A calibration method, device and robot for lidar

Abstract

This application relates to a calibration method, apparatus, and robot for a lidar. The method includes acquiring a calibration point cloud of the lidar, wherein the calibration point cloud includes the angle and measured distance of each point cloud; acquiring the actual distance between each point cloud and the lidar; and determining distance compensation parameters for the lidar at each angle based on the measured distance and the actual distance. This method addresses the adverse effects of varying thickness and / or density of the radome at different locations on ranging accuracy by optimizing the point cloud quality and improving the ranging accuracy of the point cloud through distance compensation.

Description

Technical Field

[0001] This application relates to the field of lidar technology, and in particular to a lidar calibration method, apparatus and robot. Background Technology

[0002] A lidar system is a radar system that uses laser beams to detect the position, velocity, and other characteristics of a target. Its working principle involves emitting a detection signal (laser beam) towards the target, then comparing the received signal reflected back from the target (target echo) with the emitted signal. After appropriate processing, information about the target can be obtained, such as its distance, azimuth, altitude, velocity, attitude, and even shape, thereby enabling the detection, tracking, and identification of the target object.

[0003] During normal rotation, lidar may collide with other external objects, causing radar malfunctions. Therefore, a radome is designed to house the rotating part of the lidar inside, preventing interference from external objects. However, due to the manufacturing process of the radome, it is impossible to achieve uniform thickness and / or density at different locations. This results in inaccurate ranging at certain angles during lidar ranging, causing ranging errors and affecting ranging accuracy. Summary of the Invention

[0004] The main technical problem addressed by the embodiments of this application is to provide a calibration method, device, and robot for lidar, in order to solve the adverse effects of different thicknesses and / or densities of the radome on ranging during lidar ranging, and to improve the accuracy of point cloud ranging by performing distance compensation, optimizing point cloud quality.

[0005] In a first aspect, embodiments of this application provide a calibration method for a lidar, the method comprising:

[0006] Acquire the calibration point cloud of the lidar, wherein the calibration point cloud includes the angle and measurement distance of each point cloud;

[0007] Obtain the actual distance between each point cloud and the lidar;

[0008] Based on the measured distance and the actual distance, the distance compensation parameters for the lidar at each angle are determined.

[0009] In some embodiments, acquiring the calibration point cloud of the lidar includes:

[0010] The lidar is mounted on a rotating platform;

[0011] Based on the range of the lidar, multiple targets with different distances and angles are set around the lidar.

[0012] Point cloud data generated on each target during the rotation of the lidar and the rotation table are collected to obtain the calibration point cloud.

[0013] In some embodiments, the calibration point cloud is a plurality of concentric point clouds centered on the lidar, and determining the distance compensation parameters of the lidar at each angle based on the measured distance and the actual distance includes:

[0014] The calibration point cloud is classified according to the distance category to obtain a first point cloud dataset for the first type of distance and a second point cloud dataset for the second type of distance for each angle.

[0015] Obtain the first actual distance set of the first type of distance and the second actual distance set of the second type of distance for each angle;

[0016] Based on the first point cloud dataset and the first actual distance set, determine the distance compensation parameters for each angle in the first type of distance;

[0017] Based on the second point cloud dataset and the second actual distance set, the distance compensation parameters for each angle in the second type of distance are determined.

[0018] In some embodiments, before determining the distance compensation parameters for the lidar at each angle based on the measured distance and the actual distance, the method further includes:

[0019] Remove noise from the calibration point cloud;

[0020] The removal of noise from the calibration point cloud includes:

[0021] Calculate the absolute value of the first difference in the measured distances between adjacent angular point clouds on the same target;

[0022] Obtain two point clouds of adjacent angles where the absolute value of the first difference is greater than the first threshold;

[0023] Calculate the absolute value of the second difference between the measured distance and the actual distance of the two point clouds respectively;

[0024] Remove the point cloud with the larger absolute value of the second difference from the two point clouds obtained.

[0025] In some embodiments, the step of removing noise from the calibration point cloud further includes:

[0026] Calculate the absolute value of the third difference between the measured distance and the actual distance for each point in the calibration point cloud;

[0027] Remove point clouds whose absolute value of the third difference is greater than the second threshold.

[0028] In some embodiments, the method further includes:

[0029] Acquire point cloud data generated by the lidar during actual ranging;

[0030] Based on the angle and distance information of each point cloud in the point cloud data, determine the distance compensation parameters corresponding to each point cloud;

[0031] The point cloud data is compensated according to the distance compensation parameters to obtain the target point cloud data.

[0032] In some embodiments, determining the distance compensation parameter for each angle in the first type of distance based on the first point cloud dataset and the first actual distance set includes:

[0033] Establish the curve equation between the first point cloud dataset and the first actual distance set corresponding to each angle;

[0034] The parameters of the curve equation are solved using the least squares method;

[0035] Based on the solved parameters, the distance compensation parameters for each angle in the first type of distance are determined.

[0036] Secondly, embodiments of this application provide a calibration device for a lidar, comprising:

[0037] The first acquisition module is used to acquire the calibration point cloud of the lidar, wherein the calibration point cloud includes the angle and measurement distance of each point cloud;

[0038] The second acquisition module is used to acquire the actual distance between each point cloud and the lidar;

[0039] The determination module is used to determine the distance compensation parameters of the lidar at each angle based on the measured distance and the actual distance.

[0040] Thirdly, this application provides a lidar system, including:

[0041] At least one processor, and

[0042] The memory communicatively connected to the at least one processor, wherein,

[0043] The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of the first aspect.

[0044] Fourthly, this application provides a robot that includes the lidar described in the third aspect.

[0045] The beneficial effects of this application's embodiments are as follows: Unlike related technologies, the lidar calibration method provided in this application acquires a calibration point cloud of the lidar, which includes the angle and measurement distance of each point cloud. The actual distance between each point cloud and the lidar is then obtained. Based on the measured distance and the actual distance, distance compensation parameters corresponding to each angle of the lidar are determined. This method addresses the adverse effects of varying thickness and / or density of the radome at different locations on distance measurement. By performing distance compensation, it optimizes the point cloud quality and improves the point cloud ranging accuracy. Attached Figure Description

[0046] One or more embodiments are illustrated by way of example with reference to the accompanying drawings. These illustrations do not constitute a limitation on the embodiments. Elements having the same reference numerals in the drawings are denoted as similar elements. Unless otherwise stated, the figures in the drawings are not to be limited by scale.

[0047] Figure 1 This is a schematic diagram illustrating the application environment of a lidar calibration method provided in an embodiment of this application;

[0048] Figure 2 This is a schematic diagram of a single circular ring calibration point cloud provided in an embodiment of this application;

[0049] Figure 3 This is a schematic flowchart of a lidar calibration method provided in an embodiment of this application;

[0050] Figure 4 This is a schematic diagram of a target placement scenario provided in an embodiment of this application;

[0051] Figure 5 This is a schematic diagram of a calibration point cloud of concentric circles provided in an embodiment of this application;

[0052] Figure 6 This is a schematic diagram of the result of fitting a calibration point cloud of concentric circles provided in an embodiment of this application;

[0053] Figure 7 This is a schematic diagram showing the average error before and after distance compensation, provided in an embodiment of this application.

[0054] Figure 8 This is a schematic diagram of a calibration device for a lidar provided in an embodiment of this application;

[0055] Figure 9 This is a schematic diagram of the structure of a lidar provided in an embodiment of this application;

[0056] Figure 10 This is a schematic diagram of the structure of a robot provided in an embodiment of this application. Detailed Implementation

[0057] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0058] It should be noted that, unless there is a conflict, the various features in the embodiments of this application can be combined with each other, all of which are within the protection scope of this application. Furthermore, although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than the module division in the device or the order in the flowchart. In addition, the terms "first," "second," and "third" used herein do not limit the data or execution order, but only distinguish identical or similar items with essentially the same function and effect.

[0059] Unless otherwise defined, all technical and scientific terms used in this specification have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of this application. The term "and / or" as used in this specification includes any and all combinations of one or more of the associated listed items.

[0060] Furthermore, the technical features involved in the various embodiments of this application described below can be combined with each other as long as they do not conflict with each other.

[0061] Please see Figure 1 , Figure 1 The application environment diagram of the lidar calibration method provided in this application embodiment is as follows: Figure 1 As shown, the application environment includes target objects such as robot 10, coffee table, sofa, and potted plants.

[0062] Robot 10 is equipped with a LiDAR 11, which scans target objects to obtain point cloud data. Based on this point cloud data, the distance from the target object to the LiDAR is calculated. It should be noted that to protect the LiDAR from interference during normal operation, a radome is often installed around it. Due to the manufacturing process and materials of the radome, the thickness and density of each part of the radome cannot be completely consistent, or the radome may be twisted at some angles during assembly. This can lead to inaccurate point cloud data from certain angles, resulting in ranging deviations and affecting the LiDAR's ranging accuracy. Therefore, the LiDAR also performs distance compensation processing on the point cloud data at different angles and distances, calculating the accurate distance based on the processed point cloud data. This enables the robot to guide its movement and obstacle avoidance.

[0063] In this embodiment, the robot 10 includes mobile robots, such as cleaning robots, pet robots, handling robots, care robots, remote monitoring robots, sweeping robots, etc. Among them, cleaning robots include, but are not limited to, sweeping robots, vacuuming robots, mopping robots, or floor washing robots.

[0064] In this embodiment, the lidar includes a transmitter, a receiver, a processor, and a rotating mechanism. The transmitter is a device that emits laser light; the receiver is a device that receives laser light; the processor is primarily responsible for controlling the transmitter to emit laser light and processing the laser signals received by the receiver to calculate the distance information of the target object; the rotating mechanism is the lidar's mounting frame, used for orientation adjustment. The transmitter, receiver, and processor are mounted on the rotating mechanism, which rotates at a stable speed, enabling the lidar to scan the surrounding environment and generate real-time point cloud information.

[0065] The point cloud data generated by lidar includes the angle and distance of each point. The angle is the angle of the laser spot formed on the target object in polar coordinates, and the distance is the distance from the laser spot on the target object to the lidar.

[0066] Please see Figure 2 , Figure 2 A schematic diagram of a single circular ring calibration point cloud is provided, such as... Figure 2 As shown in (a), a ring-shaped target 22 is set at a certain distance around the lidar 21. When the lidar rotates 360 degrees, a ring-shaped target 22 is generated on the target 22. Figure 2 The point cloud data in (b) is affected by the radar dome. Figure 2 In (b), point clouds with significant distance errors compared to normal point clouds appear on both the right and bottom sides of the point cloud data. It is understandable that point cloud data at different distances will exhibit varying degrees of distance error at the same angle. For example, if the lidar rotates 90 degrees, a concave or convex point cloud may appear at 200mm. Due to the influence of different thicknesses and / or densities of the radome, concave or convex point clouds may also exist at 400mm, 600mm, and 800mm. These concave or convex point clouds reduce the robot's ranging accuracy towards target objects, affecting the robot's normal operation.

[0067] During normal operation, the robot's point cloud ranging at some angles is inaccurate due to differences in thickness and / or density at different locations of the radome. To address this issue, this application provides a calibration method for a lidar, which performs distance compensation on the obtained point cloud data to obtain processed point cloud data. Based on the processed point cloud data, accurate distance information is calculated to guide the robot's movement and obstacle avoidance.

[0068] Specifically, please refer to Figure 3 , Figure 3 This is a flowchart illustrating a calibration method for a lidar provided in an embodiment of this application. The method includes the following steps:

[0069] Step S1: Obtain the calibration point cloud of the lidar, which includes the angle and measurement distance of each point cloud;

[0070] Step S2: Obtain the actual distance between each point cloud and the lidar;

[0071] Step S3: Determine the distance compensation parameters for the lidar at each angle based on the measured distance and the actual distance.

[0072] The calibration point cloud is obtained by placing a target around the lidar for calibration. The points in the calibration point cloud are arranged in ascending order of their generation time. From the calibration point cloud, the angle and distance information of each point can be obtained.

[0073] Angle refers to the angle of the laser spot formed when a laser hits a target object in polar coordinates. It should be noted that during distance compensation, the distance is compensated based on the angle of the point cloud and the obtained compensation parameter model at that angle.

[0074] Distance measurement is the process by which a lidar processor calculates the distance between each spot formed on the target and the lidar based on the emitted laser information and the echo laser information when the lidar performs laser scanning and ranging on the target.

[0075] The actual distance refers to the straight-line distance between the center of the lidar and the center of the target after the rotary table rotates at a certain angle. It can be understood that, given the target distance and the rotation angle of the rotary table, this straight-line distance is a fixed value.

[0076] During normal operation, due to limitations in the manufacturing process of the radome, it's impossible to ensure uniform thickness and density across all parts of the lidar. This can lead to significant errors in point cloud data at certain angles during lidar ranging, affecting ranging accuracy. To address this, distance compensation parameters are determined for each point cloud location based on both the measured and actual distances. These parameters are then used to compensate for the distance in the point cloud data, thereby improving the ranging accuracy.

[0077] In some embodiments, acquiring the calibration point cloud of the lidar includes:

[0078] The lidar is mounted on a rotating platform.

[0079] Based on the range of the lidar, multiple targets at different distances and angles are set around the lidar.

[0080] Point cloud data generated on each target during the rotation of the lidar and the rotation table are collected to obtain the calibration point cloud.

[0081] A rotating platform is a device that can place a lidar without obstruction and rotate 360 ​​degrees.

[0082] To perform distance compensation processing on point clouds at various distances and angles, it is necessary to obtain point clouds at different distances and angles. Based on the range of the lidar, multiple targets at different distances and angles are set up around the lidar to collect point cloud data generated by the lidar on each target.

[0083] The targets include homogeneous targets and heterogeneous targets. Homogeneous targets are those made of the same material, such as homogeneous white cardboard or plastic sheets. Heterogeneous targets, made of different materials, have varying absorption and dispersion capabilities for laser light, which can affect the clarity of the laser spot during use. Therefore, in this embodiment, the target only needs to be homogeneous.

[0084] Specifically, please refer to Figure 4 , Figure 4 A schematic diagram of target placement is provided. A lidar 32 is placed on a rotating platform 31. Based on the range of the lidar 32, the closest distance between the lidar 32 and target J is set to 0.1m, and the farthest distance to target A is set to 1.2m. The distances to other targets are set between 0.1m and 1.2m, and each target has a different distance. Simultaneously, the minimum angular interval between adjacent targets is set to 10 degrees, and the maximum angular interval is set to 60 degrees. Specifically, the angular interval between targets A and B is 10 degrees, between targets B and C is 10 degrees, between targets C and D is 10 degrees, between targets D and E is 10 degrees, between targets E and F is 10 degrees, between targets F and G is 30 degrees, between targets G and H is 20 degrees, between targets H and I is 20 degrees, and between targets I and G is 60 degrees. It should be noted that when setting the angle of the target, the angle interval between any two adjacent targets is an integer multiple of the minimum angle interval.

[0085] In some embodiments, the calibration point cloud is a plurality of concentric point clouds centered on the lidar, and determining the distance compensation parameters of the lidar at each angle based on the measured distance and the actual distance includes:

[0086] The calibration point cloud is classified according to the distance category to obtain a first point cloud dataset for the first type of distance and a second point cloud dataset for the second type of distance for each angle.

[0087] Obtain the first actual distance set of the first type of distance and the second actual distance set of the second type of distance for each angle;

[0088] Based on the first point cloud dataset and the first actual distance set, determine the distance compensation parameter corresponding to the first type of distance for each angle;

[0089] Based on the second point cloud dataset and the second actual distance set, the distance compensation parameter corresponding to the second type of distance for each angle is determined.

[0090] Targets at different distances and angles are set up around the lidar. Point cloud data is collected from each target as the lidar rotates to a preset angle. For example, starting from 0 degrees, the rotating platform rotates 5 degrees each time, collecting point cloud data from the lidar for two seconds. When the platform reaches 360 degrees, the data collection ends, resulting in a calibration point cloud of multiple concentric circles centered on the lidar. Figure 5 As shown, Figure 5 A schematic diagram of a calibration point cloud of a concentric circle is provided. At some angles of the concentric circle, such as the upper left, right and upper right sides, point clouds with varying degrees of convexity or depression appear. The measured distance of the point cloud at these angles will have a distance deviation compared with the actual distance.

[0091] Based on the actual distance between the calibration point cloud and the lidar, the point cloud data at each angle can be divided into two categories: a first-range point cloud dataset and a second-range point cloud dataset. For example, combining... Figure 5As can be seen, distances less than 550mm corresponding to each angle in the calibration point cloud are defined as the first type of distance, and the point cloud set within the first type of distance is defined as the first point cloud dataset. Based on the first point cloud dataset and the first actual distance set of the first type of distance at that angle, the distance compensation parameter corresponding to the first type of distance at that angle is determined by curve fitting. Distances greater than 350mm corresponding to each angle are defined as the second type of distance, and the point cloud set within the second type of distance is defined as the second point cloud dataset. Based on the second point cloud dataset and the second actual distance set of the second type of distance at that angle, the distance compensation parameter corresponding to the second type of distance at that angle is determined by curve fitting. It should be noted that there is a 350mm to 550mm overlap between the distance compensation parameter models of the first type of distance and the second type of distance to prevent obvious layering at the boundary. When the distance measurement range is between 350mm and 550mm, 450mm can be used as the dividing line. For distances less than 450mm, the distance compensation parameter model corresponding to the first type of distance should be used; for distances greater than 450mm, the distance compensation parameter model corresponding to the second type of distance should be used. For distances equal to 450mm, either of the above distance compensation parameter models can be used. The setting of the dividing line is based on practical experience. Alternatively, when the distance measurement range is between 350mm and 550mm, either the distance compensation parameter model corresponding to the first type of distance or the distance compensation parameter model corresponding to the second type of distance can be randomly selected without setting a dividing line.

[0092] In theory, the point clouds in the concentric circles formed by the collected calibration point clouds should be evenly distributed on the track of each ring. However, due to the defects of the lidar itself or the influence of external factors, some point clouds may be offset too much when the lidar scans the target, which affects the accuracy of subsequent data fitting and needs to be removed.

[0093] In some embodiments, before determining the distance compensation parameters for the lidar at each angle based on the measured distance and the actual distance, the method further includes:

[0094] Remove noise from the calibration point cloud;

[0095] The removal of noise from the calibration point cloud includes:

[0096] Calculate the absolute value of the first difference in the measured distances between adjacent angular point clouds on the same target;

[0097] Obtain two point clouds of adjacent angles where the absolute value of the first difference is greater than the first threshold;

[0098] Calculate the absolute value of the second difference between the measured distance and the actual distance of the two point clouds respectively;

[0099] Remove the point cloud with the larger absolute value of the second difference from the two point clouds obtained.

[0100] For example, if the absolute value of the first difference in the measured distance between two adjacent point clouds on the same target is greater than the first threshold of 20 mm, it indicates that there are point clouds in these two adjacent point clouds that deviate from the same circular track. The absolute value of the difference between the measured distance and the actual distance of these two adjacent point clouds is calculated, and the point cloud with the larger absolute value of the difference is removed. The first threshold is set by those skilled in the art based on practical experience.

[0101] In some embodiments, the step of removing noise from the calibration point cloud further includes:

[0102] Calculate the absolute value of the third difference between the measured distance and the actual distance for each point in the calibration point cloud;

[0103] Remove point clouds whose absolute value of the third difference is greater than the second threshold.

[0104] During LiDAR scanning, due to interference from the external environment or inherent errors in the LiDAR itself, the measured distance of some point clouds may differ significantly from the actual distance. These point clouds need to be removed before data processing. For example, if the actual distance of some point clouds is 70mm, but the measured distance is 30mm, the absolute value of the third difference between the actual and measured distances is 40mm, which exceeds the set second threshold of 30mm. This indicates that the point cloud is an abnormal point cloud and needs to be removed. The second threshold is set by those skilled in the art based on practical experience.

[0105] In some embodiments, determining the distance compensation parameter corresponding to the first type of distance for each angle based on the first point cloud dataset and the first actual distance set includes:

[0106] Establish the curve equation between the first point cloud dataset corresponding to each angle and the first actual distance set;

[0107] The parameters of the curve equation are solved using the least squares method;

[0108] Based on the solved parameters, the distance compensation parameters corresponding to the first type of distance are determined for each angle.

[0109] In this embodiment, to ensure that the average deviation of the point cloud within 1m of the lidar is no greater than 5mm, the distance of the point cloud corresponding to each angle is fitted using the mathematical function equation of a cubic curve. The point cloud data from each 5-degree rotation of the rotary table is linearly interpolated into 2-degree point cloud data. During cubic curve fitting, the fitting is performed based on point cloud data for each 2-degree interval. It should be noted that if the angle measured during actual ranging is not a multiple of 2, distance compensation is performed using the curve equation parameters of an angle closer to a multiple of 2.

[0110] In some embodiments, for example, when the angle is 0 degrees, the first point cloud dataset Y of the first type of distance is [101,392,508], and the first actual distance set S of the first distance is [95,383,500]. A correspondence between the first point cloud dataset Y and the first actual distance set S is established:

[0111] Y*B=S

[0112] Where B is the calibration parameter.

[0113] B = [a1, b1, c1, d1] can be solved using the least squares method. Similarly, the calibration parameters for each angle in the first type of distance can be calculated using the same method.

[0114] The equation of the cubic curve is:

[0115] a1Y 3 +b1Y 2 +c1Y+d1=dist1

[0116] Where a1, b1, c1, and d1 are coefficients, and dist1 is the predicted distance of the first type of distance.

[0117] The distance compensation parameters corresponding to the first type of distance can be determined based on the predicted distance of the first type of distance.

[0118] In some embodiments, determining the distance compensation parameter for each angle corresponding to the second type of distance based on the second point cloud dataset and the second actual distance set includes:

[0119] Establish the curve equation between the second point cloud dataset corresponding to each angle and the second actual distance set;

[0120] The parameters of the curve equation are solved using the least squares method;

[0121] Based on the solved parameters, the distance compensation parameters corresponding to the second type of distance for each angle are determined.

[0122] In some embodiments, for example, when the angle is 0 degrees, the second point cloud dataset X of the second type of distance is [392,508,615,733,880,1010,1212], and the second actual distance set T of the second distance is [383,500,602,722,870,1002,1200]. The correspondence between the second point cloud dataset X and the second actual distance set T is established as follows:

[0123] X*A=T

[0124] Where A is the calibration parameter.

[0125] A = [a², b², c², d²] can be solved using the least squares method. Similarly, the calibration parameters for each angle in the second type of distance can be calculated using the same method.

[0126] The equation of the cubic curve is:

[0127] a2X 3 +b2X 2 +c2X+d2=dist2

[0128] Where a2, b2, c2, and d2 are coefficients, and dist2 is the predicted distance of the second type of distance.

[0129] The distance compensation parameters corresponding to the second type of distance can be determined based on the predicted distance of the second type of distance.

[0130] In some embodiments, the method further includes:

[0131] Acquire point cloud data generated by the lidar during actual ranging;

[0132] Based on the angle and distance information of each point cloud in the point cloud data, determine the distance compensation parameters corresponding to each point cloud;

[0133] The point cloud data is compensated according to the distance compensation parameters to obtain the target point cloud data.

[0134] Please refer to Figure 6 , Figure 6 This is a schematic diagram of the result of fitting a calibration point cloud of concentric circles according to an embodiment of this application, as shown below. Figure 6As shown, the "dot"-shaped point cloud 42 represents the collected calibration point cloud data, and the "X"-shaped point cloud 41 represents the fitted target point cloud data. Each point cloud records angle and distance information. The concentric circles formed by the "dot"-shaped point cloud data show depressions or bulges at some angles, and some point clouds even deviate from their normal circular paths. The "X"-shaped target point cloud shows uniform transitions between point clouds, and the rings formed by point clouds on the same path are relatively regular, without obvious depressions or bulges.

[0135] Please refer to Figure 7 , Figure 7 This is a schematic diagram illustrating the average error before and after distance compensation, provided in an embodiment of this application. Figure 7 (a) is a distribution of the average error values ​​between the measured distance and the actual distance of the same target point cloud without distance compensation. Figure 7 (b) This is a distribution diagram of the average error between the compensated distance and the actual distance of the same target point cloud after distance compensation. It can be seen that without distance compensation, the average error of the point cloud data at multiple distances exceeds 5mm, for example, at distances of 400mm, 800mm, and 1000mm. After distance compensation, the average error of the point cloud data at each distance is within 5mm, indicating that the point cloud data after distance compensation is uniformly distributed at each distance, without any depressions or bulges.

[0136] In summary, the lidar calibration method provided in this application obtains a calibration point cloud of the lidar, which includes the angle and measurement distance of each point cloud. It then obtains the actual distance between each point cloud and the lidar, and determines the distance compensation parameters corresponding to each angle of the lidar based on the measured distance and the actual distance. This method addresses the adverse effects of varying thickness and / or density of the radome at different locations on distance measurement by optimizing the point cloud quality and improving the point cloud ranging accuracy through distance compensation.

[0137] Please see Figure 8 , Figure 8 This is a schematic diagram of a calibration device for a lidar according to an embodiment of this application. The device 100 includes: a first acquisition module 101, a second acquisition module 102, and a determination module 103.

[0138] The first acquisition module 101 is used to acquire the calibration point cloud of the lidar, which includes the angle and measurement distance of each point cloud. The second acquisition module 102 is used to acquire the actual distance between each point cloud and the lidar. The determination module 103 is used to determine the distance compensation parameters of the lidar at each angle based on the measured distance and the actual distance.

[0139] In the embodiments of this application, the calibration device for LiDAR can also be constructed from hardware components. For example, the calibration of LiDAR can be constructed from one or more chips, and the chips can work in coordination to complete the calibration method for LiDAR described in the above embodiments. Furthermore, the calibration device for LiDAR can also be constructed from various logic devices, such as general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), microcontrollers, ARM (Acorn RISC Machine) or other programmable logic devices, discrete gate or transistor logic, discrete hardware components, or any combination of these components.

[0140] The lidar calibration device in this embodiment can be a device with an operating system. This operating system can be Android, iOS, or other possible operating systems; this embodiment does not specifically limit its use.

[0141] It should be noted that the above-described lidar calibration device can execute the lidar calibration method provided in the embodiments of this application, and has the corresponding functional modules and beneficial effects of the method. Technical details not described in detail in the lidar calibration device embodiments can be found in the lidar calibration method provided in the embodiments of this application.

[0142] This application also provides a lidar; please refer to [link / reference]. Figure 9 , Figure 9 This is a schematic diagram of a lidar structure provided in an embodiment of this application. The lidar 200 includes at least one processor 201 and a memory 202 communicatively connected to the at least one processor 201. The memory 202 stores instructions executable by the at least one processor 201. These instructions, when executed by the at least one processor 201, enable the at least one processor 201 to perform the lidar calibration method described in any of the above-described method embodiments. The processor 201 and the memory 202 can be connected via a bus or other means. Figure 9 Taking the example of a connection between China and Israel via a bus.

[0143] Processor 201 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), a hardware chip, or any combination thereof; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The aforementioned PLD can be a complex programmable logic device (CPLD), a field-programmable gate array (FPGA), a generic array logic (GAL), or any combination thereof.

[0144] The memory 202, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as the program instructions / modules corresponding to the lidar calibration method in the embodiments of this application. The processor 201, by running the non-transitory software programs, instructions, and modules stored in the memory 202, can implement the lidar calibration method in any of the above method embodiments, that is, it can achieve... Figure 3 The entire process.

[0145] This application also provides a robot, please refer to... Figure 10 , Figure 10 This is a schematic diagram of the structure of a robot provided in an embodiment of this application. The robot 300 includes a lidar 200 and a controller 301. The lidar 200 is communicatively connected to the controller 301. The controller 301 is used to send ranging commands to the lidar 200 so that the lidar 200 can perform ranging. It can be understood that the ranging command can be sent to the robot 300 from an external terminal, and the controller 301 forwards the ranging command to the lidar 200. The external terminal can be a fixed terminal or a mobile terminal, such as an electronic device like a computer, mobile phone, or tablet, and is not limited thereto.

[0146] This application provides a computer-readable storage medium, such as a memory including program code, which can be executed by a processor to complete the lidar calibration method in the above embodiments. For example, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CDROM), magnetic tape, floppy disk, and optical data storage device, etc.

[0147] This application provides a computer program product comprising one or more lines of program code stored in a computer-readable storage medium. The processor of a lidar reads the program code from the computer-readable storage medium and executes the program code to complete the lidar calibration method steps provided in the above embodiments.

[0148] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and not to limit them; under the concept of this application, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of this application as described above, which are not provided in detail for the sake of brevity; although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A calibration method for a lidar, characterized in that, The method includes: Acquire the calibration point cloud of the lidar, wherein the calibration point cloud includes the angle and measurement distance of each point cloud; Obtain the actual distance between each point cloud and the lidar; Based on the measured distance and the actual distance, determine the distance compensation parameters of the lidar at each angle; The calibration point cloud is a plurality of concentric point clouds centered on the lidar. Determining the distance compensation parameters for the lidar at each angle based on the measured distance and the actual distance includes: The calibration point cloud is classified according to the distance category to obtain a first point cloud dataset for the first type of distance and a second point cloud dataset for the second type of distance for each angle. Obtain the first actual distance set of the first type of distance and the second actual distance set of the second type of distance for each angle; Based on the first point cloud dataset and the first actual distance set, determine the distance compensation parameters for each angle in the first type of distance; Based on the second point cloud dataset and the second actual distance set, the distance compensation parameters for each angle in the second type of distance are determined.

2. The method according to claim 1, characterized in that, The acquisition of the calibration point cloud of the lidar includes: The lidar is mounted on a rotating platform; Based on the range of the lidar, multiple targets with different distances and angles are set around the lidar. Point cloud data generated on each target during the rotation of the lidar and the rotation table are collected to obtain the calibration point cloud.

3. The method according to claim 2, characterized in that, Before determining the distance compensation parameters for the lidar at each angle based on the measured distance and the actual distance, the method further includes: Remove noise from the calibration point cloud; The removal of noise from the calibration point cloud includes: Calculate the absolute value of the first difference in the measured distances between adjacent angular point clouds on the same target; Obtain two point clouds of adjacent angles where the absolute value of the first difference is greater than the first threshold; Calculate the absolute value of the second difference between the measured distance and the actual distance of the two point clouds respectively; Remove the point cloud with the larger absolute value of the second difference from the two point clouds obtained.

4. The method according to claim 3, characterized in that, The process of removing noise from the calibration point cloud also includes: Calculate the absolute value of the third difference between the measured distance and the actual distance for each point in the calibration point cloud; Remove point clouds whose absolute value of the third difference is greater than the second threshold.

5. The method according to claim 1, characterized in that, The method further includes: Acquire point cloud data generated by the lidar during actual ranging; Based on the angle and distance information of each point cloud in the point cloud data, determine the distance compensation parameters corresponding to each point cloud; The point cloud data is compensated according to the distance compensation parameters to obtain the target point cloud data.

6. The method according to claim 1, characterized in that, The step of determining the distance compensation parameters for each angle in the first type of distance based on the first point cloud dataset and the first actual distance set includes: Establish the curve equation between the first point cloud dataset and the first actual distance set corresponding to each angle; The parameters of the curve equation are solved using the least squares method; Based on the solved parameters, the distance compensation parameters for each angle in the first type of distance are determined.

7. A calibration device for a lidar, used in the method described in any one of claims 1 to 6, characterized in that, include: The first acquisition module is used to acquire the calibration point cloud of the lidar, wherein the calibration point cloud includes the angle and measurement distance of each point cloud; The second acquisition module is used to acquire the actual distance between each point cloud and the lidar; The determination module is used to determine the distance compensation parameters of the lidar at each angle based on the measured distance and the actual distance.

8. A lidar, characterized in that, include: At least one processor, and A memory communicatively connected to the at least one processor, wherein, The memory stores instructions executable by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the method according to any one of claims 1-6.

9. A robot, characterized in that, Including the lidar as described in claim 8.

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