CN106897686A - A kind of airborne LIDAR electric inspection process point cloud classifications method - Google Patents
A kind of airborne LIDAR electric inspection process point cloud classifications method Download PDFInfo
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- CN106897686A CN106897686A CN201710088491.3A CN201710088491A CN106897686A CN 106897686 A CN106897686 A CN 106897686A CN 201710088491 A CN201710088491 A CN 201710088491A CN 106897686 A CN106897686 A CN 106897686A
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- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
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Abstract
The invention discloses a kind of sorting technique of airborne LIDAR electric inspection process cloud data.After noise suppression preprocessing being carried out in acquisition airborne LIDAR electric inspection process cloud data and to original point cloud, build the different type of ground objects sorting algorithms based on airborne LIDAR electric inspection process cloud data, point cloud filtering algorithm first according to design realizes that ground point is separated with non-ground points, then the High Precision Automatic extraction of the atural object such as electric force pole tower, power line and vegetation in original point cloud data is carried out successively, the precision and automaticity of the classification of electric inspection process cloud data are greatly improved, for power circuit polling malfunction elimination provides guarantee.
Description
First, technical field
The present invention relates to the Data Classification Technology in a kind of Mapping remote sensing technology field, under particularly a kind of airborne LIDAR technology
Electric inspection process laser radar point cloud data sorting technique method.
2nd, technical background
Laser radar (Light Detection And Ranging, LiDAR) is one and is sent by by sensor
Laser carrys out the active remote sensing technology of distance between determination sensor and object.LiDAR data is in three dimensions in irregular
The point set of distribution, discrete " point cloud " is presented in the distributional pattern of three dimensions.Transmission line of electricity can be carried out using airborne LIDAR
Patrol and examine, can investigate the failure and hidden danger of transmission line of electricity by patrolling and examining cloud data, wherein between atural object and power line away from
From detection be an important content, it is necessary to from patrolling and examining in cloud data of obtaining extract ground, vegetation to corresponding power line away from
From so that it is determined that whether the distance meets the criterion distance of power network specification, if hypotelorism, being set as hidden danger.And this is hidden
The accurate and efficient judgement suffered from depends on the high-precision classification for patrolling and examining cloud data, but traditional electric inspection process cloud data classification
Method is mainly semiautomatic extraction method, and the method typically first distinguishes ground and non-ground points using conventional filtering algorithm, then
Other types of ground objects are manually divided, the method has obvious shortcoming with deficiency：
(1) often data volume is larger for electric inspection process cloud data, in region with a varied topography, conventional filtering algorithm often precision
It is relatively low, it is necessary to artificial later stage modification, has that workload is big, it is impossible to reach the real-time of data quick-processing and hidden troubles removing
Property require；
(2) for the Type division of other different atural objects on electric inspection process circuit, conventional method relies on artificial division, exists
Many-sided shortcoming such as workload is big, project cost is high, the cycle is long, labour intensity is big, in airborne LIDAR electric inspection process on a large scale
Do not have economic feasibility in mesh.
So during classifying to electric inspection process cloud data, during using conventional sorting methods, inevitable area
Much to bother and inefficiency.Therefore, prior art high, the exploitation that there is low automaticity, low production efficiency, project cost
Cycle is long, the low many-sided shortcoming of Result Precision.How on the premise of electric inspection process cloud data precise classification is ensured, improve
Automaticity and production efficiency, project cost is reduced, shortening the construction cycle turns into that this area scientific and technical personnel are anxious to be resolved to be asked
Topic.
3rd, the content of the invention
In order to solve existing Mapping remote sensing technology sorting technique problems present in the classification of airborne LIDAR cloud data,
Improve production efficiency, it is an object of the invention to provide a kind of airborne LIDAR electric inspection process cloud data sorting technique.It is ensureing
In the case of cloud data terrain classification precision, the automaticity and efficiency of electric inspection process cloud data classification, pole are improve
Big reduces project development cost, shortens the project cycle, overcomes conventional electric power and patrols and examines the presence of cloud data taxonomic methods
Shortcoming.
The object of the present invention is achieved like this：Obtaining airborne LIDAR electric inspection process cloud data and to carry out denoising pre-
After treatment, designed by efficient algorithm, the differentiation of ground point and non-ground points is realized first with filtering algorithm, then successively
Realize automatically extracting for the atural object key element such as electric force pole tower, power line, vegetation.
Airborne LIDAR electric inspection process cloud data sorting technique is as follows：
(1) treatment is filtered to original point cloud using morphology reversed interpolation filtering algorithm, obtain ground point cloud with it is non-
Ground point cloud；
(2) a cloud normalized is carried out to non-ground points cloud data；
(3) according to a cloud normalization result, normalization point cloud is divided into two parts, is highly more than by one height threshold of setting
Threshold value is power line and part shaft tower point cloud normalization data, is vegetation and part shaft tower point cloud normalizing highly less than threshold value
Change data；
(4) determination of shaft tower position：Shaft tower quantity N and the distance threshold D of adjacent shaft tower that setting original point cloud is included,
Each the consecutive points cloud quantity of point in K distance domains in test point cloud in power line and part shaft tower point cloud normalization data,
And the consecutive points cloud quantity put according to each carries out a cloud descending sort, take top n after sequence and two-by-two horizontal range more than D
Point is used as tower spotting point；
(5) extraction of shaft tower normalization point cloud：According to the shaft tower position horizontal coordinate for determining, in power line and part shaft tower
Will be in the range of certain level distance threshold in point cloud normalization data and vegetation and part shaft tower point cloud normalization data
Point cloud is divided into shaft tower normalization point cloud；
(6) vegetation and power line normalization data reduction：By power line and part shaft tower point cloud normalization data remainder
Divide and be classified as power line normalization point cloud, vegetation and part shaft tower point cloud normalization data remainder are classified as vegetation normalization point
Cloud；
(7) all kinds of normalization point clouds are recovered into elevation, finally gives sorted ground point, electric force pole tower point, power line
Point and vegetation point.
This invention has advantages below compared with prior art：
(1) the classification thinking that conventional electric power patrols and examines cloud data has been changed, by point cloud Normalized Scale and original point cloud chi
Degree is combined is classified, and improves the precision and efficiency of cloud data classification.
(2) relatively low project cost, shorter data processing cycle
The classification of airborne LIDAR electric inspection process cloud data realizes full automatic classification, greatly the project of saving can open
Hair cost, shortens the cycle of data processing.
4th, illustrate
The present invention is further described with reference to the accompanying drawings and examples.
Fig. 1 is flow chart of the method for the present invention；
Fig. 2 is the original point cloud chart of experimental data；
Fig. 3 is ground point cloud classifications result；
Fig. 4 is that non-ground points cloud normalizes result；
Fig. 5 is electric force pole tower point cloud classifications result；
Fig. 6 is power line point cloud classifications result；
Fig. 7 is vegetation point cloud classifications result.
5th, specific embodiment：
For the technical characterstic for illustrating this programme can be understood, below by specific embodiment, and its accompanying drawing is combined, to this hair
It is bright to be described in detail.
As shown in Fig. 1 method flows, the present invention：A kind of airborne LIDAR electric inspection process point cloud classifications method, methods described bag
Include following steps：
The differentiation of step 1, ground point and non-ground points：Raw power is patrolled and examined using morphology reversed interpolation filtering algorithm
Point cloud is filtered, and is divided into ground point and non-ground points two parts.Original point cloud data is carried out into grid partition first, lattice are set
Net width be d, in detecting each grid elevation minimum point as initial ground seed point, according to initial ground point and corrosion window
Size does morphology erosion operation to cloud data first, and morphological dilations fortune is done to cloud data further according to expansion window size
Calculate, finally the point by height value in each grid less than setting height threshold value h1 makees sparse ground point, according to extraction sparsely
Millet cake carries out anti-distance weighting interpolation arithmetic, obtains the interpolation elevation of each grid, then left point in each grid is concentrated into high
Point of the journey less than setting height threshold value h2 is divided into ground point, finally gives ground point cloud after high-precision classification, then remaining
Point cloud is non-ground points cloud；
Step 2, the normalization of non-ground points cloud：The ground point that step 1 is obtained is real using inverse distance-weighting algorithm interpolation
Border ground grid elevation, the corresponding grid ground elevation value of non-ground points elevation is subtracted each other the normalization point for obtaining non-ground points
Cloud.
Step 3, electric force pole tower, power line and vegetation normalization point cloud rough sort：A height threshold H is set, will be normalized
Point cloud is divided into two parts, is power line and part shaft tower point cloud normalization data highly more than threshold value, highly less than threshold value
Be vegetation and part shaft tower point cloud normalization data；
Step 4, the determination of shaft tower position：The shaft tower quantity N that includes of setting original point cloud and adjacent shaft tower apart from threshold
Value D, each the consecutive points cloud number of point in K distance domains in test point cloud in power line and part shaft tower point cloud normalization data
Amount, and the consecutive points cloud quantity put according to each carries out a cloud descending sort, takes after sequence top n and horizontal range is more than two-by-two
The point of D as tower spotting point, using horizontal coordinate X, Y of tower spotting point as position coordinates；
Step 5, the extraction of shaft tower normalization point cloud：According to the shaft tower position horizontal X, the Y-coordinate that determine, in power line and portion
Dividing will be in certain level distance threshold Dxy in shaft tower point cloud normalization data and vegetation and part shaft tower point cloud normalization data
In the range of point cloud be divided into shaft tower normalization point cloud；
Step 6, vegetation and power line normalization data reduction：Power line and part shaft tower point cloud normalization data is remaining
Part is classified as power line normalization point cloud, and vegetation and part shaft tower point cloud normalization data remainder are classified as into vegetation normalization
Point cloud；
All kinds of normalization point clouds are recovered elevation by step 7：To all kinds of normalization cloud datas for obtaining, by each normalizing
The elevation for changing point is added with corresponding grid elevation, obtains a mysorethorn border elevation, finally gives sorted ground point, electric force pole tower
Point, power line point and vegetation point.
Claims (3)
1. a kind of sorting technique of airborne LIDAR electric inspection process cloud data, it is characterized in that：Patrolled airborne LIDAR electric power is obtained
Inspection cloud data and after carrying out noise suppression preprocessing to original point cloud, builds based on airborne LIDAR electric inspection process cloud data not
With type of ground objects sorting algorithm, the point cloud filtering algorithm first according to design realizes that ground point is separated with non-ground points, then
The High Precision Automatic extraction of the atural object such as electric force pole tower, power line and vegetation in original point cloud data is carried out successively, so as to realize
The efficient classification of airborne LIDAR electric inspection process cloud data.
2. the airborne LIDAR electric inspection process cloud data sorting technique according to claim, it is characterized in that：It is wherein airborne
LIDAR electric inspection process point cloud filtering method：1. a cloud lattice is carried out according to the grid window size of setting to original point cloud data
Networking is processed；2. cloud level journey is put in each grid of detection cloud data, using lowest elevation point as initial ground seed point；3. root
Morphological erosion and dilation operation are carried out successively according to the point-to-point cloud of initial seed, obtain sparse ground point；4. to sparse ground point
Anti- distance weighting interpolation is carried out, ground grid interpolation elevation is obtained, the point by elevation in left point cloud less than setting height threshold value
Also ground point is subdivided into, the ground point that classification is completed is finally given.
3. the airborne LIDAR electric inspection process cloud data sorting technique according to claim, it is characterized in that：It is wherein airborne
LIDAR electric inspection process point cloud other terrain classification methods：1. non-ground points cloud is normalized；2. setting height threshold
Value, power line is divided into part shaft tower point cloud normalization data and vegetation and part shaft tower point cloud normalization by normalization point cloud
The two parts such as data；3. detection of the neighborhood apart from point data is carried out in power line and part shaft tower point cloud normalization data, and
Descending sort is carried out by neighborhood point quantity, according to distance threshold between shaft tower quantity and shaft tower, shaft tower position coordinates is determined；4. by electricity
With shaft tower coordinate level in the line of force and part shaft tower point cloud normalization data and in vegetation and part shaft tower point cloud normalization data
Point in contiguous range is divided into shaft tower normalization point, and left point in power line and part shaft tower point cloud normalization data is divided
It is power line normalization point, left point in vegetation and part shaft tower point cloud normalization data is divided into vegetation normalization point；⑤
Shaft tower, power line, the vegetation normalization point elevation that will be obtained finally give sorted plus corresponding ground grid interpolation elevation
Electric force pole tower point, power line point and vegetation point.
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Cited By (18)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN107818550A (en) * | 2017-10-27 | 2018-03-20 | 广东电网有限责任公司机巡作业中心 | A kind of point cloud top portion noise elimination method based on LiDAR |
| CN108230336A (en) * | 2017-12-29 | 2018-06-29 | 国网通用航空有限公司 | A kind of cloud shaft tower extracting method and device |
| CN108562885A (en) * | 2018-03-29 | 2018-09-21 | 同济大学 | A kind of ultra-high-tension power transmission line airborne LiDAR point cloud extracting method |
| CN109254586A (en) * | 2018-09-19 | 2019-01-22 | 绵阳紫蝶科技有限公司 | Point and non-thread upper point classification, electric power line drawing and path planning method on line |
| CN109390873A (en) * | 2018-11-30 | 2019-02-26 | 贵州电网有限责任公司 | A method of predicting that screen of trees is threatened using point cloud data in electric transmission line channel |
| CN109492699A (en) * | 2018-11-21 | 2019-03-19 | 国网江苏省电力有限公司扬州供电分公司 | Passway for transmitting electricity method for three-dimensional measurement and device |
| CN109657569A (en) * | 2018-11-30 | 2019-04-19 | 贵州电网有限责任公司 | More vegetation areas transmission of electricity corridor hidden danger point quick extraction method based on cloud analysis |
| CN109872384A (en) * | 2018-12-29 | 2019-06-11 | 中国科学院遥感与数字地球研究所 | A kind of shaft tower automation modeling method based on airborne LIDAR point cloud data |
| CN109948414A (en) * | 2018-12-29 | 2019-06-28 | 中国科学院遥感与数字地球研究所 | Electric power corridor scene classification method based on LiDAR point cloud feature |
| CN110070544A (en) * | 2019-06-06 | 2019-07-30 | 江苏省农业科学院 | One planting fruit-trees target three-dimensional data compensation method and compensation system |
| CN111157530A (en) * | 2019-12-25 | 2020-05-15 | 国网宁夏电力有限公司电力科学研究院 | Unmanned aerial vehicle-based safety detection method for power transmission line |
| CN111598780A (en) * | 2020-05-14 | 2020-08-28 | 山东科技大学 | Terrain adaptive interpolation filtering method suitable for airborne LiDAR point cloud |
| CN111895907A (en) * | 2020-06-18 | 2020-11-06 | 南方电网数字电网研究院有限公司 | Electricity tower point cloud extraction method, system and equipment |
| CN112033393A (en) * | 2020-08-25 | 2020-12-04 | 国网天津市电力公司 | Three-dimensional route planning method and device based on laser radar point cloud data |
| CN112883878A (en) * | 2021-02-24 | 2021-06-01 | 武汉大学 | Automatic point cloud classification method under transformer substation scene based on three-dimensional grid |
| CN112945198A (en) * | 2021-02-02 | 2021-06-11 | 贵州电网有限责任公司 | Automatic detection method for power transmission line iron tower inclination based on laser LIDAR point cloud |
| CN113009452A (en) * | 2019-12-20 | 2021-06-22 | 广东科诺勘测工程有限公司 | Laser point cloud electric power tower extraction method |
| CN113487555A (en) * | 2021-07-01 | 2021-10-08 | 中国电建集团贵州电力设计研究院有限公司 | Point cloud gridding-based power transmission line hidden danger point rapid detection method |
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| CN108230336A (en) * | 2017-12-29 | 2018-06-29 | 国网通用航空有限公司 | A kind of cloud shaft tower extracting method and device |
| CN108562885A (en) * | 2018-03-29 | 2018-09-21 | 同济大学 | A kind of ultra-high-tension power transmission line airborne LiDAR point cloud extracting method |
| CN108562885B (en) * | 2018-03-29 | 2021-12-31 | 同济大学 | High-voltage transmission line airborne LiDAR point cloud extraction method |
| CN109254586A (en) * | 2018-09-19 | 2019-01-22 | 绵阳紫蝶科技有限公司 | Point and non-thread upper point classification, electric power line drawing and path planning method on line |
| CN109492699A (en) * | 2018-11-21 | 2019-03-19 | 国网江苏省电力有限公司扬州供电分公司 | Passway for transmitting electricity method for three-dimensional measurement and device |
| CN109657569A (en) * | 2018-11-30 | 2019-04-19 | 贵州电网有限责任公司 | More vegetation areas transmission of electricity corridor hidden danger point quick extraction method based on cloud analysis |
| CN109390873A (en) * | 2018-11-30 | 2019-02-26 | 贵州电网有限责任公司 | A method of predicting that screen of trees is threatened using point cloud data in electric transmission line channel |
| CN109948414A (en) * | 2018-12-29 | 2019-06-28 | 中国科学院遥感与数字地球研究所 | Electric power corridor scene classification method based on LiDAR point cloud feature |
| CN109872384B (en) * | 2018-12-29 | 2021-03-09 | 中国科学院遥感与数字地球研究所 | Automatic tower modeling method based on airborne LIDAR point cloud data |
| CN109872384A (en) * | 2018-12-29 | 2019-06-11 | 中国科学院遥感与数字地球研究所 | A kind of shaft tower automation modeling method based on airborne LIDAR point cloud data |
| CN110070544A (en) * | 2019-06-06 | 2019-07-30 | 江苏省农业科学院 | One planting fruit-trees target three-dimensional data compensation method and compensation system |
| CN113009452B (en) * | 2019-12-20 | 2024-03-19 | 广东科诺勘测工程有限公司 | Laser point cloud power tower extraction method |
| CN113009452A (en) * | 2019-12-20 | 2021-06-22 | 广东科诺勘测工程有限公司 | Laser point cloud electric power tower extraction method |
| CN111157530A (en) * | 2019-12-25 | 2020-05-15 | 国网宁夏电力有限公司电力科学研究院 | Unmanned aerial vehicle-based safety detection method for power transmission line |
| CN111157530B (en) * | 2019-12-25 | 2022-08-12 | 国网宁夏电力有限公司电力科学研究院 | Unmanned aerial vehicle-based safety detection method for power transmission line |
| CN111598780A (en) * | 2020-05-14 | 2020-08-28 | 山东科技大学 | Terrain adaptive interpolation filtering method suitable for airborne LiDAR point cloud |
| CN111598780B (en) * | 2020-05-14 | 2022-03-18 | 山东科技大学 | Terrain adaptive interpolation filtering method suitable for airborne LiDAR point cloud |
| CN111895907A (en) * | 2020-06-18 | 2020-11-06 | 南方电网数字电网研究院有限公司 | Electricity tower point cloud extraction method, system and equipment |
| CN111895907B (en) * | 2020-06-18 | 2023-02-03 | 南方电网数字电网研究院有限公司 | Electricity tower point cloud extraction method, system and equipment |
| CN112033393A (en) * | 2020-08-25 | 2020-12-04 | 国网天津市电力公司 | Three-dimensional route planning method and device based on laser radar point cloud data |
| CN112945198A (en) * | 2021-02-02 | 2021-06-11 | 贵州电网有限责任公司 | Automatic detection method for power transmission line iron tower inclination based on laser LIDAR point cloud |
| CN112883878A (en) * | 2021-02-24 | 2021-06-01 | 武汉大学 | Automatic point cloud classification method under transformer substation scene based on three-dimensional grid |
| CN113487555A (en) * | 2021-07-01 | 2021-10-08 | 中国电建集团贵州电力设计研究院有限公司 | Point cloud gridding-based power transmission line hidden danger point rapid detection method |
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