data and the plane equation, and the point cloud data of the adjacent plane is retained.

[0018] Furthermore, the method of utilizing the difference between different coordinate points in the point cloud data and the plane equation to remove the interfering coordinate points in the point cloud data and retain the point cloud data of the adjacent plane specifically includes:

[0019] Using random iteration to construct multiple plane equations for the coordinate points in the point cloud data, for each plane equation;

[0020] Count the distance differences between the coordinate points and the plane in each plane calculation point cloud data, and select the point cloud data of the coordinate points whose distance is less than the set value as the adjacent point cloud data;

[0021] The point cloud data of the adjacent plane is retained by utilizing the proportion of the neighbor point cloud data in the point cloud data.

[0022] Furthermore, after acquiring the point cloud data corresponding to at least two reference elements in the static scene, the method further includes:

[0023] The coordinate points in the point cloud data that do not meet the scene conditions are removed by using the position features of the reference elements in the static scene, and the point cloud data of the reference elements are retained.

[0024] Further, the determining the plane distance between the first plane and the second plane, and calculating the height measurement accuracy of the laser radar according to the plane distance, specifically includes:

[0025] Determine a plane distance between the first plane and the second plane using a plane equation corresponding to the first plane and a plane equation corresponding to the second plane;

[0026] Using a measuring device to obtain a real distance between the first plane and the second plane;

[0027] The height measurement accuracy of the laser radar is calculated by comparing the plane distance with the real distance.

[0028] According to a second aspect of the present application, a device for determining height measurement accuracy is provided, the device comprising:

[0029] An acquisition unit, used to acquire point cloud data corresponding to at least two reference elements in a static scene;

[0030] A fitting unit, used for selecting point cloud data corresponding to two reference elements, and fitting the point cloud data corresponding to the two reference elements respectively to obtain a first plane and a second plane by using a plane fitting method;

[0031] A determination unit is used to determine a plane distance between the first plane and the second plane, and calculate the height measurement accuracy of the laser radar according to the plane distance.

[0032] Furthermore, the acquisition unit includes:

[0033] A setting module, used to pre-set at least two horizontally parallel reference elements in a static scene, and obtain original point cloud data scanned by the laser radar in the static scene;

[0034] The acquisition module is used to acquire point cloud data corresponding to at least two reference elements from the original point cloud data.

[0035] Furthermore, the reference element includes at least a plane and / or a line. When the reference element includes a plane, the acquisition module is specifically used to acquire point cloud data corresponding to at least two planes from the original point cloud data by utilizing the spatial characteristics of the plane.

[0036] When the reference element includes a line, the acquisition module is specifically used to divide the original point cloud data by utilizing the reflectivity difference between the line and other elements in the static scene, and to acquire point cloud data corresponding to at least two lines from the original point cloud data.

[0037] Furthermore, the device also includes:

[0038] The first elimination unit is used to eliminate coordinate points that do not meet the plane conditions in the point cloud data after acquiring the point cloud data corresponding to at least two reference elements in the static scene, using the difference between different coordinate points in the point cloud data and the plane equation, and retain the point cloud data of the adjacent plane.

[0039] Furthermore, the first elimination unit is specifically used to use random iteration to construct multiple plane equations for the coordinate points in the point cloud data, for each plane equation; statistically calculate the distance difference between the coordinate points in the point cloud data of each plane and the plane, and select the point cloud data of the coordinate points whose distance is less than a set value as the neighboring point cloud data; and retain the point cloud data of the neighboring plane by utilizing the proportion of the neighbor point cloud data in the point cloud data.

[0040] Furthermore, the device also includes:

[0041] The second elimination unit is used to eliminate the coordinate points that do not meet the scene conditions in the point cloud data after obtaining the point cloud data corresponding to at least two reference elements in the static scene, using the position characteristics of the reference elements in the static scene, and retain the point cloud data of the reference elements.

[0042] Furthermore, the determination unit is specifically used to determine the plane distance between the first plane and the second plane using the plane equation corresponding to the first plane and the plane equation corresponding to the second plane; obtain the true distance between the first plane and the second plane using a measuring device; and calculate the height measurement accuracy of the laser radar by comparing the plane distance with the true distance.

[0043] According to a third aspect of the present application, a storage medium is provided, on which a computer program is stored, and when the program is executed by a processor, the above-mentioned method for determining the height measurement accuracy is implemented.

[0044] According to a fourth aspect of the present application, a device for determining height measurement accuracy is provided, comprising a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, wherein the processor implements the above-mentioned method for determining height measurement accuracy when executing the program.

[0045] By means of the above technical scheme, the present application provides a method and device for determining the height measurement accuracy. Compared with the current method of using a known rectangular target plate to determine the height measurement accuracy of a laser radar, the present application obtains point cloud data corresponding to at least two reference elements in a static scene, selects point cloud data corresponding to the two reference elements, and uses a plane fitting method to fit the point cloud data corresponding to the two reference elements to obtain a first plane and a second plane, determines the plane distance between the first plane and the second plane, and calculates the height measurement accuracy of the laser radar according to the plane distance. This process eliminates the ranging error of the laser radar itself by fitting the plane, thereby avoiding the edge blur error caused by using edge height measurement, and

[0046] The distance between the two objects can objectively reflect the measured height of the lidar and improve the accuracy of the height measurement.

[0047] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0049] Figure 1 is a schematic diagram of laser radar scanning points in one embodiment of the present application;

[0050] Figure 2 It is a flowchart of a method for determining high measurement accuracy in one embodiment of the present application;

[0051] Figure 3 yes Figure 2 A schematic flow chart of a specific implementation of step 101;

[0052] Figure 4 This is a schematic diagram of a scene of laser radar high precision measurement in one embodiment of the present application;

[0053] Figure 5 is a flow chart of a method for determining height measurement accuracy in another embodiment of the present application;

[0054] Figure 6 is a flow chart of a method for determining height measurement accuracy in another embodiment of the present application;

[0055] Figure 7 yes Figure 2 A schematic flow chart of a specific implementation of step 103

[0056] Figure 8 It is a structural schematic diagram of a device for determining high measurement accuracy in one embodiment of the present application;

[0057] Fig. 9 It is a schematic diagram of the device structure of a computer device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0058] The present application will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that the embodiments and features in the embodiments of the present application can be combined with each other without conflict.

[0059] Normally, some vehicle-mounted optical radars, especially those using MEMS micromirrors for scanning, have inconsistencies in the longitudinal angle range of their scanning due to the inconsistency of the MEMS micromirrors during batch production and the creep caused by device aging after leaving the factory. This causes inaccurate high-angle measurements, that is, the internal parameters are not accurate enough, and the point cloud data will show a distortion phenomenon of being "stretched" or "compressed" longitudinally, resulting in deviations in the measurement of the target height and the longitudinal size of the target itself. Generally speaking, the greater the deviation between the longitudinal field of view angle and the true value, the more the point cloud data is "stretched" or "compressed".

[0060] If the laser radar has a large deviation in measuring the height of the target, it will affect the target detection rate and the accuracy of the target position and size. For example, when using two laser radars with a large FOV overlap area, the point cloud data of the two laser radars will be layered after the point cloud data of the same target is spliced.

[0061] This can cause the perception algorithm to miss or misdetect targets. When passing through a height limit pole, if the height of the height limit pole given by the LiDAR is inaccurate, the vehicle may hit the height limit pole. If the point cloud data on the ground is layered, the detection of road boundaries may also miss or misdetect. When performing sensor fusion, the point cloud data of the LiDAR cannot overlap well with the image of the camera, which also affects the recognition of fused targets.

[0062] Related technologies In the actual use of LiDAR for algorithm development, since LiDAR has the problem of point cloud "creep" after leaving the factory, the LiDAR has been installed on the vehicle at this time, and it is difficult to measure accurately with high-precision equipment in the laboratory. As a solution after the LiDAR is installed on the vehicle, a known rectangular target plate is usually used to compare the height value of the target measured by the LiDAR with the height value of the actual target to determine the height measurement accuracy of the LiDAR. However, considering that the point cloud obtained by the LiDAR scan is discrete, and the LiDAR spot has a certain size that will cause edge blur. When using certain scanning methods that are not strictly horizontal lines, the radar boundary will be very uneven, resulting in a large subjectivity in the determination of the target boundary and errors, resulting in large errors in the height measurement accuracy.

[0063] For example, as shown in Figure 1, the implementation box in the figure is the actual rectangular target plate, the circle is the scanning point of the laser radar, the laser scanning points overlapping with the solid line box will be presented in the point cloud data of the target plate, and the dotted line box is the target plate fitted according to the point cloud data of the target plate. It can be seen that the size of the target plate fitted in the first case is obviously smaller than the actual target plate size, and this measurement method has a large error.

[0064] In order to solve this problem, this embodiment provides a method for determining the height measurement accuracy, such as Figure 2 As shown, the method can be applied to the server corresponding to the laser radar, including the following steps:

[0065] 101. Obtain point cloud data corresponding to at least two reference elements in a static scene.

[0066] Among them, the static scene includes at least two reference elements, where the reference elements can be static reference objects contained in the scene, such as walls, ceilings, and the ground, etc., or static reference objects set in the scene, such as lines and planes with different reflectivity from other static reference objects. In a static scene, in order to ensure the accuracy of the measurement, the reference elements to be measured usually need to be horizontally parallel. For example, at least two reference elements can be two parallel horizontal planes, or a plane and a straight line parallel to the horizontal plane.

[0067] In another implementation scenario, in addition to being two planes, at least two reference elements may also be horizontal line features, and there is a certain distance between the feature and the ground, such as a thin horizontal bar, a pipeline in a basement, etc. In this scenario, the horizontal line can be used to obtain the distance between the feature and the ground by fitting a straight line to the feature.

[0068] In this embodiment, point cloud data is a set of coordinate points in three-dimensional space, which can be used to build three-dimensional models, perform object recognition and environmental perception and other applications. Generally, point cloud data is generated by scanning of 3D devices, such as laser radar, TOF camera, etc. Here, laser radar can use automatic measurement of a large number of points on the surface of an object, and then output point cloud data with a certain data file, and scan to generate point cloud data. Laser radar is a measurement equipment that integrates laser scanning and positioning and attitude systems. The laser radar system includes a laser and a receiving system. The laser generates and emits a beam of light pulses, which hits the object and reflects back, and is finally received by the receiver. The receiver accurately measures the propagation time of the light pulse from emission to reflection. Since the speed of light is known, the propagation time can be converted into a measurement of distance. Combined with the height of the laser and the laser scanning angle, the three-dimensional coordinates of the reference element in each static scene can be accurately calculated. Since the laser radar uses three-dimensional space to discretize the point cloud data format, the point cloud data finally scanned is a set of discrete points.

[0069] The executor of the embodiment of the present invention can be a device for determining height measurement accuracy. At least two reference elements can be provided through a static scene, and point cloud data of the laser radar in the static scene can be collected. The distance between the two reference elements can be obtained through the point cloud data, and whether the height measurement of the laser radar is accurate can be reflected based on the distance between the two reference elements.

[0070] 102. Select point cloud data corresponding to two reference elements, and use a plane fitting method to fit the point cloud data corresponding to the two reference elements to obtain a first plane and a second plane.

[0071] It is understandable that since the laser radar has a certain measurement error, the point cloud data of the reference element in the point cloud data of the static scene scanned by the laser radar has a certain thickness. It is necessary to extract the point cloud data corresponding to the reference element from the point cloud data of the static scene, and use the plane fitting method to fit the point cloud data corresponding to the reference element to obtain a plane.

[0072] In the actual height measurement scenario, two reference boundaries are required to measure the height. Here, the point cloud data corresponding to the two reference elements are selected as the two reference boundaries. For example, the ceiling and the ground are selected as the two reference boundaries. In order to ensure the accuracy of the measured height, the two selected reference elements need to be controlled within a certain distance range, for example, between 2-5 meters.

[0073] Here, the plane fitting method can be used to fit the point cloud data using the least squares method, Chebyshev best approximation method, eigenvalue method, etc. Since the point cloud data is usually a geometric figure composed of a large number of discrete 3D coordinate points, plane fitting refers to finding the most suitable plane in the point cloud data, which can approximately fit the plane of the reference element. The point cloud data corresponding to the two reference elements are fitted to obtain the first plane and the second plane.

[0074] 103 determines a plane distance between the first plane and the second plane, and calculates the height measurement accuracy of the laser radar according to the plane distance.

[0075] Normally, when plane fitting is used to fit point cloud data, the plane parameters of the two reference elements can be obtained, and the plane equation is constructed according to the plane parameters corresponding to the two reference elements. Since the two reference elements are selected to be horizontally parallel, the first plane and the second plane fitted by the two reference elements are also horizontally parallel. The distance between the first plane and the second plane can be solved through two mutually parallel plane equations.

[0076] It can be understood that the plane distance between the first plane and the second plane is the actual measured height value obtained by measuring using the laser radar. Whether the actual measured height value meets the measurement accuracy needs to be compared with the true measurement value. Here, a high-precision laser rangefinder or dimension can be used to measure the true distance between the two reference elements, and the height measurement accuracy of the laser radar is calculated by the difference between the plane distance and the actual distance.

[0077] Compared with the current method of using a known rectangular target plate to determine the height measurement accuracy of a laser radar, the method for determining the height measurement accuracy provided in the embodiment of the present application obtains point cloud data corresponding to at least two reference elements in a static scene, selects point cloud data corresponding to the two reference elements, and uses a plane fitting method to fit the point cloud data corresponding to the two reference elements to obtain a first plane and a second plane, determines the plane distance between the first plane and the second plane, and calculates the height measurement accuracy of the laser radar based on the plane distance. This process eliminates the ranging error of the laser radar itself by fitting the plane, avoids the edge blur error caused by using edge height measurement, objectively reflects the measurement height of the laser radar through the distance between the planes, and improves the accuracy of the height measurement accuracy.

[0078] In actual application scenarios, it is necessary to control the height measurement accuracy of the vehicle-mounted laser radar within a certain range. In the process of using the laser radar for algorithm development, this application can determine the height measurement accuracy of the laser radar through the above process, and further determine whether the height measurement accuracy is within an acceptable range.

[0079] Specifically, in the above embodiment, if Figure 3As shown, step 101 includes the following steps:

[0080] 201. Pre-set at least two horizontally parallel reference elements in a static scene to obtain original point cloud data obtained by scanning a laser radar in the static scene.

[0081] 202. Acquire point cloud data corresponding to at least two reference elements from the original point cloud data.

[0082] Among them, the reference elements include at least planes and / or lines. When the reference elements include planes, the spatial characteristics of the planes can be used to obtain point cloud data corresponding to at least two planes from the original point cloud data. When the reference elements include lines, the reflectivity difference between the lines and other elements in the static scene can be used to divide the original point cloud data, and the point cloud data corresponding to at least two lines can be obtained from the original point cloud data.

[0083] In actual usage scenarios, the floor and ceiling of a garage are usually balanced with each other and have a certain height distance, which meets the conditions in a static scene. The ceiling and floor can be used as two reference elements in a static scene. Figure 4 To measure high-precision scenes for LiDAR, Figure 4 In the process, the vehicle-mounted laser radar scans two parallel planes, the ground and the ceiling, in the static scene to obtain the original point cloud data scanned by the laser radar in the static scene.

[0084] Furthermore, considering the position differences of the coordinate points in the point cloud data on the plane, the coordinate points in the point cloud data that do not meet the plane conditions can be eliminated. In the above embodiment, Figure 5 As shown, after step 101, the method further includes the following steps:

[0085] 104. Using the difference between different coordinate points in the point cloud data and the plane equation, coordinate points in the point cloud data that do not meet the plane condition are eliminated, and point cloud data adjacent to the plane are retained.

[0086] It is understandable that most of the coordinate points in the point cloud data are close to the plane. Not all coordinate points are in the same plane. However, there are still some coordinate points that have errors with the plane. These coordinate points are out of the fitting plane and do not meet the plane conditions and need to be eliminated. Here, we can compare the coordinate points in the point cloud data with the establishment of different fitting planes, obtain the coordinate points with large errors and eliminate them, so that the fitting plane covers as few error coordinate points as possible, and use point cloud data that fits the plane as closely as possible to fit the plane.

[0087] Specifically, multiple plane equations can be constructed for the coordinate points in the point cloud data using random iteration. For each plane equation, the distance difference between the coordinate points in each plane calculation point cloud data and the plane is counted, and the point cloud data of the coordinate points whose distance is less than the set value is selected as the neighboring point cloud data. The point cloud data of the neighboring plane is retained by using the proportion of the neighboring point cloud data in the point cloud data. The retained point cloud data here is the point cloud data that fits the plane.

[0088] Considering the distribution differences of the coordinate points in the point cloud data in the scene, the coordinate points in the point cloud data that do not meet the scene conditions can be eliminated. Further, in the above embodiment, Figure 6 As shown, after step 101, the method further includes the following steps:

[0089] 105. Using the positional features of the reference element in the static scene, coordinate points in the point cloud data that do not meet the scene conditions are removed, and the point cloud data of the reference element is retained.

[0090] It is understandable that not all elements in a static scene are reference elements. Here, the position features of the reference elements can be stored in advance, and other elements in the point cloud data can be converted into reference elements according to the position features of the reference elements.

[0091] Of course, if the position features of other elements are close to the point cloud data of the reference element, the point cloud data of other elements can be eliminated through the color information mapped from the corresponding point cloud data of the reference element. Generally, the color information mapped from the point cloud data with high reflected light intensity is brighter, and the color information mapped from the point cloud data with low reflected light intensity is darker.

[0092] Specifically, in the above embodiment, Figure 7 As shown, step 103 includes the following steps:

[0093] 301. Determine a plane distance between the first plane and the second plane by using a plane equation corresponding to the first plane and a plane equation corresponding to the second plane.

[0094] 302. Use a measuring device to obtain a real distance between the first plane and the second plane.

[0095] 303. Calculate the height measurement accuracy of the laser radar by comparing the plane distance with the real distance.

[0096] In this embodiment, the measuring device can be a high-precision rangefinder or a ruler. The distance between the first plane and the second plane measured by the measuring device is the actual distance D. The plane distance calculated using the equation corresponding to the first plane and the equation corresponding to the second plane is d. Then the height measurement accuracy of the laser radar is (dD) / D. Generally, the smaller the absolute value corresponding to the height measurement accuracy is, the higher the height measurement accuracy of the laser radar is.

[0097] Furthermore, the plane equation of the reference element obtained by plane fitting corresponds to plane fitting parameters, such as the variance of the coordinate points. The plane fitting parameters can reflect the coupling process of the laser radar ranging and angle measurement, and the plane fitting parameters can be used to evaluate the measurement accuracy of the laser radar.

[0098] In this embodiment, point cloud data corresponding to two reference elements in a static scene are obtained by laser radar scanning. There is no need to find edges, but planes are found, which eliminates the error of edge blur. The measurement error of the laser radar itself can be eliminated by fitting a large number of planes. Therefore, the distance between the two reference elements calculated by obtaining point cloud data through the laser radar can accurately reflect the measured height of the laser radar. By comparing the measured height with the actual height, the height measurement accuracy of the laser radar can be obtained. Without the need for special equipment and difficult-to-find sites, the height measurement accuracy of the laser radar can be determined simply, accurately and objectively.

[0099] Further, as Figure 1-Figure 7 The specific implementation of the method, the embodiment of the present application provides a device for determining the height measurement accuracy, such as Figure 8 As shown, the device includes: an acquisition unit 41, a fitting unit 42, and a determination unit 43.

[0100] An acquisition unit 41 is used to acquire point cloud data corresponding to at least two reference elements in a static scene;

[0101] A fitting unit 42, configured to select point cloud data corresponding to two reference elements, and respectively fit the point cloud data corresponding to the two reference elements using a plane fitting method to obtain a first plane and a second plane;

[0102] The determination unit 43 is used to determine the plane distance between the first plane and the second plane, and calculate the height measurement accuracy of the laser radar according to the plane distance.

[0103] Compared with the current method of using a known rectangular target plate to determine the height measurement accuracy of a laser radar, the device for determining height measurement accuracy provided by an embodiment of the present invention obtains point cloud data corresponding to at least two reference elements in a static scene, selects point cloud data corresponding to the two reference elements, and uses a plane fitting method to fit the point cloud data corresponding to the two reference elements to obtain a first plane and a second plane, determines the plane distance between the first plane and the second plane, and calculates the height measurement accuracy of the laser radar based on the plane distance. This process eliminates the ranging error of the laser radar itself by fitting the plane, avoids the edge blur error caused by using edge height measurement, objectively reflects the measurement height of the laser radar through the distance between the planes, and improves the accuracy of the height measurement accuracy.

[0104] In a specific application scenario, the acquisition unit 41 includes:

[0105] A setting module, used to pre-set at least two horizontally parallel reference elements in a static scene, and obtain original point cloud data scanned by the laser radar in the static scene;

[0106] The acquisition module is used to acquire point cloud data corresponding to at least two reference elements from the original point cloud data.

[0107] In a specific application scenario, the reference element includes at least a plane and / or a line. When the reference element includes a plane, the acquisition module is specifically used to acquire point cloud data corresponding to at least two planes from the original point cloud data by using the spatial characteristics of the plane;

[0108] When the reference element includes a line, the acquisition module is specifically used to divide the original point cloud data by utilizing the reflectivity difference between the line and other elements in the static scene, and to acquire point cloud data corresponding to at least two lines from the original point cloud data.

[0109] In a specific application scenario, the device further includes:

[0110] The first elimination unit is used to eliminate coordinate points that do not meet the plane conditions in the point cloud data after acquiring the point cloud data corresponding to at least two reference elements in the static scene, using the difference between different coordinate points in the point cloud data and the plane equation, and retain the point cloud data of the adjacent plane.

[0111] In a specific application scenario, the first elimination unit is specifically used to use random iteration to construct multiple plane equations for the coordinate points in the point cloud data, for each plane equation; statistically calculate the distance difference between the coordinate points in the point cloud data of each plane and the plane, and select the point cloud data of the coordinate points whose distance is less than the set value as the neighboring point cloud data; and utilize the proportion of the neighbor point cloud data in the point cloud data to retain the point cloud data of the neighboring plane.

[0112] In a specific application scenario, the device further includes:

[0113] The second elimination unit is used to eliminate the coordinate points that do not meet the scene conditions in the point cloud data after obtaining the point cloud data corresponding to at least two reference elements in the static scene, using the position characteristics of the reference elements in the static scene, and retain the point cloud data of the reference elements.

[0114] In a specific application scenario, the determination unit 43 is specifically used to determine the plane distance between the first plane and the second plane using the plane equation corresponding to the first plane and the plane equation corresponding to the second plane; use a measuring device to obtain the true distance between the first plane and the second plane; and calculate the height measurement accuracy of the laser radar by comparing the plane distance with the true distance.

[0115] Based on the above Figure 2-Figure 7 The method shown in the embodiment of the present application accordingly provides a storage medium on which a computer program is stored, and when the program is executed by a processor, the above-mentioned Figure 2-Figure 8 The method for determining the height measurement accuracy is shown.

[0116] Based on this understanding, the technical solution of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.), and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each implementation scenario of the present application.

[0117] Based on the above Figure 2-Figure 7 The method shown, and Figure 8 In order to achieve the above-mentioned purpose, the embodiment of the present application also provides a physical device for determining the height accuracy, which can be a computer, a smart phone, a tablet computer, a smart watch, a server, or a network device, etc. The physical device includes a storage medium and a processor; the storage medium is used to store a computer program; the processor is used to execute the computer program to achieve the above-mentioned Figure 2-Figure 7 The method for determining the height measurement accuracy is shown.

[0118] Optionally, the physical device may also include a user interface, a network interface, a camera, a radio frequency (RF) circuit, a sensor, an audio circuit, a WI-FI module, etc. The user interface may include a display, an input unit such as a keyboard, etc., and the optional user interface may also include a display, an input unit such as a keyboard, etc.

[0119] It may include a USB interface, a card reader interface, etc. The network interface may optionally include a standard wired interface, a wireless interface (such as a WI-FI interface), etc.

[0120] In an exemplary embodiment, see Fig. 9 The physical device includes a communication bus, a processor, a memory and a communication interface, and may also include an input / output interface and a display device, wherein each functional unit can communicate with each other through the bus. The memory stores a computer program, and the processor is used to execute the program stored in the memory and execute the method for determining the height measurement accuracy in the above embodiment.

[0121] Those skilled in the art will appreciate that the physical device structure for determining the height measurement accuracy provided in this embodiment does not constitute a limitation on the physical device, and may include more or fewer components, or a combination of certain components, or different component arrangements.

[0122] The storage medium may also include an operating system and a network communication module. The operating system is a program that manages the hardware and software resources of the physical device for determining the height measurement accuracy, and supports the operation of the information processing program and other software and / or programs. The network communication module is used to realize the communication between the components inside the storage medium, and the communication with other hardware and software in the information processing physical device.

[0123] Through the description of the above implementation methods, those skilled in the art can clearly understand that the present application can be implemented by means of software plus the necessary general hardware platform, or by hardware. By applying the technical solution of the present application, compared with the current existing methods, the present application eliminates the ranging error of the laser radar itself by fitting the plane, avoids the edge blurring error caused by edge height measurement, objectively reflects the measurement height of the laser radar through the distance between the planes, and improves the accuracy of the height measurement precision.

[0124] Those skilled in the art will appreciate that the accompanying drawings are only schematic diagrams of a preferred implementation scenario, and the modules or processes in the accompanying drawings are not necessarily necessary for implementing the present application. Those skilled in the art will appreciate that the modules in the devices in the implementation scenario can be distributed in the devices of the implementation scenario according to the description of the implementation scenario, or can be changed accordingly and located in one or more devices different from the present implementation scenario. The modules of the above-mentioned implementation scenario can be combined into one module, or can be further split into multiple submodules.

[0125] The above serial numbers of this application are only for description and do not represent the advantages and disadvantages of the implementation scenarios. The above disclosure is only a few specific implementation scenarios of this application, but this application is not limited to them, and any changes that can be thought of by technicians in this field should fall within the scope of protection of this application.

Claims

1. A method for determining height measurement accuracy, characterized in that: include: Acquire point cloud data corresponding to at least two reference elements in a static scene; Selecting point cloud data corresponding to two reference elements, and fitting the point cloud data corresponding to the two reference elements using a plane fitting method to obtain a first plane and a second plane; A plane distance between the first plane and the second plane is determined, and a height measurement accuracy of the laser radar is calculated according to the plane distance.

2. The method according to claim 1, characterized in that The obtaining of point cloud data corresponding to at least two reference elements in the static scene specifically includes: Pre-setting at least two horizontally parallel reference elements in a static scene, and obtaining original point cloud data obtained by scanning the laser radar in the static scene; Point cloud data corresponding to at least two reference elements are acquired from the original point cloud data.

3. The method according to claim 2, characterized in that The reference element includes at least a plane and / or a line. When the reference element includes a plane, acquiring point cloud data corresponding to at least two reference elements from the original point cloud data specifically includes: Using the spatial features of the plane, acquiring point cloud data corresponding to at least two planes from the original point cloud data; When the reference element includes a line, acquiring point cloud data corresponding to at least two reference elements from the original point cloud data specifically includes: The original point cloud data is divided by utilizing the reflectivity difference between the lines and other elements in the static scene, and point cloud data corresponding to at least two lines are obtained from the original point cloud data.

4. The method according to claim 1, characterized in that After acquiring the point cloud data corresponding to at least two reference elements in the static scene, the method further includes: The coordinate points in the point cloud data that do not meet the plane condition are eliminated by utilizing the difference between different coordinate points in the point cloud data and the plane equation, and the point cloud data of the adjacent plane is retained.

5. The method according to claim 4, characterized in that The method of utilizing the difference between different coordinate points in the point cloud data and the plane equation to remove the interfering coordinate points in the point cloud data and retain the point cloud data of the adjacent plane specifically includes: Using random iteration to construct multiple plane equations for the coordinate points in the point cloud data, for each plane equation; Count the distance differences between the coordinate points and the plane in each plane calculation point cloud data, and select the point cloud data of the coordinate points whose distance is less than the set value as the adjacent point cloud data; The point cloud data of the adjacent plane is retained by utilizing the proportion of the neighbor point cloud data in the point cloud data.

6. The method according to claim 1, characterized in that After acquiring the point cloud data corresponding to at least two reference elements in the static scene, the method further includes: The coordinate points in the point cloud data that do not meet the scene conditions are removed by using the position features of the reference elements in the static scene, and the point cloud data of the reference elements are retained.

7. The method according to any one of claims 1 to 6, characterized in that The determining the plane distance between the first plane and the second plane, and calculating the height measurement accuracy of the laser radar according to the plane distance, specifically includes: Determine a plane distance between the first plane and the second plane using a plane equation corresponding to the first plane and a plane equation corresponding to the second plane; Using a measuring device to obtain a real distance between the first plane and the second plane; The height measurement accuracy of the laser radar is calculated by comparing the plane distance with the real distance.

8. A device for determining height measurement accuracy, characterized in that: include: An acquisition unit, used to acquire point cloud data corresponding to at least two reference elements in a static scene; A fitting unit, used for selecting point cloud data corresponding to two reference elements, and fitting the point cloud data corresponding to the two reference elements respectively to obtain a first plane and a second plane by using a plane fitting method; A determination unit is used to determine a plane distance between the first plane and the second plane, and calculate the height measurement accuracy of the laser radar according to the plane distance.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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