CN110660027A - Laser point cloud continuous profile ground filtering method for complex terrain - Google Patents
Laser point cloud continuous profile ground filtering method for complex terrain Download PDFInfo
- Publication number
- CN110660027A CN110660027A CN201910799867.0A CN201910799867A CN110660027A CN 110660027 A CN110660027 A CN 110660027A CN 201910799867 A CN201910799867 A CN 201910799867A CN 110660027 A CN110660027 A CN 110660027A
- Authority
- CN
- China
- Prior art keywords
- grid
- point cloud
- terrain
- grids
- points
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 46
- 238000001914 filtration Methods 0.000 title claims abstract description 21
- 238000006243 chemical reaction Methods 0.000 claims abstract description 23
- 238000012545 processing Methods 0.000 claims abstract description 14
- 230000000750 progressive effect Effects 0.000 claims abstract description 7
- 238000000638 solvent extraction Methods 0.000 claims abstract description 5
- 230000000903 blocking effect Effects 0.000 claims description 5
- 230000008859 change Effects 0.000 claims description 3
- 230000011218 segmentation Effects 0.000 claims description 3
- 238000004422 calculation algorithm Methods 0.000 abstract description 5
- 230000008569 process Effects 0.000 abstract description 4
- 238000010586 diagram Methods 0.000 description 3
- 238000011161 development Methods 0.000 description 2
- 230000018109 developmental process Effects 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000009877 rendering Methods 0.000 description 2
- 238000007792 addition Methods 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000007635 classification algorithm Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012805 post-processing Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T3/00—Geometric image transformations in the plane of the image
- G06T3/06—Topological mapping of higher dimensional structures onto lower dimensional surfaces
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10028—Range image; Depth image; 3D point clouds
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20024—Filtering details
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30181—Earth observation
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Processing Or Creating Images (AREA)
Abstract
The invention discloses a ground filtering method for a laser point cloud continuous section aiming at complex terrain, which belongs to the technical field of laser scanning point cloud data processing, and firstly, a grid neighborhood height difference method is utilized to extract a terrain feature line in point cloud data; integrally partitioning the original point cloud data by taking the terrain feature lines as boundary constraint conditions; then, respectively carrying out gridding processing on the point cloud data after the block division, and extracting initial ground points; carrying out section coordinate conversion on the point cloud data in a direction vertical to the topographic feature line, and encrypting ground points at the complex terrain; carrying out progressive sectional profile curve fitting along the profile direction; and finally, calculating the distance between the point and the curve, and solving the error in the distance as a threshold value for filtering. The invention reduces the complexity of the filtering process, enables the whole filtering processing algorithm aiming at the complex terrain to be more efficient, and can obtain more accurate ground point data.
Description
Technical Field
The invention belongs to the technical field of laser scanning point cloud data processing, and particularly relates to a laser point cloud continuous profile ground filtering method for complex terrains.
Background
With the introduction of the concept of "digital earth," digitization has become a hot issue of attention today. In recent years, the demand for three-dimensional geographic spatial information is gradually increased, and the development of an effective, rapid and accurate acquisition mode and processing technology of spatial information data is a key factor for meeting the above problems. The three-dimensional laser scanning technology is an active measurement technology, can acquire three-dimensional coordinates and laser intensity of laser foot points, is limited by factors such as an operation mode, huge data volume, complex processing process and the like along with the increase of the density of development points of the technology, and still does not meet the requirements in the directions of data processing, application and the like, and the algorithm implementation aspect of data post-processing needs to be improved continuously. At present, the existing algorithm has the problems of low automation level of the processing process, various parameters and the like, still needs a large amount of manual participation, and the three-dimensional laser scanning technology is still continuously developed, so that the requirements of data processing and classification algorithms are more and more urgent with the unique advantages and wide application prospects. In mountainous terrain, the terrain has a large variety of ground features and large terrain fluctuation, and the data processing difficulty in the complex environment is increased accordingly. Therefore, it is urgently needed to develop a ground filtering algorithm which can be applied to complex terrain.
Disclosure of Invention
Aiming at the technical problems in the prior art, the invention provides the ground filtering method for the laser point cloud continuous section of the complex terrain, which has reasonable design, overcomes the defects of the prior art and has good effect.
In order to achieve the purpose, the invention adopts the following technical scheme:
a laser point cloud continuous section ground filtering method for complex terrain comprises the following steps:
step 1: traversing the lowest points of all grids, calculating the height difference between the lowest point of the grid and the lowest point of the neighborhood grid, and extracting a topographic feature line comprising a ridge line, a valley line, a top slope line and a bottom slope line in the laser point cloud data by utilizing a grid neighborhood height difference method;
step 2: integrally partitioning the original point cloud data by taking the terrain characteristic line as a constraint condition;
and step 3: respectively carrying out gridding processing on the point cloud data partitioned in the step 2, and extracting initial grid ground points;
and 4, step 4: performing two-dimensional section coordinate conversion on the initial grid ground points according to the direction of the terrain feature line, and encrypting the initial grid ground points of the complex terrain area;
and 5: taking the profile points converted from the initial grid ground points as a basic point cloud, and adopting a progressive sectional profile curve fitting method to fit accurate ground points in a sectional manner;
step 6: and calculating the distance between other point clouds in the grid and the fitting curve, calculating the median error sigma of the distance, and filtering by taking 2 sigma as a distance threshold.
Preferably, in step 2, the method specifically comprises the following steps:
step 2.1: the method comprises the following steps of classifying grids by taking grids to which terrain feature lines belong as constraints:
step 2.1.1: selecting grid GiWherein i is 1,2, … …;
step 2.1.2: judging grid GiIf there are topographic feature points in it, if grid GiMarking grid G without inner topographic feature pointsiForming a clustering grid;
step 2.1.3: find the grid G i8 neighborhood grid NGmWherein m is more than or equal to 0 and less than or equal to 9, if the neighborhood grid meets the following three conditions: (1) not labeled as a clustering grid; (2) presence of point cloud data; (3) no topographic feature points exist in the grid; mark the grid NGmIs a clustering grid and is added to a grid set CGx，1≤x≤N；
Step 2.1.4: NG with new label as clustering gridmIs a centerSearching 8 neighborhood grids until no grid meeting the requirement exists, and collecting the grids CGxMarking a serial number N;
step 2.1.5: starting with the grids which are not marked as clustering grids again, repeating the steps 2.1.1-2.1.4, and judging all grids except the grids containing the topographic feature points, wherein all grids are marked as clustering grids;
step 2.2: classifying point clouds in grids to which the terrain feature lines belong;
dividing point cloud in a grid network with the terrain feature lines into two parts by taking the terrain feature lines as boundaries; respectively searching the grid set CG to which each part of adjacent grids belongsxThe point cloud of the part in the grid is classified to the Nth grid set CGx(ii) a And traversing all grids with terrain feature lines to finish point cloud blocking.
Preferably, in step 4, the method specifically comprises the following steps:
step 4.1: converting the section coordinate constrained by the terrain characteristic line;
searching a terrain characteristic line corresponding to the point cloud data by taking the point cloud partitioned in the step 2 as a unit, and then calculating an included angle alpha between the terrain characteristic line and the positive direction of the X axis; taking (alpha +90) ° as a section direction, carrying out two-dimensional section coordinate conversion on continuous section grid point clouds, and sequentially adding the point clouds into a section conversion point cloud set P;
step 4.2: encrypting ground points of the complex terrain area;
and (3) taking the grid in which the terrain feature line is positioned as a complex terrain area with terrain change, traversing all grids with the terrain feature line, averagely dividing the grids into three small grids according to the direction of the terrain feature line, then respectively extracting the lowest point of each small grid as a ground point for encryption, carrying out two-dimensional profile coordinate conversion according to the method in the step 4.1, and adding the two-dimensional profile coordinate conversion into a profile conversion point cloud set P.
Preferably, in step 5, the method specifically comprises the following steps:
step 5.1: fitting an initial curve:
sequentially carrying out initial curve fitting on grid points in each section in the section conversion point cloud set P by using a least square curve fitting method;
step 5.2: and (3) fitting a progressive curve:
calculating the curvature of each grid point in the section fitting curves one by one, carrying out line segmentation on the grid point with the maximum curvature of each section fitting curve, and encrypting in the segmented grid according to the step 4.2; and respectively carrying out quadratic curve fitting on the two segmented lines to obtain accurate ground points.
The invention has the following beneficial technical effects:
firstly, extracting a topographic feature line by using a grid neighborhood height difference method, and then integrally partitioning original point cloud data according to the topographic feature line; and finally, filtering the section of the point cloud data in the direction vertical to the topographic feature line. The method mainly takes the terrain feature line as a constraint condition to obtain the optimal angle for showing the profile features, divides the complex terrain into simple terrains, and converts three-dimensional point cloud data into two-dimensional profile data, thereby reducing the complexity of the filtering process, accelerating the processing speed of the filtering algorithm, and also being capable of obtaining more accurate ground point data.
Drawings
Fig. 1 is a flowchart of a laser point cloud continuous section ground filtering method for complex terrain according to the present invention.
FIG. 2 is a schematic diagram illustrating a point cloud block rendering comparison according to the present invention.
Fig. 3 is a schematic diagram of a center point of a mesh and an 8-neighborhood mesh in the present invention.
FIG. 4 is a schematic diagram of the meshing of point clouds in the present invention.
FIG. 5 is a schematic cross-sectional view of a point cloud according to the present invention.
Detailed Description
The invention is described in further detail below with reference to the following figures and detailed description:
the basic idea of the invention is: the method is characterized in that the ground point cloud of the complex terrain in the mountainous area is automatically extracted by taking the skeleton line characteristics of the mountain area point cloud data as the basis and comprehensively using a profile conversion and curve fitting method and combining simplified means such as blocking, segmenting and the like.
As shown in fig. 1, a laser point cloud continuous section ground filtering method for complex terrain includes the following steps:
step 1: traversing the lowest points of all grids, calculating the height difference between the lowest point of the grid and the lowest point of the neighborhood grid, and extracting a topographic feature line comprising a ridge line, a valley line, a top slope line and a bottom slope line in the laser point cloud data by utilizing a grid neighborhood height difference method;
step 2: integrally partitioning the original point cloud data by taking the terrain characteristic line as a constraint condition; as shown in fig. 3, the method specifically includes the following steps:
step 2.1: the method comprises the following steps of classifying grids by taking grids to which terrain feature lines belong as constraints:
step 2.1.1: selecting grid GiWherein i is 1,2, … …;
step 2.1.2: judging grid GiIf there are topographic feature points in it, if grid GiMarking grid G without inner topographic feature pointsiForming a clustering grid;
step 2.1.3: find the grid G i8 neighborhood grid NGmWherein m is more than or equal to 0 and less than or equal to 9, if the neighborhood grid meets the following three conditions: (1) not labeled as a clustering grid; (2) presence of point cloud data; (3) no topographic feature points exist in the grid; mark the grid NGmIs a clustering grid and is added to a grid set CGx，1≤x≤N；
Step 2.1.4: NG with new label as clustering gridmSearching 8 neighborhood grids for the center until no grid meeting the requirement exists, and collecting the grids by CGxMarking a serial number N;
step 2.1.5: starting with the grids which are not marked as clustering grids again, repeating the steps 2.1.1-2.1.4, and judging all grids except the grids containing the topographic feature points, wherein all grids are marked as clustering grids;
step 2.2: classifying point clouds in grids to which the terrain feature lines belong;
dividing point cloud in a grid network with the terrain feature lines into two parts by taking the terrain feature lines as boundaries; respectively searching the grid set to which each partial adjacent grid belongsCGxThe point cloud of the part in the grid is classified to the Nth grid set CGx(ii) a And traversing all grids with terrain feature lines to finish point cloud blocking. A point cloud blocking rendering pair is shown in fig. 2.
And step 3: respectively carrying out gridding processing on the point cloud data partitioned in the step 2, and extracting initial grid ground points as shown in fig. 4;
and 4, step 4: performing two-dimensional section coordinate conversion on the initial grid ground points according to the direction of the terrain feature line, and encrypting the initial grid ground points of the complex terrain area; as shown in fig. 5, the method specifically includes the following steps:
step 4.1: converting the section coordinate constrained by the terrain characteristic line;
searching a terrain characteristic line corresponding to the point cloud data by taking the point cloud partitioned in the step 2 as a unit, and then calculating an included angle alpha between the terrain characteristic line and the positive direction of the X axis; taking (alpha +90) ° as a section direction, carrying out two-dimensional section coordinate conversion on continuous section grid point clouds, and sequentially adding the point clouds into a section conversion point cloud set P;
step 4.2: encrypting ground points of the complex terrain area;
and (3) taking the grid in which the terrain feature line is positioned as a complex terrain area with terrain change, traversing all grids with the terrain feature line, averagely dividing the grids into three small grids according to the direction of the terrain feature line, then respectively extracting the lowest point of each small grid as a ground point for encryption, carrying out two-dimensional profile coordinate conversion according to the method in the step 4.1, and adding the two-dimensional profile coordinate conversion into a profile conversion point cloud set P.
And 5: taking the profile points converted from the initial grid ground points as a basic point cloud, and adopting a progressive sectional profile curve fitting method to fit accurate ground points in a sectional manner; the method specifically comprises the following steps:
step 5.1: fitting an initial curve:
sequentially carrying out initial curve fitting on grid points in each section in the section conversion point cloud set P by using a least square curve fitting method;
step 5.2: and (3) fitting a progressive curve:
calculating the curvature of each grid point in the section fitting curves one by one, carrying out line segmentation on the grid point with the maximum curvature of each section fitting curve, and encrypting in the segmented grid according to the step 4.2; and respectively carrying out quadratic curve fitting on the two segmented lines to obtain accurate ground points.
It is to be understood that the above description is not intended to limit the present invention, and the present invention is not limited to the above examples, and those skilled in the art may make modifications, alterations, additions or substitutions within the spirit and scope of the present invention.
Claims (4)
1. A laser point cloud continuous section ground filtering method for complex terrain is characterized by comprising the following steps: the method comprises the following steps:
step 1: traversing the lowest points of all grids, calculating the height difference between the lowest point of the grid and the lowest point of the neighborhood grid, and extracting a topographic feature line comprising a ridge line, a valley line, a top slope line and a bottom slope line in the laser point cloud data by utilizing a grid neighborhood height difference method;
step 2: integrally partitioning the original point cloud data by taking the terrain characteristic line as a constraint condition;
and step 3: respectively carrying out gridding processing on the point cloud data partitioned in the step 2, and extracting initial grid ground points;
and 4, step 4: performing two-dimensional section coordinate conversion on the initial grid ground points according to the direction of the terrain feature line, and encrypting the initial grid ground points of the complex terrain area;
and 5: taking the profile points converted from the initial grid ground points as a basic point cloud, and adopting a progressive sectional profile curve fitting method to fit accurate ground points in a sectional manner;
step 6: and calculating the distance between other point clouds in the grid and the fitting curve, calculating the median error sigma of the distance, and filtering by taking 2 sigma as a distance threshold.
2. The laser point cloud continuous-profile ground filtering method for complex terrain according to claim 1, characterized in that: in the step 2, the method specifically comprises the following steps:
step 2.1: the method comprises the following steps of classifying grids by taking grids to which terrain feature lines belong as constraints:
step 2.1.1: selecting grid GiWherein i is 1,2, … …;
step 2.1.2: judging grid GiIf there are topographic feature points in it, if grid GiMarking grid G without inner topographic feature pointsiForming a clustering grid;
step 2.1.3: find the grid Gi8 neighborhood grid NGmWherein m is more than or equal to 0 and less than or equal to 9, if the neighborhood grid meets the following three conditions: (1) not labeled as a clustering grid; (2) presence of point cloud data; (3) no topographic feature points exist in the grid; mark the grid NGmIs a clustering grid and is added to a grid set CGx，1≤x≤N；
Step 2.1.4: NG with new label as clustering gridmSearching 8 neighborhood grids for the center until no grid meeting the requirement exists, and collecting the grids by CGxMarking a serial number N;
step 2.1.5: starting with the grids which are not marked as clustering grids again, repeating the steps 2.1.1-2.1.4, and judging all grids except the grids containing the topographic feature points, wherein all grids are marked as clustering grids;
step 2.2: classifying point clouds in grids to which the terrain feature lines belong;
dividing point cloud in a grid network with the terrain feature lines into two parts by taking the terrain feature lines as boundaries; respectively searching the grid set CG to which each part of adjacent grids belongsxThe point cloud of the part in the grid is classified to the Nth grid set CGx(ii) a And traversing all grids with terrain feature lines to finish point cloud blocking.
3. The laser point cloud continuous-profile ground filtering method for complex terrain according to claim 1, characterized in that: in step 4, the method specifically comprises the following steps:
step 4.1: converting the section coordinate constrained by the terrain characteristic line;
searching a terrain characteristic line corresponding to the point cloud data by taking the point cloud partitioned in the step 2 as a unit, and then calculating an included angle alpha between the terrain characteristic line and the positive direction of the X axis; taking (alpha +90) ° as a section direction, carrying out two-dimensional section coordinate conversion on continuous section grid point clouds, and sequentially adding the point clouds into a section conversion point cloud set P;
step 4.2: encrypting ground points of the complex terrain area;
and (3) taking the grid in which the terrain feature line is positioned as a complex terrain area with terrain change, traversing all grids with the terrain feature line, averagely dividing the grids into three small grids according to the direction of the terrain feature line, then respectively extracting the lowest point of each small grid as a ground point for encryption, carrying out two-dimensional profile coordinate conversion according to the method in the step 4.1, and adding the two-dimensional profile coordinate conversion into a profile conversion point cloud set P.
4. The laser point cloud continuous-profile ground filtering method for complex terrain according to claim 1, characterized in that: in step 5, the method specifically comprises the following steps:
step 5.1: fitting an initial curve:
sequentially carrying out initial curve fitting on grid points in each section in the section conversion point cloud set P by using a least square curve fitting method;
step 5.2: and (3) fitting a progressive curve:
calculating the curvature of each grid point in the section fitting curves one by one, carrying out line segmentation on the grid point with the maximum curvature of each section fitting curve, and encrypting in the segmented grid according to the step 4.2; and respectively carrying out quadratic curve fitting on the two segmented lines to obtain accurate ground points.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910799867.0A CN110660027B (en) | 2019-08-28 | 2019-08-28 | Laser point cloud continuous profile ground filtering method for complex terrain |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910799867.0A CN110660027B (en) | 2019-08-28 | 2019-08-28 | Laser point cloud continuous profile ground filtering method for complex terrain |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110660027A true CN110660027A (en) | 2020-01-07 |
| CN110660027B CN110660027B (en) | 2023-03-31 |
Family
ID=69036470
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910799867.0A Active CN110660027B (en) | 2019-08-28 | 2019-08-28 | Laser point cloud continuous profile ground filtering method for complex terrain |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110660027B (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112764651A (en) * | 2021-02-01 | 2021-05-07 | 飞燕航空遥感技术有限公司 | Method and system for drawing three-dimensional point cloud profile of browser |
| CN113109793A (en) * | 2021-03-22 | 2021-07-13 | 中交广州航道局有限公司 | Adaptive resolution water depth curved surface filtering method and device |
| CN114463507A (en) * | 2022-04-11 | 2022-05-10 | 国家电投集团科学技术研究院有限公司 | Road identification method and device |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102663237A (en) * | 2012-03-21 | 2012-09-12 | 武汉大学 | Point cloud data automatic filtering method based on grid segmentation and moving least square |
| CN102930509A (en) * | 2012-09-18 | 2013-02-13 | 广州建通测绘技术开发有限公司 | Intelligent filtering method for airborne laser point cloud data |
| RU2013123952A (en) * | 2013-05-24 | 2014-11-27 | Федеральное государственное бюджетное образовательное учреждение высшего профессионального образования "Сибирская государственная геодезическая академия" (ФГБОУ ВПО "СГГА") | METHOD FOR DETERMINING OBJECT CONTROL POINT COORDINATES USING A LASER LASER SCANNER |
| CN106503674A (en) * | 2016-10-27 | 2017-03-15 | 北京林业大学 | A kind of point cloud filtering method for being applied to complex topographic territory |
| CN106529469A (en) * | 2016-11-08 | 2017-03-22 | 华北水利水电大学 | Unmanned aerial vehicle airborne LiDAR point cloud filtering method based on adaptive gradient |
| CN106887020A (en) * | 2015-12-12 | 2017-06-23 | 星际空间（天津）科技发展有限公司 | A kind of road vertical and horizontal section acquisition methods based on LiDAR point cloud |
| US20170294026A1 (en) * | 2016-04-08 | 2017-10-12 | Thinkware Corporation | Method and apparatus for generating road surface, method and apparatus for processing point cloud data, computer program, and computer readable recording medium |
| US20180081035A1 (en) * | 2016-09-22 | 2018-03-22 | Beijing Greenvalley Technology Co., Ltd. | Method and device for filtering point cloud data |
| CN109299739A (en) * | 2018-09-26 | 2019-02-01 | 速度时空信息科技股份有限公司 | The method that vehicle-mounted laser point cloud is filtered based on the surface fitting of normal vector |
| CN109410265A (en) * | 2019-01-22 | 2019-03-01 | 江苏省测绘工程院 | A kind of TIN filtering innovatory algorithm based on previous DEM auxiliary |
| CN109886895A (en) * | 2019-02-28 | 2019-06-14 | 重庆交通大学 | local fitting filtering method |
-
2019
- 2019-08-28 CN CN201910799867.0A patent/CN110660027B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102663237A (en) * | 2012-03-21 | 2012-09-12 | 武汉大学 | Point cloud data automatic filtering method based on grid segmentation and moving least square |
| CN102930509A (en) * | 2012-09-18 | 2013-02-13 | 广州建通测绘技术开发有限公司 | Intelligent filtering method for airborne laser point cloud data |
| RU2013123952A (en) * | 2013-05-24 | 2014-11-27 | Федеральное государственное бюджетное образовательное учреждение высшего профессионального образования "Сибирская государственная геодезическая академия" (ФГБОУ ВПО "СГГА") | METHOD FOR DETERMINING OBJECT CONTROL POINT COORDINATES USING A LASER LASER SCANNER |
| CN106887020A (en) * | 2015-12-12 | 2017-06-23 | 星际空间（天津）科技发展有限公司 | A kind of road vertical and horizontal section acquisition methods based on LiDAR point cloud |
| US20170294026A1 (en) * | 2016-04-08 | 2017-10-12 | Thinkware Corporation | Method and apparatus for generating road surface, method and apparatus for processing point cloud data, computer program, and computer readable recording medium |
| US20180081035A1 (en) * | 2016-09-22 | 2018-03-22 | Beijing Greenvalley Technology Co., Ltd. | Method and device for filtering point cloud data |
| CN106503674A (en) * | 2016-10-27 | 2017-03-15 | 北京林业大学 | A kind of point cloud filtering method for being applied to complex topographic territory |
| CN106529469A (en) * | 2016-11-08 | 2017-03-22 | 华北水利水电大学 | Unmanned aerial vehicle airborne LiDAR point cloud filtering method based on adaptive gradient |
| CN109299739A (en) * | 2018-09-26 | 2019-02-01 | 速度时空信息科技股份有限公司 | The method that vehicle-mounted laser point cloud is filtered based on the surface fitting of normal vector |
| CN109410265A (en) * | 2019-01-22 | 2019-03-01 | 江苏省测绘工程院 | A kind of TIN filtering innovatory algorithm based on previous DEM auxiliary |
| CN109886895A (en) * | 2019-02-28 | 2019-06-14 | 重庆交通大学 | local fitting filtering method |
Non-Patent Citations (6)
| Title |
|---|
| ABDUL NURUNNABI等: "Robust Locally Weighted Regression Techniques for Ground Surface Points Filtering in Mobile Laser Scanning Three Dimensional Point Cloud Data", 《IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING》 * |
| MAOYI TIAN等: "Ground point filtering method of vehicle-borne laser point cloud in urban street", 《COMPUTER MODELLING & NEW TECHNOLOGIES》 * |
| 丁少鹏等: "一种顾及地形的点云自适应坡度滤波方法", 《遥感信息》 * |
| 卢秀山等: "根据地形坡度旋转的车载激光点云形态学滤波", 《遥感信息》 * |
| 李乐林等: "一种顾及地形复杂度的LiDAR点云多尺度滤波方法", 《测绘科学》 * |
| 李鹏飞等: "车载移动测量系统在路面平整度评价中的应用", 《北京测绘》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112764651A (en) * | 2021-02-01 | 2021-05-07 | 飞燕航空遥感技术有限公司 | Method and system for drawing three-dimensional point cloud profile of browser |
| CN113109793A (en) * | 2021-03-22 | 2021-07-13 | 中交广州航道局有限公司 | Adaptive resolution water depth curved surface filtering method and device |
| CN113109793B (en) * | 2021-03-22 | 2024-10-01 | 中交广州航道局有限公司 | Adaptive resolution depth of water curved surface filtering method and device |
| CN114463507A (en) * | 2022-04-11 | 2022-05-10 | 国家电投集团科学技术研究院有限公司 | Road identification method and device |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110660027B (en) | 2023-03-31 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110660027B (en) | Laser point cloud continuous profile ground filtering method for complex terrain | |
| CN116310192B (en) | Urban building three-dimensional model monomer reconstruction method based on point cloud | |
| CN110176018B (en) | Pattern spot merging method for keeping structural ground feature contour characteristics | |
| CN106022259B (en) | A kind of mountain road extracting method based on laser point cloud three-dimensional feature descriptive model | |
| CN108257142B (en) | Method for extracting slope unit in DEM (digital elevation model) | |
| CN114332366B (en) | Digital urban single house point cloud elevation 3D feature extraction method | |
| CN109919944B (en) | Combined superpixel graph-cut optimization method for complex scene building change detection | |
| CN111340723B (en) | Terrain-adaptive airborne LiDAR point cloud regularization thin plate spline interpolation filtering method | |
| CN104574512A (en) | Multi-scale DEM (digital elevation model) construction method considering topographical semantic information | |
| CN114926602B (en) | Building singleization method and system based on three-dimensional point cloud | |
| CN104866840A (en) | Method for recognizing overhead power transmission line from airborne laser point cloud data | |
| CN114862715A (en) | TIN (triangulated irregular network) progressive encryption denoising method fusing terrain feature semantic information | |
| CN116468873A (en) | Building model reconstruction method based on airborne TomoSAR point cloud | |
| CN112184725A (en) | Structured light strip center extraction method for asphalt pavement image | |
| CN115272866A (en) | Mountain landform form classification method based on positive and negative landforms and application | |
| CN104951752A (en) | Method for extracting houses from airborne laser point cloud data | |
| CN115544789B (en) | River valley plain extraction method based on digital elevation model and gradient cost distance | |
| Costantino et al. | Features and ground automatic extraction from airborne LiDAR data | |
| CN117274517A (en) | Method for generating light-weight body frame model of urban building based on three-dimensional model | |
| CN105321168B (en) | A kind of method of the automatic compilation mountain area raised path through fields in three-dimensional laser point cloud | |
| CN117671167B (en) | Heuristic DEM (digital elevation model) comprehensive method based on mountain shadow analysis | |
| Yuan et al. | DEM generation from airborne LIDAR data | |
| Zou et al. | An adaptive strips method for extraction buildings from light detection and ranging data | |
| Liu et al. | Terrain-adaptive ground filtering of airborne LIDAR data based on saliency-aware thin plate spline | |
| CN117171856B (en) | Highway railway digital information modeling method based on handheld Lidar |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |