CN110471074B - Method for improving planar laser point cloud distance measurement precision - Google Patents

Method for improving planar laser point cloud distance measurement precision Download PDF

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CN110471074B
CN110471074B CN201910753161.0A CN201910753161A CN110471074B CN 110471074 B CN110471074 B CN 110471074B CN 201910753161 A CN201910753161 A CN 201910753161A CN 110471074 B CN110471074 B CN 110471074B
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路静峰
洪溪森
林坚
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Lingwei Technology Xiamen Co ltd
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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
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/02—Systems using the reflection of electromagnetic waves other than radio waves
    • G01S17/06—Systems determining position data of a target
    • G01S17/08—Systems determining position data of a target for measuring distance only
    • 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
    • G01S7/4802—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
    • 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
    • G01S7/4808—Evaluating distance, position or velocity data
    • 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
    • G01S7/481—Constructional features, e.g. arrangements of optical elements
    • G01S7/4817—Constructional features, e.g. arrangements of optical elements relating to scanning

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Abstract

The invention provides a method for improving the precision of planar laser point cloud ranging, which comprises the following steps: step 1, controlling a laser radar to work, obtaining point clouds, and calculating the space coordinates of each point in the point clouds; step 2, converting the space coordinates of each point, the vertical opening angle gamma of the point cloud and the horizontal opening angle theta into two-dimensional matrix elements, and obtaining the position coordinates of each point in the matrix; step 3, searching each effective element A in the two-dimensional matrix according to the position coordinates of each point in the step 2, and creating a window with the size of G x G by taking each effective element A as a center, wherein G is an odd number and comprises a plurality of effective elements K1, K2, … and Kn; step 4, determining whether the central point A in each window is a discrete point; and 5, outputting the central point A which is a non-discrete point to obtain an accurate distance. The plane laser point cloud distance measurement precision method effectively solves the problem of precision of laser distance measurement in the prior art.

Description

Method for improving planar laser point cloud distance measurement precision
Technical Field
The invention relates to a method for improving the point cloud distance measurement precision of planar laser points, in particular to a calculation process of a discrete point algorithm for improving the point cloud distance measurement precision of planar laser points and filtering.
Background
The laser radar is used as an important device and has important application in the fields of security monitoring, automatic driving and the like, when the laser radar emits laser and reflects back to receive, an ideal result is that a very thin curved surface corresponding to a real object is displayed, but a test surface in reality is not an ideal ultrathin curved surface but a point cloud three-dimensional image with a certain thickness, so that the test precision is seriously influenced, and the use of the laser radar is limited. The invention processes the received point cloud data to recover the point cloud data to be close to an ideal curved surface state, thereby improving the precision of distance measurement and simultaneously filtering discrete points in the air.
Disclosure of Invention
The invention provides a method for improving the precision of planar laser point cloud ranging, which at least solves the problem of the precision of laser ranging in the prior art.
The invention provides a method for improving the precision of planar laser point cloud ranging, which comprises the following steps:
step 1, controlling a laser radar to work, obtaining point clouds, and calculating the space coordinates of each point in the point clouds;
step 2, converting the space coordinates of each point, the vertical opening angle gamma of the point cloud and the horizontal opening angle theta into two-dimensional matrix elements, and obtaining the position coordinates of each point in the matrix;
step 3, searching each effective element A in the two-dimensional matrix according to the position coordinates of each point in the step 2, and creating a window with the size of G x G by taking each effective element A as a center, wherein G is an odd number and comprises a plurality of effective elements K1, K2, … and Kn;
step 4, determining whether the central point A in each window is a discrete point;
and 5, outputting the central point A which is a non-discrete point to obtain an accurate distance.
Further, the step 1 specifically comprises: controlling a laser radar to work, rotating the angle of a galvanometer to obtain a plurality of points to obtain point cloud; calculating the point cloud according to the rotating angle of the galvanometer and the Tof of each point to obtain the space coordinate K of the Cartesian coordinate system of each pointi(x,y,z)。
Further, the step 2 specifically includes: according to the space coordinate K of each pointi(x, y, z) and the vertical opening angle γ and the horizontal opening angle θ of the dot cloud are converted into elements in a two-dimensional matrix MatrixL, and the position coordinate KM of each dot in the matrix is obtainediThe predetermined resolution of the two-dimensional matrix is K × N.
Further, the step 3 specifically includes: in the two-dimensional matrix MatrixL, each effective element a is searched from left to right and from top to bottom according to the position coordinates of each point in step 2, a window with the size of G × G is created by taking each effective element a as the center, G is an odd number, and the window comprises a plurality of effective elements K1, K2, … and Kn.
Further, the step 4 specifically includes: setting a threshold AK and a threshold DK of filtering points in each window of G × G, searching each effective element in the window, and calculating a central point A in the window to each effective elementThe Tof difference of each effective element point K1, K2, …, Kn is set as TjIf T isjIf DK, the counter is equal to Count +1, until the last valid point, compare Count with AK, if Count>AK, the central point is determined as an undispersed point, and the flag of the point is true.
Further, the step 4 further includes recording all the satisfied conditions T except the center point Aj< point of DK zkCounting the number of the points, setting the number as sk, and using a formula
Figure BDA0002167533160000021
Replacing z of the window center point A space coordinate (x, y, z) with z
Figure BDA0002167533160000022
And obtaining the accurate distance between the window and the laser radar.
Further, the step 4 further includes recording all the satisfied conditions T except the center point AjThe Tof average value of the point less than DK is calculated by the rotation angle of the galvanometer when the laser radar obtains the point, and the average space coordinate of A is obtained
Figure BDA0002167533160000023
And obtaining the accurate distance between the window and the laser radar.
Further, the method for calculating the spatial coordinates of each point in the step is as follows: setting one point in the point cloud as K, obtaining an included angle alpha between a connecting line of the point K and a Cartesian coordinate system origin O and OY, and obtaining an included angle beta between a connecting line of a projection point B of the point K on a ZOX plane and the coordinate system origin O and an OZ coordinate axis, according to a formula: lOKObtaining a straight-line distance between a point K and an origin O of a Cartesian coordinate system by Tof C/2, wherein C is the speed of light; and calculating the Cartesian space coordinate of the point K by using the included angle alpha and the included angle beta.
Further, the cartesian space coordinate of the point K is calculated as x ═ lOK*cos(90°-α)*sinβ；z＝lOK*cos(90°-α)*cosβ；y＝lOK*cosα。
Furthermore, the coordinates KM of each point in the two-dimensional matrix in step 2iThe specific calculation process is as follows: in the following formula, W represents the length of the two-dimensional matrix in the x-axis direction within a range of a horizontal opening angle perpendicular to the Z-axis, H represents the length of the two-dimensional matrix in the y-axis direction within a range of a vertical opening angle, xyz is a spatial coordinate value of each point, and W is 2 × Z × tg (θ/2); h2 x z tg (γ/2); resolution _ x ═ W/(K-1); resolution _ y ═ H/(N-1); Row-W/2-0.5 + x/Resolution _ x; column ═ N/2-0.5+ y/Resolution _ y, coordinates KMiIs (row, column).
Compared with the prior art, the method has the advantages that the accurate average distance between each element in the two-dimensional matrix and the laser radar is obtained by performing two-dimensional matrix conversion on each point in the point cloud and analyzing the discrete points, and the average Cartesian coordinates and the average tof of each point in the point cloud are obtained, so that the effect of accurate laser ranging is achieved. Particularly, with the application of the invention, the distance measurement precision can be improved in a direct proportion along with the improvement of the density of the point cloud points, namely, the higher the density of the point cloud is, the thinner the obtained point cloud curved surface is, and the shape of the point cloud curved surface is closer to that of a real object. The unprocessed point cloud is increased along with the density of the point cloud, the measured distance divergence is obviously increased, and the point cloud is more overstaffed.
Drawings
FIG. 1 is a schematic diagram of a space point transformed into Cartesian coordinates according to Tof according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a sliding search filtering and averaging algorithm according to an embodiment of the present invention;
FIG. 3 is a point cloud without filtering and mean-plus-range accuracy algorithm in the mirror-frames of embodiment 6 of the present invention;
FIG. 4 is a point cloud after applying filtering and average enhanced range accuracy algorithm in embodiment 6mirror-frames of the present invention;
FIG. 5 is a point cloud without filtering and mean-plus-range accuracy algorithm in the mirror-frames of example 6 of the present invention;
FIG. 6 is a point cloud after applying filtering and average enhanced range accuracy algorithm in embodiment 6mirror-frames of the present invention;
FIG. 7 is a point cloud after applying filtering and mean-plus-range accuracy algorithm in embodiment 12mirror-frames of the present invention;
FIG. 8 is a point cloud after applying filtering and mean-plus-range accuracy algorithm in embodiment 12mirror-frames of the present invention;
FIG. 9 is a point cloud after applying filtering and mean-plus-range accuracy algorithm in embodiment 12mirror-frames of the present invention;
FIG. 10 is a point cloud after applying filtering and mean-plus-range accuracy algorithm in embodiment 12mirror-frames of the present invention;
FIG. 11 is a point cloud after applying a filtering and average enhancing distance-measuring accuracy algorithm in an embodiment 24mirror-frames of the present invention;
FIG. 12 is a point cloud after applying filtering and mean-plus-range accuracy algorithm in embodiment 24mirror-frames of the present invention;
FIG. 13 is a point cloud after applying filtering and mean-plus-range accuracy algorithm in embodiment 24mirror-frames of the present invention;
FIG. 14 is a point cloud after applying the filtering and average enhancing distance-measuring accuracy algorithm under 24mirror-frames according to the embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood by those skilled in the art, the technical solutions in the embodiments of the present invention will be clearly and completely described below, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments.
The method of the embodiment of the invention comprises the following steps:
step 1, calculating the point cloud according to the rotating angle of the galvanometer and the obtained Tof (time of fly air time) to obtain a space coordinate K of a Cartesian coordinate system of each pointi(x, y, z), the calculation formula is as follows:
at any moment, the galvanometer mems generates a specific gesture, and after the laser hits the rotating mirror, the laser outputs current gesture data to the processor, wherein the current gesture data mainly comprise two data, one is an included angle α between a connecting line of a current point K and a Cartesian coordinate system origin O and an OY, and the other is an included angle β between a connecting line of a projection point B of the K on a ZOX plane and the coordinate system origin O and an OZ coordinate axis, a schematic diagram is shown in FIG. 1, and the following formula is provided:
lOKtof C/2; where C is the speed of light- - (1);
x＝lOK*cos(90°-α)*sinβ；----------------------(2)；
z＝lOK*cos(90°-α)*cosβ；---------------------(3)；
y＝lOK*cosα；----------------------------------------(4)；
step 2, according to the space coordinate K of each pointi(x, y, z) and the vertical opening angle γ and the horizontal opening angle θ of the dot cloud are converted into elements in a two-dimensional matrix MatrixL, and the position coordinate KM of each dot in this matrix is obtainedi(row, column), the two-dimensional matrix is a preset resolution K N;
the specific calculation process is as follows:
in the following formula, W represents the length of a specific plane in the x-axis direction within a horizontal open angle range perpendicular to the Z-axis, H represents the length of the specific plane in the y-axis direction within a vertical open angle range, and xyz is a spatial coordinate value of the specific point;
W＝2*z*tg(θ/2)；-----------------------------(5)
H＝2*z*tg(γ/2)；-------------------------------(6)
Resolution_x＝W/(K-1)；-------------------------------(7)
Resolution_y＝H/(N-1)；---------------------------------(8)
Row＝W/2-0.5+x/Resolution_x；------------------------(9)
Column＝N/2-0.5+y/Resolution_y；------------------------(10)
thus, each point has two coordinates, a space Cartesian coordinate system coordinate and a position coordinate in a two-dimensional matrix;
step 3, in the two-dimensional matrix, searching each effective element from left to right and from top to bottom according to the position coordinates of each point in the step 2, and creating a window with the size of G by taking each effective element A as the center, wherein G is an odd number, such as 5, 7, 9, 11, 13, 15, 17, 19 and the like, and the window comprises a plurality of effective elements K1, K2, K3 and the like, as shown in a schematic diagram 2;
step 4, in each window with the size of G, firstly setting a filtering point threshold AK, then setting a distance threshold DK, then searching each effective element, and calculating the following 2 numerical values: first, the Tof difference from the center point A to each of the effective element points K1, K2, K3, etc. is calculated as TjIf T isjIf DK, the counter is equal to Count +1, until the last valid point, compare Count with AK, if Count>AK, the central point is determined as a non-discrete point, and the flag of the point is true; two, recording all satisfied conditions T except the center point Aj< point of DK zkAssuming that the number of points satisfying the above condition is sk, the calculation is performed
Figure BDA0002167533160000041
Figure BDA0002167533160000042
Replacing z of the spatial coordinates A (x, y, z) of the center point by z
Figure BDA0002167533160000043
Wherein, step 4 can also adopt the method of recording all the satisfied conditions T except the central point AjThe Tof average value of the point less than DK is calculated by the rotation angle of the galvanometer when the laser radar obtains the point, and the average space coordinate of A is obtained
Figure BDA0002167533160000044
The obtained coordinate precision is higher;
step 5, outputting all the points of which the flag is true;
the comparison of the effects before and after the application of the algorithm is shown in fig. 3-fig. 14, wherein mirrorframe means a point cloud set obtained after scanning by scanning galvanometer mems in each field, and 6 mirrorframes means a point cloud map obtained by superposing a 6-field scanning point cloud set in one frame of data. Fig. 3, 4, 7, 8, 11, and 12 show first angle detection for the test surface, and fig. 5, 6, 9, 10, 13, and 14 show second angle detection for the test surface.
According to the embodiment of the invention, the accurate average distance between each element in the two-dimensional matrix and the laser radar is obtained by performing two-dimensional matrix conversion on each point in the point cloud and analyzing the discrete points, and the average Cartesian coordinate and the average tof of each point in the point cloud are obtained, so that the effect of accurate laser ranging is achieved. Meanwhile, as can be seen from comparison of the drawings in the embodiments of the present invention, after the algorithm processing in the embodiments of the present invention, the precision of the detection effect can be effectively improved, and the influence of the discrete point on the precision can be removed.
Finally, it should be noted that the above-mentioned embodiments are only used for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above-mentioned embodiments, it should be understood by those skilled in the art that the modifications and equivalents of the specific embodiments of the present invention can be made by those skilled in the art after reading the present specification, but these modifications and variations do not depart from the scope of the claims of the present application.

Claims (6)

1. A method for improving the distance measurement precision of a planar laser point cloud is characterized by comprising the following steps:
step 1, controlling a laser radar to work, obtaining point clouds, and calculating the space coordinates of each point in the point clouds;
step 2, converting the space coordinates of each point, the vertical opening angle gamma of the point cloud and the horizontal opening angle theta into two-dimensional matrix elements, and obtaining the position coordinates of each point in the matrix;
step 3, searching each effective element A in the two-dimensional matrix according to the position coordinates of each point in the step 2, and creating a window with the size of G x G by taking each effective element A as a center, wherein G is an odd number and comprises a plurality of effective elements K1, K2, … and Kn;
step 4, determining whether the central point A in each window is a discrete point;
step 5, outputting a central point A which is a non-discrete point to obtain an accurate distance;
the step 2 specifically comprises the following steps: converting the space coordinates Ki (x, y, z) of each point and the vertical open angle gamma and the horizontal open angle theta of the point cloud into elements in a two-dimensional matrix MatrixL, and obtaining the position coordinates KM I of each point in the matrix, wherein the preset resolution of the two-dimensional matrix is K x N; the method for calculating the space coordinates of each point in the step is as follows: setting one point in the point cloud as K, obtaining an included angle alpha between a connecting line of the point K and a Cartesian coordinate system origin O and OY, and obtaining an included angle beta between a connecting line of a projection point B of the point K on a ZOX plane and the coordinate system origin O and an OZ coordinate axis, according to a formula: obtaining the linear distance between a point K and the original point O of the Cartesian coordinate system by Tof C/2, wherein C is the speed of light; calculating the Cartesian space coordinate of the point K by using the included angle alpha and the included angle beta; the cartesian space coordinate calculation formula of the point K is x ═ OK ═ cos (90 ° - α) × sin β; z ═ l OK ═ cos (90 ° - α) × cos β; y ═ l OK × cos α; the specific calculation process of the coordinates KM i of each point in the two-dimensional matrix in step 2 is as follows: in the following formula, W represents the length of the two-dimensional matrix in the x-axis direction within a range of a horizontal opening angle perpendicular to the Z-axis, H represents the length of the two-dimensional matrix in the y-axis direction within a range of a vertical opening angle, xyz is a spatial coordinate value of each point, and W is 2 × Z × tg (θ/2); h2 x z tg (γ/2); resolution _ x ═ W/(K-1); resolution _ y ═ H/(N-1); Row-W/2-0.5 + x/Resolution _ x; column ═ N/2-0.5+ y/Resolution _ y, with the coordinate KM i (row, Column).
2. The method for improving the precision of planar laser point cloud ranging according to claim 1, wherein the step 1 specifically comprises: controlling a laser radar to work, rotating the angle of a galvanometer to obtain a plurality of points to obtain point cloud; and calculating the point cloud according to the rotating angle of the galvanometer and the Tof of each point to obtain the space coordinates K i (x, y, z) of the cartesian coordinate system of each point.
3. The method for improving the precision of planar laser point cloud ranging according to claim 1, wherein the step 3 specifically comprises: in the two-dimensional matrix MatrixL, each effective element a is searched from left to right and from top to bottom according to the position coordinates of each point in step 2, a window with the size of G × G is created by taking each effective element a as the center, G is an odd number, and the window comprises a plurality of effective elements K1, K2, … and Kn.
4. The method for improving the precision of planar laser point cloud ranging according to claim 3, wherein the step 4 specifically comprises: in each window with the size of G × G, setting a filtering point threshold AK and a distance threshold DK, searching each effective element in the window, calculating the Tof difference from a central point A in the window to each effective element point K1, K2, … and Kn, setting the Tof difference as T j, if T j is less than DK, setting a counter as Count +1 until the last effective point, comparing Count and AK, and if Count > AK, determining that the central point is a non-discrete point and setting the flag of the point as true.
5. The method of claim 4, wherein the step 4 further comprises recording z k of all points satisfying the condition T j < DK except the center point A, counting the number of the points, and setting the number as sk, and replacing the z of the spatial coordinates (x, y, z) of the center point A of the window with the accurate distance between the window and the laser radar.
6. The method for improving the precision of planar laser point cloud ranging as claimed in claim 4, wherein the step 4 further comprises recording the Tof average value of all points satisfying the condition T j < DK except the central point A, and obtaining the accurate distance between the window for obtaining the average space coordinate of A and the laser radar through calculation of the rotation angle of the galvanometer when the laser radar obtains the point.
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