A ranging method, apparatus, and electronic device

Abstract

This application relates to the field of lidar technology, specifically to a ranging method, device, and electronic equipment. The method involves setting up multiple sets of targets and collecting point cloud data from a reference target and two high-reflectivity targets with different reflectivities in each set. A correction model is determined based on theoretical distance, and then the point cloud generated by actual ranging is corrected according to the correction model to obtain an updated distance value. This method can stably and effectively correct the distance of "convex hull" distortion point clouds formed by special materials in lidar ranging. It is not only highly stable and robust, but also does not affect ranging of normal materials, and it does not change the original point cloud contour shape of the object. This allows for optimized output point clouds for lidar products, reducing ranging errors.

Description

Technical Field

[0001] This application relates to the field of lidar technology, specifically to a ranging method, device, and electronic device. Background Technology

[0002] When using lidar for ranging, objects may exist at various distances and angles in the scene, and different objects have different reflectivities. Common ranging methods include triangulation-based ranging, where lidar measures distance by receiving the light spot emitted by the transmitter; the offset of the light spot's position in the receiver during the ranging process determines the distance to the target object. Another example is time-of-flight (TOF) ranging, where lidar uses the round-trip flight time of the data signal between the receiver and transmitter to measure the distance between the target object and the lidar.

[0003] For these common lidar ranging methods, the accuracy of ranging is affected by the varying reflectivity of the target object due to factors such as material (or brightness). For example, in triangulation, when the sensor receives a beam of light returning from an object at an angle, a significant portion of its energy is diffusely reflected elsewhere without reaching the sensor. However, when the sensor receives a beam of light returning perpendicularly from the object, the energy is particularly abundant. For certain high-brightness materials, such as aluminum alloy baseboards, the energy at the angled location is diffusely reflected even more severely, while at the perpendicular receiver, the energy is more abundant and the beam is wider, essentially capturing all the energy of the light spot. Therefore, the point cloud generated at this location will exhibit distance distortion, with the distortion being most severe at the perpendicular location and less severe near the edges, thus appearing as a "convex hull" distorted point cloud.

[0004] like Figure 1a and Figure 1b As shown, Figure 1a The rectangular frame represents the "convex" distorted point cloud portion. This point cloud should output a straight point cloud, but because the beam is energy-saturated near the vertical receiver, the reflected spot is larger than at other locations. After extracting the centroid of the spot using the normal method, the calculated distance will be deviated. Figure 1b The point cloud region pointed to by the middle arrow is an "inwardly convex" distorted point cloud. Generally speaking, TOF ranging lidar tends to exhibit "inwardly convex" distorted point clouds in vertical areas of this type of special material. For triangulation lidar, the installation position of the transmitter and receiver (based on the baseline, with the laser on the left and the receiver on the right, or the laser on the right and the receiver on the left) determines whether the "convex hull" is "inward" or "outward." These distorted point clouds will affect the accuracy of lidar ranging. Summary of the Invention

[0005] The embodiments of this application mainly address the technical problem that existing lidar systems suffer from large ranging deviations due to point cloud distortion in the vertical region of reflected light.

[0006] To solve the above-mentioned technical problems, one technical solution adopted in this application is: providing a ranging method, including:

[0007] The range of the lidar is obtained, and at least two sets of targets are set according to the range. Each set of targets includes a first high-reflectivity target, a second high-reflectivity target, and a reference target. The reflectivity of the first high-reflectivity target is less than that of the second high-reflectivity target, and the reflectivity of the reference target is less than that of the first high-reflectivity target. Point cloud data of the lidar on each set of targets is collected. The point cloud data includes distance data and brightness data. Based on the point cloud data of the targets and the theoretical distances corresponding to the targets at different preset distances, a correction model is determined. The distance of the point cloud information generated by the lidar during actual ranging is corrected according to the correction model to obtain an updated distance value.

[0008] Optionally, the correction model includes a first correction model and a second correction model. Determining the correction model based on the point cloud data of the target and the theoretical distance corresponding to the target at different preset distances includes: drawing a distance-brightness curve based on the point cloud data, and segmenting the distance-brightness curve based on different preset distances; for each segment, calculating the first correction model corresponding to each segment based on the brightness data corresponding to the first high-reflectivity target and the reference target, combined with the theoretical distance at the corresponding preset distance; and calculating the second correction model corresponding to each segment based on the brightness data corresponding to the second high-reflectivity target and the reference target, combined with the theoretical distance at the corresponding preset distance.

[0009] Optionally, for each curve segment, based on the brightness data corresponding to the first high-reflectivity target and the reference target, and combined with the theoretical distance at the corresponding preset distance, the first correction model corresponding to each curve segment is calculated, including: selecting a curve segment, and inputting the point cloud data of the first high-reflectivity target and the reference target within the distance corresponding to the selected curve and their theoretical distance into the initial correction model to determine the first parameter of the first correction model, thereby obtaining the first correction model corresponding to the selected curve.

[0010] Optionally, the point cloud information includes distance measurements and brightness measurements. The step of performing distance correction on the point cloud information generated by the lidar during actual ranging based on the correction model includes: combining the distance-brightness curve to determine a target curve segment based on the distance measurements; determining a target correction model based on the brightness measurements and the target curve segment; and performing distance correction on the distance measurements based on the target correction model and the point cloud information.

[0011] Optionally, the target curve segment includes a reference target distance-brightness curve segment corresponding to the reference target within the distance segment corresponding to the distance measurement value, and a first high-reflectivity distance-brightness curve corresponding to the first high-reflectivity target. Determining the target correction model based on the brightness measurement value and the target curve segment includes: obtaining the reference target distance-brightness function corresponding to the reference target distance-brightness curve segment and the first high-reflectivity distance-brightness function corresponding to the first high-reflectivity distance-brightness curve; substituting the distance measurement value into the reference target distance-brightness function and the first high-reflectivity distance-brightness function to obtain the reference target standard brightness value and the first high-reflectivity standard brightness value corresponding to the distance measurement value; and determining the target correction model based on the brightness measurement value, the reference target standard brightness value, and the first high-reflectivity standard brightness value.

[0012] Optionally, determining the target correction model based on the measured brightness value, the reference target standard brightness value, and the first high-reflection standard brightness value includes: if the measured brightness value is greater than the reference target standard brightness value and less than the first high-reflection standard brightness value, then the first correction model is used as the target correction model; if the measured brightness value is greater than or equal to the first high-reflection standard brightness value, then the second correction model is used as the target correction model; if the measured brightness value is less than or equal to the reference target standard brightness value, then no target correction model is determined, and the measured distance value is used as the target distance.

[0013] Optionally, setting at least two sets of targets according to the measurement range includes: determining a test range according to the measurement range; determining a preset step size and the number of target sets according to the test range; and determining the position of each set of targets according to the preset step size and the number of target sets.

[0014] To address the aforementioned technical problems, another technical solution adopted in this application is: providing a ranging device, comprising: a target setting module for acquiring the range of a lidar, and setting at least two sets of targets according to the range, wherein each set of targets includes a first high-reflectivity target, a second high-reflectivity target, and a reference target, and the reflectivity of the first high-reflectivity target is less than that of the second high-reflectivity target, and the reflectivity of the reference target is less than that of the first high-reflectivity target; a data acquisition module for acquiring point cloud data of the lidar at each set of targets, the point cloud data including distance data and brightness data; a model determination module for determining a correction model based on the point cloud data of the targets and the theoretical distance corresponding to the targets at different preset distances; and a correction module for performing distance correction on the point cloud information generated by the lidar during actual ranging according to the correction model to obtain an updated distance value.

[0015] To solve the above-mentioned technical problems, another technical solution adopted in the embodiments of this application is: to provide an electronic device, comprising:

[0016] At least one processor;

[0017] A memory that is communicatively connected to the at least one processor;

[0018] The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the ranging method described above.

[0019] To solve the above-mentioned technical problems, another technical solution adopted in the embodiments of this application is to provide a robot, including the electronic device described above.

[0020] Unlike related technologies, this application provides a ranging method, device, electronic device, and robot. This method involves setting up multiple sets of targets and collecting point cloud data from a baseline target and two high-reflectivity targets with different reflectivities in each set. A correction model is determined by combining this data with theoretical distance data. The point cloud generated from actual ranging is then corrected based on the correction model to obtain an updated distance value. This method can stably and effectively correct the distance in lidar ranging for "convex hull" distortion point clouds formed by special materials. It is not only highly stable and robust, but also does not affect ranging for normal materials, and it does not alter the original point cloud contour shape of the object. This allows for optimized output point clouds for lidar products, reducing ranging errors. Attached Figure Description

[0021] 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.

[0022] Figure 1a This is a schematic diagram of a distorted point cloud that is "convex".

[0023] Figure 1b This is a schematic diagram of a distorted point cloud that is "convex inward";

[0024] Figure 2 This is a schematic flowchart of a ranging method provided in an embodiment of this application;

[0025] Figure 3 This is a schematic diagram of a method for setting a target according to an embodiment of this application;

[0026] Figure 4 This is a schematic diagram of a method for determining a correction model provided in an embodiment of this application;

[0027] Figure 5 This is a schematic diagram of the distance-brightness curve of a single target provided in an embodiment of this application;

[0028] Figure 6 This is an example of a distance-brightness curve for a white target, a high-reflectivity target 1, and a high-reflectivity target 2 provided in an embodiment of this application;

[0029] Figure 7 This is a schematic diagram of a method for distance correction of actually measured point cloud data provided in an embodiment of this application;

[0030] Figure 8 This is a schematic diagram of a method for determining a target correction model provided in an embodiment of this application;

[0031] Figure 9 This is a schematic diagram of the structure of a ranging device provided in an embodiment of this application;

[0032] Figure 10 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application;

[0033] Figure 11 This is a schematic diagram of the structure of a robot provided in an embodiment of this application. Detailed Implementation

[0034] 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. It should be noted that, unless otherwise specified, the various features in the embodiments of this application can be combined with each other, all 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 may be performed with a different module division or order than that shown in the device schematic diagram or the flowchart.

[0035] 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.

[0036] This application provides a ranging method applied to a lidar ranging device, such as a robot equipped with lidar. Figure 2 As shown, the method includes:

[0037] S11. Obtain the range of the lidar, and set at least two sets of targets according to the range. Each set of targets includes a first high-reflectivity target, a second high-reflectivity target, and a reference target. The reflectivity of the first high-reflectivity target is less than that of the second high-reflectivity target, and the reflectivity of the reference target is less than that of the first high-reflectivity target. Here, a high-reflectivity target refers to a target with a surface made of a high-reflectivity material, and the reflectivity of the high-reflectivity material is greater than that of the reference target surface material.

[0038] Specifically, such as Figure 3 As shown, setting at least two sets of targets according to the range includes:

[0039] S111. Determine the test range based on the measurement range. For example, under normal circumstances, the range within 0.15m is already the blind zone of the lidar, while beyond 1.5m, the point cloud fluctuates significantly under common materials, and the "convex hull" feature is not obvious in the point cloud with large fluctuations. Therefore, the test range can be selected within the range of 0.15m-1.5m to correct the "convex hull" distortion point cloud within this range. For another example, for highly reflective materials, such as the recharging identification of a robot vacuum cleaner, lattice material identification may be required, and recharging identification or lattice identification requires a distance of 3m. Therefore, the test range can also be selected within the range of 0.15m-3m to correct the "convex hull" distortion point cloud within this range. It is understood that a reasonable test range can be set according to different application scenarios of the lidar. This application does not limit the specific range value in the embodiments. Theoretically, the method provided in this application can correct the distortion point cloud at all distances within the range of the lidar, obtaining a ranging result with very small deviation.

[0040] S112. Determine the preset step size and the number of target groups based on the test range.

[0041] S113. Determine the position of each target group according to the preset step size and the number of target groups.

[0042] In this embodiment, taking the range of 0.15m-3m as an example, targets can be set according to a certain step size. This step size can be roughly understood as the distance between adjacent targets. For example, targets can be placed at distances of 0.2m, 0.5m, 0.8m, 1.2m, 1.6m, 2.2m, and 2.8m within the range of the lidar. It should be noted that the preset step size can be a fixed value or multiple different values ​​depending on the test range. For example, in the above example, the step size corresponding to 0.2m, 0.5m, and 0.8m in the 0.15-1m range is 0.3m; the step size corresponding to 1.2m and 1.6m (and the aforementioned 0.8m) in the 1m-2m range is 0.4m; and the step size corresponding to 2.2m and 2.8m (and the aforementioned 1.6m) in the 2m-3m range is 0.6m. It is understood that the above examples are only for illustrative purposes and do not limit the value of the preset step size.

[0043] The targets mentioned in this application embodiment refer to a set of targets, including a reference target and two high-reflectivity targets with different reflectivities. The reflectivity of the first high-reflectivity target is less than that of the second high-reflectivity target, and the reflectivity of the reference target is less than that of the first high-reflectivity target. Since white materials have a wide reflectivity in everyday life—for example, the calibration targets used in triangulation are typically white—a white target can be selected as the reference target in this application embodiment. The material of the high-reflectivity targets can be selected based on the application of the LiDAR. For example, when the LiDAR needs to perform lattice recognition (recharging: the robot vacuum returns to the base station for charging), a lattice plate can be selected as the high-reflectivity target; when the LiDAR needs to use baseboards for wall alignment, baseboards can be selected as the high-reflectivity target.

[0044] To better collect reflected light data, the target length for each distance can be determined based on factors such as the actual application scenario, the length of the resulting distorted point cloud, and the feasibility of target placement. For example, the target length can be set to 10cm at a distance of 0.2m, 20cm at 0.5m, 35cm at 0.8m, 40cm at 1.2m, and 45cm at 1.6m, 2.2m, and 2.8m. It is understood that the above material selection and target length settings are merely illustrative examples; in actual applications, other suitable high-reflectivity materials can be selected, or target lengths with different values ​​can be set.

[0045] S12. Collect point cloud data of the lidar on each group of targets, wherein the point cloud data includes distance data and brightness data.

[0046] After the targets are positioned as described above, point cloud data of the targets is collected using a lidar. The point cloud data includes data such as the angle, distance, or brightness of each target. Among these, the brightness data is related to the reflectivity of the target.

[0047] S13. Based on the point cloud data of the target and the theoretical distances corresponding to the target at different preset distances, determine the correction model. The correction model includes a first correction model and a second correction model. The first correction model is obtained based on the point cloud data of the first high-reflectivity target, and the second correction model is obtained based on the point cloud data of the second high-reflectivity target. For details, please refer to... Figure 4 The step of determining the correction model based on the point cloud data of the target and the theoretical distance corresponding to the target at different preset distances includes:

[0048] S131. Draw a distance-brightness curve based on the point cloud data, and segment the distance-brightness curve based on different preset distances.

[0049] Firstly, when analyzing point cloud data, considering the application scenario and calibration efficiency, it's common practice to analyze only a few rotations of the LiDAR point cloud. This means the LiDAR only needs to operate (rotate) for a brief few seconds (e.g., more than 2 seconds but no more than 5 seconds) at the calibration station for data acquisition. Based on this, the reference target and the high-reflectivity target to be processed can each yield 7 points in a coordinate system, where the x-axis of each point represents distance and the y-axis represents brightness. Interpolation is performed on the 7 data points of both types of targets to obtain interpolation functions and curves. Based on the interpolation function, the brightness corresponding to the point cloud data of the light spot on the target within the range of 0.15m-3m can be calculated. This allows the brightness at various distances to be obtained through this function, avoiding the limitations of the calibration station space caused by the inability to effectively place numerous targets and the high costs associated with using guide rails.

[0050] Please combine Figure 5 , Figure 5 This application provides an example of a distance-brightness curve corresponding to point cloud data of a single target (0.4m target). The horizontal axis represents the distance of the point cloud under the target, and the vertical axis represents the brightness of the point cloud under the target. As shown in the figure, the dark black dots forming the horizontal line in the middle represent the point cloud generated under the reference target material, while the light black dots forming the lower left curve represent the point cloud generated by the high-reflectivity target. For each curve, the average brightness mPeak near the minimum distance of the reference target is taken, and the maximum brightness MaxPeak near the minimum distance of the high-reflectivity target is taken. Taking a 0.4m target as an example, the average brightness of the white target and the maximum brightness of the high-reflectivity target within the range of 0.4m ± 0.015m can be taken, and denoted as mPeak and MaxPeak respectively.

[0051] Then, based on the aforementioned interpolation function and curve, range-brightness calibration is performed on each individual radar. Data from a reference target and a high-reflectivity target are collected at each of the aforementioned ranges. In this embodiment, a high-reflectivity target made of kickboard material is used as an example. For the distorted point cloud situation caused by the kickboard "convex hull," the distance and brightness of the "convex hull" point cloud and the normal point clouds on the left and right sides can be collected to establish a range-brightness coordinate system. A range-brightness curve is plotted with distance as the horizontal axis and brightness as the vertical axis. It should be noted that this operation is performed for each target range.

[0052] Please combine Figure 6 , Figure 6 Here are examples of distance-brightness curves for white target, high-reflectivity target 1, and high-reflectivity target 2. Figure 6 With the above Figure 5 These are all examples of distance-brightness curves corresponding to targets, but Figure 6 and Figure 5 Slightly different, Figure 5 It is a curve of point cloud data. Figure 6 It is a curve obtained by fitting the point cloud data corresponding to the target. Figure 6 The three curves in the diagram correspond from top to bottom to the second high-reflectivity target (high-reflectivity 2 in the diagram), the first high-reflectivity target (high-reflectivity 1 in the diagram), and the reference target (the white target in the diagram). The blank area on the left side of the diagram without curves is the blind zone of the lidar. The curves are segmented by dashed lines in the diagram. d1, d2, d3, and d4 all belong to preset distances. For example, d1 corresponds to a distance of 0.2m-0.5m, d2 corresponds to a range of 0.5m-0.8m, and so on. Their values ​​are not limited here.

[0053] S132. For each curve segment, based on the brightness data corresponding to the first high-reflectivity target and the reference target, and combined with the theoretical distance at the corresponding preset distance, calculate the first correction model corresponding to each curve segment; based on the brightness data corresponding to the second high-reflectivity target and the reference target, and combined with the theoretical distance at the corresponding preset distance, calculate the second correction model corresponding to each curve segment.

[0054] Taking the first correction model as an example, based on the brightness data corresponding to the first high-reflectivity target and the reference target, and combined with the theoretical distance at the corresponding preset distance, the first correction model corresponding to each curve segment is calculated, including: selecting a curve segment, and inputting the point cloud data of the first high-reflectivity target and the reference target and their theoretical distance within the distance corresponding to the selected curve into the initial correction model to determine the first parameter of the first correction model, thereby obtaining the first correction model corresponding to the selected curve.

[0055] Please combine Figure 6Taking the curve corresponding to segment d1 as an example, the point cloud data of the first high-reflectivity target and the reference target at a distance of 0.2m-0.5m are input into the initial correction model. In this embodiment, an initial correction model is provided: dis_new = dis - a * 1 / (exp(mPeak - peak) + 1) + b, where dis_new is the theoretical distance between the target and the lidar, dis is the distance data of the first high-reflectivity target, peak is the brightness data of the first high-reflectivity target, and mPeak is the average brightness value near the minimum distance of the reference target. For example, in a certain experiment, the applicant calculated the first parameters corresponding to segment d1 as a = 3.5 and b = 0.1, and thus derived the first correction model corresponding to the distance of segment d1 as dis_new = dis – 3.5 * 1 / (exp(mPeak - peak) + 1) + 0.1.

[0056] Similarly, for the second correction model, based on the brightness data corresponding to the second high-reflectivity target and the reference target, and combined with the theoretical distance at the corresponding preset distance, the second correction model corresponding to each curve segment is calculated, including: selecting a curve segment, and inputting the point cloud data of the second high-reflectivity target and the reference target within the distance corresponding to the selected curve and their theoretical distance into the initial correction model to determine the second parameter of the second correction model, thereby obtaining the second correction model corresponding to the selected curve.

[0057] like Figure 6 As shown, taking the d1 segment as an example, the point cloud data of the second high-reflectivity target and the reference target at 0.2m-0.5m are substituted into the initial correction model dis_new=dis-a*1 / (exp(mPeak-peak)+1)+b, where dis_new is the theoretical distance between the target and the lidar, dis is the distance data of the second high-reflectivity target, peak is the brightness data of the second high-reflectivity target, and mPeak is the average brightness value near the minimum distance of the reference target. For example, in a certain experiment, the applicant calculated the second parameters corresponding to the d1 segment as a=3, b=0.15, and thus derived the second correction model corresponding to the distance of the d1 segment as dis_new=dis–3*1 / (exp(mPeak-peak)+1)+0.15.

[0058] Based on the above method, two sets of parameters (first parameter and second parameter) corresponding to each curve segment are calculated to determine the correction model corresponding to each curve segment, including the first correction model and the second correction model. It is understood that the initial correction model provided above in this application is only a preferred example and is not limited to the formula in the above example. For example, in some other embodiments, the initial correction model can also be dis_new = dis - a * (peak - mPeak) + b), where dis_new is the theoretical distance between the target and the lidar, dis is the distance data of the first / second high-reflectivity target, peak is the brightness data of the first / second high-reflectivity target, mPeak is the average brightness near the minimum distance of the reference target, and a and b are parameters to be determined. The two sets of parameters corresponding to each curve segment can be calculated separately using the above method.

[0059] S14. Based on the correction model, perform distance correction on the point cloud information generated by the lidar during actual ranging to obtain an updated distance value. The point cloud information includes distance measurements and brightness measurements.

[0060] Please combine Figure 7 The step of performing distance correction on the point cloud information generated by the lidar during actual ranging based on the correction model includes:

[0061] S141. Combining the distance-brightness curve, determine the target curve segment based on the distance measurement value; determine the target correction model based on the brightness measurement value and the target curve segment. Wherein, as... Figure 6 As shown, the target curve segment includes the reference target distance-brightness curve segment corresponding to the reference target within the distance segment corresponding to the distance measurement value, and the first high-reflectivity distance-brightness curve corresponding to the first high-reflectivity target. Please refer to... Figure 8 The step of determining the target correction model based on the measured brightness value and the target curve segment includes:

[0062] S1411. Obtain the reference target distance-brightness function corresponding to the reference target distance-brightness curve segment and the first high-reflectivity distance-brightness function corresponding to the first high-reflectivity distance-brightness curve, respectively. Both the reference target distance-brightness function and the first high-reflectivity distance-brightness function can be obtained through methods such as... Figure 6 The distance-brightness curve shown is obtained.

[0063] S1412. Substitute the distance measurement value into the reference target distance-brightness function and the first high-reflectivity distance-brightness function, respectively, to obtain the reference target standard brightness value and the first high-reflectivity standard brightness value corresponding to the distance measurement value. This distance measurement value is the distance data calculated from the point cloud obtained during the actual ranging process of the lidar. By substituting this distance measurement value into the above two curve functions, the reference target standard brightness value and the first high-reflectivity standard brightness value at the corresponding distance can be obtained.

[0064] S1413. Determine the target correction model based on the measured brightness value, the reference target standard brightness value, and the first high-reflection standard brightness value. Specifically, compare the measured brightness value with the reference target standard brightness value and the first high-reflection standard brightness value respectively:

[0065] a. If the measured brightness value is greater than the reference target standard brightness value and less than the first high-reflection standard brightness value, then the first correction model will be used as the target correction model so that distance correction can be performed subsequently through the target correction model.

[0066] b. If the measured brightness value is greater than or equal to the first high-reflection standard brightness value, then the second correction model is used as the target correction model.

[0067] c. If the measured brightness value is less than or equal to the standard brightness value of the reference target, then a targetless correction model is determined, and the measured distance value is taken as the target distance.

[0068] S142. Based on the target correction model and the point cloud information, perform distance correction on the distance measurement value.

[0069] For point cloud information generated by lidar during actual ranging, if the distance of the point cloud is within the range of the correction model, the corresponding target correction model can be calculated using the distance measurement value and brightness measurement value in the point cloud information. Then, the distance measurement value is substituted into the target correction model for distance correction to obtain the updated distance value.

[0070] For example, if the measured distance corresponding to the point cloud data of a certain spot is in the range of [0.35, 0.45], and the brightness of the point is greater than the first high-reflectivity standard brightness value mPeak corresponding to the distance in the range of [0.35, 0.45], the target correction model corresponding to it is determined as the second correction model. Substituting the distance measurement value and brightness measurement value of the point cloud into the target correction model, the corrected distance can be obtained, and the updated distance value can be obtained. The "convex hull" in the point cloud image can also be eliminated based on the updated distance value.

[0071] It is understandable that the first / second high-reflectivity target in the above embodiments uses a high-reflectivity material as an example. The principle is that the point cloud of a high-reflectivity material is distorted because its reflectivity is higher than that of a white target. Conversely, the point cloud of a low-reflectivity material is distorted because its reflectivity is lower than that of a white target. Therefore, in practical applications, a low-reflectivity material can also be used as the material for the first / second low-reflectivity target. Analogous to the method used for the first / second high-reflectivity target, the distorted point cloud can be corrected for distance correction. The principle is the same as in the above embodiments and will not be repeated here.

[0072] The ranging method provided in this application sets up multiple sets of targets and collects point cloud data of a reference target and two high-reflectivity targets with different reflectivities in each set. A correction model is determined by combining theoretical distance data, and then the point cloud generated by actual ranging is corrected according to the correction model to obtain an updated distance value. This method can stably and effectively correct the distance of "convex hull" distortion point clouds formed by special materials in lidar ranging. It is not only highly stable and robust, but also does not affect the ranging of normal materials, and it does not change the original point cloud contour shape of the object. This optimizes the output point cloud for lidar products and reduces ranging deviation.

[0073] This application provides a ranging device; please refer to [link / reference]. Figure 9 , Figure 9 This is a schematic diagram of a ranging device provided in an embodiment of this application. The device 100 includes: a target setting module 101, a data acquisition module 102, a model determination module 103, and a correction module 104.

[0074] Specifically, the target setting module 101 can acquire the range of the lidar and set at least two sets of targets according to the range. Each set of targets includes a first high-reflectivity target, a second high-reflectivity target, and a reference target. The reflectivity of the first high-reflectivity target is less than that of the second high-reflectivity target, and the reflectivity of the reference target is less than that of the first high-reflectivity target. The data acquisition module 102 can acquire point cloud data of the lidar at each set of targets. The point cloud data includes distance data and brightness data. The model determination module 103 can determine a correction model based on the point cloud data of the targets and the theoretical distances corresponding to the targets at different preset distances. The correction module 104 can perform distance correction on the point cloud information generated by the lidar during actual ranging based on the correction model to obtain updated distance values.

[0075] In the embodiments of this application, the ranging device can also be constructed from hardware components. For example, the ranging device can be constructed from one or more chips, and the chips can work in coordination to complete the ranging method described in the above embodiments. Furthermore, the ranging device 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.

[0076] The ranging device in this application embodiment can be a device with an operating system. This operating system can be Android, iOS, or other possible operating systems; this application embodiment does not specifically limit the specific operating system used.

[0077] It should be noted that the above-described ranging device can execute the ranging 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 ranging device embodiments can be found in the ranging method provided in the embodiments of this application.

[0078] This application also provides an electronic device; please refer to [link / reference]. Figure 10 , Figure 10 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device 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 ranging method 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 10 Taking the example of a connection between China and Israel via a bus.

[0079] 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.

[0080] 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 ranging 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 ranging method in any of the above method embodiments, that is, it can achieve... Figure 2 The entire process.

[0081] This application also provides a robot, please refer to [link / reference]. Figure 11 , Figure 11 This is a schematic diagram of the structure of a robot provided in an embodiment of this application. The robot 300 includes an electronic device 200 and a controller 301. The electronic device 200 is communicatively connected to the controller 301. The controller 301 is used to send a ranging command to the electronic device 200 so that the electronic device 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 electronic device 200. The external terminal can be a fixed terminal or a mobile terminal, such as a computer, mobile phone, tablet, or other electronic device, and is not limited thereto.

[0082] This application provides a computer-readable storage medium, such as a memory including program code, which can be executed by a processor to perform the ranging 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.

[0083] 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 ranging method steps provided in the above embodiments.

[0084] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented using software and a general-purpose hardware platform, or of course, using hardware. Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0085] 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 method of ranging, characterized by, include: The range of the lidar is obtained, and at least two sets of targets are set according to the range. Each set of targets includes a first high reflectivity target, a second high reflectivity target and a reference target. The reflectivity of the first high reflectivity target is less than that of the second high reflectivity target, and the reflectivity of the reference target is less than that of the first high reflectivity target. Collect point cloud data of the lidar on each group of targets, the point cloud data including distance data and brightness data; Based on the point cloud data of the target and the theoretical distances corresponding to the target at different preset distances, a correction model is determined. Based on the correction model, the point cloud information generated by the lidar during actual ranging is corrected to obtain an updated distance value. The point cloud information includes distance measurement value and brightness measurement value. The step of performing distance correction on the point cloud information generated by the lidar during actual ranging based on the correction model includes: Combining the distance-brightness curve, a target curve segment is determined based on the distance measurement value; wherein, the target curve segment includes the reference target distance-brightness curve segment corresponding to the reference target within the distance segment corresponding to the distance measurement value, and the first high-reflectivity distance-brightness curve corresponding to the first high-reflectivity target; Obtain the reference target distance-brightness function corresponding to the reference target distance-brightness curve segment and the first high-reflection distance-brightness function corresponding to the first high-reflection distance-brightness curve, respectively; substitute the distance measurement value into the reference target distance-brightness function and the first high-reflection distance-brightness function, respectively, to obtain the reference target standard brightness value and the first high-reflection standard brightness value corresponding to the distance measurement value; determine the target correction model based on the brightness measurement value, the reference target standard brightness value, and the first high-reflection standard brightness value. Based on the target correction model and the point cloud information, the distance measurement value is corrected.

2. The method of claim 1, wherein, The correction model includes a first correction model and a second correction model. The step of determining the correction model based on the point cloud data of the target and the theoretical distances corresponding to the target at different preset distances includes: A distance-brightness curve is plotted based on the point cloud data, and the distance-brightness curve is segmented based on different preset distances; For each curve segment, based on the brightness data corresponding to the first high-reflectivity target and the reference target, and combined with the theoretical distance at the corresponding preset distance, the first correction model corresponding to each curve segment is calculated respectively; based on the brightness data corresponding to the second high-reflectivity target and the reference target, and combined with the theoretical distance at the corresponding preset distance, the second correction model corresponding to each curve segment is calculated respectively.

3. The method according to claim 2, characterized in that, For each curve segment, based on the brightness data corresponding to the first high-reflectivity target and the reference target, and combined with the theoretical distance at the corresponding preset distance, the first correction model corresponding to each curve segment is calculated, including: Select a curve segment, and input the point cloud data of the first high-reflection target and the reference target within the distance corresponding to the selected curve, along with their theoretical distances, into the initial correction model to determine the first parameter of the first correction model, thereby obtaining the first correction model corresponding to the selected curve.

4. The method of claim 1, wherein, The step of determining the target correction model based on the measured brightness value, the reference target standard brightness value, and the first high-reflection standard brightness value includes: If the measured brightness value is greater than the reference target standard brightness value and less than the first high-reflection standard brightness value, then the first correction model is used as the target correction model. If the measured brightness value is greater than or equal to the first high-reflection standard brightness value, then the second correction model is used as the target correction model; If the measured brightness value is less than or equal to the standard brightness value of the reference target, then a targetless correction model is determined, and the measured distance value is taken as the target distance.

5. The method of claim 1, wherein, Setting at least two sets of targets according to the range includes: Determine the test range based on the stated range; Determine the preset step size and the number of target groups based on the test range; The position of each target group is determined based on the preset step size and the number of target groups.

6. A ranging device, characterized in that, include: The target setting module is used to acquire the range of the lidar and set at least two sets of targets according to the range. Each set of targets includes a first high-reflectivity target, a second high-reflectivity target and a reference target. The reflectivity of the first high-reflectivity target is less than that of the second high-reflectivity target, and the reflectivity of the reference target is less than that of the first high-reflectivity target. The data acquisition module is used to acquire point cloud data of the lidar on each group of targets, and the point cloud data includes distance data and brightness data; The model determination module is used to determine the correction model based on the point cloud data of the target and the theoretical distance corresponding to the target at different preset distances; The correction module is used to perform distance correction on the point cloud information generated by the lidar during actual ranging based on the correction model, so as to obtain an updated distance value. The point cloud information includes distance measurement value and brightness measurement value. Specifically, the correction module is used for: Combining the distance-brightness curve, a target curve segment is determined based on the distance measurement value; wherein, the target curve segment includes the reference target distance-brightness curve segment corresponding to the reference target within the distance segment corresponding to the distance measurement value, and the first high-reflectivity distance-brightness curve corresponding to the first high-reflectivity target; Obtain the reference target distance-brightness function corresponding to the reference target distance-brightness curve segment and the first high-reflection distance-brightness function corresponding to the first high-reflection distance-brightness curve, respectively; substitute the distance measurement value into the reference target distance-brightness function and the first high-reflection distance-brightness function, respectively, to obtain the reference target standard brightness value and the first high-reflection standard brightness value corresponding to the distance measurement value; determine the target correction model based on the brightness measurement value, the reference target standard brightness value, and the first high-reflection standard brightness value. Based on the target correction model and the point cloud information, the distance measurement value is corrected.

7. An electronic device, comprising: include: At least one processor; A memory that is communicatively connected to the at least one processor; The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the ranging method according to any one of claims 1 to 5.

8. A robot, characterized in that Including the electronic device as described in claim 7.

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