CN116148822B - Unmanned multi-laser radar automatic calibration method applied to surface mine - Google Patents
Unmanned multi-laser radar automatic calibration method applied to surface mine Download PDFInfo
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- 239000011159 matrix material Substances 0.000 claims description 12
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/48—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00
- G01S7/497—Means for monitoring or calibrating
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
Abstract
The application discloses an unmanned multi-laser radar automatic calibration method applied to an open-pit mine, which comprises the steps of pre-arranging a calibration plate in the environment of the open-pit mine; after the mine car is arranged at a preset position, acquiring point clouds scanned by a laser radar at the same moment at present, and preprocessing the scanned point clouds to obtain point clouds of a calibration plate; sorting the obtained point clouds based on the calibration plate distance of the preset point location arrangement; constructing and establishing a constraint relation of the laser radar to the same calibration plate, and carrying out optimization solution on an initial value; and carrying out multi-frame NDT point cloud registration by using the original point cloud scanned by the laser radar, and matching the initial value obtained by the optimization solution as an initialization value of an NDT algorithm to obtain a final calibration parameter. According to the automatic calibration method, the characteristic extraction can be automated, the characteristic of the calibration plate is not required to be manually extracted, the external parameter initial value between the laser radars is automatically obtained, the manual parameter adjustment is not required, and the influence of the laser radar installation error is avoided.
Description
Technical Field
The application relates to the technical field of unmanned multi-laser radar, in particular to an unmanned multi-laser radar automatic calibration method applied to an open mine.
Background
The multi-laser radar calibration method is usually a non-automatic calibration method, such as manual parameter adjustment based on human eye observation, and registration is carried out by using laser point clouds of areas scanned by the laser radars in pairs to obtain a calibration result, wherein the point cloud registration algorithm needs a better initial value, otherwise, the initial value cannot be converged, and the initial value acquisition is realized by a manual parameter adjustment mode;
the defects of the prior art are that the existing non-automatic calibration process is complex, the efficiency is low, the precision is poor, and the subsequent sensing fusion and mapping effects can be influenced.
Disclosure of Invention
The application aims to overcome the defects in the prior art, and aims to solve the problems in the background art by adopting the automatic calibration method of the multi-laser radar applied to the unmanned surface mine.
An unmanned multi-laser radar automatic calibration method applied to an open mine comprises the following specific steps:
s1, in the environment of an open mine, arranging calibration plates according to preset points;
s2, after the mine car is arranged at a preset position, acquiring point clouds scanned by a laser radar at the same moment, and preprocessing the scanned point clouds to obtain a point cloud set of a calibration plate;
s3, sorting the obtained point clouds based on the calibration plate distance of the preset point location arrangement;
s4, constructing and establishing a constraint relation of the laser radar to the same calibration plate, and carrying out optimization solution on an initial value;
and S5, carrying out multi-frame NDT point cloud registration by using the original point cloud scanned by the laser radar, and using the initial value obtained by the optimization solution as an initialization value of an NDT algorithm to obtain a final calibration parameter by matching.
As a further aspect of the application: the specific steps of the step S2 include:
after the mine car is arranged at a preset position, acquiring point clouds scanned by a laser radar at the same moment at present;
performing point cloud filtering operation on the acquired point cloud based on a distance threshold, wherein the point cloud comprises non-calibration plate point cloud, ground point cloud and surrounding clutter target point cloud;
clustering and dividing the point cloud after filtering operation to obtain the point cloud set of the calibration plate.
As a further aspect of the application: the specific steps of the step S3 include:
acquiring the barycenter coordinates of each calibration plate of the point clouds of the calibration plates, calculating the distance between the barycenter and the laser radar, and sequencing the point clouds of the calibration plates according to the distance to obtain the nearest calibration plate as a first calibration plate;
and calculating the distances between the centroids of the rest calibration plates and the first calibration plate by taking the first calibration plate as a node, and sorting the point clouds of the calibration plates according to the distances between the centroids.
As a further aspect of the application: the specific steps of the step S4 include:
s41, performing plane fitting on the ordered point cloud sets of the calibration plates, and extracting normal vectors of the calibration plates;
N＝{{n 1 ，n 2 ，…，n k } 1 ，{n 1 ，n 2 ，…，n k } 2 ，…，{n 1 ，n 2 ，…，n k } m }k，m∈R；
n k ＝(a k ，b k ，c k )a，b，c，k∈R；
wherein N is a set of normal vectors of all laser radar calibration plates, k represents the number of the calibration plates, m represents the number of the laser radars, and N k Representing the normal vector extracted from the kth calibration plate;
taking the normal vector included angle of the calibration plate with the same number scanned by the laser radar as a loss value, and iteratively optimizing and solving an initial value R of the rotation matrix through an LM algorithm;
extracting normal vectors n1, n2, n1= (a 1, b1, c 1) of a first calibration plate through two laser radars, obtaining (a 2, b2, c 2) after n2 is transformed by a matrix R, and calculating an included angle between n2 and n1, wherein the calculation formula is as follows:
the formula for deriving the least squares cost function is:
step S42, taking the centroid distance of the calibration plates with the same number scanned by the laser radar as another loss value, and iteratively optimizing and solving a translation matrix initial value T by an LM algorithm;
D＝{d 1 ，d 2 …d k }k∈R；
the method comprises the steps that D is marked as a centroid distance set of a laser radar scanned to the same calibration plate, k represents the number of the calibration plates, and dk represents the centroid distance calculated by the kth calibration plate;
the barycenter points p1, p2, p1= (x 1, y1, z 1) of the first calibration plate obtained through the two laser radars, the coordinates of p2 after the transformation of the translation matrix T are (x 2, y2, z 2), the distance between p2 and p1 is calculated, and the calculation formula is as follows:
the formula for deriving the least squares cost function is:
as a further aspect of the application: the specific steps of the step S5 include:
and (3) carrying out multi-frame NDT point cloud registration by using the original point cloud scanned by the laser radar, and matching R and T which are optimally solved in the step (S4) to obtain final calibration parameters R, T by taking the R and T as initialization values of an NDT algorithm.
Compared with the prior art, the application has the following technical effects:
by adopting the technical scheme, the specific calibration sites are established, the point clouds of the calibration plates are automatically segmented, and the characteristics are extracted. And sorting the calibration plates according to the distance threshold. And the normal vector and the centroid distance of the same calibration plate scanned by the laser radar are used as loss values, and the initial external parameters are optimized and solved without manual acquisition. The full-automatic calibration is suitable for the offline calibration of mass production vehicle types, and the efficiency is improved. Therefore, the problems that in the prior art, the non-automatic calibration process is complex, the efficiency is low, the precision is poor, and the follow-up perception fusion and the map building effect are affected are solved.
Drawings
The following detailed description of specific embodiments of the application refers to the accompanying drawings, in which:
FIG. 1 is a schematic diagram of steps of an automatic calibration method according to an embodiment of the present disclosure;
FIG. 2 is a schematic illustration of a calibration plate setup according to an embodiment of the present disclosure.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Referring to fig. 1, in an embodiment of the present application, a method for automatically calibrating multiple laser radars applied to unmanned surface mine comprises the following specific steps:
step S1, in the environment of an open mine, as shown in FIG. 2, a calibration plate setting schematic diagram is shown, and specifically, the calibration plate arrangement is carried out according to preset points in the diagram;
step S2, after the mine car is arranged at a preset position, acquiring point clouds scanned by a laser radar at the same moment, and preprocessing the scanned point clouds to obtain point clouds of a calibration plate, wherein the method comprises the following specific steps of:
step S21, firstly, setting a mine car at a preset position in an open-pit mine, and then acquiring point clouds scanned by a laser radar of the mine car at the same moment;
step S22, performing point cloud filtering operation on the acquired point cloud based on a distance threshold, wherein the point cloud comprises non-calibration plate point cloud, ground point cloud and surrounding clutter target point cloud;
clustering and dividing the point cloud after filtering operation to obtain the point cloud set of the calibration plate.
Step S3, sorting the obtained point clouds based on the calibration plate distance of the preset point location arrangement, wherein the specific steps comprise:
s31, obtaining mass center coordinates of each calibration plate of point clouds of the calibration plates, calculating the distance between the mass center and the laser radar, and sequencing the point clouds of the calibration plates according to the distance to obtain the nearest calibration plate as a first calibration plate;
and S32, calculating the distances between the centroids of the rest calibration plates and the first calibration plate by taking the first calibration plate as a node, and sequencing the point clouds of the calibration plates according to the distances between the centroids.
S4, constructing and establishing a constraint relation of the laser radar to the same calibration plate, and carrying out optimization solution on an initial value, wherein the method specifically comprises the following steps of:
and S41, performing plane fitting on the ordered calibration plate point cloud sets, and extracting normal vectors of all the calibration plates.
The method comprises the steps that a set of normal vectors of all laser radar calibration plates is recorded as N, k represents the number of the calibration plates, m represents the number of the laser radars, nk represents the normal vector extracted from the kth calibration plate, and the normal vector is expressed as:
N＝{{n 1 ，n 2 ，…，n k } 1 ，{n 1 ，n 2 ，…，n k } 2 ，…，{n 1 ，n 2 ，…，n k } m }k，m∈R；
n k ＝(a k ，b k ，c k )a，b，c，k∈R；
and taking the normal vector included angle of the calibration plates with the same number scanned by the laser radar as a loss value, and solving the initial value R of the rotation matrix through iteration optimization by an LM algorithm.
Assuming that n1 and n2 are normal vectors of a first calibration plate extracted by two radars respectively, n 1= (a 1, b1, c 1), n2 is obtained after transformation of a matrix R, and an included angle with n1 is calculated by a calculation formula:
deriving a least squares cost function:
and S42, taking the centroid distance of the calibration plates with the same number scanned by the laser radar as another loss value, and iteratively optimizing and solving a translation matrix initial value T by an LM algorithm.
The centroid distance set of the laser radar scanned to the same calibration plate is marked as D, k represents the number of the calibration plates, dk represents the centroid distance calculated by the kth calibration plate, and the centroid distance is expressed as:
D＝{d 1 ，d 2 …d k }k∈R；
assuming that p1 and p2 are mass center points of a No. 1 calibration plate calculated by two radars respectively, and the coordinates of p2 after transformation of a translation matrix T are (x 2, y2 and z 2), calculating the distance from p1, wherein the calculation formula is as follows:
deriving a least squares cost function:
and S5, carrying out multi-frame NDT point cloud registration by using the original point cloud scanned by the laser radar, and matching R and T which are optimally solved in the step S4 and are used as initialization values of an NDT algorithm to obtain final calibration parameters R, T.
In the embodiment, the method is used for automatic calibration, automatic feature extraction is carried out, and the feature of a calibration plate is not required to be manually extracted. And automatically acquiring an external parameter initial value between the laser radars without manually adjusting parameters. The calibration plate extraction is not affected by the mounting error of the laser radar. The laser radar calibration method is suitable for laser radar calibration on large vehicles. Calibrating precision: within 70m of about 2cm
Although embodiments of the present application have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the spirit and scope of the application as defined by the appended claims and their equivalents.
Claims (4)
1. The unmanned multi-laser radar automatic calibration method applied to the surface mine is characterized by comprising the following specific steps of:
s1, in the environment of an open mine, arranging calibration plates according to preset points;
s2, after the mine car is arranged at a preset position, acquiring point clouds scanned by a laser radar at the same moment, and preprocessing the scanned point clouds to obtain a point cloud set of a calibration plate;
s3, sorting the obtained point clouds based on the calibration plate distance of the preset point location arrangement;
s4, constructing and establishing a constraint relation of the laser radar to the same calibration plate, and carrying out optimization solution on an initial value, wherein the specific steps comprise:
s41, performing plane fitting on the ordered point cloud sets of the calibration plates, and extracting normal vectors of the calibration plates;
；
；
wherein ,Nfor each set of normal vectors of the lidar calibration plate,krepresenting the number of calibration plates,mrepresenting the number of lidars,n k representative pair ofkNormal vectors extracted by the calibration plates;
taking the normal vector included angle of the calibration plate with the same number scanned by the laser radar as a loss value, and iteratively optimizing and solving an initial value R of the rotation matrix through an LM algorithm;
extracting normal vectors n1, n2, n1= (a 1, b1, c 1) of a first calibration plate through two laser radars, obtaining (a 2, b2, c 2) after n2 is transformed by a matrix R, and calculating an included angle between n2 and n1, wherein the calculation formula is as follows:
；
the formula for deriving the least squares cost function is:
；
step S42, taking the centroid distance of the calibration plates with the same number scanned by the laser radar as another loss value, and iteratively optimizing and solving a translation matrix initial value T by an LM algorithm;
；
the method comprises the steps that D is marked as a centroid distance set of a laser radar scanned to the same calibration plate, k represents the number of the calibration plates, and dk represents the centroid distance calculated by the kth calibration plate;
the barycenter points p1, p2, p1= (x 1, y1, z 1) of the first calibration plate obtained through the two laser radars, the coordinates of p2 after the transformation of the translation matrix T are (x 2, y2, z 2), the distance between p2 and p1 is calculated, and the calculation formula is as follows:
the formula for deriving the least squares cost function is:
；
and S5, carrying out multi-frame NDT point cloud registration by using the original point cloud scanned by the laser radar, and using the initial value obtained by the optimization solution as an initialization value of an NDT algorithm to obtain a final calibration parameter by matching.
2. The automatic calibration method for the unmanned multi-laser radar applied to the surface mine according to claim 1, wherein the specific steps of the step S2 include:
after the mine car is arranged at a preset position, acquiring point clouds scanned by a laser radar at the same moment at present;
performing point cloud filtering operation on the acquired point cloud based on a distance threshold, wherein the point cloud comprises non-calibration plate point cloud, ground point cloud and surrounding clutter target point cloud;
clustering and dividing the point cloud after filtering operation to obtain the point cloud set of the calibration plate.
3. The automatic calibration method for the unmanned multi-laser radar applied to the surface mine according to claim 1, wherein the specific steps of the step S3 include:
acquiring the barycenter coordinates of each calibration plate of the point clouds of the calibration plates, calculating the distance between the barycenter and the laser radar, and sequencing the point clouds of the calibration plates according to the distance to obtain the nearest calibration plate as a first calibration plate;
and calculating the distances between the centroids of the rest calibration plates and the first calibration plate by taking the first calibration plate as a node, and sorting the point clouds of the calibration plates according to the distances between the centroids.
4. The automatic calibration method for the unmanned multi-laser radar applied to the surface mine according to claim 1, wherein the specific steps of the step S5 include:
and (3) carrying out multi-frame NDT point cloud registration by using the original point cloud scanned by the laser radar, and matching R and T which are optimally solved in the step (S4) to obtain final calibration parameters R, T by taking the R and T as initialization values of an NDT algorithm.
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