Method and device for determining internal reference error of laser radar and electronic equipment

Abstract

The embodiment of the invention discloses a method and device for determining an internal reference error of a laser radar and electronic equipment, and the method comprises the steps: controlling a target laser radar disposed on a rigid rotary table to carry out the point cloud data collection of a preset space, and obtaining the spatial point cloud data; determining plane point cloud data corresponding to at least one plane included in the preset space based on the space point cloud data; each plane corresponds to one group of plane point cloud data, and each group of plane point cloud data comprises a plurality of point clouds; determining an internal reference error of the target laser radar based on the plane point cloud data; according to the invention, the rigid turntable rotates to control the laser radar to collect the data of the preset space, so that the external parameters in the collected data do not have errors, the correlation between the internal parameters and the external parameters of the laser radar is separated, and only the internal parameter error of the laser radar is determined.

Description

Technical Field

[0001] The present disclosure relates to the technical field of radar device internal parameter correction, and in particular to a method, device, and electronic device for determining a laser radar internal parameter error. Background Art

[0002] Existing lidar internal calibration schemes typically use the manufacturer's internal calibration values ​​directly without secondary calibration. However, quality differences exist between lidar manufacturers' machines before shipment, making it difficult to accurately assess the quality of individual lidars during use. This makes it difficult to trace the subsequent errors caused by installing lidars of varying precision in equipment. Summary of the Invention

[0003] In order to solve the above technical problems, the present disclosure is proposed. Embodiments of the present disclosure provide a method, apparatus, and electronic device for determining an intrinsic parameter error of a laser radar.

[0004] According to one aspect of an embodiment of the present disclosure, a method for determining an intrinsic parameter error of a laser radar is provided, characterized by comprising:

[0005] Control the target laser radar set on the rigid turntable to collect point cloud data of the preset space to obtain spatial point cloud data;

[0006] Determining plane point cloud data corresponding to at least one plane included in the preset space based on the spatial point cloud data; each plane corresponds to a group of plane point cloud data, and each group of plane point cloud data includes multiple point clouds;

[0007] Based on the planar point cloud data, an intrinsic parameter error of the target laser radar is determined.

[0008] Optionally, the controlling of the target laser radar disposed on the rigid turntable to collect point cloud data of a preset space to obtain spatial point cloud data includes:

[0009] Controlling the rigid turntable to rotate at a preset angular resolution;

[0010] When the rigid turntable rotates to at least one angle, the target laser radar collects point cloud data of the preset space to obtain the spatial point cloud data.

[0011] Optionally, when the rigid turntable rotates to at least one angle, the target laser radar collects point cloud data of the preset space to obtain the spatial point cloud data, including:

[0012] When the rigid turntable rotates to at least one angle, the target laser radar collects point cloud data of the preset space to obtain point cloud data in at least one radar coordinate system;

[0013] Perform coordinate system conversion on the point cloud data in the at least one radar coordinate system to obtain the spatial point cloud data in the same coordinate system.

[0014] Optionally, determining, based on the spatial point cloud data, plane point cloud data corresponding to at least one plane included in the preset space includes:

[0015] Determining at least one point cloud included in the spatial point cloud data as a reference point to obtain at least one reference point;

[0016] At least one of the planar point cloud data is determined by region growing using at least one of the reference points as an initial position.

[0017] Optionally, determining at least one point cloud included in the spatial point cloud data as a reference point to obtain at least one reference point includes:

[0018] Dividing the spatial point cloud data into a plurality of voxel grids of a preset size;

[0019] A point cloud closest to the center of gravity in each voxel grid is obtained as the reference point to obtain at least one reference point.

[0020] Optionally, the determining of at least one of the plane point cloud data by region growing with at least one of the reference points as the initial position includes:

[0021] Taking at least one of the reference points as an initial position, determining at least one initial plane equation corresponding to at least one of the reference points;

[0022] determining, based on a positional relationship between at least one of the reference points, whether the at least one initial plane equation corresponds to a plane;

[0023] In response to the existence of at least one initial plane equation of the reference point corresponding to a plane, determining a target plane equation based on at least one initial plane equation corresponding to the plane;

[0024] The plane point cloud data is determined based on the target plane equation.

[0025] Optionally, taking at least one reference point as an initial position and determining at least one initial plane equation corresponding to at least one reference point includes:

[0026] Taking each of the at least one reference point as an initial position, determining a preset number of neighboring points corresponding to the reference point based on a nearest neighbor classification algorithm;

[0027] The initial plane equation corresponding to the reference point is determined using the preset number of neighboring points and the reference point.

[0028] Optionally, determining whether the at least one initial plane equation corresponds to a plane based on the positional relationship between at least one of the reference points includes:

[0029] Determining at least one pair of adjacent reference points based on a positional relationship between at least one of the reference points; the adjacent reference points include two reference points with closest positional relationship;

[0030] Determining normal vectors corresponding to the two reference points according to initial plane equations corresponding to the two reference points included in the adjacent reference points;

[0031] According to the relationship between the two normal vectors, it is determined whether the two initial plane equations corresponding to the adjacent reference points correspond to a plane.

[0032] Optionally, determining whether the two initial plane equations corresponding to the adjacent reference points correspond to a plane based on the relationship between the two normal vectors includes:

[0033] Determining the angle between the two normal vectors;

[0034] According to the relationship between the included angle and a preset angle threshold, it is determined whether the two initial plane equations corresponding to the adjacent reference points correspond to a plane.

[0035] Optionally, determining the target plane equation based on at least one initial plane equation corresponding to a plane includes:

[0036] Obtain at least one set of plane parameters according to at least one of the initial plane equations; each of the initial plane equations corresponds to a set of plane parameters;

[0037] Determining target plane parameters according to weighted processing of the at least one set of plane parameters;

[0038] The target plane equation is determined according to the target plane parameters.

[0039] Optionally, determining the intrinsic parameter error of the target laser radar based on the planar point cloud data includes:

[0040] Determining a corresponding target plane equation according to the plane corresponding to the planar point cloud data;

[0041] Constructing a correspondence between an intrinsic parameter error and a point cloud coordinate measured by the target laser radar according to a distance value measured by the target laser radar and an angle value corresponding to the distance value;

[0042] Constructing a residual formula according to the target plane equation and each point cloud coordinate included in the plane point cloud data;

[0043] The intrinsic parameter error of the target laser radar is determined by least squares fitting.

[0044] Optionally, it also includes:

[0045] Correcting the spatial point cloud data obtained by the target laser radar based on the internal parameter error to obtain corrected point cloud data;

[0046] Obtaining true value point cloud data corresponding to the preset space;

[0047] The state of the target laser radar is determined based on the error between the corrected point cloud data and the true point cloud data.

[0048] According to another aspect of an embodiment of the present disclosure, a device for determining an intrinsic parameter error of a laser radar is provided, comprising:

[0049] The data acquisition module is used to control the target laser radar set on the rigid turntable to collect point cloud data of the preset space to obtain spatial point cloud data;

[0050] a plane extraction module, configured to determine, based on the spatial point cloud data, plane point cloud data corresponding to at least one plane included in the preset space; each plane corresponds to a group of plane point cloud data, and each group of plane point cloud data includes a plurality of point clouds;

[0051] An error determination module is used to determine the intrinsic parameter error of the target laser radar based on the planar point cloud data.

[0052] Optionally, the data acquisition module is specifically used to control the rigid turntable to rotate at a preset angular resolution; when the rigid turntable rotates to at least one angle, the target laser radar collects point cloud data of the preset space to obtain the spatial point cloud data.

[0053] Optionally, the data acquisition module is used to collect point cloud data for the preset space when the rigid turntable is rotated to at least one angle by the target laser radar to obtain point cloud data in at least one radar coordinate system; perform coordinate system conversion on the point cloud data in the at least one radar coordinate system to obtain the spatial point cloud data in the same coordinate system.

[0054] Optionally, the plane extraction module includes:

[0055] a point cloud thinning unit, configured to determine at least one point cloud included in the spatial point cloud data as a reference point, and obtain at least one reference point;

[0056] The region growing unit is used to determine at least one of the planar point cloud data by region growing with at least one of the reference points as the initial position.

[0057] Optionally, the point cloud thinning unit is specifically used to divide the spatial point cloud data into multiple voxel grids of preset size; obtain the point cloud closest to the center of gravity point in each voxel grid as the reference point, and obtain at least one reference point.

[0058] Optionally, the region growing unit is specifically used to determine at least one initial plane equation corresponding to at least one of the reference points with at least one of the reference points as the initial position; determine whether the at least one initial plane equation corresponds to a plane based on the positional relationship between at least one of the reference points; in response to the existence of an initial plane equation of at least one of the reference points corresponding to a plane, determine a target plane equation based on at least one of the initial plane equations corresponding to a plane; and determine the plane point cloud data based on the target plane equation.

[0059] Optionally, when the region growth unit takes at least one of the reference points as the initial position and determines at least one initial plane equation corresponding to at least one of the reference points, it is used to take each of the at least one reference point as the initial position and determine a preset number of neighboring points corresponding to the reference point based on a nearest neighbor classification algorithm; and determine the initial plane equation corresponding to the reference point using the preset number of neighboring points and the reference point.

[0060] Optionally, when the region growth unit determines whether the at least one initial plane equation corresponds to a plane based on the positional relationship between at least one of the reference points, it is used to determine at least one pair of adjacent reference points based on the positional relationship between at least one of the reference points; the adjacent reference points include the two reference points with the closest positional relationship; according to the initial plane equations corresponding to the two reference points included in the adjacent reference points, the normal vectors corresponding to the two reference points are determined; according to the relationship between the two normal vectors, it is determined whether the two initial plane equations corresponding to the adjacent reference points correspond to a plane.

[0061] Optionally, when the region growth unit determines whether the two initial plane equations corresponding to the adjacent reference points correspond to a plane based on the relationship between the two normal vectors, it is used to determine the angle between the two normal vectors; and determine whether the two initial plane equations corresponding to the adjacent reference points correspond to a plane based on the relationship between the angle and a preset angle threshold.

[0062] Optionally, when determining the target plane equation based on at least one initial plane equation corresponding to a plane, the region growing unit is used to obtain at least one set of plane parameters based on at least one initial plane equation; each initial plane equation corresponds to a set of plane parameters; the target plane parameters are determined based on weighted processing of the at least one set of plane parameters; and the target plane equation is determined based on the target plane parameters.

[0063] Optionally, the error determination module is specifically used to determine the corresponding target plane equation based on the plane corresponding to the planar point cloud data; construct a correspondence between the intrinsic parameter error and the point cloud coordinates measured by the target lidar based on the distance value measured by the target lidar and the angle value corresponding to the distance value; construct a residual formula based on the target plane equation and each point cloud coordinate included in the planar point cloud data; and determine the intrinsic parameter error of the target lidar through least squares fitting.

[0064] Optionally, the device further comprises:

[0065] A radar state determination module is used to perform correction on the spatial point cloud data obtained by the target laser radar based on the internal parameter error to obtain corrected point cloud data; obtain the true point cloud data corresponding to the preset space; and determine the state of the target laser radar based on the error between the corrected point cloud data and the true point cloud data.

[0066] According to another aspect of the present disclosure, there is provided an electronic device, including:

[0067] a memory for storing a computer program product;

[0068] The processor is configured to execute the computer program product stored in the memory, and when the computer program product is executed, the method described in any one of the above embodiments is implemented.

[0069] According to another aspect of the embodiments of the present disclosure, a computer-readable storage medium is provided, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the method described in any one of the above embodiments is implemented.

[0070] According to another aspect of the embodiments of the present disclosure, a computer program product is provided, including computer program instructions, which implement the method described in any of the above embodiments when executed by a processor.

[0071] Based on the method, device and electronic device for determining the intrinsic parameter error of the laser radar provided in the above-mentioned embodiments of the present disclosure, a target laser radar set on a rigid turntable is controlled to collect point cloud data of a preset space to obtain spatial point cloud data; based on the spatial point cloud data, plane point cloud data corresponding to at least one plane included in the preset space is determined; each of the planes corresponds to a group of the plane point cloud data, and each group of the plane point cloud data includes multiple point clouds; based on the plane point cloud data, the intrinsic parameter error of the target laser radar is determined; the present application controls the laser radar to collect data of the preset space by rotating the rigid turntable, so that there is no error in the external parameters in the collected data, thereby separating the correlation between the internal and external parameters of the laser radar, and only determining the intrinsic parameter error of the laser radar.

[0072] The technical solution of the present disclosure is further described in detail below through the accompanying drawings and examples. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0074] The present disclosure can be more clearly understood from the following detailed description with reference to the accompanying drawings, in which:

[0075] Figure 1 is a flowchart of a method for determining a laser radar internal parameter error provided by an exemplary embodiment of the present disclosure;

[0076] Figure 2 This disclosure Figure 1 A schematic flow chart of step 102 in the embodiment shown;

[0077] Figure 3 1 is a schematic diagram of a coordinate system conversion process in a method for determining a laser radar intrinsic parameter error provided by an exemplary embodiment of the present disclosure;

[0078] Figure 4 This disclosure Figure 1 A schematic flow chart of step 104 in the embodiment shown;

[0079] Figure 5 This disclosure Figure 4 A schematic flow chart of step 1042 in the embodiment shown;

[0080] Figure 6 This disclosure Figure 1 A schematic flow chart of step 106 in the embodiment shown;

[0081] Figure 7 1 is a schematic structural diagram of a device for determining an internal parameter error of a laser radar provided by an exemplary embodiment of the present disclosure;

[0082] Figure 8 A block diagram of an electronic device according to an embodiment of the present disclosure is illustrated. DETAILED DESCRIPTION

[0083] Below, the exemplary embodiments according to the present disclosure will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present disclosure, rather than all the embodiments of the present disclosure, and it should be understood that the present disclosure is not limited to the exemplary embodiments described herein.

[0084] It should be noted that the relative arrangement of components and steps, the numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present disclosure unless specifically stated otherwise.

[0085] Those skilled in the art will understand that the terms "first" and "second" in the embodiments of the present disclosure are only used to distinguish different steps, devices or modules, and do not represent any specific technical meanings, nor do they indicate a necessary logical order between them.

[0086] It should also be understood that in the embodiments of the present disclosure, “a plurality of” may refer to two or more than two, and “at least one” may refer to one, two, or more than two.

[0087] It should also be understood that any component, data or structure mentioned in the embodiments of the present disclosure can generally be understood as one or more, unless explicitly limited or otherwise indicated in the context.

[0088] In addition, the term "and / or" in this disclosure is merely a description of the association relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this disclosure generally indicates that the related objects are in an "or" relationship. The data referred to in this disclosure can include unstructured data such as text, images, and videos, as well as structured data.

[0089] It should also be understood that the description of the various embodiments in this disclosure focuses on the differences between the various embodiments, and the same or similar aspects thereof can be referenced with each other. For the sake of brevity, they will not be described one by one.

[0090] At the same time, it should be understood that for the convenience of description, the sizes of the various parts shown in the drawings are not drawn according to the actual proportional relationship.

[0091] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the present disclosure, its application, or uses.

[0092] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered part of the specification.

[0093] It should be noted that like reference numerals and letters refer to like items in the following figures, and therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0094] The embodiments of the present disclosure can be applied to electronic devices such as terminal devices, computer systems, and servers, and can operate in conjunction with numerous other general-purpose or special-purpose computing system environments or configurations. Examples of well-known terminal devices, computing systems, environments, and / or configurations suitable for use with electronic devices such as terminal devices, computer systems, and servers include, but are not limited to, personal computer systems, server computer systems, thin clients, thick clients, handheld or laptop devices, microprocessor-based systems, set-top boxes, programmable consumer electronics, network personal computers, minicomputer systems, mainframe computer systems, and distributed cloud computing technology environments including any of the above systems, among others.

[0095] Electronic devices such as terminal devices, computer systems, and servers can be described in the general context of computer system-executable instructions (such as program modules) executed by a computer system. Generally, program modules can include routines, programs, object programs, components, logic, data structures, etc., which perform specific tasks or implement specific abstract data types. Computer systems / servers can be implemented in a distributed cloud computing environment, where tasks are performed by remote processing devices linked via a communication network. In a distributed cloud computing environment, program modules can be located on local or remote computing system storage media, including storage devices.

[0096] Application Overview

[0097] In the process of realizing the present disclosure, the inventors discovered that in the prior art, the internal parameters of the laser radar are usually not calibrated twice.

[0098] Exemplary Methods

[0099] Figure 1 This is a flow chart of a method for determining the internal parameter error of a laser radar provided by an exemplary embodiment of the present disclosure. This embodiment can be applied to electronic devices, such as Figure 1 As shown, the following steps are included:

[0100] Step 102 : Control the target laser radar set on the rigid turntable to collect point cloud data of the preset space to obtain spatial point cloud data.

[0101] The rigid turntable's rotation axis has sufficient rigidity to ensure that the target laser radar remains in the same plane during rotation, and the rigid turntable's rotation angle is highly accurate. Optionally, in the disclosed embodiment, the target laser radar is fixed to the rigid turntable. When installed, the rotation axis within the target laser radar is perpendicular to the rotation axis of the rigid turntable, ensuring that the point cloud range of a single scan covers the entire preset space.

[0102] Optionally, the target laser radar can be any mechanical rotating radar, such as a single-line radar, a multi-line radar, etc.

[0103] Step 104 : determining plane point cloud data corresponding to at least one plane included in the preset space based on the spatial point cloud data.

[0104] Each plane corresponds to a set of plane point cloud data, and each set of plane point cloud data includes multiple point clouds.

[0105] In one embodiment, the preset space is a bounded, finite space that includes at least one plane, such as a regularly shaped room. This embodiment aggregates point clouds corresponding to the same plane in the spatial point cloud data to form plane point cloud data, providing a data basis for determining the intrinsic parameter error of the target lidar.

[0106] Step 106: Determine the intrinsic parameter error of the target laser radar based on the planar point cloud data.

[0107] Optionally, after obtaining the planar point cloud data, the internal and external parameters are estimated using an optimizer using point-surface constraints to determine the internal and external parameter errors of the target laser radar. In this embodiment, due to the accuracy of the rigid turntable angle and the sufficient rigidity of the rigid turntable shaft, all errors come from the internal part of the target laser radar (i.e., the internal parameter errors introduced during the laser radar assembly process), i.e., ranging and angular measurement errors. This embodiment minimizes the point cloud errors caused by the shaft rigidity and angular errors, so the correction of the internal angle of the target laser radar is a technical problem to be solved by this embodiment, including: optimizing and estimating the vertical angle error and the horizontal angle error. When the target laser radar leaves the factory, most devices are prone to relatively large vertical installation system errors due to installation differences. The vertical installation deviation usually causes the plane stratification of the point cloud (the point cloud data collected on the same plane do not belong to the same plane point cloud data set). Therefore, the vertical angular deviation of the target laser radar is a relatively large system deviation. This embodiment mainly solves the technical problem of the vertical angular deviation of the target laser radar. That is, the internal parameter error referred to in this embodiment is the vertical angular error (vertical deflection angle).

[0108] The method for determining the internal parameter error of the laser radar provided in the above-mentioned embodiment of the present disclosure controls the target laser radar set on a rigid turntable to collect point cloud data of a preset space to obtain spatial point cloud data; determines the plane point cloud data corresponding to at least one plane included in the preset space based on the spatial point cloud data; each of the planes corresponds to a group of the plane point cloud data, and each group of the plane point cloud data includes a plurality of point clouds; determines the internal parameter error of the target laser radar based on the plane point cloud data; the present application controls the laser radar to collect data of the preset space by rotating the rigid turntable, so that there is no error in the external parameters in the collected data, thereby separating the correlation between the internal and external parameters of the laser radar, and only determining the internal parameter error of the laser radar.

[0109] like Figure 2 As shown in the above Figure 1 Based on the embodiment shown, step 102 may include the following steps:

[0110] Step 1021: Control the rigid turntable to rotate at a preset angular resolution.

[0111] In this embodiment, angular resolution refers to the minimum angle at which the laser radar can distinguish adjacent targets during observation; the preset angular resolution can be set according to the application scenario, for example, 0.5 degrees. In this embodiment, a rigid turntable is determined to rotate to different angles at a preset angular resolution for stationary acquisition, and acquisition is stopped after the turntable rotates one circle (360 degrees). Point cloud data at different angles in the preset space is obtained. Optionally, the number of angular samplings of the rigid turntable can be set to the number of angular samplings actually used.

[0112] Step 1022 : When the rigid turntable rotates to at least one angle, the target laser radar collects point cloud data of the preset space to obtain spatial point cloud data.

[0113] Optionally, when the rigid turntable is rotated to at least one angle, the target laser radar collects point cloud data of the preset space, and performs coordinate transformation on the collected point cloud data in the radar coordinate system to obtain spatial point cloud data.

[0114] Optionally, when the target laser radar rotates to each angle in a rigid state, the turntable angle corresponding to the angle and the point cloud collected at the angle are obtained; the point cloud data included in all angles in the preset space can be obtained by rotating one circle in the rigid state; in addition, when the target laser radar is collecting data, the point cloud data obtained is in the coordinate system corresponding to the position of the target laser radar at that time. If the point cloud data collected at different angles are directly combined, spatial point cloud data cannot be obtained, but rather repeated and superimposed chaotic data. Therefore, before combining the point cloud data of multiple angles, this embodiment first converts the point cloud data of multiple angles into a coordinate system. After converting all the point cloud data to the same coordinate system, the point cloud data corresponding to multiple angles in the same coordinate system are combined to obtain spatial point cloud data. Optionally, step 1022 may include:

[0115] When the rigid turntable rotates to at least one angle, the target laser radar collects point cloud data of the preset space to obtain point cloud data in at least one radar coordinate system;

[0116] The point cloud data in at least one radar coordinate system is subjected to coordinate system conversion to obtain spatial point cloud data in the same coordinate system.

[0117] Optionally, in this embodiment, point cloud data in at least one radar coordinate system can be converted to a turntable coordinate system corresponding to the rigid turntable to obtain spatial point cloud data. Optionally, the process of converting a radar coordinate system to the turntable coordinate system can include translation and rotation. A translation operation can be performed first and then a rotation operation, or a rotation operation can be performed first and then a translation operation, both of which achieve the purpose of coordinate system conversion. Furthermore, any commonly used coordinate system conversion method in the prior art is applicable to the coordinate system conversion in this embodiment, and this embodiment does not impose any limitations.

[0118] In some optional examples, the coordinate system conversion is achieved by first performing a translation operation and then performing a rotation operation, combined with Figure 3 As shown, the specific conversion process is as follows:

[0119] Combining the points in the radar coordinate system with the coordinates of the radar coordinate system origin at the center of the turntable coordinate system, we can obtain the coordinates of the vector OP in the xl-yl-zl (radar coordinate system), expressed as: OP = O-Ol + Ol-P, where O-Ol represents the vector from point O to point OI, Ol-P represents the vector from point OI to point P, and + represents vector addition. The resulting OP is the intermediate coordinate system XYZO (red coordinate system, whose origin coincides with the turntable coordinate system and whose rotation angle is the same as the radar coordinate system) obtained by moving the radar coordinate system to coincide with O, thus achieving translation in the coordinate system conversion. In this embodiment, since the rigid turntable is selected around the y-axis, the coordinates of the turntable axis in the radar coordinate system are (0, 1, 0).

[0120] The Rodriguez formula is used to rotate a vector in space by a certain angle around a specific axis to obtain the rotated vector. Since the rotation angle of the intermediate coordinate system is the same as that of the radar coordinate system, the coordinates of the turntable axis in the intermediate coordinate system (XYZO) are also (0, 1, 0). The Rodriguez formula can be used to convert the OP to the turntable coordinate system.

[0121] Alternatively, all point cloud data can be expressed in the intermediate coordinate system (XYZO). The Rodriguez formula can be applied to OP. For example, if the point is rotated by θ degrees around the axis OY, the resulting OP' is the coordinate of the rotated vector in the XYZO coordinate system (intermediate coordinate system). This is equivalent to keeping OP stationary and rotating the intermediate coordinate system (XYZO) where OP is located by the same angle in the opposite direction. Rotating the intermediate coordinate system around OY by -θ degrees results in a new coordinate system X'-Y'-Z'-O' (where O' coincides with point O). The coordinates of OP in X'-Y'-Z'-O' are equivalent to the coordinates of OP' in XYZO. X'-Y'-Z'-O' is the turntable coordinate system, which means that the point cloud is converted to the turntable coordinate system.

[0122] like Figure 4 As shown in the above Figure 1 Based on the embodiment shown, step 104 may include the following steps:

[0123] Step 1041 : Determine at least one point cloud included in the spatial point cloud data as a reference point to obtain at least one reference point.

[0124] Optionally, the spatial point cloud data is segmented into a plurality of voxel grids of a preset size, and at least one point cloud in the voxel grid is determined as a reference point to obtain at least one reference point. For example, any point cloud in each voxel grid is determined as the reference point, or the point cloud in each voxel grid that is closest to the center of gravity is determined as the reference point.

[0125] To improve processing efficiency, this embodiment downsamples the spatial point cloud data using a thinning method. The resulting point cloud is used as the reference point. The number of reference points is much smaller than the number of spatial point cloud data, thus significantly improving the efficiency of determining the planar point cloud data. Optionally, the spatial point cloud data is segmented into multiple voxel grids of a preset size; the point cloud closest to the center of gravity in each voxel grid is obtained as the reference point, resulting in at least one reference point.

[0126] In this embodiment, since the spatial point cloud data is three-dimensional point cloud data, the voxel grid is a three-dimensional grid, for example, the voxel grid is a cube with a preset side length; to achieve downsampling, this embodiment only retains one point cloud in each voxel grid. Optionally, the center of gravity of the voxel grid is first determined (vector addition can be performed based on all point clouds included in the voxel grid, and the sum is divided by the number of point clouds to obtain the coordinates of the center of gravity point), and the point cloud closest to the center of gravity in the voxel grid is determined as the reference point. Since the reference point is closest to the center of gravity, it can express the entire voxel grid, thereby improving the accuracy of subsequent processing.

[0127] Step 1042 : Using at least one reference point as an initial position, determine at least one plane point cloud data by region growing.

[0128] This embodiment uses a region growing method to segment the spatial point cloud data, and divides the spatial point cloud data into planes, that is, determines the plane point cloud data corresponding to at least one plane included in the preset space. The region growing method is to use a reference point as the origin to expand the reference points adjacent to the reference point to determine whether they are in the same plane. If they are in the same plane, other adjacent reference points are continued to be expanded based on the plane determined by the two reference points until the plane boundary is reached. When the reference points that are not in the same plane are identified, the plane point cloud data corresponding to a plane is determined for all the reference points in the same plane. This embodiment implements plane segmentation through region growing, thereby improving the accuracy of plane segmentation.

[0129] like Figure 5 As shown in the above Figure 4 Based on the illustrated embodiment, step 1042 may include the following steps:

[0130] Step 501: Taking at least one reference point as an initial position, determine at least one initial plane equation corresponding to the at least one reference point.

[0131] Optionally, each of at least one reference point is used as the initial position, and a preset number of neighboring points corresponding to the reference point is determined based on a nearest neighbor classification algorithm; the initial plane equation corresponding to the reference point is determined using the preset number of neighboring points and the reference point.

[0132] In this embodiment, a KNN algorithm is used to determine a preset number of neighboring points corresponding to a reference point. The preset number can be pre-set based on the specific scenario, for example, 80. The coordinates of multiple points can be used to determine a corresponding plane, which is expressed using an initial plane equation, for example, ax + by + cz + d = 0, where the coordinates of the preset number of neighboring points and the reference point satisfy the x, y, and z components of the initial plane equation.

[0133] Step 502: Determine whether at least one initial plane equation corresponds to a plane based on the positional relationship between at least one reference point.

[0134] In this embodiment, the corresponding initial plane equation is determined based on each reference point. Each time, the initial plane equations corresponding to two reference points adjacent in position are compared to determine whether these two planes correspond to the same plane. The situation of corresponding to the same plane is further expanded to the initial plane equations corresponding to other adjacent reference points, thereby realizing the judgment of whether at least one initial plane equation corresponds to a plane.

[0135] Step 503 : In response to the existence of an initial plane equation of at least one reference point corresponding to a plane, a target plane equation is determined based on at least one initial plane equation corresponding to the plane.

[0136] Optionally, at least one set of plane parameters is obtained based on at least one initial plane equation; each initial plane equation corresponds to a set of plane parameters; for example, when the initial plane equation is ax+by+cz+d=0, the plane parameters are a, b and c, and the corresponding plane parameters can be obtained through each initial plane equation.

[0137] The target plane parameters are determined based on weighted processing of at least one set of plane parameters. In this embodiment, the weight values ​​corresponding to the plane parameters are determined by the number of initial plane equations included in the known plane. For example, when the determined plane includes 3 initial plane equations, the weight values ​​corresponding to the new plane parameters are 1 / 3+1, that is, 0.25. After the weighted summation of the plane parameters is used to obtain the updated plane parameters, the weight values ​​corresponding to the newly determined plane parameters are 1 / 4+1, that is, 0.2. Similarly, the target plane parameters can be obtained.

[0138] Determine the target plane equation based on the target plane parameters.

[0139] After the target plane parameters are determined, the target plane equation obtained by recombining multiple initial plane equations can be directly determined.

[0140] Step 504: determine plane point cloud data based on the target plane equation.

[0141] In this embodiment, after determining the target plane equation, all point clouds that conform to the target plane equation are considered planar point cloud data corresponding to that plane. Optionally, because the target plane equation is obtained by updating the initial plane equations corresponding to multiple reference points, each of the multiple reference points and all point clouds in the voxel grid corresponding to that reference point are considered planar point cloud data corresponding to that plane. This embodiment uses the reference point as the initial plane and expands the plane size toward adjacent reference points to determine the planar point cloud data corresponding to the complete plane included in the preset space, thereby improving the efficiency and accuracy of plane determination.

[0142] Optionally, step 502 may include:

[0143] Based on the positional relationship between at least one reference point, at least one pair of adjacent reference points is determined; adjacent reference points include the two reference points that are closest to each other. Optionally, the reference points in a pair of adjacent reference points can also be reference points in other adjacent reference points. For example, adjacent reference point A includes reference points a and b, adjacent reference point B includes reference points b and c, and so on, so that all reference points can be associated.

[0144] According to the initial plane equations corresponding to the two reference points included in the adjacent reference points, the normal vectors corresponding to the two reference points are determined. In this embodiment, after the initial plane equation is determined, the normal vector perpendicular to the initial plane can be determined, and thus the normal vector corresponding to each reference point can be determined.

[0145] According to the relationship between the two normal vectors, it is determined whether the two initial plane equations corresponding to the adjacent reference points correspond to a plane.

[0146] Optionally, the angle between the two normal vectors is determined; and based on the relationship between the angle and a preset angle threshold, it is determined whether the two initial plane equations corresponding to adjacent reference points correspond to a plane.

[0147] In this embodiment, the preset angle threshold can be set according to the specific application scenario. When the included angle is greater than the preset angle threshold, it can be determined that the two initial plane equations do not correspond to a plane; when the included angle is less than the preset angle threshold, it can be determined that the two initial plane equations correspond to a plane.

[0148] like Figure 6 As shown in the above Figure 1 Based on the illustrated embodiment, step 106 may include the following steps:

[0149] Step 1061 : Determine the corresponding target plane equation according to the plane corresponding to the planar point cloud data.

[0150] Step 1062: Construct a corresponding relationship between the intrinsic parameter error and the point cloud coordinates measured by the target laser radar based on the distance value measured by the target laser radar and the angle value corresponding to the distance value.

[0151] Optionally, the internal reference model is constructed as shown in the following formula (1-3) (the error term is applied to the original measurement data):

[0152]

[0153] in, is the initial value of the vertical angle of the target lidar (the factory value, which can usually be 0); δ is the error of the vertical angle (that is, the internal parameter error that needs to be determined in this embodiment); θ is the horizontal emission angle corresponding to the point cloud collected by the target lidar; Xcorrect, Ycorrect, and Zcorrect are the coordinate values ​​of the point cloud on the x-axis, y-axis, and z-axis after error correction, respectively; distance is the distance value returned by the target lidar.

[0154] Step 1063 : constructing a residual formula according to the target plane equation and the coordinates of each point cloud included in the plane point cloud data.

[0155] Step 1064: Determine the intrinsic parameter error of the target lidar through least squares fitting.

[0156] In this embodiment, after obtaining the spatial point cloud data in the turntable coordinate system, the extracted plane point cloud data is used to construct the least squares residual using ceres (which can be achieved through the optimization library to minimize the distance from the point cloud to the plane). Through multiple iterations, the intrinsic parameter error is changed in each iteration to make the residual smaller until the convergence condition is reached (for example, the intrinsic parameter error changes, but the residual no longer becomes smaller; or the number of iterations reaches a preset number, etc.), and the corresponding intrinsic parameter error is determined. The intrinsic parameter of the target lidar is corrected with the intrinsic parameter error to obtain the corrected vertical deviation angle, that is, the internal parameter correction of the target lidar is realized, thereby improving the accuracy of the target lidar.

[0157] In some optional embodiments, the method provided in this embodiment may further include:

[0158] Correcting the spatial point cloud data obtained by the target laser radar based on the internal parameter error to obtain corrected point cloud data;

[0159] Obtain the true value point cloud data corresponding to the preset space;

[0160] Optionally, since a stand-mounted scanner has high precision, this embodiment uses a stand-mounted scanner to scan a preset space, and the obtained point cloud is used as the true point cloud data.

[0161] The status of the target lidar is determined based on the error between the corrected point cloud data and the true point cloud data.

[0162] Optionally, the corrected point cloud data corresponds to the turntable coordinate system, while the true point cloud data corresponds to the frame scanner coordinate system. Therefore, in order to determine the error, the corrected point cloud data and the true point cloud data need to be converted to the same coordinate system, for example, the corrected point cloud data is converted from the turntable coordinate system to the frame scanner coordinate system, or the true point cloud data is converted to the turntable coordinate system. In this embodiment, the state of the target laser radar can be determined based on the error. Optionally, the distribution of the error can be statistically analyzed to evaluate the final calibration effect. If the error is within a set range (the set range can be set according to the actual application scenario), the state of the target laser radar is determined to be usable. If the error exceeds the set range, it is determined that there are other problems with the target laser radar, and the state of the target laser radar is unusable.

[0163] Any of the methods for determining lidar intrinsic parameter errors provided in the embodiments of the present disclosure can be executed by any appropriate device with data processing capabilities, including but not limited to: a terminal device and a server. Alternatively, any of the methods for determining lidar intrinsic parameter errors provided in the embodiments of the present disclosure can be executed by a processor, such as by invoking corresponding instructions stored in a memory to execute any of the methods for determining lidar intrinsic parameter errors mentioned in the embodiments of the present disclosure. This will not be further described below.

[0164] Exemplary devices

[0165] Figure 7 FIG. 1 is a schematic diagram of a structure of a device for determining an internal parameter error of a laser radar provided by an exemplary embodiment of the present disclosure. Figure 7 As shown, the device provided in this embodiment includes:

[0166] The data acquisition module 71 is used to control the target laser radar set on the rigid turntable to collect point cloud data of the preset space to obtain spatial point cloud data.

[0167] The plane extraction module 72 is configured to determine plane point cloud data corresponding to at least one plane included in a preset space based on the spatial point cloud data.

[0168] Each plane corresponds to a set of plane point cloud data, and each set of plane point cloud data includes multiple point clouds.

[0169] The error determination module 73 is used to determine the intrinsic parameter error of the target laser radar based on the planar point cloud data.

[0170] The device for determining the internal parameter error of the laser radar provided in the above-mentioned embodiment of the present disclosure controls the target laser radar set on a rigid turntable to collect point cloud data of a preset space to obtain spatial point cloud data; determines the plane point cloud data corresponding to at least one plane included in the preset space based on the spatial point cloud data; each of the planes corresponds to a group of the plane point cloud data, and each group of the plane point cloud data includes a plurality of point clouds; determines the internal parameter error of the target laser radar based on the plane point cloud data; the present application controls the laser radar to collect data of the preset space by rotating the rigid turntable, so that there is no error in the external parameters in the collected data, thereby separating the correlation between the internal and external parameters of the laser radar, and only determining the internal parameter error of the laser radar.

[0171] In some optional embodiments, the data acquisition module 71 is specifically used to control the rigid turntable to rotate at a preset angular resolution; when the rigid turntable rotates to at least one angle, the target laser radar collects point cloud data of the preset space to obtain spatial point cloud data.

[0172] Optionally, the data acquisition module 71 collects point cloud data for a preset space when the target laser radar is rotated to at least one angle on the rigid turntable to obtain spatial point cloud data. When the target laser radar is rotated to at least one angle on the rigid turntable, the data acquisition module 71 collects point cloud data for a preset space to obtain point cloud data in at least one radar coordinate system; performs coordinate system conversion on the point cloud data in at least one radar coordinate system to obtain spatial point cloud data in the same coordinate system.

[0173] In some optional embodiments, the plane extraction module 72 includes:

[0174] a point cloud thinning unit, configured to determine at least one point cloud included in the spatial point cloud data as a reference point, thereby obtaining at least one reference point;

[0175] The region growing unit is used to determine at least one of the planar point cloud data by region growing with at least one reference point as the initial position.

[0176] Optionally, the point cloud thinning unit is specifically used to divide the spatial point cloud data into multiple voxel grids of preset size; obtain the point cloud closest to the center of gravity point in each voxel grid as a reference point, and obtain at least one reference point.

[0177] Optionally, the region growing unit is specifically used to determine at least one initial plane equation corresponding to at least one reference point with at least one reference point as the initial position; determine whether at least one initial plane equation corresponds to a plane based on the positional relationship between at least one reference point; in response to the existence of an initial plane equation of at least one reference point corresponding to a plane, determine a target plane equation based on at least one initial plane equation corresponding to a plane; and determine plane point cloud data based on the target plane equation.

[0178] Optionally, when the region growing unit determines at least one initial plane equation corresponding to at least one reference point with at least one reference point as the initial position, it is used to determine a preset number of neighboring points corresponding to the reference point based on a nearest neighbor classification algorithm with each reference point of the at least one reference point as the initial position; and determine the initial plane equation corresponding to the reference point with the preset number of neighboring points and the reference point.

[0179] Optionally, when the region growing unit determines whether at least one initial plane equation corresponds to a plane based on the positional relationship between at least one reference point, it is used to determine at least one pair of adjacent reference points based on the positional relationship between at least one reference point; the adjacent reference points include two reference points with the closest positional relationship; based on the initial plane equations corresponding to the two reference points included in the adjacent reference points, the normal vectors corresponding to the two reference points are determined; based on the relationship between the two normal vectors, it is determined whether the two initial plane equations corresponding to the adjacent reference points correspond to a plane.

[0180] Optionally, when the region growing unit determines whether the two initial plane equations corresponding to adjacent reference points correspond to a plane based on the relationship between the two normal vectors, it is used to determine the angle between the two normal vectors; and determines whether the two initial plane equations corresponding to the adjacent reference points correspond to a plane based on the relationship between the angle and a preset angle threshold.

[0181] Optionally, when determining a target plane equation based on at least one initial plane equation corresponding to a plane, the region growing unit is used to obtain at least one set of plane parameters based on the at least one initial plane equation; each initial plane equation corresponds to a set of plane parameters; the target plane parameters are determined based on weighted processing of at least one set of plane parameters; and the target plane equation is determined based on the target plane parameters.

[0182] In some optional embodiments, the error determination module 73 is specifically used to determine the corresponding target plane equation based on the plane corresponding to the planar point cloud data; construct a correspondence between the intrinsic parameter error and the point cloud coordinates measured by the target lidar based on the distance value measured by the target lidar and the angle value corresponding to the distance value; construct a residual formula based on the target plane equation and each point cloud coordinate included in the plane point cloud data; and determine the intrinsic parameter error of the target lidar through least squares fitting.

[0183] In some optional embodiments, the apparatus provided in this embodiment may further include:

[0184] The radar state determination module is used to perform correction on the spatial point cloud data obtained by the target lidar based on the internal parameter error to obtain corrected point cloud data; obtain the true point cloud data corresponding to the preset space; and determine the state of the target lidar based on the error between the corrected point cloud data and the true point cloud data.

[0185] Exemplary electronic devices

[0186] Below, reference Figure 8 The electronic device according to the embodiment of the present disclosure is described. The electronic device may be either or both of the first device and the second device, or a standalone device independent of them, and the standalone device may communicate with the first device and the second device to receive collected input signals from them.

[0187] Figure 8 A block diagram of an electronic device according to an embodiment of the present disclosure is illustrated.

[0188] like Figure 8 As shown, the electronic device includes one or more processors and memory.

[0189] The processor may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions.

[0190] The memory may store one or more computer program products, and the memory may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory. The non-volatile memory may include, for example, read-only memory (ROM), a hard disk, flash memory, etc. One or more computer program products may be stored on the computer-readable storage medium, and the processor may execute the computer program product to implement the method for determining the lidar intrinsic parameter error of each embodiment of the present disclosure described above and / or other desired functions.

[0191] In one example, the electronic device may further include an input device and an output device, and these components are interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0192] In addition, the input device may also include, for example, a keyboard, a mouse, and the like.

[0193] The output device can output various information to the outside, including determined distance information, direction information, etc. The output device can include, for example, a display, a speaker, a printer, a communication network and a remote output device connected thereto, and the like.

[0194] Of course, to simplify, Figure 8 Only some of the components related to the present disclosure in the electronic device are shown, and components such as a bus, an input / output interface, etc. are omitted. In addition, the electronic device may further include any other appropriate components according to specific application scenarios.

[0195] In addition to the above-mentioned methods and devices, an embodiment of the present disclosure may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to execute the steps of the method for determining the lidar internal parameter error according to various embodiments of the present disclosure described in the above part of this specification.

[0196] The computer program product may be written in any combination of one or more programming languages ​​to implement the operations of the disclosed embodiments, including object-oriented programming languages ​​such as Java, C++, and conventional procedural programming languages ​​such as C or similar programming languages. The program code may be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0197] In addition, an embodiment of the present disclosure may also be a computer-readable storage medium having computer program instructions stored thereon, which, when executed by a processor, causes the processor to execute the steps of the method for determining the lidar internal parameter error according to various embodiments of the present disclosure described in the above part of this specification.

[0198] The computer-readable storage medium can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can, for example, include but is not limited to a system, device or component of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination thereof. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0199] The basic principles of the present disclosure have been described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, and effects mentioned in this disclosure are merely illustrative and not restrictive, and should not be construed as necessarily possessed by each embodiment of the present disclosure. Furthermore, the specific details disclosed above are provided for illustrative purposes and to facilitate understanding, rather than as limitations. These details do not limit the present disclosure to necessarily being implemented using these specific details.

[0200] Each embodiment in this specification is described in a progressive manner, with each embodiment focusing on its differences from the other embodiments. References to the same or similar parts between the various embodiments are sufficient. For system embodiments, since they largely correspond to method embodiments, their description is relatively simple. For relevant parts, references to the description of the method embodiments are sufficient.

[0201] The block diagrams of the devices, devices, equipment, and systems involved in this disclosure are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As will be appreciated by those skilled in the art, these devices, devices, equipment, and systems can be connected, arranged, or configured in any manner. Words such as "include," "comprise," "have," and the like are open-ended words, meaning "including but not limited to," and can be used interchangeably therewith. The words "or" and "and" used herein refer to the words "and / or" and can be used interchangeably therewith, unless the context clearly indicates otherwise. The word "such as" used herein refers to the phrase "such as but not limited to," and can be used interchangeably therewith.

[0202] The methods and apparatus of the present disclosure may be implemented in many ways. For example, the methods and apparatus of the present disclosure may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above order of steps for the method is for illustration only, and the steps of the method of the present disclosure are not limited to the order specifically described above unless otherwise specified. In addition, in some embodiments, the present disclosure may also be implemented as programs recorded in a recording medium, which include machine-readable instructions for implementing the methods according to the present disclosure. Thus, the present disclosure also covers recording media that store programs for executing the methods according to the present disclosure.

[0203] It should also be noted that in the apparatus, device, and method of the present disclosure, each component or each step can be decomposed and / or recombined. Such decomposition and / or recombination should be regarded as equivalent solutions of the present disclosure.

[0204] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of the present disclosure. Therefore, the present disclosure is not intended to be limited to the aspects shown herein, but rather to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0205] The above description has been provided for the purpose of illustration and description. In addition, this description is not intended to limit the embodiments of the present disclosure to the forms disclosed herein. Although a number of example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. A method for determining the internal parameter error of a laser radar, characterized in that: include: Control the target laser radar set on the rigid turntable to collect point cloud data of the preset space to obtain spatial point cloud data; Determining plane point cloud data corresponding to at least one plane included in the preset space based on the spatial point cloud data; each plane corresponds to a group of plane point cloud data, and each group of plane point cloud data includes multiple point clouds; Based on the planar point cloud data, an intrinsic parameter error of the target laser radar is determined.

2. The method according to claim 1, characterized in that The control of the target laser radar set on the rigid turntable to collect point cloud data of the preset space to obtain spatial point cloud data includes: Controlling the rigid turntable to rotate at a preset angular resolution; When the rigid turntable is rotated to at least one angle, the target laser radar collects point cloud data of the preset space, and performs coordinate transformation on the collected point cloud data in the radar coordinate system to obtain the spatial point cloud data.

3. The method according to claim 1 or 2, characterized in that The determining, based on the spatial point cloud data, plane point cloud data corresponding to at least one plane included in the preset space includes: Dividing the spatial point cloud data into a plurality of voxel grids of a preset size, determining at least one point cloud in the voxel grid as a reference point, and obtaining at least one reference point; At least one of the planar point cloud data is determined by region growing using at least one of the reference points as an initial position.

4. The method according to claim 3, characterized in that The determining of at least one of the plane point cloud data by region growing using at least one of the reference points as an initial position includes: Taking at least one of the reference points as an initial position, determining at least one initial plane equation corresponding to at least one of the reference points; determining, based on a positional relationship between at least one of the reference points, whether the at least one initial plane equation corresponds to a plane; In response to the existence of at least one initial plane equation of the reference point corresponding to a plane, determining a target plane equation based on at least one initial plane equation corresponding to the plane; The plane point cloud data is determined based on the target plane equation.

5. The method according to claim 4, characterized in that The determining of at least one initial plane equation corresponding to at least one reference point using at least one reference point as an initial position includes: Taking each of the at least one reference point as an initial position, determining a preset number of neighboring points corresponding to the reference point based on a nearest neighbor classification algorithm; The initial plane equation corresponding to the reference point is determined using the preset number of neighboring points and the reference point.

6. The method according to claim 4 or 5, characterized in that The determining, based on the positional relationship between at least one of the reference points, whether the at least one initial plane equation corresponds to a plane includes: Determining at least one pair of adjacent reference points based on a positional relationship between at least one of the reference points; the adjacent reference points include two reference points with closest positional relationship; Determining normal vectors corresponding to the two reference points according to initial plane equations corresponding to the two reference points included in the adjacent reference points; According to the relationship between the two normal vectors, it is determined whether the two initial plane equations corresponding to the adjacent reference points correspond to a plane.

7. The method according to claim 6, characterized in that The determining, based on the relationship between the two normal vectors, whether the two initial plane equations corresponding to the adjacent reference points correspond to a plane includes: Determining the angle between the two normal vectors; According to the relationship between the included angle and a preset angle threshold, it is determined whether the two initial plane equations corresponding to the adjacent reference points correspond to a plane.

8. The method according to any one of claims 4 to 7, characterized in that: The determining of the target plane equation based on at least one initial plane equation corresponding to a plane comprises: Obtain at least one set of plane parameters according to at least one of the initial plane equations; each of the initial plane equations corresponds to a set of plane parameters; Determining target plane parameters according to weighted processing of the at least one set of plane parameters; The target plane equation is determined according to the target plane parameters.

9. The method according to any one of claims 1 to 8, characterized in that: The determining of the intrinsic parameter error of the target laser radar based on the planar point cloud data includes: Determining a corresponding target plane equation according to the plane corresponding to the planar point cloud data; Constructing a correspondence between an intrinsic parameter error and a point cloud coordinate measured by the target laser radar according to a distance value measured by the target laser radar and an angle value corresponding to the distance value; Constructing a residual formula according to the target plane equation and each point cloud coordinate included in the plane point cloud data; The intrinsic parameter error of the target laser radar is determined by least squares fitting.

10. The method according to any one of claims 1 to 9, characterized in that: Also includes: Correcting the spatial point cloud data obtained by the target laser radar based on the internal parameter error to obtain corrected point cloud data; Obtaining true value point cloud data corresponding to the preset space; The state of the target laser radar is determined based on the error between the corrected point cloud data and the true point cloud data.

11. A device for determining an internal parameter error of a laser radar, characterized in that: include: The data acquisition module is used to control the target laser radar set on the rigid turntable to collect point cloud data of the preset space to obtain spatial point cloud data; a plane extraction module, configured to determine, based on the spatial point cloud data, plane point cloud data corresponding to at least one plane included in the preset space; each plane corresponds to a group of plane point cloud data, and each group of plane point cloud data includes a plurality of point clouds; An error determination module is used to determine the intrinsic parameter error of the target laser radar based on the planar point cloud data.

12. An electronic device, characterized in that: include: a memory for storing a computer program product; A processor is configured to execute the computer program product stored in the memory, and when the computer program product is executed, implements the method described in any one of claims 1 to 10.

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