CN109697710A - A kind of filtering processing algorithm based on layering grid - Google Patents
A kind of filtering processing algorithm based on layering grid Download PDFInfo
- Publication number
- CN109697710A CN109697710A CN201710987958.8A CN201710987958A CN109697710A CN 109697710 A CN109697710 A CN 109697710A CN 201710987958 A CN201710987958 A CN 201710987958A CN 109697710 A CN109697710 A CN 109697710A
- Authority
- CN
- China
- Prior art keywords
- point
- elevation
- grid
- data
- laser
- 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.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
-
- 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
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30248—Vehicle exterior or interior
- G06T2207/30252—Vehicle exterior; Vicinity of vehicle
Landscapes
- Engineering & Computer Science (AREA)
- Quality & Reliability (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Length Measuring Devices By Optical Means (AREA)
- Optical Radar Systems And Details Thereof (AREA)
Abstract
Vehicle-borne Laser Scanning system provides a kind of quick, efficient, high-precision technological means for the acquisition of three-dimensional spatial information.It is different according to the carrying platform of sensor, laser load measuring system be can be retouched into and airborne lidar system and Vehicle-borne Laser Scanning system are divided into.Devise a kind of filtering method based on graded mesh.This method according to the elevation at IMU central point in Vehicle-borne Laser Scanning system (inertial navigation center), filters out the part non-ground points in the laser data of corresponding moment acquisition first；Then grid is divided to remaining laser point, and is layered based on elevation；Last point cloud level difference and layering feature according in grid is precisely separated out ground point and non-ground points.
Description
Technical field
It is a kind of based on layering grid filtering processing algorithm be to be related to computerized algorithm field.
Background technique
Laser scanning measurement technology provides a kind of quick, efficient, high-precision technology for the acquisition of three-dimensional spatial information
Means.It is different according to the carrying platform of sensor, laser load measuring system be can be retouched into and airborne lidar system and vehicle are divided into
Carry laser scanning system.Airborne lidar system is mainly for the production of digital elevation model (DEM) and digital surface model
(DSM) development of its data processing technique is more mature[1-2].And Vehicle-borne Laser Scanning system lays particular emphasis on acquisition City Building and stands
Face information, the characters of ground object and type complexity of the big acquisition of data information amount therefore, for Vehicle-borne Laser Scanning Data Post
The research that the research of technology more lags the especially filtering of Vehicle-borne Laser Scanning data, the classification of typical feature etc. restricts
The application and development of Vehicle-borne Laser Scanning measuring system[3]。
Many scholars domestic at present study the processing of Vehicle-borne Laser Scanning data, and Shi Wen is medium to be first proposed
Vehicle-borne Laser Scanning range image segmentation method based on projection dot density[4].Lu Xiushan etc. is close for the distribution of laser point cloud
Degree extracts the laggard row information of point cloud data gridization[5].Laser scanning point is projected to two-dimensional horizontal grid by Wu Fenfang etc.
In, and data point classification is determined according to the maximum value of projection point height[6].Tan is beautifully adorned equal using based on grid and region segmentation
The method combined carries out entity division, then by calculating atural object spatial form feature (outer bounding box, physical height etc.), to reality
Body is classified[7].Yang Yang etc. proposes that a kind of vehicle-mounted laser point cloud filtering method based on scan line will be scanned using gradient difference
Line is divided into different line segment aggregates, assigns respective attributes to each line segment aggregate to classify.
Summary of the invention
The 1.1 filtering algorithm processes based on graded mesh
1) data prediction: by the height for passing through guiding center position at the elevation of laser point collected GPS moment corresponding with the point
Journey compares, and elevation filter out for the first time as non-ground points greater than the laser point that the corresponding moment passes through guiding center elevation.
2) on the basis of filtering out for the first time, laser point data is subjected to grid piecemeal by horizontal coordinate, by elevation coordinate
Carry out data hierarchy.
3) elevation of laser point cloud data in grid and set threshold value are compared again；If the elevation of point cloud data
Less than threshold value, then the point is ground point；If the elevation of point cloud data is greater than threshold value, using the point as point to be located.
4) finally according to the layering feature of grid points cloud where point to be located, accurately judge the point whether be ground point or
Non-ground points.
Water of the Vehicle-borne Laser Scanning data filtering methods according to measured zone size, by pretreated data based on cloud
Flat coordinate carries out the horizontal piecemeal of regular grid, carries out data hierarchy based on height value[11].It extracts in measured zone and owns first
The horizontal coordinate of data and the maximum value X of elevationmax, Ymax, HmaxWith minimum value Xmin, Ymin, Hmin.Then according to point cloud data
X, Y, H value are the side length of a grid by dx and dy, and dh is the height of a grid, by the entire point cloud data for surveying area be divided into m ×
N grid, wherein each grid includes l layer data.
Position where each point to be located:
The structural body of each ground point undetermined is defined as follows in algorithm:
struct points
{
public:
double X;The abscissa X of // definition data point；
double Y;The ordinate Y of // definition data point；
double H;The elevation coordinate H of // definition data point；
int row;// define line number of the point to be located in grid；
int column;// define row number of the point to be located in grid；
int layer;// define level number of the point to be located in grid；
points *next;The pointer of next point is directed toward in // definition；
Points()
{
X=0;Y=0;H=0;row=column=layer=0;next=null;// to the construction of variable progress assignment
Function；
}
}
In point cloud data height difference treated point to be located, only least a portion of ground point is not separated.These ground points
It is substantially the ground point that tree crown covers, or is not above onboard system and passes through the canopy shape object of guiding center point height coordinate and cover
Ground point.The grid layered characteristic of these points is identical.Assuming that the maximum number of plies is n (n > 2) in a certain grid points cloud, then the 1st layer
Data are ground point, and do not have point cloud data distribution in the 2nd layer to i-th layer (2≤i < n), at m (i < m≤n)
Layer has data distribution.According to this feature, selection meets the lattice of features above in point to be located.
Claims (5)
1. a kind of its characteristic of filtering processing algorithm pretreatment based on layering grid.
2. data prediction: by the height for passing through guiding center position at the elevation of laser point collected GPS moment corresponding with the point
Journey compares, and elevation filter out for the first time as non-ground points greater than the laser point that the corresponding moment passes through guiding center elevation.
3. laser point data is carried out grid piecemeal by horizontal coordinate, is carried out by elevation coordinate on the basis of filtering out for the first time
Data hierarchy.
4. the elevation of laser point cloud data in grid and set threshold value are compared again；If the elevation of point cloud data is less than
Threshold value, then the point is ground point；If the elevation of point cloud data is greater than threshold value, using the point as point to be located.
5. finally according to the layering feature of grid points cloud where point to be located, accurately judge whether the point is ground point or non-
Millet cake.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710987958.8A CN109697710A (en) | 2017-10-21 | 2017-10-21 | A kind of filtering processing algorithm based on layering grid |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710987958.8A CN109697710A (en) | 2017-10-21 | 2017-10-21 | A kind of filtering processing algorithm based on layering grid |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109697710A true CN109697710A (en) | 2019-04-30 |
Family
ID=66225582
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710987958.8A Pending CN109697710A (en) | 2017-10-21 | 2017-10-21 | A kind of filtering processing algorithm based on layering grid |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109697710A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110349092A (en) * | 2019-05-27 | 2019-10-18 | 香港理工大学深圳研究院 | A kind of cloud filtering method and equipment |
| CN114518108A (en) * | 2020-11-18 | 2022-05-20 | 郑州宇通客车股份有限公司 | Positioning map construction method and device |
| CN114820747A (en) * | 2022-06-28 | 2022-07-29 | 安徽继远软件有限公司 | Air route planning method, device, equipment and medium based on point cloud and live-action model |
-
2017
- 2017-10-21 CN CN201710987958.8A patent/CN109697710A/en active Pending
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110349092A (en) * | 2019-05-27 | 2019-10-18 | 香港理工大学深圳研究院 | A kind of cloud filtering method and equipment |
| CN114518108A (en) * | 2020-11-18 | 2022-05-20 | 郑州宇通客车股份有限公司 | Positioning map construction method and device |
| CN114518108B (en) * | 2020-11-18 | 2023-09-08 | 宇通客车股份有限公司 | Positioning map construction method and device |
| CN114820747A (en) * | 2022-06-28 | 2022-07-29 | 安徽继远软件有限公司 | Air route planning method, device, equipment and medium based on point cloud and live-action model |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106199557B (en) | A kind of airborne laser radar data vegetation extracting method | |
| CN105184250B (en) | A kind of terrain classification method of electric power corridor airborne LiDAR point cloud data | |
| CN105160309B (en) | Three lanes detection method based on morphological image segmentation and region growing | |
| CN105242279B (en) | A kind of slip mass change detecting method based on laser radar technique | |
| Ramiya et al. | Segmentation based building detection approach from LiDAR point cloud | |
| CN107273902B (en) | A method of pylon point cloud is automatically extracted from on-board LiDAR data | |
| CN110490888B (en) | Highway geometric feature vectorization extraction method based on airborne laser point cloud | |
| CN106033611B (en) | A kind of mountain top point extracting method in dem data | |
| CN104463164B (en) | It is a kind of based on umbrella frame method and crown height than trees canopy structure information extracting method | |
| CN103020637B (en) | A kind of buildings end face point cloud data segmentation method based on K-plane algorithm | |
| CN107871126A (en) | Model recognizing method and system based on deep-neural-network | |
| CN117836667A (en) | Static and non-static object point cloud identification method based on road side sensing unit | |
| CN105513127A (en) | Rod-shaped object regular three-dimensional modeling method and rod-shaped object regular three-dimensional modeling system based on density peak clustering | |
| CN112132840B (en) | Vehicle-mounted street tree point cloud classification and feature information extraction method | |
| CN104376595A (en) | Method for generating three-dimensional roads on basis of airborne LiDAR (light detection and ranging) and GIS (geographic information system) cooperation | |
| CN108288059A (en) | A kind of building waste monitoring method based on high-definition remote sensing technology | |
| CN115605777A (en) | Dynamic target point cloud rapid identification and point cloud segmentation method based on road side sensing unit | |
| CN109697710A (en) | A kind of filtering processing algorithm based on layering grid | |
| CN110222586A (en) | A kind of calculating of depth of building and the method for building up of urban morphology parameter database | |
| CN106971156A (en) | Rare earth mining area remote sensing information extraction method based on object-oriented classification | |
| CN107451982A (en) | A kind of high canopy density standing forest tree crown area acquisition methods based on unmanned plane image | |
| CN108061901A (en) | The method that 3D electric power line models are rebuild based on airborne laser radar point cloud data | |
| CN107798657B (en) | A kind of vehicle-mounted laser point cloud filtering method based on circular cylindrical coordinate | |
| JP5283100B1 (en) | Solar power generation potential evaluation device and solar power generation potential evaluation program | |
| CN109410265B (en) | A kind of TIN filtering innovatory algorithm based on previous DEM auxiliary |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20190430 |
|
| WD01 | Invention patent application deemed withdrawn after publication |