BUCKET COORDINATES CALIBRATION PROCEDURES AND DEVICE, UPDATE PROCEDURES AND DEVICE AND EXCAVATORS

Abstract

A method and device for calibrating bucket coordinates, a method and device for updating, and an excavator.The bucket coordinate calibration procedure comprises the following: acquiring angle sensor data and radar point cloud data of a bucket (step 21); determining the coordinates of bucket teeth in the bucket under the radar coordinate system and according to the radar point cloud data of the bucket (step 22); determining the coordinates of the bucket teeth in the bucket under the excavator coordinate system and according to the angle sensor data of the bucket (step 23); and determining a coordinate calibration matrix according to the coordinates of the bucket teeth in the bucket under the radar coordinate system and under the excavator coordinate system (step 24), wherein the coordinate calibration matrix is ​​a calibration matrix that calibrates the coordinates of the bucket teeth in the bucket under the radar coordinate system to coordinates in the excavator coordinate system.The present method can utilize a laser radar and an excavator angle sensor, and bucket coordinates are calibrated without the addition of external calibration equipment.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application is based on and claims priority from Chinese Patent Application No. 202211674601.1, filed on December 26, 2022, the disclosure of which is incorporated herein by reference in its entirety. TECHNICAL FIELD

[0002] The present disclosure relates to the intelligent field of construction machinery, particularly to a method and apparatus for calibrating coordinates of a bucket, an updating method and apparatus, and an excavator. GENERAL STATE OF THE ART

[0003] In view of the operating conditions of the excavator for excavating the bulk material of the related art, it is desirable that a size of a material pile be recognized and a position of a target excavation point be recognized by a detecting device, and that the excavator can be informed of a target position to realize the unmanned automatic excavation operation of the excavator. SUMMARY

[0004] According to one aspect of the present disclosure, a method for calibrating coordinates of a bucket is provided. The method includes: acquiring lidar point cloud data and angle sensor data of the bucket of an excavator; determining coordinates of a center bucket tooth of the bucket in a lidar coordinate system according to the lidar point cloud data of the bucket; determining the coordinates of the center bucket tooth of the bucket in an excavator coordinate system according to the angle sensor data of the bucket; and determining a coordinate calibration matrix according to the coordinates of the center bucket tooth of the bucket in the lidar coordinate system and in the excavator coordinate system, wherein the coordinate calibration matrix is ​​a calibration matrix for calibrating the coordinates of the center bucket tooth of the bucket in the lidar coordinate system to the excavator coordinate system.

[0005] In some embodiments of the present disclosure, collecting the lidar point cloud data and the angle sensor data of the excavator bucket includes: collecting the lidar point cloud data and the angle sensor data of the bucket in a plurality of different positions.

[0006] In some embodiments of the present disclosure, determining the coordinates of the center blade tooth of the bucket in the lidar coordinate system according to the lidar point cloud data of the bucket comprises: determining the coordinates of the center blade tooth of the bucket in the lidar coordinate system according to the lidar point cloud data of the bucket acquired at each of the different positions.

[0007] In some embodiments of the present disclosure, determining the coordinates of the center bucket tooth of the bucket in the excavator coordinate system according to the bucket angle sensor data includes determining the coordinates of the center bucket tooth of the bucket in the excavator coordinate system according to the bucket angle sensor data acquired at each of the different positions.

[0008] In some embodiments of the present disclosure, determining the coordinates of the center blade tooth of the bucket in the lidar coordinate system according to the lidar point cloud data of the bucket acquired at each of the different positions comprises: determining the coordinates of the center blade tooth of the bucket in the lidar coordinate system using an implicit shape model algorithm according to the lidar point cloud data of the bucket acquired at each of the different positions.

[0009] In some embodiments of the present disclosure, determining the coordinates of the center bucket tooth of the bucket in the excavator coordinate system according to the angle sensor data of the bucket acquired at each of the different positions comprises: calculating a forward kinematics resolution of the excavator to determine the coordinates of the center bucket tooth of the bucket in the excavator coordinate system according to the angle sensor data of the bucket acquired at each of the different positions.

[0010] In some embodiments of the present disclosure, determining the coordinate calibration matrix according to the coordinates of the center bucket tooth of the bucket in the lidar coordinate system and in the excavator coordinate system includes: constructing a plurality of data pairs of the coordinates of the lidar coordinate system and the coordinates of the excavator coordinate system according to the coordinates of the center bucket tooth of the bucket in the lidar coordinate system and in the excavator coordinate system, and dividing the plurality of data pairs into training set data and test set data; determining the coordinate calibration matrix according to the training set data; and verifying the coordinate calibration matrix using the test set data.

[0011] In some embodiments of the present disclosure, the coordinate calibration matrix is ​​a coordinate rotation and translation transformation matrix.

[0012] In some embodiments of the present disclosure, determining the coordinate calibration matrix according to the training set data comprises: initializing respective parameters, wherein the respective parameters include iteration times; randomly selecting a predetermined number of first data pairs; determining whether the first data pairs are collinear; and determining the coordinate calibration matrix by a direct linear transformation if the first data pairs are not collinear.

[0013] In some embodiments of the present disclosure, determining the coordinate calibration matrix according to the training set data further includes: transforming the coordinates of the lidar coordinate system into second data pairs to acquire the coordinates of the excavator coordinate system using the coordinate calibration matrix, wherein the second data pairs are different data pairs from the first data pairs in the training set data; calculating a distance deviation between the transformed coordinates of the excavator coordinate system and the actual coordinates of the excavator coordinate system; determining whether the distance deviation is less than a predetermined distance threshold; recording conditionally compliant interior points and updating the coordinate calibration matrix according to the iteration times and the determination result of the distance deviation;and calculating an interior point probability and updating the iteration times according to the interior point probability.;

[0014] According to another aspect of the present disclosure, a method for updating a coordinate calibration is provided. The method includes: determining whether an online error of a coordinate calibration matrix is ​​greater than a predetermined allowable error; determining whether a number of data pairs of acquired position points reaches a predetermined position point number if the online error of the coordinate calibration matrix is ​​greater than the predetermined allowable error; determining a new coordinate calibration matrix using the method according to any of the embodiments described above if the number of data pairs of acquired position points is equal to the predetermined position point number; and updating the coordinate calibration matrix.

[0015] In some embodiments of the present disclosure, the method further comprises: acquiring the lidar point cloud data of the bucket and determining a coordinate of the center bucket tooth of the bucket in the lidar coordinate system if the number of data pairs of the acquired position points is less than the predetermined position point number; acquiring the angle sensor data of the bucket and determining a coordinate of the center bucket tooth of the bucket in the excavator coordinate system; and accumulating the number of data pairs of the position points and subsequently performing the determining again if the number of data pairs of the acquired position points reaches the predetermined position point number.

[0016] In some embodiments of the present disclosure, determining a coordinate of the center blade tooth of the bucket in the lidar coordinate system comprises: acquiring a coordinate of the center blade tooth of the bucket in the lidar coordinate system using an implicit shape model algorithm; determining whether a model similarity is greater than a predetermined similarity; and using the acquired one coordinate of the center blade tooth of the bucket in the lidar coordinate system if the model similarity is greater than the predetermined similarity.

[0017] In some embodiments of the present disclosure, determining a coordinate of the center bucket tooth of the bucket in the excavator coordinate system includes: calculating a forward kinematic resolution of the excavator based on the angle sensor data and determining the one coordinate of the center bucket tooth of the bucket in the excavator coordinate system.

[0018] According to another aspect of the present disclosure, an apparatus for calibrating coordinates of a blade is provided.The apparatus comprises: a data acquisition module configured to acquire lidar point cloud data and angle sensor data of an excavator's bucket; a positioning module configured to determine coordinates of a bucket center tooth in a lidar coordinate system according to the lidar point cloud data of the bucket; and to determine coordinates of the bucket center tooth in an excavator coordinate system according to the bucket angle sensor data; and a calibration module configured to determine a coordinate calibration matrix according to the coordinates of the bucket center tooth in the lidar coordinate system and in the excavator coordinate system, wherein the coordinate calibration matrix is ​​a calibration matrix for calibrating the coordinates of the bucket center tooth in the lidar coordinate system to the excavator coordinate system.

[0019] In some embodiments of the present disclosure, the apparatus for calibrating the coordinates of the blade is configured to perform the method according to any of the embodiments described above.

[0020] According to another aspect of the present disclosure, a device for updating a coordinate calibration is provided.The apparatus comprises: a determining device configured to determine whether an online error of a coordinate calibration matrix is ​​greater than a predetermined allowable error; and to determine whether a number of data pairs of acquired position points reaches a predetermined position point number if the online error of the coordinate calibration matrix is ​​greater than the predetermined allowable error; a device for calibrating coordinates of a bucket configured to determine a new coordinate calibration matrix using a method for calibrating coordinates of a bucket if the number of data pairs of the acquired position points is equal to the predetermined position point number; and an updating device configured to update the coordinate calibration matrix.

[0021] In some embodiments of the present disclosure, the apparatus for calibrating the coordinates of the blade is the apparatus for calibrating the coordinates of the blade according to any of the embodiments described above.

[0022] In some embodiments of the present disclosure, the device is configured to perform the method for updating the coordinate calibration according to any of the embodiments described above.

[0023] According to another aspect of the present disclosure, a computing device is provided. The device comprises: a memory for storing instructions; a processor configured to execute the instructions, such that the computing device performs the method according to any of the embodiments described above.

[0024] According to another aspect of the present disclosure, a calibration system is provided. The system includes a lidar, an angle sensor, and further comprising at least one of a computing device, a means for updating a coordinate calibration, and a device for calibrating coordinates of a blade, wherein the computing device is the computing device according to any one of the embodiments described above, the means for updating a coordinate calibration is the means for updating the coordinate calibration according to any one of the embodiments described above, and the device for calibrating the coordinates of the blade is the device for calibrating the coordinates of the blade according to any one of the embodiments described above.

[0025] According to another aspect of the present disclosure, an excavator is provided. The excavator includes a lidar, and further comprising at least one of a computing device, a means for updating a coordinate calibration, and a device for calibrating coordinates of a bucket, wherein the computing device is the computing device according to any one of the above-described embodiments, the means for updating a coordinate calibration is the means for updating the coordinate calibration according to any one of the above-described embodiments, and the device for calibrating the coordinates of the bucket is the device for calibrating the coordinates of the bucket according to any one of the above-described embodiments.

[0026] According to another aspect of the present disclosure, a computer-readable storage medium is provided, the computer-readable storage medium having computer instructions stored thereon that, when executed by a processor, perform the method for updating the coordinate calibration according to any of the embodiments described above. BRIEF DESCRIPTION OF THE ACCOMPANIMENTAL DRAWINGS

[0027] In order to more clearly explain the embodiments of the present disclosure or the technical solutions of the relevant technical field, the accompanying drawings to be used in describing the embodiments or the relevant technical field are briefly introduced below. It should be understood that the accompanying drawings described below are merely some embodiments of the present disclosure. A person skilled in the art can also obtain other accompanying drawings based on these accompanying drawings, assuming no inventive effort is involved. Fig. 1 is a schematic view of the operating conditions of excavating the bulk material by the excavator according to some embodiments of the present disclosure. Fig. 2 is a schematic view of the method for calibrating the coordinates of the blade according to some embodiments of the present disclosure. Fig. 3 is a schematic view of the method for calibrating the coordinates of the blade according to other embodiments of the present disclosure. Fig. 4 is a schematic view of the method for updating coordinate calibration according to some embodiments of the present disclosure. Fig. 5 is a schematic view of the method for updating the coordinate calibration according to other embodiments of the present disclosure. Fig. 6 is a schematic view of the apparatus for calibrating the coordinates of the blade according to some embodiments of the present disclosure. Fig. 7 is a schematic view of the device for updating the coordinate calibration according to some embodiments of the present disclosure. Fig. 8 is a structural view of the computing device according to some embodiments of the present disclosure. DETAILED DESCRIPTION

[0028] The technical solution in the embodiments of the present disclosure will be clearly and fully described below with reference to the accompanying drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only some of the embodiments of the present disclosure, not all of the embodiments. The following descriptions of at least one embodiment, which are indeed merely illustrative, are in no way intended to limit the present disclosure, nor its application or use. Based on the embodiments of the present disclosure, all other embodiments obtained by those skilled in the art, assuming no inventive effort is involved, fall within the scope of the present disclosure.

[0029] Unless otherwise indicated, the relative arrangements, numerical expressions, and numerical values ​​of the components and steps set forth in these examples are not intended to limit the scope of the present invention.

[0030] At the same time, it is understood that for the sake of convenience of description, the dimensions of the various parts shown in the accompanying drawings are not drawn according to actual proportional relationships.

[0031] Techniques, methods, and devices known to those skilled in the art may not be explained in detail. However, such techniques, methods, and devices are to be considered part of the granted description, where appropriate.

[0032] In all examples shown and explained herein, each specific value is intended to be exemplary only and not limiting. Thus, other examples in the embodiments may have different values.

[0033] It should be noted that similar reference numerals and letters in the following accompanying drawings represent similar items, so that once an item is defined in an accompanying drawing, further discussion thereof is required in the following accompanying drawings.

[0034] The inventors have discovered through studies that the size of the material pile and the position of the target excavation point are detected by a lidar mounted outside the excavator at the excavation site of the bulk material. The lidar is mounted outside the excavator. The position of the bucket is detected by an angle sensor mounted on the excavator. The function of automatic excavation for the target excavation point is implemented by planning and controlling a bucket trajectory. In this scenario, to achieve accurate excavation, the target excavation point must be accurately measured, and the accurate measurement is carried out under the premise of coordinate calibration, i.e., the coordinates of the bucket and the target excavation point in the lidar coordinate system are accurately calibrated in the excavator coordinate system to ensure that the coordinates of the bucket and the coordinates of the target excavation point in the excavator coordinate system are uniform.

[0035] Common coordinate calibration methods in related technology include direct measurement, manual point selection, and scenario characterization. These related technology methods also have the following drawbacks: the calibration accuracy of the direct measurement method is low, and both the direct measurement method and the manual point selection method are prone to various errors, including those caused by artificial manipulation.

[0036] The inventors have also found through studies that the direct measurement method, the manual point selection method and the scenario characterization method also have the following deficiencies: 1) The manual point selection method is time-consuming and labor-consuming. Both the direct measurement method and the manual point selection method require manual intervention. Calibrating a large number of fixtures may result in a heavy workload, making it difficult to implement mass production of the intelligent system, whether using the direct measurement method or the manual point selection method. 2) The scenario characterization procedure requires the design of a specific scenario for the calibration of the sensors, so that it is not possible to implement an online calibration of the sensors. 3) The methods described above lack the function of online verification of calibration errors during operation, and the calibration cannot be updated online, especially when the relative position of the excavator and the lidar changes.

[0037] The related technology concerns a sensor positioning system capable of calculating the positions of the sensors of at least one or more self-driving vehicles based on areal data. However, it requires multiple measurement transformations; and it lacks error verification and online update functions.

[0038] The related art also discloses a method for calibrating a machine-mounted sensor device capable of implementing the calibration of the machine-mounted devices. However, it is necessary to acquire and register topographic point clouds with multiple features twice to implement the calibration of the machine-mounted sensor device; and it is not possible to perform online calibration when the relative positions of the sensors change.

[0039] With other related technologies, offline calibration can be implemented. However, a specific common calibration target, including a checkerboard calibration board and an L-shape, is required; and online calibration cannot be performed.

[0040] Another related technique can implement offline calibration. However, it requires a dedicated calibration board. Second, to acquire the first coordinate value of each feature point on the calibration board in the undercarriage coordinate system of the operating machine, it is necessary to perform manual measurement with a measuring tool such as a tape measure or to control the operating machine to perform automatic measurement. However, manual measurement is laborious, and accurate measurement is difficult, and there is no explanation of how to perform automatic measurement. Third, online calibration cannot be performed.

[0041] In view of at least one of the above technical problems, the present disclosure provides a method and apparatus for calibrating bucket coordinates, an updating method and apparatus, and an excavator that can calibrate bucket coordinates using the excavator's lidar and angle sensor without adding an external calibration device. The present disclosure will be explained below with reference to the embodiments.

[0042] Fig. 1 is a schematic view of the operating conditions of excavating the bulk material by the excavator according to some embodiments of the present disclosure. Fig. The operating scenario shown in Figure 1 includes an excavator, a material to be excavated, a lidar scanning device and a transport vehicle (as an unloading point, in Fig. 1 (not shown). The excavator is parked near the material to be excavated, allowing its excavation radius to cover the area of ​​the material. In another scenario, if the total amount or location of the material changes with the excavation site, the excavator can move as the location of the material changes.

[0043] In some embodiments of the present disclosure, the lidar may be a lidar scanning device.

[0044] In some embodiments of the present disclosure, as in Fig. As shown in Figure 1, the lidar scanning device is deployed outside the excavated material and configured to acquire the point cloud of the excavated material to determine a suitable target excavation point; and is simultaneously configured to acquire the point cloud of the bucket. When the center bucket tooth of the bucket is visible outside the material, it is possible to acquire the point cloud containing the center bucket tooth and to acquire the coordinates PL of the center bucket tooth of the bucket in the lidar coordinate system through an implicit shape model algorithm.

[0045] In some embodiments of the present disclosure, as in Fig. 1, angle sensors are mounted on the excavator to detect the angle information of the excavator, and the coordinates PW of the center bucket tooth of the bucket in the excavator coordinate system are calculated by a forward kinematics algorithm.

[0046] To achieve accurate excavation, coordinate calibration must be performed. In the above-described embodiments of the present disclosure, the coordinates of the center bucket tooth of the bucket in the lidar coordinate system are calibrated to the excavator coordinate system, and the coordinates of the center bucket tooth and the coordinates of the target excavation point in the excavator coordinate system are implemented to be consistent.

[0047] Fig. 2 is a schematic view of the method for calibrating coordinates of a blade according to some embodiments of the present disclosure. Preferably, the present embodiment can be performed by the apparatus for calibrating coordinates of a blade of the present disclosure, or the calibration system of the present disclosure, or the computing device of the present disclosure, or the device for updating a coordinate calibration of the present disclosure. The method according to the embodiment of the Fig. 2 may comprise at least one of steps 21 to 24.

[0048] In step 21, the lidar point cloud data and the angle sensor data of the bucket are collected.

[0049] In some embodiments of the present disclosure, step 21 may include: collecting the lidar point cloud data and the angle sensor data of the bucket in a plurality of different positions.

[0050] In step 22, the coordinates of the center blade tooth of the blade in the lidar coordinate system are determined according to the lidar point cloud data of the blade.

[0051] In some embodiments of the present disclosure, step 22 may include determining the coordinates of the center blade tooth of the blade in the lidar coordinate system according to the lidar point cloud data of the blade acquired at each of the different positions.

[0052] In some embodiments of the present disclosure, step 22 may include determining the coordinates of the center blade tooth of the blade in the lidar coordinate system according to the lidar point cloud data of the blade acquired at each of the different positions using an implicit shape model algorithm according to the lidar point cloud data of the blade acquired at each of the different positions.

[0053] In step 23, the coordinates of the center bucket tooth of the bucket in the excavator coordinate system are determined according to the angle sensor data of the bucket.

[0054] In some embodiments of the present disclosure, step 23 may include determining the coordinates of the center bucket tooth of the bucket in the excavator coordinate system according to the bucket angle sensor data acquired at each of the different positions.

[0055] In some embodiments of the present disclosure, step 23 may include calculating a forward kinematic resolution of the excavator to determine the coordinates of the center bucket tooth of the bucket in the excavator coordinate system according to the bucket angle sensor data acquired at each of the different positions.

[0056] In step 24, the coordinate calibration matrix is ​​determined according to the coordinates of the center bucket tooth of the bucket in the lidar coordinate system and in the excavator coordinate system, wherein the coordinate calibration matrix is ​​a calibration matrix for calibrating the coordinates of the center bucket tooth of the bucket in the lidar coordinate system into the excavator coordinate system.

[0057] In some embodiments of the present disclosure, the coordinate calibration matrix is ​​a coordinate rotation and translation transformation matrix.

[0058] In some embodiments of the present disclosure, step 24 may include at least one of steps 241 through 243.

[0059] In step 241, a plurality of data pairs of the coordinates of the lidar coordinate system and the coordinates of the excavator coordinate system are constructed according to the coordinates of the center bucket tooth of the bucket in the lidar coordinate system and in the excavator coordinate system, and the plurality of data pairs are divided into training set data and test set data.

[0060] In step 242, the coordinate calibration matrix is ​​determined according to the training set data.

[0061] In step 243, the coordinate calibration matrix is ​​verified using the test set data.

[0062] In the present disclosure, the bucket coordinates are calibrated using the lidar and angle sensor of the excavator without adding an external calibration device.

[0063] Fig. 3 is a schematic view of the method for calibrating coordinates of a blade according to other embodiments of the present disclosure. Preferably, the present embodiment can be performed by the apparatus for calibrating coordinates of a blade of the present disclosure, or the calibration system of the present disclosure, or the computing device of the present disclosure, or the device for updating a coordinate calibration of the present disclosure. The method according to the embodiment of the Fig. 3 may include at least one of steps 31 to 36.

[0064] In step 31, the lidar point cloud data and the angle sensor data of the bucket are collected at N different positions.

[0065] In some embodiments of the present disclosure, N different positions means that the bucket moves to N different spatial positions relative to the excavator coordinate system, and N is determined during the process, with the value of N being at least greater than 4. The bucket point cloud data is acquired based on the lidar coordinate system. The angle sensor data is the data acquired based on the excavator coordinate system.

[0066] In some embodiments of the present disclosure, it is possible to allow N different spatial positions to be greatly different during data acquisition through system detection and control, for example, the acquisition is performed according to different rotation intervals, different blade attitudes, and different boom angles, in order to avoid the phenomenon of data correlation caused by excessively concentrated acquisition positions, which leads to the problem of being unable to converge when calculating the coordinate calibration matrix in the subsequent steps.

[0067] In some embodiments of the present disclosure, it is possible to allow the bucket to be located at N different spatial positions within the range of the lidar's view angle during data acquisition through system detection and control, so that the lidar can acquire as many point clouds of the bucket as possible, thereby ensuring the configuration accuracy of the subsequently used implicit mode algorithm and improving similarity.

[0068] In some embodiments of the present disclosure, during the data collection process for single calibration, the upper vehicle rotation system, excavator arm, and bucket can move freely, but the positions of the excavator's lower vehicle and the lidar should be kept relatively fixed. Otherwise, the calibration may be inaccurate due to changes in the relative positions of the excavator and the lidar. Of course, the system may also be configured to perform recalibration when it detects that the relative positions are changing.

[0069] In step 32, the N coordinates PL of the center blade tooth of the blade in the lidar coordinate system are calculated using an implicit shape model algorithm.

[0070] In some embodiments of the present disclosure, step 32 may include calculating the coordinates PL of the center blade tooth of the blade in the lidar coordinate system using an implicit shape model algorithm according to the lidar point cloud of the blade acquired by the blade at each of the different positions.

[0071] In step 33, the N corresponding coordinates PW of the center bucket tooth of the bucket in the excavator coordinate system are calculated based on the forward kinematic resolution of the angle sensor.

[0072] In some embodiments of the present disclosure, step 33 may include: detecting the coordinates PW of the center bucket tooth of the bucket in the excavator coordinate system based on the forward kinematics resolution according to the angle sensor data of the bucket detected at each of the different positions.

[0073] In step 34, the data pairs (PL, PW) are constructed and randomly divided into training set data and test set data.

[0074] In some embodiments of the present disclosure, step 34 may include constructing the data pairs (PL, PW) by one-to-one correspondence of the coordinates PL, PW of the center blade tooth of the blade acquired in the above-described step, and randomly dividing the data pairs (PL, PW) into a training set and a test set, wherein the division ratio may be set according to the number of cumulatively acquired points.

[0075] In step 35, the coordinate calibration matrix R|T is acquired by applying a RANSAC (Random Sample Consensus) estimation algorithm to the training set data.

[0076] In some embodiments of the present disclosure, step 35 may include calculating the coordinate calibration matrix R|T by applying a RANSAC estimation algorithm to the training set data according to the coordinate calibration model PW = R*PL+T. The RANSAC estimation algorithm used in the present disclosure can avoid the perturbation of the noisy data, so that the estimated R|T is accurate and reliable.

[0077] In some embodiments of the present disclosure, the task of coordinate calibration is to find a suitable R|T to transform the coordinates of the center bucket tooth of the bucket in the lidar coordinate system to the excavator coordinate system and calculate the error with the corresponding points, so that it is desirable that all data pairs have a minimum mean square error, ie, calculating a minimum value of formula (1). RE2=1N∗∑i=1N((PW−(R∗PL+T)) 2)

[0078] The inventors have found through studies that the matrix is ​​calculated directly by using the least squares method and DLT (direct linear transformation), but this method sometimes leads to unstable calculation results due to reasons such as the numerical calculation of the matrix, inaccurate point correspondence, especially the presence of outliers.

[0079] In the above-described steps of the solution according to the present disclosure, the blade coordinates are calculated using the implicit shape model algorithm according to the blade point cloud, and at the bottom layer, the actual blade point cloud is registered with the model point cloud. Since there is a registration similarity between the field-captured blade point cloud and the model prototype, the higher the similarity, the higher the accuracy of the calculated blade coordinates will be. That is, the accuracy of the blade coordinates is affected by the model similarity. Therefore, in the present solution, the coordinate calibration matrix R|T is calculated using the random sampling estimation algorithm RANSAC.

[0080] In some embodiments of the present disclosure, step 242 of the embodiment according to Fig. 2 or step 35 according to the embodiment of the Fig. 3 comprise at least one of steps 351 to 359.

[0081] In step 351, the relevant parameters are initialized, wherein the relevant parameters include parameters such as iteration times, a threshold, a maximum interior point number, and an interior point probability.

[0082] Steps 352 to 359 are iterative calculations.

[0083] In step 352, a predetermined number of first data pairs are randomly selected, where the data pairs are point pairs.

[0084] In some embodiments of the present disclosure, the predetermined number may be set to 4.

[0085] In some embodiments of the present disclosure, step 352 may include randomly selecting 4 pairs of points.

[0086] In step 353, it is determined whether the first data pairs are collinear. If so, the flow returns to step 351.

[0087] In step 354, the coordinate calibration matrix is ​​determined by the direct linear transformation if the first data pairs are not collinear, ie, R|T i is calculated using DLT.

[0088] In step 355, the lidar coordinates PL i of the lidar coordinate system in the second data pairs using the coordinate calibration matrix R|T i transformed to the coordinates PW i of the excavator coordinate system, where the second data pairs are different data pairs than the first data pairs in the training set data.

[0089] In step 35, the distance deviation between the coordinates of the excavator coordinate system acquired by transformation and the actual coordinates of the excavator coordinate system is calculated.

[0090] In step 357, it is determined whether the distance deviation is less than a predetermined distance threshold.

[0091] In step 358, the conditional interior points are recorded and the coordinate calibration matrix R|T is updated according to the iteration times and the determination result of the distance deviation.

[0092] In step 359, the interior point probability is calculated and the iteration times are updated according to the interior point probability.

[0093] It is verified that the RANSAC estimation algorithm used in the present disclosure can stably estimate the model parameters and can estimate the parameters with high accuracy from the data set containing a large number of outliers.

[0094] In step 36, the coordinate calibration matrix R|T is verified using the test set data.

[0095] In the present disclosure, the coordinate calibration matrix R|T is verified using the test set data, and if the errors meet the usage requirements, the calibration is completed. During the subsequent operation process of the excavator, the PWs are transformed into the lidar coordinate system using R|T, and excavation is performed at the target point and unloading at the unloading point.

[0096] The method for calibrating coordinates of a blade according to the present disclosure is explained by the following specific embodiments. Specific embodiment 1:

[0097] After field testing, PL and PW are recorded from N=8 different positions.

[0098] The coordinates of the center blade tooth of the blade in the lidar coordinate system obtained by point cloud calculation are as follows. PL={3.233 2.986 3.233 3.634 3.972 2.986 2.789 2.651−0.415 −0.738 −0.888 -0.367 −0.662 −0.23 −0.52 −0.6960.057 −0.204 −0.38 −0.058 −0.266 −0.322 -0.074 0.253}

[0099] The coordinates of the center bucket tooth of the bucket in the excavator coordinate system detected by the forward kinematic resolution of the angle sensor are as follows. PW={4.27 4.20 3.94 4.04 3.60 4.6 4.53 4.43 / / x0.74 1.13 1.07 0.50 0.45 0.80 1.12 1.33 / / y0.36 0.11 −0.07 0.24 0.01 0.01 0.24 0.58} / / z

[0100] The dataset is split in a ratio of 6:4 to capture the training set as follows. PL_Training={3.233 2.986 3.233 3.634 3.972−0.415 −0.738 −0.888 −0.367 -0.6620.057 −0.204 −0.38 −0.058 -0.266} PW_Training={4.27 4.20 3.94 4.04 3.6030.74 1.13 1.07 0.50 0.45 0.800.36 0.11 −0.07 0.24 0.01}

[0101] The test set is as follows. PL_Test={2.986 2.789 2.651−0.23 -0.52 −0.696−0.322 −0.074 0.253} PW_Test={4.6 4.53 4,430.80 1.12 1,330.01 0.24 0.58}

[0102] The coordinate calibration matrix R|T is acquired using the RANSAC estimation algorithm, where: R={−0.7098 0.7033 −0.0408−0.7028 −0.7109 −0.0273−0.0482 0.0093 0.9988} T={6.85492.72140.4704}

[0103] The verification is performed by the test set res=PW_Test-(PL_Test*R+T) to capture the deviation matrix, so it is possible to calculate the error size res of each point in the x, y and z directions, ie, and the mean square error RE2 as follows: res={−0.0131 -0.0173 0.0435−0.0048 0.0131 0.0162−0.0073 0.0172 0.0088 RE2=0.0189

[0104] The maximum error is 4.35 cm, and the root mean square error is 1.89 cm. The requirements for excavating the bulk material are met.

[0105] Fig. 4 is a schematic view of the method for updating a coordinate calibration according to some embodiments of the present disclosure. Preferably, the present embodiment can be performed by the calibration system of the present disclosure or the computing device of the present disclosure or the device for updating the coordinate calibration of the present disclosure. The method according to the embodiment of the Fig. 4 may include at least one of steps 41 to 44

[0106] In step 41, it is determined whether the online error of the coordinate calibration matrix is ​​greater than the predetermined allowable error.

[0107] In step 42, it is determined whether the number of data pairs of the acquired position points reaches the predetermined position point number if the online error of the coordinate calibration matrix is ​​larger than the predetermined allowable error.

[0108] In step 43, a new coordinate calibration matrix is ​​generated using the method for calibrating coordinates of a blade according to one of the embodiments described above (e.g., the embodiment of

[0109] Fig. 2 or Fig. 3) is determined if the number of data pairs of the detected position points is equal to the number of the predetermined number of position points.

[0110] In step 44 the coordinate calibration matrix is ​​updated.

[0111] Fig. 5 is a schematic view of the method for updating a coordinate calibration according to other embodiments of the present disclosure. Preferably, the present embodiment can be performed by the calibration system of the present disclosure or the computing device of the present disclosure or the device for updating the coordinate calibration of the present disclosure. The method according to the embodiment of the Fig. 5 may comprise at least one step from step S1 to step S9.

[0112] In step S1, the automatic calibration parameters such as the position point number N, the predetermined similarity A, the division ratio B and the predetermined allowable error C are set.

[0113] In some embodiments of the present disclosure, the position point number N (the number N of position points) means that the bucket moves in N different spatial positions relative to the excavator coordinate system, and N is set during the process, wherein the value of N is at least greater than 4.

[0114] In some embodiments of the present disclosure, the predetermined similarity A refers to the degree of registration similarity between the point cloud of the blade of the implicit shape model algorithm and the point cloud of the blade of the model.

[0115] In some embodiments of the present disclosure, the split ratio B refers to the split ratio of the training set (PL, PW) to the test set (PL, PW).

[0116] In some embodiments of the present disclosure, the predetermined allowable error C refers to the error that meets the usage requirements. The automatic calibration parameters can be specified through configuration files.

[0117] In step S2, it is determined whether the online error is larger than the predetermined allowable error C.

[0118] In some embodiments of the present disclosure, the online error is calculated using the formula PW-(R*PL+T). When the system is used for the first time or when the relative positions between the excavator and the lidar change, the online error is greater than the specified allowable error. At this point, it is possible to enter the automatic calibration procedure and proceed to step S3. After successful calibration, it is possible to exit the calibration procedure directly.

[0119] In step S3, it is determined whether the number of detected point pairs (the number of data pairs of the position points) is less than the predetermined position point number N. If the number of data pairs of the detected position points is less than the predetermined position point number N, step S4 is performed; and if the number of data pairs of the detected position points is equal to the predetermined position point number N, step S8 is performed.

[0120] In step S4, the lidar point cloud data of the blade is acquired and a coordinate of the center blade tooth of the blade is determined in the lidar coordinate system.

[0121] In some embodiments of the present disclosure, step S4 may include acquiring the lidar point cloud data of the bucket and acquiring a coordinate PL1 of the center tooth of the bucket using an implicit shape model algorithm.

[0122] In some embodiments of the present disclosure, if data is acquired according to the different positions of the bucket relative to the excavator, the bucket is detected within a suitable range of the view angle of the lidar to acquire a meaningful point cloud of the bucket, thereby increasing the success rate of model registration and improving model similarity.

[0123] In step S5, it is determined whether the model similarity is greater than the predetermined similarity A.

[0124] In some embodiments of the present disclosure, if the model similarity is greater than the predetermined similarity A, it is assumed that a coordinate PL1 of the center bucket tooth of the bucket is acquired using an implicit shape model algorithm to enter step S6 to acquire the coordinates PW1 of the center bucket tooth of the bucket in the excavator coordinate system.

[0125] In step S6, the angle sensor data of the bucket is acquired and a coordinate of the bucket tooth of the bucket is determined in the excavator coordinate system.

[0126] In some embodiments of the present disclosure, step S6 may include acquiring the bucket angle sensor data and calculating a bucket center tooth coordinate PW1 based on the angle sensor kinematic resolution.

[0127] In step S7, the number of position points is accumulated.

[0128] In some embodiments of the present disclosure, step S7 may include counting the number of successfully paired point pairs (PL1, PW1); and proceeding to step S8 if the data is equal to N.

[0129] In step S8, the coordinate calibration matrix R|T is calibrated using a calibration algorithm.

[0130] In some embodiments of the present disclosure, step S8 may include: determining a new coordinate calibration matrix using the method for calibrating coordinates of a blade according to any of the embodiments described above (e.g., the embodiment of Fig. 2 or Fig. 3) if the number of data pairs of the acquired position points is equal to the predetermined number of position points.

[0131] In step S9, the coordinate calibration matrix R|T is automatically updated.

[0132] After successful calibration in the present disclosure, the R|T may be automatically updated or the operator may be reminded to decide whether to use the R|T.

[0133] To address the problems that calibration has low accuracy and is time-consuming and labor-consuming, a concrete scenario or a special calibration device is required, online verification is lacking, and online calibration, which is present in the calibration methods such as a direct measurement method, a manual point selection method, and a scenario characterization method, is not feasible, the present disclosure provides a coordinate calibration method and an automatic update method based on a lidar and an angle sensor.

[0134] First, in the present disclosure, calibration can be completed without adding a calibration device to the system in question.

[0135] On the other hand, in the present disclosure, filtering is performed using model similarity, thereby improving the calibration accuracy using the RANSAC estimation algorithm.

[0136] Third, in the present disclosure, the data set is randomly divided and the online verification function is provided.

[0137] Fourth, in the present disclosure, it is possible to perform automatic calibration and update if the relative positions of the excavator and the lidar change and if the coordinate calibration matrix generates a drift error.

[0138] Fig. 6 is a schematic view of some embodiments of the apparatus for calibrating coordinates of a blade of the present disclosure. As in Fig. 6, the apparatus for calibrating coordinates of a blade of the present disclosure may include: a data acquisition module 61, a position determination module 62, and a calibration module 63.

[0139] The data acquisition module 61 is configured to acquire lidar point cloud data and angle sensor data from the bucket of an excavator.

[0140] In some embodiments of the present disclosure, the data acquisition module 61 is configured to acquire the lidar point cloud data and the bucket angle sensor data in a plurality of different positions.

[0141] The positioning module 62 is configured to determine coordinates of a center bucket tooth of the bucket in a lidar coordinate system according to the lidar point cloud data of the bucket; and to determine the coordinates of the center bucket tooth of the bucket in an excavator coordinate system according to the angle sensor data of the bucket.

[0142] In some embodiments of the present disclosure, the position determination module 62 is configured to determine the coordinates of the center blade tooth of the bucket in the lidar coordinate system according to the lidar point cloud data of the bucket acquired at each of the different positions if the coordinates of the center blade tooth of the bucket in the lidar coordinate system are determined according to the lidar point cloud data of the bucket.

[0143] In some embodiments of the present disclosure, the position determination module 62 is configured to determine the coordinates of the center blade tooth of the bucket in the lidar coordinate system using an implicit shape model algorithm according to the lidar point cloud data of the bucket acquired at each of the different positions if the coordinates of the center blade tooth of the bucket in the lidar coordinate system are determined according to the lidar point cloud data of the bucket.

[0144] In some embodiments of the present disclosure, the position determination module 62 is configured to determine the coordinates of the center bucket tooth of the bucket in the excavator coordinate system according to the bucket angle sensor data acquired at each of the different positions if the coordinates of the center bucket tooth of the bucket in the excavator coordinate system are determined according to the bucket angle sensor data.

[0145] In some embodiments of the present disclosure, the excavator forward kinematic resolution position determination module 62 is configured to determine the bucket center tooth coordinates in the excavator coordinate system according to the bucket angle sensor data acquired at each of the different positions if the bucket center tooth coordinates in the excavator coordinate system are determined according to the bucket angle sensor data.

[0146] The calibration module 63 is configured to determine a coordinate calibration matrix according to the coordinates of the center bucket tooth of the bucket in the lidar coordinate system and in the excavator coordinate system, wherein the coordinate calibration matrix is ​​a calibration matrix for calibrating the coordinates of the center bucket tooth of the bucket in the lidar coordinate system into the excavator coordinate system.

[0147] In some embodiments of the present disclosure, the coordinate calibration matrix is ​​a coordinate rotation and translation transformation matrix.

[0148] In some embodiments of the present disclosure, the calibration module 63 is configured to construct a plurality of data pairs of the coordinates of the lidar coordinate system and the coordinates of the excavator coordinate system according to the coordinates of the center bucket tooth of the bucket in the lidar coordinate system and in the excavator coordinate system, and divide the plurality of data pairs into training set data and test set data; determine the coordinate calibration matrix according to the training set data; and verify the coordinate calibration matrix using the test set data.

[0149] In some embodiments of the present disclosure, the calibration module 63 is configured to initialize the relevant parameters if the coordinate calibration matrix is ​​determined according to the training set data, the relevant parameters including the iteration times; randomly select a predetermined number of first data pairs, determine whether the first data pairs are collinear, and determine the coordinate calibration matrix by direct linear transformation if the first data pairs are not collinear if the coordinate calibration matrix is ​​determined according to the training set data.

[0150] In some embodiments of the present disclosure, the calibration module 63 is further configured to: transform the coordinates of the lidar coordinate system into second data pairs to acquire the coordinates of the excavator coordinate system using the coordinate calibration matrix, wherein the second data pairs are different data pairs than the first data pairs in the training set data; calculate a distance deviation between the transformed coordinates of the excavator coordinate system and the actual coordinates of the excavator coordinate system; determine whether the distance deviation is less than a predetermined distance threshold;record conditional interior points and update the coordinate calibration matrix according to the iteration times and the determination result of the distance deviation, and calculate the interior point probability and update the iteration times according to the interior point probability if the coordinate calibration matrix is ​​determined according to the training set data;

[0151] In some embodiments of the present disclosure, the apparatus for calibrating coordinates of a blade is configured to perform the operations for implementing the method for calibrating coordinates of a blade according to any of the embodiments described above (for example, the embodiment of Fig. 2 or Fig. 3) to be carried out.

[0152] Fig. 7 is a schematic view of some embodiments of the device for updating a coordinate calibration of the present disclosure. As in Fig. 7, the coordinate calibration updating device of the present disclosure may include: a determining device 71, a blade coordinate calibrating device 72, and an updating device 73, wherein: The determining device 71 is configured to determine whether an online error of a coordinate calibration matrix is ​​greater than a predetermined allowable error; and to determine whether a number of data pairs of the acquired position points reaches a predetermined position point number if the online error of the coordinate calibration matrix is ​​greater than the predetermined allowable error.

[0153] The blade coordinate calibration device 72 is configured to determine a new coordinate calibration matrix using the blade coordinate calibration method if the number of data pairs of the acquired position points is equal to the predetermined position point number.

[0154] The updating device 73 is configured to update the coordinate calibration matrix.

[0155] In some embodiments of the present disclosure, the apparatus 72 for calibrating coordinates of a blade is the apparatus for calibrating coordinates of a blade according to one of the embodiments described above (for example, the embodiment of the Fig. 6).

[0156] In some embodiments of the present disclosure, the determining device 71 is further configured to acquire the lidar point cloud data of the bucket and determine a coordinate of the center bucket tooth of the bucket in the lidar coordinate system if the number of data pairs of the acquired position points is less than the predetermined position point number; acquire the angle sensor data of the bucket and determine a coordinate of the center bucket tooth of the bucket in the excavator coordinate system; and accumulate the number of data pairs of the position points, and subsequently perform the determination again as to whether the number of data pairs of the acquired position points reaches the predetermined position point number.

[0157] In some embodiments of the present disclosure, the determining device 71 is configured to acquire a coordinate of the center blade tooth of the bucket in the lidar coordinate system using an implicit shape model algorithm if a coordinate of the center blade tooth of the bucket is determined in the lidar coordinate system; determine whether the model similarity is greater than the predetermined similarity; and use the acquired coordinate of the center blade tooth of the bucket in the lidar coordinate system if the model similarity is greater than the predetermined similarity.

[0158] In some embodiments of the present disclosure, the determining device 71 is configured to calculate a forward kinematic resolution of the excavator based on the angle sensor data and to determine the one coordinate of the center bucket tooth of the bucket in the excavator coordinate system if one coordinate of the center bucket tooth of the bucket is determined in the excavator coordinate system.

[0159] In some embodiments of the present disclosure, the means for updating the coordinate calibration is configured to perform the operations for implementing the method for updating the coordinate calibration according to one of the embodiments described above (for example, the embodiment of the Fig. 4 or Fig. 5).

[0160] The above-described embodiments of the present disclosure provide a coordinate calibration device and an automatic update device based on a lidar and an angle sensor, wherein the coordinates of the bucket are calibrated and automatically updated using the lidar and the angle sensor of the excavator without adding an external calibration device, thereby realizing that the coordinates of the bucket are uniform with the coordinates of the target excavation point in the excavator coordinate system.

[0161] Fig. 8 is a view of the structure of some embodiments of the computing device of the present disclosure. As in Fig. 8, the computing device includes a memory 81 and a processor 82.

[0162] The memory 81 is configured to store instructions, the processor 82 is coupled to the memory 81, and the processor 82 is configured to perform the method according to the embodiments described above (for example, one of the embodiments of the Fig. 2 to 5) based on the instructions stored in memory.

[0163] As in Fig. As shown in Figure 8, the computing device further includes a communication interface 83 for information interaction with other devices. At the same time, the computing device also includes a bus 84, wherein the processor 82, the communication interface 83, and the memory 81 communicate with each other via the bus.

[0164] Memory 81 may include high-speed RAM or non-volatile memory, such as at least one hard disk. Memory 81 may also be a memory array. Memory 81 may also be divided into blocks that can be combined into virtual volumes according to certain rules.

[0165] Additionally, the processor 82 may be a central processing unit (CPU) or an application specific integrated circuit (ASIC) or one or more integrated circuits configured to implement the embodiments of the present disclosure.

[0166] According to another aspect of the present disclosure, as in Fig. 1, a calibration system is provided. The system comprises a lidar and an angle sensor and further comprises at least one of a computing device, a means for updating a coordinate calibration, and a device for calibrating coordinates of a blade, wherein the computing device implements the computing device according to one of the embodiments described above (for example, the embodiment of Fig. 8), the means for updating a coordinate calibration is the means for updating the coordinate calibration according to one of the embodiments described above (for example, the embodiment of the Fig. 7) and the device for calibrating coordinates of a blade is the device for calibrating coordinates of a blade according to one of the embodiments described above (for example the embodiment of Fig. 6) is.

[0167] According to another aspect of the present disclosure, as in Fig. 1, an excavator is provided. The excavator comprises a lidar and further comprises at least one of a computing device, a device for updating a coordinate calibration and a device for calibrating coordinates of a bucket, wherein the computing device comprises the computing device according to one of the embodiments described above (for example, the embodiment of Fig. 8), the means for updating a coordinate calibration is the means for updating the coordinate calibration according to one of the embodiments described above (for example, the embodiment of the Fig. 7) and the device for calibrating coordinates of a blade is the device for calibrating coordinates of a blade according to one of the embodiments described above (for example the embodiment of Fig. 6) is.

[0168] The above-described embodiments of the present disclosure provide a coordinate calibration method and an automatic update method based on a lidar and an angle sensor, wherein the coordinates of the bucket are calibrated and automatically updated using the lidar and the angle sensor of the excavator without adding an external calibration device, thereby realizing that the coordinates of the bucket are uniform with the coordinates of the target excavation point in the excavator coordinate system.

[0169] According to another aspect of the present disclosure, a computer-readable storage medium is provided, the computer-readable storage medium having computer instructions stored thereon that, when executed by a processor, implement the method for calibrating coordinates of a blade according to any of the embodiments described above (for example, the embodiment of Fig. 2 or Fig. 3) implement and / or the operations of the method for updating a coordinate calibration according to one of the embodiments described above (for example, the embodiment of the Fig. 4 or Fig. 5) implement.

[0170] In some embodiments of the present disclosure, the computer-readable storage medium may be a non-transitory computer-readable storage medium.

[0171] It will be appreciated by those skilled in the art that embodiments of the present disclosure may be provided as a method, apparatus, or computer program product. Accordingly, the present disclosure may take the form of a purely hardware embodiment, a purely software embodiment, or a combination of software and hardware aspects. Furthermore, the present disclosure may take the form of a computer program product embodied in one or more computer-usable, non-transitory storage media (including, but not limited to, disk storage, CD-ROM, optical storage device, and the like) containing computer-usable program code therein.

[0172] The present disclosure is described with reference to flowcharts and / or block diagrams of the methods, apparatus (systems), and computer program products according to embodiments of the present disclosure. It is understood that each step and / or block in the flowcharts and / or block diagrams, as well as combinations of steps and / or blocks in the flowcharts and / or block diagrams, may be implemented by computer program instructions.These computer program instructions may be provided to a processor of a general purpose computer, a special purpose computer, an embedded processing machine, or other programmable data processing apparatus to produce a machine such that the instructions, when executed by a processor of a computer or other programmable data processing apparatus, produce an apparatus for implementing a function identified in one or more steps of a flowchart and / or one or more blocks in a block diagram.

[0173] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to operate in a specific manner, such that the instructions stored in the computer-readable memory produce a product that includes an instruction device. The instruction device implements a function described in one or more steps in a flowchart or one or more blocks in a block diagram.

[0174] These computer program instructions may also be loaded onto a computer or other programmable data processing device such that a series of operational steps are performed on the computer or other programmable device to produce computer-implemented processing, such that the instructions executing on the computer or other programmable device provide steps that implement a function identified in one or more steps of the flowchart and / or one or more blocks in the block view.

[0175] The computing device, the device for calibrating coordinates of a bucket, the data acquisition module, the position determining module, the calibration module, the means for updating the coordinate calibration, the determining device, and the updating device may be implemented as a general-purpose processor, programmable logic controller (PLC), digital signal processor (DSP), application-specific integrated circuit (ASIC), field-programmable gate array (FPGA) or other programmable logic devices, individual gates or transistor logic devices, individual hardware assemblies, or any suitable combination thereof configured to perform the functions described in the present application.

[0176] So far, the present disclosure has been described in detail. In order not to dilute the concept of the present disclosure, some details known in the art will not be explained in detail. Based on the above description, one skilled in the art will fully understand how to implement the technical solutions disclosed in this document.

[0177] It will be appreciated by those of ordinary skill in the art that all or some of the steps in the above-described embodiments may be achieved by hardware or by programs for instructing relevant hardware. The programs may be stored in a non-transitory computer-readable storage medium. The aforementioned storage medium may be a read-only memory, a magnetic disk, an optical disk, and the like.

[0178] Descriptions of the present disclosure, which are provided for purposes of illustration and description, are not intended to omit or limit the present disclosure to the forms disclosed. Many modifications and variations will be apparent to those skilled in the art. The embodiments are chosen and described to better explain the principles and actual application of the present disclosure and to enable those skilled in the art to understand the present disclosure and to design various embodiments suitable for particular purposes, incorporating various modifications. QUOTES CONTAINED IN THE DESCRIPTION

[0000] This list of documents submitted by the applicant was generated automatically and is included solely for the convenience of the reader. This list is not part of the German patent or utility model application. The DPMA assumes no liability for any errors or omissions. Cited patent literature

[0000] CN 202211674601.1

[0001]

Claims

[1] A method for calibrating coordinates of a blade, comprising: Collecting lidar point cloud data and angle sensor data from an excavator's bucket; Determining coordinates of a center blade tooth of the blade in a lidar coordinate system according to the lidar point cloud data of the blade; Determining the coordinates of the center bucket tooth of the bucket in an excavator coordinate system according to the angle sensor data of the bucket; and Determining a coordinate calibration matrix according to the coordinates of the center bucket tooth of the bucket in the lidar coordinate system and in the excavator coordinate system, wherein the coordinate calibration matrix is a calibration matrix for calibrating the coordinates of the center bucket tooth of the bucket in the lidar coordinate system into the excavator coordinate system. [2] The method of claim 1, wherein: Collecting the lidar point cloud data and the angle sensor data of the excavator's bucket includes: collecting the lidar point cloud data and the angle sensor data of the bucket in a variety of different positions; determining the coordinates of the center bucket tooth of the bucket in the lidar coordinate system according to the lidar point cloud data of the bucket comprises: determining the coordinates of the center bucket tooth of the bucket in the lidar coordinate system according to the lidar point cloud data of the bucket acquired at each of the different positions; and determining the coordinates of the center bucket tooth of the bucket in the excavator coordinate system according to the angle sensor data of the bucket comprises: determining the coordinates of the center bucket tooth of the bucket in the excavator coordinate system according to the angle sensor data of the bucket acquired at each of the different positions. [3] The method of claim 2, wherein: determining the coordinates of the center blade tooth of the blade in the lidar coordinate system according to the lidar point cloud data of the blade acquired at each of the different positions comprises: determining the coordinates of the center blade tooth of the blade in the lidar coordinate system using an implicit shape model algorithm according to the lidar point cloud data of the blade acquired at each of the different positions; and determining the coordinates of the center bucket tooth of the bucket in the excavator coordinate system according to the angle sensor data of the bucket acquired at each of the different positions comprises: calculating a forward kinematics resolution of the excavator to determine the coordinates of the center bucket tooth of the bucket in the excavator coordinate system according to the angle sensor data of the bucket acquired at each of the different positions. [4] The method according to any one of claims 1 to 3, wherein determining the coordinate calibration matrix according to the coordinates of the center bucket tooth of the bucket in the lidar coordinate system and in the excavator coordinate system comprises: Constructing a plurality of data pairs of the coordinates of the lidar coordinate system and the coordinates of the excavator coordinate system according to the coordinates of the center bucket tooth of the bucket in the lidar coordinate system and in the excavator coordinate system, and dividing the plurality of data pairs into training set data and test set data; Determining the coordinate calibration matrix according to the training set data; and Verify the coordinate calibration matrix using the test set data. [5] The method according to any one of claims 1 to 3, wherein the coordinate calibration matrix is a coordinate rotation and translation transformation matrix. [6] The method of claim 4, wherein determining the coordinate calibration matrix according to the training set data comprises: Initializing relevant parameters, where the relevant parameters include iteration times; Randomly selecting a predetermined number of first data pairs; Determine whether the first data pairs are collinear; and Determine the coordinate calibration matrix by a direct linear transformation if the first data pairs are not collinear. [7] The method of claim 6, wherein determining the coordinate calibration matrix according to the training set data further comprises: Transforming the coordinates of the lidar coordinate system in second data pairs to acquire the coordinates of the excavator coordinate system using the coordinate calibration matrix, wherein the second data pairs are different data pairs than the first data pairs in the training set data; Calculating a distance deviation between the transformed coordinates of the excavator coordinate system and the actual coordinates of the excavator coordinate system; Determining whether the distance deviation is less than a predetermined distance threshold; Recording conditional interior points and updating the coordinate calibration matrix according to the iteration times and the determination result of the distance deviation; and Calculate an interior point probability and update the iteration times according to the interior point probability. [8] A method for updating a coordinate calibration, comprising: Determining whether an online error of a coordinate calibration matrix is greater than a predetermined allowable error; Determining whether a number of data pairs of acquired position points reaches a predetermined position point number if the online error of the coordinate calibration matrix is greater than the predetermined allowable error; Determining a new coordinate calibration matrix using the method according to one of claims 1 to 7 if the number of data pairs of the detected position points is equal to the predetermined number of position points; and Updating the coordinate calibration matrix. [9] The method of claim 8, further comprising: Acquiring the lidar point cloud data of the bucket and determining a coordinate of the center bucket tooth of the bucket in the lidar coordinate system if the number of data pairs of the acquired position points is less than the predetermined number of position points; Acquiring the bucket angle sensor data and determining a coordinate of the bucket's center tooth in the excavator coordinate system; and Accumulating the number of data pairs of the position points and subsequently performing the determination again whether the number of data pairs of the acquired position points reaches the predetermined position point number. [10] The method of claim 9, wherein: Determining a coordinate of the center blade tooth of the blade in the lidar coordinate system comprises: detecting a coordinate of the center blade tooth of the blade in the lidar coordinate system using an implicit shape model algorithm; determining whether a model similarity is greater than a predetermined similarity; and using the detected one coordinate of the center blade tooth of the blade in the lidar coordinate system if the model similarity is greater than the predetermined similarity; and determining a coordinate of the center bucket tooth of the bucket in the excavator coordinate system comprises: calculating a forward kinematic resolution of the excavator based on the angle sensor data and determining the one coordinate of the center bucket tooth of the bucket in the excavator coordinate system. [11] Device for calibrating coordinates of a blade, comprising: a data acquisition module configured to acquire lidar point cloud data and angle sensor data of the bucket of an excavator; a positioning module configured to determine coordinates of a center bucket tooth of the bucket in a lidar coordinate system according to the lidar point cloud data of the bucket; and to determine the coordinates of the center bucket tooth of the bucket in an excavator coordinate system according to the angle sensor data of the bucket; and a calibration module configured to determine a coordinate calibration matrix according to the coordinates of the center bucket tooth of the bucket in the lidar coordinate system and in the excavator coordinate system, wherein the coordinate calibration matrix is a calibration matrix for calibrating the coordinates of the center bucket tooth of the bucket in the lidar coordinate system into the excavator coordinate system. [12] Apparatus according to claim 11, wherein the device is configured to perform the method according to any one of claims 2 to 7. [13] Device for updating a coordinate calibration, comprising: a determining device configured to determine whether an online error of a coordinate calibration matrix is greater than a predetermined allowable error; and to determine whether a number of data pairs of acquired position points reaches a predetermined position point number if the online error of the coordinate calibration matrix is greater than the predetermined allowable error; a blade coordinate calibration device configured to determine a new coordinate calibration matrix using a blade coordinate calibration method if the number of data pairs of the acquired position points is equal to the predetermined number of position points; and an update device configured to update the coordinate calibration matrix. [14] Apparatus according to claim 13, wherein the device for calibrating the coordinates of the blade is the device according to claim 11 or 12. [15] Device according to claim 13, wherein the device is configured to perform the method according to any one of claims 8 to 10. [16] A computer device comprising: a memory for storing instructions, a processor configured to execute the instructions such that the computing device performs the method of any one of claims 1 to 10. [17] A calibration system comprising a lidar, an angle sensor, and further comprising at least one of a computing device, a means for updating a coordinate calibration, and a device for calibrating coordinates of a blade, wherein the computing device is the computing device of claim 16, the means for updating the coordinate calibration is the device of any one of claims 13 to 15, and the device for calibrating the coordinates of the blade is the device of claim 11 or 12. [18] An excavator comprising a lidar, and further comprising at least one of a computing device, a means for updating a coordinate calibration, and a device for calibrating coordinates of a bucket, wherein the computing device is the computing device according to claim 16, the means for updating the coordinate calibration is the device according to any one of claims 13 to 15, and the device for calibrating the coordinates of the bucket is the device according to claim 11 or 12. [19] A computer-readable storage medium, the computer-readable storage medium having computer instructions stored thereon which, when executed by a processor, perform the method of any one of claims 1 to 10.

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