CN109001711A - Multi-line laser radar scaling method - Google Patents
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Abstract
The present invention relates to a kind of multi-line laser radar scaling methods, comprising: by decoupling α, β, Δ z and γ, Δ x, Δ y, demarcates two parts parameter respectively.By extracting level ground feature, calibrating parameters α, β, Δ z；The spin matrix provided by GPSvR and translation matrixvT constructs constraint equation, solves calibrating parameters γ, Δ x, Δ y.Method provided by the invention is not necessarily to manual measurement calibrating parameters, does not need special calibration object, and it is convenient to operate, and demarcates high-efficient；Using level land feature and high-precision GPS information, stated accuracy can be improved.
Description
Technical Field
The invention relates to the technical field of laser radars, in particular to a multi-line laser radar calibration method.
Background
In recent years, with the rapid development of intelligent driving technology, the laser radar has the characteristics of high precision, wide range of distance measurement, no influence of light and the like, and is widely applied to the environment perception fields of obstacle detection, instant positioning, map construction and the like of intelligent driving vehicles.
In an intelligent driving system, a laser radar is generally installed on the top of an intelligent driving vehicle, and position data information acquired by the laser radar is based on a local coordinate system of the radar. In the actual use process of the laser radar, in order to unify the information of the multiple sensors, data acquired by the laser radar under the local coordinate system of the laser radar needs to be converted into the automobile body coordinate system. And in the data conversion process, point cloud data in the laser radar coordinate system is converted into an automobile body coordinate system through a transformation matrix. Therefore, in the actual use process, the laser radar needs to be calibrated in advance, namely, the transformation relation between the radar coordinate system and the automobile body coordinate system is obtained.
In the prior art, in order to realize the calibration of a coordinate system, researchers have proposed various schemes. The common calibration methods mainly comprise the traditional manual calibration and calibration object measurement methods.
The traditional manual calibration adopts a manual measurement or instrument measurement mode to measure the transformation relation between a laser radar coordinate system and a vehicle body coordinate system. Firstly, the origin position and the coordinate axis direction of a vehicle body coordinate system are determined, the origin position of a laser radar coordinate system is determined, and a caliper or a tape measure is used for measuring a distance vector between the origin of the laser radar coordinate system and the origin of the vehicle body coordinate system. And then determining the direction of the laser radar coordinate axis, and measuring the Euler angle between the laser radar coordinate axis and the vehicle body coordinate axis by using a special testing instrument, such as a three-dimensional angle measuring instrument.
The calibration object measuring method solves the external parameters of the three-dimensional laser radar through matching of a specific calibration object. Common calibration objects are planar targets, triangular targets, and three-sided targets. The method utilizes the working characteristics of invisible scanning points, single line scanning and the like of the laser radar, carries out three-dimensional reconstruction on the target on the basis of a three-dimensional coordinate system conversion model based on a space vector, and solves the calibration parameters by adopting the coordinate system conversion model.
Although the manual measurement calibration method is simple in principle, the manual measurement calibration method has high requirements on the operation precision of an operator and the precision of a measuring instrument, so that the manual measurement calibration method is not high in popularization, and the measurement precision is unstable and can change along with the level of the operator. The method for measuring the calibration object has certain disadvantages, because the reflectivity of the laser radar to objects with different colors and materials is different, the searching of the control point is difficult, meanwhile, a great deal of manpower is needed in the process of moving the control point, and the calibration precision also depends on parameters between other sensors and the control point, so the calibration method has low efficiency and low precision.
Disclosure of Invention
The invention aims to provide a multi-line laser radar calibration method aiming at the defects in the prior art, so that calibration parameters do not need to be measured manually, a specific calibration object is not needed, and the calibration efficiency and the calibration precision are improved.
In order to achieve the above object, the present invention provides a method for calibrating a multiline laser radar, comprising:
the method comprises the steps that a laser radar collects point cloud data of a ground plane where a vehicle is located;
calculating a normal vector n' of a plane equation of the ground plane under a laser radar coordinate system according to the point cloud data;
setting initial values of parameters α, beta and gamma in a rotation matrix R from a laser radar coordinate system to a vehicle body coordinate system;
calculating an initial value of a rotation matrix R according to the initial values of the parameters α, beta and gamma;
constructing a parallelism function of the coordinate axis z by using the normal vector n' of the plane equation and the initial value of the rotation matrix R;
summing the parallelism functions of the point cloud data to obtain a first optimization objective function;
performing iterative processing on the first optimization objective function according to the initial values of the parameters α and β, and terminating iteration when preset conditions are met to obtain final values of α and β;
obtaining a z-axis translation amount delta z from a laser radar coordinate system to a vehicle body coordinate system according to the final values of the alpha and β;
setting position coordinates of a fixed reference point under a laser radar coordinate system;
acquiring first attitude transformation data of the fixed reference point at different moments under a vehicle body coordinate system according to vehicle GPS data;
acquiring second position and posture conversion data of the fixed reference point at different moments under a laser radar coordinate system according to the first position and posture data;
obtaining an error function according to the first position posture transformation data and the second position posture transformation data;
summing a plurality of error functions corresponding to a plurality of groups of continuous frame data to obtain a second optimized objective function;
and obtaining parameters gamma, x-axis translation amount delta x and y-axis translation amount delta y according to the final values of alpha and beta, z-axis translation amount delta z and a second optimization objective function.
Further, the preset conditions specifically include:
the value of the optimization objective function is smaller than a first preset threshold or the iteration number reaches a second preset threshold.
Further, the constructing a parallelism function of the coordinate axis z by using the normal vector n' of the plane equation and the initial value of the rotation matrix R specifically includes:
using formulasAnd constructing a parallelism function of the coordinate axis z of the normal vector n'.
Further, the summing the parallelism functions of the point cloud data to obtain the first optimization objective function specifically includes:
collecting m sampling points on the ground plane, and calculating normal vector n of each pointi′；
Using formulasAnd calculating to obtain a first optimization objective function.
further, the calculating the initial value of the rotation matrix R according to the initial values of the parameters α, β, and γ specifically includes:
based on preset estimated values of parameters α beta and parameter gamma0 using the formulaThe initial values of the rotation matrix R are calculated.
Further, the setting of the position coordinates of the fixed reference point in the laser radar coordinate system specifically includes:
and setting the origin of the laser radar coordinate system as a fixed reference point P.
Further, the acquiring, according to the vehicle GPS data, first posture change data of the fixed reference point at different times in the vehicle body coordinate system specifically includes:
at any time t, acquiring a fixed reference point in a vehicle body coordinate system C according to vehicle GPS data0The coordinates under (t) arevPt＝T＝[Δx Δy Δz]T；
At time t +1, in the vehicle body coordinate system c0(t +1) the coordinates of the fixed reference point areWherein,vRtis a rotation matrix of the vehicle body coordinate system change,vTtthe translation matrix is changed by the vehicle body coordinate system.
Further, the acquiring, according to the first position posture data, second position posture transformation data of the fixed reference point at different times in the laser radar coordinate system specifically includes:
at any time t, lidar coordinate system Cl(t) coordinates of the fixed reference point P arelPt＝[0 0 0]T；
At time t +1, lidar coordinate system Cl(t +1) the coordinates of the fixed reference point areWherein,lRtis a rotation matrix of the changes of the laser radar coordinate system,lTtis a translation matrix of the laser radar coordinate system change.
Further, the obtaining an error function according to the first and second bit posture transformation data specifically includes:
the coordinates between the laser radar coordinate system and the vehicle body coordinate system satisfy the following relations:
vPt+1＝R*lPt+1+T；
using formulasAn error function e is obtained.
Further, the summing the error functions corresponding to the multiple groups of continuous frame data to obtain a second optimization objective function specifically includes:
using formulasAnd obtaining a second optimization objective function.
the multi-line laser radar calibration method provided by the invention comprises the steps of respectively calibrating two parts of parameters through decoupling alpha, β, delta z and gamma, delta x and delta y, calibrating the parameters alpha, β and delta z through extracting horizontal ground characteristics, and calibrating the parameters alpha, β and delta z through a rotation matrix provided by a GPS (global positioning system)vR and translation matrixvAnd T, constructing a constraint equation, and solving the calibration parameters gamma, delta x and delta y. The method provided by the invention does not need to manually measure the calibration parameters and special calibration objects, is convenient and fast to operate and has high calibration efficiency; the calibration precision can be improved by using the flat ground characteristics and the high-precision GPS information.
Drawings
FIG. 1 is a schematic diagram of a relative position relationship of a coordinate system according to an embodiment of the present invention;
fig. 2 is a flowchart of a method for calibrating a multiline lidar according to an embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the present invention will be described in further detail with reference to the accompanying drawings, and it is apparent that the described embodiments are only a part of the embodiments of the present invention, not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a schematic diagram of a relative position relationship of a coordinate system according to an embodiment of the present invention. As shown in FIG. 1, the lidar coordinate system x ' y ' z ' is denoted as ClThe vehicle body coordinate system xyz is recorded as C0the origin of the vehicle body coordinate system is set as the projection point of the vehicle head central point on the ground, the xy plane of the vehicle body coordinate system coincides with the ground, α, beta and gamma are the rotation angles of the two coordinate systems along the directions of the x, y and z axes, and delta x, delta y and delta z are the laser radar coordinate system ClRelative vehicle body coordinate system C0The translation amounts along the directions of the x axis, the y axis and the z axis respectively.
The transformation relation between the laser radar coordinate system and the vehicle body coordinate system is as follows:
by obtaining the parameters α, β, γ, Δ x, Δ y, Δ zDetermining a laser radar coordinate system ClRelative to the vehicle body coordinate system C0the technical scheme of the invention respectively calibrates two parts of parameters by decoupling α, beta, delta z and gamma, delta x and delta y, calibrates the parameters α, beta and delta z by extracting horizontal ground characteristics, and obtains a rotation matrix by an NDT algorithmlR and translation matrixlT, and rotation matrix provided by GPSvR translation matrixvAnd T, constructing a constraint equation, and solving the calibration parameters gamma, delta x and delta y.
Fig. 2 is a flowchart of a method for calibrating a multiline lidar according to an embodiment of the invention. As shown in fig. 2, the method specifically includes the following steps:
step 101, collecting point cloud data of a ground plane where a vehicle is located by a laser radar;
and the laser radar fixed at the top of the vehicle acquires point cloud data of a ground plane where the vehicle is located in a laser radar coordinate system.
102, calculating a normal vector of a plane equation of a ground plane in a laser radar coordinate system according to the point cloud data;
calculating the ground plane in the laser radar coordinate system C by using RANSAC (Random sample consensus) algorithm according to the collected point cloud data by using the ground as a reference planelThe following plane equation:
z′＝Ax′+By′+C (4)
the normal vector of the plane equation is: n [ -A-B1]T(5)
103, setting initial values of parameters in a rotation matrix R from a laser radar coordinate system to a vehicle body coordinate system;
the preset estimated values of the parameters alpha and beta are set as initial values, and the initial value of gamma is 0.
104, calculating an initial value of a rotation matrix according to the initial value of the parameter;
substituting the initial values of the parameters α, β, γ set in step 103 into the formula
And calculating to obtain an initial value of the rotation matrix R.
105, constructing a parallelism function of a coordinate axis z by utilizing a normal vector of a plane equation and an initial value of a rotation matrix;
the plane normal vector and the z-axis in the vehicle body coordinate system satisfy the equation:
constructing a parallelism function of a normal vector n' and a coordinate axis z:
wherein, the smaller the value of f is, the better the xy plane parallelism of the ground and the vehicle body coordinate system is.
Step 106, summing the parallelism functions of the point cloud data to obtain a first optimized objective function;
collecting m sampling points on the ground plane, and calculating normal vector n of each pointi′；
Using formulas
And calculating the sum of the parallelism functions of all the sampling points to obtain a first optimized objective function.
Step 107, performing iterative processing on the first optimization objective function according to the initial values of the parameters, and terminating the iterative processing when preset conditions are met to obtain final values of the first parameter and the second parameter;
wherein the preset conditions specifically include: the value of the optimization objective function is smaller than a first preset threshold or the iteration number reaches a second preset threshold.
Specifically, a Particle Swarm Optimization (PSO) algorithm is adopted, and equation (9) is used as an optimization objective function.
the method comprises the steps of taking preset estimated values of parameters α and β as initial values, taking a parallelism function F as an optimization objective function by a PSO algorithm, updating a group of α and β values, calculating the parallelism function F according to the updated α and β values, judging whether the value of F is smaller than a first preset threshold value epsilon or whether the iteration frequency reaches a preset second preset threshold value K, and if any one of the conditions is met, terminating iteration to obtain final values of the first parameter α and the second parameter β.
The particle swarm optimization algorithm in the step can be replaced by a multi-objective optimization algorithm such as an artificial immune system algorithm, a distribution estimation algorithm and the like.
108, obtaining the z-axis translation amount from the laser radar coordinate system to the vehicle body coordinate system according to the final values of the first parameter and the second parameter;
and substituting the final values of α and the beta obtained by optimization into a projection equation to obtain a plane equation of the ground plane under the laser radar coordinate system, wherein a constant term C in the equation is the translation quantity delta z.
In addition, for the calibration reference plane, a flat wall surface which is right opposite to the vehicle body and is vertical to the ground plane can be used as the reference plane, and then the corresponding formula (7) is replaced by the following formula:
the corresponding equation (8) is replaced with the following equation:
the corresponding equation (9) is replaced by the following equation:
thus, α, beta and delta z in the laser radar calibration parameters are obtained.
based on the calibration results α, β, Δ z, a constraint equation is derived by using global positioning system (gps) data of the vehicle body and data obtained by a Normal Distribution Transformation (NDT) algorithm, an optimization function is constructed, and the yaw angle γ and the plane offsets Δ x, Δ y are calculated.
From the formula (1), the lidar coordinate system ClRelative to the vehicle body coordinate system C0Can be described by a rotation matrix R and a translation vector T.
The vehicle-mounted GPS system is fixed on a vehicle body, the GPS can obtain the pose transformation of the vehicle body coordinate system from the moment t to the moment t +1, and the NDT algorithm can be used for calculating the pose transformation of the laser radar coordinate system from the moment t to the moment t + 1. The laser radar is fixed on the vehicle body, so that the transformation relation between the laser radar coordinate system and the vehicle body coordinate system is fixed at the time t and the time t + 1. Using the above constraint relationships, a constraint equation can be constructed.
Step 109, setting position coordinates of a fixed reference point under a laser radar coordinate system;
and setting the origin of the laser radar coordinate system as a fixed reference point P.
Step 110, acquiring first attitude transformation data of a fixed reference point at different moments under a vehicle body coordinate system according to vehicle GPS data;
the vehicle-mounted GPS system is fixed on the vehicle body, and the GPS can obtain the pose transformation of the vehicle body coordinate system from the moment t to the moment t + 1:
at any time t, acquiring a fixed reference point in a vehicle body coordinate system C according to vehicle GPS data0The coordinates under (t) are:
vPt＝T＝[Δx Δy Δz]T(10)
at time t +1, in the vehicle body coordinate system C0(t +1), the fixed reference point satisfies the equation:
vPt＝vRt*vPt+1+vTt(11)
thus, the fixed reference point is in the body coordinate system C0The coordinates under (t +1) are:
wherein,vRtis a rotation matrix of the vehicle body coordinate system change,vTtthe translation matrix is changed by the vehicle body coordinate system.
Step 111, acquiring second position and posture conversion data of the fixed reference point at different moments under the laser radar coordinate system according to the first position and posture data;
at any time t, lidar coordinate system Cl(t) coordinates of the fixed reference point P arelPt＝[0 0 0]T；
At time t +1, lidar coordinate system Cl(t +1), the fixed reference point satisfies the equation:
lPt＝lRt*lPt+1+lTt(13)
thus, the reference point is fixed in the lidar coordinate system C0The coordinates under (t +1) are:
wherein,lRtis a rotation matrix of the changes of the laser radar coordinate system,lTtis a translation matrix of the laser radar coordinate system change.
Step 112, obtaining an error function according to the first attitude transformation data and the second attitude transformation data;
the coordinates between the laser radar coordinate system and the vehicle body coordinate system satisfy the following relations:
vPt+1＝R*lPt+1+T (15)
substituting equation (12) and equation (14) into equation (15) minimizes the error on both sides of the equation,
an error function is obtained:
step 113, summing a plurality of error functions corresponding to a plurality of groups of continuous frame data to obtain a second optimized objective function;
collecting data among a plurality of groups of continuous frames, and summing a plurality of obtained error functions to obtain a second optimized objective function:
and obtaining a second optimization objective function.
In addition, the algorithm NDT used for point cloud matching may be replaced by an iterative closest point ICP (iterative closest point) algorithm.
And step 114, obtaining a third parameter, an x-axis translation amount and a y-axis translation amount according to the first parameter, the final value of the second parameter, the z-axis translation amount and the second optimization objective function.
and substituting the final values of the first parameter alpha and the second parameter β and the z-axis translation amount delta z into a formula (16), and minimizing the formula (16) to obtain third parameters gamma, delta x and delta y.
finally, the resulting α, β, γ, Δ x, Δ y, Δ z are substituted into the rotation matrix R, T to obtain the transformation relationship between the two coordinate systems.
the multi-line laser radar calibration method provided by the invention comprises the steps of respectively calibrating two parts of parameters through decoupling alpha, β, delta z and gamma, delta x and delta y, calibrating the parameters alpha, β and delta z through extracting horizontal ground characteristics, and calibrating the parameters alpha, β and delta z through a rotation matrix provided by a GPS (global positioning system)vR and translation matrixvAnd T, constructing a constraint equation, and solving the calibration parameters gamma, delta x and delta y. The method provided by the invention does not need to manually measure the calibration parameters and special calibration objects, is convenient and fast to operate and has high calibration efficiency; the calibration precision can be improved by using the flat ground characteristics and the high-precision GPS information.
Those of skill would further appreciate that the various illustrative components and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied in hardware, a software module executed by a processor, or a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are merely exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. A multiline laser radar calibration method is characterized by comprising the following steps:
the method comprises the steps that a laser radar collects point cloud data of a ground plane where a vehicle is located;
calculating a normal vector n' of a plane equation of the ground plane under a laser radar coordinate system according to the point cloud data;
setting initial values of parameters α, beta and gamma in a rotation matrix R from a laser radar coordinate system to a vehicle body coordinate system;
calculating an initial value of a rotation matrix R according to the initial values of the parameters α, beta and gamma;
constructing a parallelism function of the coordinate axis z by using the normal vector n' of the plane equation and the initial value of the rotation matrix R;
summing the parallelism functions of the point cloud data to obtain a first optimization objective function;
performing iterative processing on the first optimization objective function according to the initial values of the parameters α and β, and terminating iteration when preset conditions are met to obtain final values of α and β;
obtaining a z-axis translation amount delta z from a laser radar coordinate system to a vehicle body coordinate system according to the final values of the alpha and β;
setting position coordinates of a fixed reference point under a laser radar coordinate system;
acquiring first attitude transformation data of the fixed reference point at different moments under a vehicle body coordinate system according to vehicle GPS data;
acquiring second position and posture conversion data of the fixed reference point at different moments under a laser radar coordinate system according to the first position and posture data;
obtaining an error function according to the first position posture transformation data and the second position posture transformation data;
summing a plurality of error functions corresponding to a plurality of groups of continuous frame data to obtain a second optimized objective function;
and obtaining parameters gamma, x-axis translation amount delta x and y-axis translation amount delta y according to the final values of alpha and beta, z-axis translation amount delta z and a second optimization objective function.
2. The method according to claim 1, wherein the preset conditions specifically include:
the value of the optimization objective function is smaller than a first preset threshold or the iteration number reaches a second preset threshold.
3. The method according to claim 1, wherein the constructing the parallelism function of the coordinate axes z using the normal vector n' of the plane equation and the initial values of the rotation matrix R specifically comprises:
using formulasAnd constructing a parallelism function of the coordinate axis z of the normal vector n'.
4. The method of claim 1, wherein summing the parallelism functions of the plurality of point cloud data to obtain a first optimization objective function specifically comprises:
collecting m sampling points on the ground plane, and calculating normal vector n of each pointi′；
Using formulasAnd calculating to obtain a first optimization objective function.
5. the method according to claim 1, wherein said calculating initial values of the rotation matrix R from initial values of the parameters α, β, γ comprises in particular:
using a formula based on preset estimated values of the parameters alpha and β and the parameter gamma being 0The initial values of the rotation matrix R are calculated.
6. The method according to claim 1, wherein the setting of the position coordinates of the fixed reference point in the lidar coordinate system specifically comprises:
and setting the origin of the laser radar coordinate system as a fixed reference point P.
7. The method according to claim 1, wherein the acquiring first attitude transformation data of the fixed reference point at different times in the vehicle body coordinate system according to the vehicle GPS data specifically comprises:
at any time t, acquiring a fixed reference point in a vehicle body coordinate system C according to vehicle GPS data0(t) sit downIs marked asvPt＝T＝[Δx Δy Δz]T；
At time t +1, in the vehicle body coordinate system C0(t +1) the coordinates of the fixed reference point areWherein,vRtis a rotation matrix of the vehicle body coordinate system change,vTtthe translation matrix is changed by the vehicle body coordinate system.
8. The method according to claim 1, wherein the obtaining second pose transformation data of the fixed reference point at different times under the lidar coordinate system according to the first pose data specifically comprises:
at any time t, lidar coordinate system Cl(t) coordinates of the fixed reference point P arelPt＝[0 0 0]T；
At time t +1, lidar coordinate system Cl(t +1) the coordinates of the fixed reference point areWherein,lRtis a rotation matrix of the changes of the laser radar coordinate system,lTtis a translation matrix of the laser radar coordinate system change.
9. The method according to claim 7 or 8, wherein the deriving an error function from the first and second pose transformation data specifically comprises:
the coordinates between the laser radar coordinate system and the vehicle body coordinate system satisfy the following relations:vPt+1＝R*lPt+1+T；
using formulasAn error function e is obtained.
10. The method according to claim 1, wherein the summing the plurality of error functions corresponding to the plurality of sets of consecutive frame data to obtain the second optimization objective function specifically comprises:
using formulasAnd obtaining a second optimization objective function.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2007218738A (en) * | 2006-02-16 | 2007-08-30 | Kumamoto Univ | Calibration device, target detection device, and calibration method |
| CN101699313A (en) * | 2009-09-30 | 2010-04-28 | 北京理工大学 | Method and system for calibrating external parameters based on camera and three-dimensional laser radar |
| CN104463877A (en) * | 2014-12-12 | 2015-03-25 | 武汉理工大学 | Shoreline registration method based on information of radar image and electronic sea chart |
| CN105277950A (en) * | 2015-09-29 | 2016-01-27 | 西安知几天线技术有限公司 | Laser radar coordinate conversion method based on vehicle body coordinate system |
| CN107870324A (en) * | 2017-05-09 | 2018-04-03 | 吉林大学 | The caliberating device and method of a kind of multi-line laser radar |
-
2018
- 2018-06-05 CN CN201810570557.7A patent/CN109001711B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2007218738A (en) * | 2006-02-16 | 2007-08-30 | Kumamoto Univ | Calibration device, target detection device, and calibration method |
| CN101699313A (en) * | 2009-09-30 | 2010-04-28 | 北京理工大学 | Method and system for calibrating external parameters based on camera and three-dimensional laser radar |
| CN104463877A (en) * | 2014-12-12 | 2015-03-25 | 武汉理工大学 | Shoreline registration method based on information of radar image and electronic sea chart |
| CN105277950A (en) * | 2015-09-29 | 2016-01-27 | 西安知几天线技术有限公司 | Laser radar coordinate conversion method based on vehicle body coordinate system |
| CN107870324A (en) * | 2017-05-09 | 2018-04-03 | 吉林大学 | The caliberating device and method of a kind of multi-line laser radar |
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