CN114812503A - Cliff point cloud extraction method based on airborne laser scanning - Google Patents
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Abstract
The invention relates to the technical field of engineering surveying and mapping, in particular to a cliff point cloud extraction method based on airborne laser scanning. Scanning the scarp area along a designed route by adopting airborne laser scanning equipment; resolving the scanned data to obtain a point cloud LAS0 of an engineering coordinate system; performing straight line fitting on the upper edge line of the cliff to obtain a line segment AB, wherein the line of construction is vertical to AB; based on the normal of the line segment AB, carrying out rigid coordinate transformation on the point cloud LAS0 to obtain an initial point cloud LAS1 of the cliff surface; and (3) classifying ground points and non-ground points by using point cloud processing software, extracting a ground point cloud LAS2 from an initial point cloud LAS1 of the cliff surface, and generating a contour line of the cliff surface. The method eliminates the reverse slope form of the point cloud of the cliff, obviously reduces the slope, is convenient for extracting ground points, and finally obtains the point cloud of the ground points.
Description
Technical Field
The invention relates to the technical field of engineering surveying and mapping, in particular to a cliff point cloud extraction method based on airborne laser scanning.
Background
Airborne laser scanning integrates high-precision position and Pose (POS) and a laser scanner, has high precision, point cloud density can reach 50-100/square meter, and multiple echoes can filter vegetation influence, and is applied to the fields of topographic mapping, forest general survey and the like. The ground surface information of the cliff is necessary for geological disaster evaluation and treatment, in the prior art, multiple projection intersection points of the cliff reverse slope are not considered by airborne Lidar point cloud filtering software, the slope is negative, and when ground points are extracted by point cloud filtering, the ground surface information of the cliff region rock wall is ignored frequently, so that the ground surface information is lost.
As is apparent from fig. 7, for the cliff reverse slope, the projection vertical line and the cliff reverse slope terrain line have a plurality of projection intersection points (O, P, Q), which are different from the general terrain having only one projection intersection point, and the digital elevation model DEM cannot express the special terrain because the projection intersection points are mutually overlapped.
Although the ground-based Lidar can also scan the reverse slope of the cliff, the scanning view angle is difficult to cover the whole cliff area.
Therefore, an effective filtering method needs to be researched to efficiently extract rock wall points in the cliff area, so as to provide a data base for cliff contour drawing.
Disclosure of Invention
The invention aims to provide a cliff point cloud extraction method based on airborne laser scanning, which aims at overcoming the defects of the prior art, eliminates the reverse slope form of the cliff point cloud, obviously reduces the slope, is convenient for ground point extraction, and finally obtains the ground point cloud.
The invention discloses a cliff point cloud extraction method based on airborne laser scanning, which comprises the following steps of:
scanning the scarp area along a designed route by adopting airborne laser scanning equipment;
resolving the scanned data to obtain a point cloud LAS0 of an engineering coordinate system;
performing straight line fitting on the upper edge line of the cliff to obtain a line segment AB, wherein the line of construction is vertical to AB;
based on the normal of the line segment AB, carrying out rigid coordinate transformation on the point cloud LAS0 to obtain an initial point cloud LAS1 of the cliff surface;
and (3) classifying ground points and non-ground points by using point cloud processing software, extracting a ground point cloud LAS2 from an initial point cloud LAS1 of the cliff surface, and generating a contour line of the cliff surface.
Preferably, the rigid coordinate transformation of the point cloud LAS0 based on the normal of the line segment AB includes:
and performing primary rigid transformation on the point cloud LAS0 of the engineering coordinate system to rotate the ordinate axis of the point cloud LAS0 to the normal direction of the line segment AB.
Preferably, after the first rigid transformation, a second rigid transformation is further performed, including:
and taking the elevation coordinate after the first rigid transformation as a new x-axis coordinate, converting the x-axis coordinate after the first rigid transformation into a new y-axis coordinate, and converting the y-axis coordinate after the first rigid transformation into a new elevation coordinate to obtain an initial point cloud LAS1 of the steep cliff surface under a new coordinate system.
Preferably, the classifying the ground points and the non-ground points by the point cloud processing software includes:
selecting a plurality of seed points, and generating a triangulation network through the selected seed points;
surveying the distance and the gradient of each seed point and the triangular net;
and (4) performing layer-by-layer iteration by repeating the steps, and classifying the ground points and the non-ground points.
Preferably, after the ground point cloud LAS2 is extracted, the method further includes:
constructing an irregular triangulation network according to the ground point cloud LAS 2;
and tracking the contour line to generate the contour line of the cliff surface.
Preferably, when the airborne laser scanning equipment is used for scanning, the designed route surface is parallel to the scarp surface.
Preferably, the distance between the line surface and the cliff surface is 80-120 m.
Preferably, when the onboard laser scanning device scans, the scanning angle is not less than 60 °.
Preferably, when the airborne laser scanning device scans, the overlapping degree of the flight paths is greater than 35%.
Preferably, the data obtained by scanning is resolved by a high-precision POS data resolving and/or laser point cloud data resolving method.
The invention has the beneficial effects that:
1. the method comprises the steps of performing straight line fitting on an upper edge line of the cliff to obtain a line segment AB, performing rigid coordinate transformation on an engineering coordinate system point cloud LAS0 scanned by airborne laser based on a normal line of the line segment AB, classifying ground points and non-ground points by adopting point cloud processing software, and extracting a ground point cloud LAS2 to generate a contour line of the cliff surface to finally obtain the ground points. The method eliminates the reverse slope form of the cliff point cloud, is convenient for extracting ground points, and further generates the contour line (depth) of the cliff surface.
2. Based on the normal of the line segment AB, performing first rigid transformation on the point cloud LAS0 to enable the ordinate axis of the point cloud LAS0 to rotate to the normal direction of the line segment AB, after the first rigid transformation, taking the elevation coordinate after the first rigid transformation as a new x-axis coordinate, converting the x-axis coordinate after the first rigid transformation into a new y-axis coordinate, converting the y-axis coordinate after the first rigid transformation into a new elevation coordinate, and realizing the second rigid transformation, so that the initial point cloud LAS1 of the cliff surface under a new coordinate system can be obtained, and a foundation is provided for subsequent extraction of ground point cloud.
3. The point cloud processing software selects a plurality of seed points and generates a triangulation network through the selected seed points; surveying the distance and the gradient between each seed point and the triangular net; by repeating the steps, the layer-by-layer iteration is carried out, the ground points and the non-ground points can be classified, and then the ground point cloud LAS2 is extracted from the initial point cloud LAS1 of the cliff surface.
Drawings
Fig. 1 is a schematic flow chart of a cliff point cloud extraction method based on airborne laser scanning according to the invention;
FIG. 2 is a schematic normal to line segment AB of the present invention;
FIG. 3 is a schematic diagram of rigid transformation of a point P on an original point cloud;
FIG. 4 is a schematic diagram of a point cloud LAS0 of an engineering coordinate system according to the present invention;
FIG. 5 is a schematic view of an initial point cloud LAS1 of the cliff face of the present invention;
FIG. 6 is a schematic contour line of the cliff face of the present invention;
FIG. 7 is a schematic view of a reverse slope topographical line of the present invention.
Detailed Description
In order to make the technical problems, technical solutions and advantageous effects to be solved by the present application clearer, the present application is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
In order to solve the technical problems, the invention adopts the technical scheme that: the method comprises the steps of performing rigid coordinate transformation on a point cloud of a cliff region, eliminating a reverse slope form to obtain an initial point cloud LAS1 of a cliff surface, and then extracting ground points from the LAS1 by using point cloud processing software Terrasolid.
Example one
In order to implement the above technical solution, the present embodiment provides a cliff point cloud extraction method based on airborne laser scanning, and fig. 1 shows a schematic flow chart of the cliff point cloud extraction method based on airborne laser scanning according to the preferred embodiment of the present application, and for convenience of description, only the parts related to the present embodiment are shown, and the details are as follows:
s1, scanning the scarp area along the designed route by using airborne laser scanning equipment;
s2, resolving the scanned data to obtain a point cloud LAS0 of the engineering coordinate system;
s3, performing straight line fitting on the upper edge line of the cliff to obtain a line segment AB, wherein the line segment AB is perpendicular to the line segment AB;
s4, rigid coordinate transformation is carried out on the point cloud LAS0 based on the normal line of the line segment AB to obtain an initial point cloud LAS1 of the cliff surface;
and S5, classifying ground points and non-ground points by using point cloud processing software, extracting a ground point cloud LAS2 from the initial point cloud LAS1 of the cliff surface, and generating a contour line of the cliff surface.
For S1, the airborne laser scanning equipment is set on the unmanned aerial vehicle, and the unmanned aerial vehicle is remotely controlled to scan according to the designed air route. When scanning, the designed route surface is parallel to the cliff surface, the distance between the route surface and the cliff surface is about 100m, the scanning angle is not less than 60 degrees, and the route overlapping degree is more than 35 percent.
For S2, after scanning and acquiring data, the embodiment adopts precision POS data solution and laser point cloud data solution algorithm to analyze the acquired data to obtain the engineering coordinate system (XYH) 0 The point cloud LAS 0. Engineering coordinate system (XYH) 0 And a coordinate system specified for the project engineering, wherein the coordinate system comprises an x axis, a y axis and an h axis, and the directions of the axes belong to the technical common knowledge in the field and are not described in detail herein.
For S3, the top edge line of the entire scarp is drawn based on the existing data and a straight line fitting is performed on the top edge line to obtain segment AB, which is perpendicular to AB. The normal direction of AB is shown in fig. 2.
For S4, the rigid coordinate transformation performed on the point cloud LAS0 (see fig. 4) includes a first rigid transformation and a second rigid transformation.
Wherein the first rigid transformation process is as follows:
performing a first rigid transformation on the point cloud LAS0, rotating the ordinate axis by an angle θ to the normal direction of the line segment AB, and overlapping the normal direction (see FIG. 2) to obtain a coordinate system (XYH) 1 。
Wherein the content of the first and second substances,
After the first rigid transformation, performing a second rigid transformation, comprising:
and taking the elevation coordinate after the first rigid transformation as a new x-axis coordinate, converting the x-axis coordinate after the first rigid transformation into a new y-axis coordinate, and converting the y-axis coordinate after the first rigid transformation into a new elevation coordinate to obtain an initial point cloud LAS1 of the steep cliff surface under a new coordinate system.
As shown in fig. 3, the first rigid transformation and the second rigid transformation are illustrated by taking p as any point on the original point cloud. p 'refers to the corresponding point of p points after the first rigid transformation, and p' refers to the corresponding point of p points after the second rigid transformation. Wherein the coordinates of the point p are (x, y, h), the coordinates of the point p 'are (x', y ', h'), and the coordinates of the point p 'are (x', y ', h'). p is rotated by theta around the origin to obtain a point p ', the corresponding x and y coordinates are also rotated by theta into x' and y ', and the elevation h' is equal to h.
And performing a second rigid transformation on p ', converting the coordinate value of the elevation h' into an x axis (x) after the second rigid transformation, converting the coordinate value of the x 'into a y axis (y) after the second rigid transformation, converting the coordinate value of the y' into a z axis (h) after the second rigid transformation, and obtaining a coordinate system (XYH) 2 The point cloud LAS1 (i.e. the initial point cloud of the scarp face) of (a) is shown in fig. 5.
Wherein the content of the first and second substances,
after the second rigid transformation, the value of x "is equal to the original elevation h, x" is used as a horizontal coordinate, the projection direction is consistent with the normal direction (as shown in fig. 7), the reverse slope form of the cliff is eliminated, the slope degree of the cliff surface is obviously reduced, the initial point cloud LAS1 of the cliff surface is obtained, and the form of the cliff can be visually reflected. And because the reverse slope form is eliminated, the gradient of the depth coordinate surface is obviously reduced, and the efficient extraction of the rock wall earth surface information of the cliff area is realized.
For S5, point cloud processing software Terrasolid is used, based on an irregular triangulation network TIN encryption filtering algorithm, a sparse TIN is generated through a plurality of lower seed points, then the distance and the gradient of each point and the TIN are considered, iterative encryption is carried out layer by layer, ground points and non-ground points (vegetation) are classified, and a ground point cloud LAS2 is extracted through LAS 1.
From the point cloud LAS2, an irregular triangulation network TIN is then constructed, followed by contour tracing, which may generate contours (depths), as shown in fig. 6.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application.
Claims (10)
1. A cliff point cloud extraction method based on airborne laser scanning is characterized by comprising the following steps:
scanning the scarp area along a designed route by adopting airborne laser scanning equipment;
resolving the scanned data to obtain a point cloud LAS0 of an engineering coordinate system;
performing straight line fitting on the upper edge line of the cliff to obtain a line segment AB, wherein the line of construction is vertical to AB;
based on the normal of the line segment AB, carrying out rigid coordinate transformation on the point cloud LAS0 to obtain an initial point cloud LAS1 of the cliff surface;
and (3) classifying ground points and non-ground points by using point cloud processing software, extracting a ground point cloud LAS2 from an initial point cloud LAS1 of the cliff surface, and generating a contour line of the cliff surface.
2. The cliff point cloud extraction method based on airborne laser scanning as claimed in claim 1, wherein the rigid coordinate transformation of the point cloud LAS0 based on the normal of the line segment AB comprises:
and performing primary rigid transformation on the point cloud LAS0 of the engineering coordinate system to rotate the ordinate axis of the point cloud LAS0 to the normal direction of the line segment AB.
3. The cliff point cloud extraction method based on airborne laser scanning as claimed in claim 2, wherein after the first rigid transformation, a second rigid transformation is further performed, comprising:
and taking the elevation coordinate after the first rigid transformation as a new x-axis coordinate, converting the x-axis coordinate after the first rigid transformation into a new y-axis coordinate, and converting the y-axis coordinate after the first rigid transformation into a new elevation coordinate to obtain an initial point cloud LAS1 of the steep cliff surface under a new coordinate system.
4. The cliff point cloud extraction method based on airborne laser scanning of claim 1, wherein the classification of ground points and non-ground points with point cloud processing software comprises:
selecting a plurality of seed points, and generating a triangulation network through the selected seed points;
surveying the distance and the gradient of each seed point and the triangular net;
and (4) performing layer-by-layer iteration by repeating the steps, and classifying the ground points and the non-ground points.
5. The cliff point cloud extraction method based on airborne laser scanning as claimed in claim 1, wherein after extracting the ground point cloud LAS2, the method further comprises:
constructing an irregular triangulation network according to the ground point cloud LAS 2;
and tracking the contour line to generate the contour line of the cliff surface.
6. The cliff point cloud extraction method based on airborne laser scanning as claimed in claim 1, wherein the designed route surface is parallel to the cliff surface when scanning is performed by using an airborne laser scanning device.
7. The cliff point cloud extraction method based on airborne laser scanning of claim 6, wherein the distance between the route surface and the cliff surface is 80-120 m.
8. The cliff point cloud extraction method based on airborne laser scanning as claimed in claim 6, wherein the airborne laser scanning device scans at a scanning angle of not less than 60 °.
9. The cliff point cloud extraction method based on airborne laser scanning as claimed in claim 6, wherein the lane overlapping degree is greater than 35% when the airborne laser scanning device scans.
10. The cliff point cloud extraction method based on airborne laser scanning as claimed in claim 1, wherein the data obtained by scanning is solved by adopting a high-precision POS data solving method and a laser point cloud data solving method.
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Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2013072705A (en) * | 2011-09-27 | 2013-04-22 | Kumagai Gumi Co Ltd | Detection method of discontinuity surface on rock bed slope and detection device of the same |
| CN106123845A (en) * | 2015-05-07 | 2016-11-16 | 国家测绘地理信息局第六地形测量队 | Slope displacement monitoring method based on three-dimensional laser scanning technique |
| CN108107444A (en) * | 2017-12-28 | 2018-06-01 | 国网黑龙江省电力有限公司检修公司 | Substation's method for recognizing impurities based on laser data |
| CN110298103A (en) * | 2019-06-25 | 2019-10-01 | 中国电建集团成都勘测设计研究院有限公司 | The steep Dangerous Rock Body investigation method of height based on unmanned aerial vehicle onboard three-dimensional laser scanner |
| CN111639626A (en) * | 2020-06-11 | 2020-09-08 | 深圳市泰沃德自动化技术有限公司 | Three-dimensional point cloud data processing method and device, computer equipment and storage medium |
| US20210074012A1 (en) * | 2018-02-23 | 2021-03-11 | Tsinghua University | Dynamic scene three-dimensional reconstruction method, apparatus and system, server, and medium |
| CN112556666A (en) * | 2020-11-24 | 2021-03-26 | 湖北省水利水电规划勘测设计院 | Linear lofting method for complex terrain situation |
| US20210248814A1 (en) * | 2020-02-07 | 2021-08-12 | Ke.Com (Beijing)Technology Co., Ltd | Systems and methods for constructing 3d panaroma model |
| CN113345009A (en) * | 2021-05-31 | 2021-09-03 | 湖南大学 | Unmanned aerial vehicle dynamic obstacle detection method based on laser odometer |
| CN113837892A (en) * | 2021-08-25 | 2021-12-24 | 湖北省水利水电规划勘测设计院 | Slope farmland dividing method based on 3S technology |
| CN114266987A (en) * | 2022-03-03 | 2022-04-01 | 水利部长江勘测技术研究所 | Intelligent identification method for high slope dangerous rock mass of unmanned aerial vehicle |
-
2022
- 2022-04-14 CN CN202210393225.2A patent/CN114812503B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2013072705A (en) * | 2011-09-27 | 2013-04-22 | Kumagai Gumi Co Ltd | Detection method of discontinuity surface on rock bed slope and detection device of the same |
| CN106123845A (en) * | 2015-05-07 | 2016-11-16 | 国家测绘地理信息局第六地形测量队 | Slope displacement monitoring method based on three-dimensional laser scanning technique |
| CN108107444A (en) * | 2017-12-28 | 2018-06-01 | 国网黑龙江省电力有限公司检修公司 | Substation's method for recognizing impurities based on laser data |
| US20210074012A1 (en) * | 2018-02-23 | 2021-03-11 | Tsinghua University | Dynamic scene three-dimensional reconstruction method, apparatus and system, server, and medium |
| CN110298103A (en) * | 2019-06-25 | 2019-10-01 | 中国电建集团成都勘测设计研究院有限公司 | The steep Dangerous Rock Body investigation method of height based on unmanned aerial vehicle onboard three-dimensional laser scanner |
| US20210248814A1 (en) * | 2020-02-07 | 2021-08-12 | Ke.Com (Beijing)Technology Co., Ltd | Systems and methods for constructing 3d panaroma model |
| CN111639626A (en) * | 2020-06-11 | 2020-09-08 | 深圳市泰沃德自动化技术有限公司 | Three-dimensional point cloud data processing method and device, computer equipment and storage medium |
| CN112556666A (en) * | 2020-11-24 | 2021-03-26 | 湖北省水利水电规划勘测设计院 | Linear lofting method for complex terrain situation |
| CN113345009A (en) * | 2021-05-31 | 2021-09-03 | 湖南大学 | Unmanned aerial vehicle dynamic obstacle detection method based on laser odometer |
| CN113837892A (en) * | 2021-08-25 | 2021-12-24 | 湖北省水利水电规划勘测设计院 | Slope farmland dividing method based on 3S technology |
| CN114266987A (en) * | 2022-03-03 | 2022-04-01 | 水利部长江勘测技术研究所 | Intelligent identification method for high slope dangerous rock mass of unmanned aerial vehicle |
Non-Patent Citations (2)
| Title |
|---|
| 朱依民等: "基于 PTD 和改进曲面拟合的高山区水电工程机载激光雷达点云滤波方法", 《水利水电科技进展》, vol. 41, no. 01, 31 January 2021 (2021-01-31) * |
| 陆峰;王俊奇;: "Fisher模型在岩体裂隙面模拟中的应用", 中国水利水电科学研究院学报, no. 04, 15 December 2010 (2010-12-15) * |
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