CN102062860A

CN102062860A - Foundation laser radar data registration method based on single tree position and surface information

Info

Publication number: CN102062860A
Application number: CN2009102377622A
Authority: CN
Inventors: 倪文俭; 过志锋; 孙国清; 黄华兵
Original assignee: Institute of Remote Sensing Applications of CAS
Current assignee: Institute of Remote Sensing Applications of CAS
Priority date: 2009-11-18
Filing date: 2009-11-18
Publication date: 2011-05-18

Abstract

The invention relates to an automatic registration method between multi-station foundation laser radar data based on single tree position and surface information and is mainly applied to the three-dimensional forest scene reconstruction based on multi-station foundation laser radar data. By the automatic registration method between multi-station foundation laser radar data, in the automatic alignment process, an additive foundation laser radar posture measuring device is used, the number of registration parameters required by registration need to be determined to reduce the number of control points required by registration. Simultaneously the similarity of a triangle formed by a plurality of single trees is utilized to recognize the homonymy single trees automatically, and control points are provided for the registration. By the automatic registration method between multi-station foundation laser radar data based on single tree position and surface information, the accuracy requirements and dependence for the control points in the registration process can be reduced, and the automatic registration of the multi-station foundation laser radar data under a forest environment can be realized.

Description

Foundation laser radar data registration method based on single-tree position and surface information

Technical Field

The invention relates to an automatic registration method between ground-based laser radar data based on single-tree position and earth surface information, in particular to automatic registration between multi-station ground-based laser radar data in a forest scene, which is mainly applied to three-dimensional forest scene reconstruction based on the multi-station ground-based laser radar data.

Background

Ground-based lidar data has been applied gradually to detailed measurements of structural parameters of singles, including the location and size of singles (Hopkinson et al 2004; Henning and radke 2006). The spatial distribution of forest canopy components is primarily considered on the forest stand scale (Henning and Radtke 2006). Due to the shielding between the single trees, it is difficult to accurately describe the forest using the ground-based lidar data at the forest stand level using single-station data. Multi-station data is used, which requires registration of the multi-station data. In the process of three-dimensional reconstruction of archaeology and buildings, the feature points can be used for registration because the study object has specific corner points and other features. Forests are different from artificial buildings in that there are no specific characteristic points available for direct use. Researchers have therefore attempted to place artificial targets in forest scenes as control points for registration. In addition, some methods for automatic registration without using artificial targets have been reported so far. Most of these methods are directed to artificial objects, such as buildings or industrial components. It is common practice to select homonymous points or homonymous surfaces in a common area of the two station data, whereby the registration parameters are calculated (Goshtasby 1998). The most well known algorithm at present is the Iterative Closest Point algorithm (ICP) proposed by Besl and McKay (Besl and McKay 1992). Although many researchers have improved on the limitations of this algorithm (Gendron et al 1998; Goshtasby 1998; Dalley and Flynn 2002; Estepar et al 2004; Kim et al 2004; Chetverikov et al 2005), this algorithm requires a large overlap between the two stations of data to be registered, often requiring an overlap of more than 50%, and only one object or plane of interest within the overlap region, and a high dot density (Estepar et al 2004; Kim et al 2004; Chetverikov et al 2005). These requirements are not met under forest conditions.

Henning and Radtke proposed a registration method for forest scenes (Henning and Radtke 2008). The method divides the registration process into two steps, firstly selects control points through the ground to carry out coarse registration of data, and then selects more accurate control points from a trunk to carry out fine registration. Although the method has obvious surface characteristics, the density of forest stands is lower, and the method is more applicable when the vegetation is less under the forest; under the condition that the surface features are unknown, the single wood is surrounded by dead branches, and when the shielding is serious, the control points required by the registration are difficult to select, so that the success rate is greatly reduced.

Disclosure of Invention

The key of the multi-station foundation laser radar data registration lies in the selection of control points, while under the forest scene condition, the accurate control points are difficult to select, and the existing data registration algorithm under the forest scene usually requires that a ground table and a trunk have obvious characteristic points for utilization.

The invention mainly solves the problem of reducing the requirement and the degree of dependence of a registration process on the precision of a control point. According to the invention, the number of registration parameters required to be determined for registration is reduced by adding the ground-based laser radar attitude measurement device, so that the number of control points required for registration is reduced, meanwhile, homonymous trees are automatically identified by utilizing the similarity of triangles formed by a plurality of single trees, control points are provided for registration, and the coincidence of the positions of the single trees and the ground surface in a repeated area is used as the basis for calculating the registration parameters, so that the dependence on accurate control points is reduced.

The present invention can be realized by the following scheme.

An automatic registration method for multi-station foundation laser radar data uses an additional foundation laser radar attitude measurement device in the automatic alignment process, and reduces the number of registration parameters required to be determined by registration so as to reduce the number of control points required by registration.

In the above scheme, preferably, the attitude of the instrument may be measured along the X and Y axes of the ground-based lidar using the tilt sensor.

Preferably, the automatic registration method can be used for automatic registration of forest scenes, and the identification of the trunk point cloud can be performed by using a vertical filtering method.

Preferably, in the alignment process, the central position of the trunk is used as a control point, the two stations of data to be registered are called primary data and secondary data, and the corresponding trunk is found in the primary data and the secondary data through the following three steps:

step 1, separating ground points and vegetation points by using a variable scale method;

step 2, respectively carrying out vertical filtering on the main data and the auxiliary data to extract trunk point clouds and calculate the central coordinates of the trunk;

and 3, identifying the corresponding trunk by using the rule that the triangle formed by the corresponding trunk in the main data and the triangle formed by the corresponding trunk in the auxiliary data have similar properties.

Preferably, the identification of the trunk is carried out by utilizing the point cloud of a section of space above the ground surface and below the canopy, and the branch and ground planting noise in the section of space is removed by utilizing the difference between the vertical distribution characteristics of the point cloud of the section of space and the point cloud of the trunk.

In the above scheme, preferably, the automatic registration method may be used for automatic registration of a forest scene, two stations of data to be registered may be referred to as primary data and secondary data, and the same trunk in the primary data and the secondary data may be identified by using similarity of triangles formed by singles.

Preferably, the automatic registration method is used for automatic registration of a forest scene and uses 4 registration parameters; 3 of them are calculated using the same trunk and the 4 th registration parameter is calculated using the surface.

Preferably, the laser radar data is firstly separated from the vegetation points by adopting a variable scale method, then the positions of the singles are identified through vertical filtering, the singles with the same name are automatically identified by utilizing the relative positions of the singles, and the singles with the same name are used as control points to obtain 3 registration parameters; and finally, determining the offset of the two stations of data in the vertical direction according to the optimal matching of the earth surface, and solving a 4 th registration parameter.

An automatic registration method among multi-station foundation laser radar data is used for automatic registration of a forest scene, an additional foundation laser radar attitude measuring device is used in the automatic alignment process, the number of registration parameters required to be determined for registration is reduced, and therefore 4 registration parameters are used, and the number of control points required for registration is reduced; separating the earth surface points from the vegetation points by a scale-variable method; identifying the trunk point cloud by using a vertical filtering method; identifying the same trunk in the main data and the auxiliary data by utilizing the similarity of triangles formed by single trees; 3 of the 4 registration parameters are calculated using the same trunk, the last of the 4 registration parameters being calculated using the surface.

The method can reduce the dependence of the registration process on the control point, and realize the automatic registration of the multi-station foundation laser radar data in the forest environment.

Drawings

FIG. 1 shows the relative position relationship between the O-X ' Y ' Z ' coordinate system and the O-XYZ coordinate system according to the present invention.

Fig. 2 shows a schematic diagram of the gamma' solution of the present invention.

Fig. 3 shows the relationship between the coordinate systems used for the primary and secondary data of the present invention.

Detailed Description

Exemplary embodiments for implementing the present invention will be described in detail below with reference to the accompanying drawings.

According to the data registration principle, 6 parameters are required to be determined in the registration process, the inclination angle sensor is used for measuring the attitude of the instrument along the X axis and the Y axis of the foundation laser radar, so that the number of the parameters required to be determined is 4, and the XOY plane can be rotated to the horizontal plane by using the measured attitude parameters. The method comprises the steps of firstly separating ground points from vegetation points by adopting a variable-scale method for laser radar data, then identifying the positions of the singles through vertical filtering, automatically identifying the singles with the same name by utilizing the relative positions of the singles, taking the singles with the same name as control points, obtaining 3 parameters of the singles, and finally determining the offset of the data of the two stations in the vertical direction according to the optimal matching of the ground surface.

The present invention is described in detail below.

Let the coordinate System (SOCS) used by the instrument to collect data be O-X ' Y ' Z ', and the corresponding coordinate System when the XOY plane is horizontal be O-XYZ, as shown in FIG. 1. Wherein OX is the projection of OX 'on a horizontal plane, OY is perpendicular to OX and points to OY' hemisphere space, and OZ is perpendicular to OX and OY and points upwards. OG and OF are respectively the projection OF OX 'and OY' in an XOY plane, alpha is the included angle between OX 'and OG, beta is the included angle between OY' and OF, and gamma is the included angle between OG and OF.

The unit coordinate vector of O-X ' Y ' Z ' is

The unit coordinate vector of O-XYZ is

The conversion relationship between them can be expressed as:

<math><mrow><mrow><msup><mover><mi>z</mi><mo>^</mo></mover><mo>′</mo></msup><mo>=</mo><msub><mover><mi>x</mi><mo>^</mo></mover><mi>s</mi></msub><mo>×</mo><msub><mover><mi>y</mi><mo>^</mo></mover><mi>s</mi></msub><mo>=</mo><mo>-</mo><mi>sin</mi><mi></mi><mi>α</mi><mi>cos</mi><mi></mi><mi>β</mi><mi>sin</mi><mi>γ</mi><mo>′</mo><mover><mi>x</mi><mo>^</mo></mover><mo>+</mo><mo>[</mo><mi>sin</mi><mi></mi><mi>α</mi><mi>cos</mi><mi></mi><mi>β</mi><mi>cos</mi><mi>γ</mi><mo>′</mo><mo>-</mo><mi>cos</mi><mi></mi><mi>α</mi><mi>sin</mi><mi>β</mi><mo>]</mo></mrow><mover><mi>y</mi><mo>^</mo></mover><mo>+</mo><mi>cos</mi><mi></mi><mi>α</mi><mi>cos</mi><mi>βγ</mi><mo>′</mo><mover><mi>z</mi><mo>^</mo></mover><mo>-</mo><mo>-</mo><mo>-</mo><mrow><mo>(</mo><munder><mn>1</mn><mo>&OverBar;</mo></munder><mo>)</mo></mrow></mrow></math>

in matrix form, can be expressed as:

<math><mrow><mi>whereM</mi><mo>=</mo><mfenced open='[' close=']'><mtable><mtr><mtd><mi>cos</mi><mi>α</mi></mtd><mtd><mn>0</mn></mtd><mtd><mi>sin</mi><mi>α</mi></mtd></mtr><mtr><mtd><mi>cos</mi><mi></mi><mi>β</mi><mi>cos</mi><mi>γ</mi><mo>′</mo></mtd><mtd><mi>cos</mi><mi></mi><mi>β</mi><mi>sin</mi><mi>γ</mi><mo>′</mo></mtd><mtd><mi>sin</mi><mi>β</mi></mtd></mtr><mtr><mtd><mo>-</mo><mi>sin</mi><mi></mi><mi>α</mi><mi>cos</mi><mi></mi><mi>β</mi><mi>sin</mi><mi>γ</mi><mo>′</mo></mtd><mtd><mi>sin</mi><mi></mi><mi>α</mi><mi>cos</mi><mi></mi><mi>β</mi><mi>cos</mi><mi>γ</mi><mo>′</mo><mo>-</mo><mi>cos</mi><mi></mi><mi>α</mi><mi>sin</mi><mi>β</mi></mtd><mtd><mi>cos</mi><mi></mi><mi>α</mi><mi>cos</mi><mi></mi><mi>β</mi><mi>sin</mi><mi>γ</mi><mo>′</mo></mtd></mtr></mtable></mfenced><mo>-</mo><mo>-</mo><mo>-</mo><mrow><mo>(</mo><mn>3</mn><mo>)</mo></mrow></mrow></math>

as shown in fig. 2, the plane XOY in fig. 1 is designated as H, and the planes GOX 'and FOY' are designated as P and Q, respectively. Passing any point C on OX ' as CE// GO (namely, CE is parallel to GO), passing E as ED// OF, wherein ED and OY ' are intersected at a point D, angle COG is alpha, angle DOF is beta, and angle FOG is gamma '. γ' can be calculated by:

wherein,

FO＝EO/tanβ，

GF²＝CO²+DO²＝(GO/cosα)²+(EO/sinβ)²，

EO＝GO·tanα

as can be seen from equations (1) - (4), the O-X 'Y' Z 'coordinate system can be converted to the O-XYZ coordinate system as long as α and β are known, so that prior to inter-station data registration, the coordinate system O-X' Y 'Z' is first transformed to O-XYZ using the respective α and β angles, respectively, and then registration is performed.

For convenience of description, the two stations of data to be registered are referred to as primary data and secondary data, and the registration process is to find the registration parameters of the coordinate system used by the secondary data transformed to the coordinate system used by the primary data. Let the coordinate system of the main data be O_m-X_mY_mZ_mThe coordinate system used for the side data is O_s-X_sY_sZ_sAs shown in FIG. 3, H is a horizontal plane and γ is O_mY_mAnd O_sY_sAngle between Δ x and Δ y, between the two systemsAlong O_mX_mAnd O_mY_mTranslation vector of (1), then O_m-X_mY_mZ_mAnd O_s-X_sY_sZ_sThe transformation relationship between can be expressed as:

as can be seen from equations (1) - (5), there are six parameters to be determined in the registration process, namely (α, β, γ, Δ X, Δ Y, Δ z), and α and β are the included angles between the X-axis and the Y-axis of the ground-based lidar data acquisition and the horizontal plane, and the included angles can be measured along the X-axis and the Y-axis by using tilt sensors. Therefore, only four parameters need to be determined in the present invention.

It is assumed that the points from the ground and the points from the vegetation in the primary and secondary data have been successfully separated and two control points have been determined, with their respective coordinates in the primary data being (x)_m，1，y_m，1)、(x_m，2，y_m，2) The coordinates in the sub data are (x) respectively_s，1，y_s，1)、(x_s，2，y_s，2) Then they satisfy the following conditions

From this can findRegistration parameters

(7)

where m＝x_m，1-x_m，2，n＝x_s，1-x_s，2，Q＝y_s，2-y_s，1

Finally, the parameter Δ z, i.e. the offset of the main and sub data in the vertical direction, needs to be determined. The initial value of Δ z may be determined by the two control points and the final value may be cycled to conform the surface to the indicator δ_G(group Match Index, GMI) minimization to determine:

<math><mrow><msub><mi>δ</mi><mi>G</mi></msub><mo>=</mo><mfrac><mn>1</mn><mi>cd</mi></mfrac><munderover><mi>Σ</mi><mrow><mi>m</mi><mo>=</mo><mn>1</mn></mrow><mi>c</mi></munderover><munderover><mi>Σ</mi><mrow><mi>n</mi><mo>=</mo><mn>1</mn></mrow><mi>d</mi></munderover><mo>|</mo><mo>|</mo><msub><mi>G</mi><mrow><mi>mn</mi><mo>,</mo><mi>master</mi></mrow></msub><mo>-</mo><mi>T</mi><mrow><mo>(</mo><msub><mi>G</mi><mrow><mi>mn</mi><mo>,</mo><mi>slave</mi></mrow></msub><mo>)</mo></mrow><mo>|</mo><mo>|</mo><mo>-</mo><mo>-</mo><mo>-</mo><mrow><mo>(</mo><mn>8</mn><mo>)</mo></mrow></mrow></math>

wherein G is_mn，masterAnd G_mn，slaveAnd the Z values of the m-th row and n-th column grids after the ground points in the main data and the auxiliary data are rasterized are obtained. c. d is the number of rows and columns on the earth surface of the primary and secondary data overlapping area.

Separation of ground point cloud and vegetation point cloud

The registration parameter calculation method adopted by the invention is explained in the foregoing under the condition that the control point is known and the ground point cloud is separated from the vegetation point cloud, and the separation of the ground point and the vegetation point cloud and the identification of the control point are explained in the following.

As described above, in a forest scene, it is difficult to find an obvious control point, and the present invention uses the center position of the trunk as the control point, so that it is necessary to first find the corresponding trunk in the primary and secondary data, and this process is completed in three steps: (1) separating ground points from vegetation points using a scaling method proposed by Huang (2008); (2) respectively carrying out vertical filtering on the main data and the auxiliary data to extract a trunk point cloud and calculate the central coordinate of a trunk; (3) the triangles formed by the corresponding trunks in the primary and secondary data should have similar properties, and the corresponding trunks are identified by using this rule.

The separation of the earth surface point and the vegetation point is the key of data registration and parameter extraction. The lowest point of the point cloud in the local range may be from the surface. The invention adopts a variable-scale method proposed by Huang (2008) to remove ground points and vegetation points. Setting grids with a series of sizes and corresponding distance thresholds, and according to the sequence from large to small, analyzing the ground point and the vegetation point through loop iteration, wherein the specific process is as follows:

(1) rasterizing the data in the horizontal direction according to the set grid size;

(2) recording the lowest points falling within each grid;

(3) calculating the distance between the point in the grid and the lowest point, if the distance is smaller than a set threshold value, retaining the point, and if not, excluding the point;

(4) repeating (1) - (4) using a smaller grid size and corresponding threshold until the set minimum grid size is used.

Identification of a trunk point cloud and generation of a trunk location list

Through the steps, the earth surface point and the vegetation point are processedThe next step is the identification of the trunk point cloud and the calculation of the center location. For a forest stand with a clean forest bottom, a section of space can be found below a canopy above the ground surface, most of point clouds in the space come from a trunk, the influence of branches and vegetation under the forest is small, and the trunk can be identified by using the point clouds. Although the effect of branches and ground vegetation is small in this space, their presence cannot be ignored. This portion of noise points can be removed by their vertical distribution features as distinguished from the trunk point cloud. Usually, the trunk point cloud is continuous in the vertical direction in the space, and the branches and the surface vegetation point cloud are discontinuous, so the point cloud can be processed by adopting a vertical filtering method. The specific process is as follows, the point cloud is rasterized in the horizontal plane by using the most grid size of the previous step, and the point cloud above each grid is divided into the height d_hIf n are consecutive_cEach volume element contains n_pAnd (4) regarding the grid as including the trunk point cloud, reserving all points on the grid, and removing all points on the grid which do not meet the conditions. After the above filtering, the mean value (x) of the coordinates of the points belonging to the same trunk_c，y_c，z_c) As shown in the following formula,

wherein (x)_i，y_i，z_i) The coordinates of points belonging to the same trunk. Thus, a tree trunk position table can be generated from the main data and the auxiliary data respectively.

Identification of corresponding trunks in primary and secondary trunk location lists

At least three trees are ensured in the overlapping area of the two-station data when the station is selected in the data acquisition process, so that the corresponding trunk can be identified by using the rule that the corresponding trunk is similar to triangles formed in the primary data and the secondary data. The specific process of identification is as follows:

the length and direction of a straight line formed by any two trees in the trunk position list are firstly calculated, and then straight line pairs with similar lengths and approximate parallelism are selected from the main trunk position list and the auxiliary trunk position list. Let i (x)_m，i，y_m，i) And j (x)_m，j，y_m，j) For two trees from the main trunk location list, p (x)_s，p，y_s，p) And q (x)_s，q，y_s，q) For two trees from the secondary trunk location list, they should at least satisfy if they are the corresponding trunks

Δd_ij＝|d_m，ij-d_s，pq|＜ε_d，Δφ_ij＝|φ_m，ij-φ_s，pq|＜ε_φ(11)

Wherein

d_{m, ij} = \sqrt{{(x_{m, i} - x_{m, j})}^{2} + {(y_{m, i} - y_{m, j})}^{2}},

φ_m，ij＝tan^-1((y_m，i-y_m，j)/(x_m，i-x_m，j))

d_{s, pq} = \sqrt{{(x_{s, p} - x_{s, q})}^{2} + {(y_{s, p} - y_{s, q})}^{2}},

φ_s，pq＝tan^-1((y_s，p-y_s，q)/(x_s，p-x_s，q))

(12)

On the basis, a third tree is respectively introduced from the main trunk position list and the auxiliary trunk position list, and forms a triangle with a straight line from the same table, so that the similarity of the two triangles is judged. Let k (x)_m，k，y_m，k) And r (x)_s，r，y_s，r) From the primary and secondary stem location lists, respectively, if three trees are the corresponding stems, they should at least satisfy:

Δd_ik＝|d_m，ik-d_s，pr|＜δ_d Δφ_ik＝|φ_m，ik-φ_s，pr|＜δ_φ(13)

and

Δd_jk＝|d_m，jk-d_s，qr|＜δ_d Δφ_jk＝|φ_m，jk-φ_s，qr|＜δ_φ(14)

most corresponding trunks can be identified by the method, but in some cases, the method is exceptional, and another limiting condition can be added for the reason that the registration parameters obtained by the method can be compared with initial values of the registration parameters, the trunks i, j, k and p, q and r are taken as corresponding trunks and are substituted into the formula (7), and two sets of registration parameters gamma can be obtained_ik，Δx_ik，Δy_ikAnd gamma_jk，Δx_jk，Δy_jkIf they satisfy:

<math><mrow><mo>|</mo><mfrac><mrow><mo>(</mo><mi>Δ</mi><msub><mi>x</mi><mi>ik</mi></msub><mo>+</mo><mi>Δ</mi><msub><mi>x</mi><mi>jk</mi></msub><mo>)</mo></mrow><mn>2</mn></mfrac><mo>-</mo><mi>Δ</mi><msub><mi>x</mi><mi>initial</mi></msub><mo>|</mo><mo><</mo><msub><mi>δ</mi><mi>x</mi></msub><mo>,</mo></mrow></math>

<math><mrow><mo>|</mo><mfrac><mrow><mo>(</mo><mi>Δ</mi><msub><mi>y</mi><mi>ik</mi></msub><mo>+</mo><mi>Δ</mi><msub><mi>y</mi><mi>jk</mi></msub><mo>)</mo></mrow><mn>2</mn></mfrac><mo>-</mo><mi>Δ</mi><msub><mi>y</mi><mi>initial</mi></msub><mo>|</mo><mo><</mo><msub><mi>δ</mi><mi>y</mi></msub><mo>,</mo></mrow></math>

|γ_ik-γ_jk|＜δ_φ (15)

they are certainly the corresponding trunks. Δ x_initialAnd Δ y_initialFor the initial values of the registration parameters Δ x and Δ y, they can be obtained by simple measurement using a scale at the time of data acquisition.

For example, when the ground point cloud and the vegetation point cloud are separated, a variable-scale scheme is adopted, the recommended grid size is 4m, 2m, 1m, 0.5m and 0.25m, and the corresponding distance threshold is 3m, 1.5m, 0.7m, 0.35m and 0.2 m.

From the data used in developing the invention, the point cloud in the space 0.3m to 1.5m above the surface comes mainly from the trunk and is less affected by the branches and vegetation on the surface.

The parameter used in the vertical filtering process is d_h＝0.05m，n_c＝10；n_p＝1；

The parameter used in the process of identifying the corresponding single wood is epsilon_d＝0.2m，ε_φ＝10°，δ_d＝0.3m，δ_φ＝20°，δ_x＝1.0m，δ_y＝1.0m.

When Δ z is determined, it is iterated cyclically in 0.01m steps from-1.0 m to find the minimum value for GMI.

While the present invention has been described with reference to exemplary embodiments, it is to be understood that the invention is not limited to the disclosed exemplary embodiments. The scope of the following claims is to be accorded the broadest interpretation so as to encompass all such modifications and equivalent structures and functions.

Claims

1. The automatic registration method for the multi-station foundation laser radar data based on the single-tree position and the earth surface information is characterized in that an additional foundation laser radar attitude measuring device is used in the automatic alignment process, the number of registration parameters required to be determined for registration is reduced, and the number of control points required for registration is reduced.

2. The method of automatic registration according to claim 1, wherein the attitude of the instrument is measured along X and Y axes of the ground based lidar using tilt sensors.

3. The automatic registration method according to claim 1, wherein the automatic registration method is used for automatic registration of forest scenes, and the identification of the trunk point cloud is performed by using a vertical filtering method.

4. The automatic registration method of claim 3,

in the alignment process, the central position of the trunk is used as a control point, two stations of data to be registered are called as main data and auxiliary data, and the corresponding trunk is found in the main data and the auxiliary data through the following three steps:

5. The automatic registration method according to claim 3 or 4,

the method comprises the steps of utilizing point clouds in a section of space above the ground surface and below a canopy to identify a trunk, and utilizing the difference between the vertical distribution characteristics of the point clouds in the section of space and the point clouds of the trunk to remove branch and ground planting noise in the section of space.

6. The automatic registration method according to claim 1, wherein the automatic registration method is used for automatic registration of forest scenes, two stations of data to be registered are called primary data and secondary data, and the same trunk in the primary data and the secondary data is identified by using the similarity of triangles formed by singles.

7. The automatic registration method according to claim 2, wherein the automatic registration method is used for automatic registration of forest scenes and uses 4 registration parameters; 3 of them are calculated using the same trunk and the 4 th registration parameter is calculated using the surface.

8. The automatic registration method according to claim 7, characterized in that the laser radar data is subjected to separation of ground points and vegetation points by a variable scale method, then the positions of the singles are identified by vertical filtering, the singles with the same name are automatically identified by using the relative positions of the singles, and the singles with the same name are used as control points to obtain 3 registration parameters; and finally, determining the offset of the two stations of data in the vertical direction according to the optimal matching of the earth surface, and solving a 4 th registration parameter.

9. An automatic registration method between multi-station foundation laser radar data, which is used for automatic registration of forest scenes and is characterized in that,

in the automatic alignment process, an additional foundation laser radar attitude measurement device is used, the number of registration parameters required to be determined by registration is reduced, and therefore 4 registration parameters are used, and the number of control points required by registration is reduced;

separating the earth surface points from the vegetation points by a scale-variable method;

identifying the trunk point cloud by using a vertical filtering method;

identifying the same trunk in the main data and the auxiliary data by utilizing the similarity of triangles formed by single trees;

3 of the 4 registration parameters are calculated using the same trunk, the last of the 4 registration parameters being calculated using the surface.