CN106097423A - LiDAR point cloud intensity correction method based on k neighbour - Google Patents
LiDAR point cloud intensity correction method based on k neighbour Download PDFInfo
- Publication number
- CN106097423A CN106097423A CN201610404193.6A CN201610404193A CN106097423A CN 106097423 A CN106097423 A CN 106097423A CN 201610404193 A CN201610404193 A CN 201610404193A CN 106097423 A CN106097423 A CN 106097423A
- Authority
- CN
- China
- Prior art keywords
- point
- cloud
- point cloud
- intensity
- neighbour
- 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
- G06T15/00—3D [Three Dimensional] image rendering
Landscapes
- Engineering & Computer Science (AREA)
- Computer Graphics (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Optical Radar Systems And Details Thereof (AREA)
Abstract
The invention discloses a kind of based onkThe LiDAR point cloud intensity correction method of neighbour, withkBased on nearest neighbor algorithm, by asking forkThe strength mean value method of Neighbor Points carries out a correction for cloud intensity level, and algorithm structure is very simple, and can effectively correct the strength information of a cloud so that the strength information after correction can the attribute information of the most real reflection surface.
Description
Technical field
The present invention relates to a kind of LiDAR point cloud intensity correction method, particularly to a kind of LiDAR point cloud based on k neighbour
Intensity correction method.
Background technology
The luminous power that laser intensity is object to the backscattering echo launching laser, echo-signal received after through inside
Conversion and amplification, the intensity level in final conversion raw LiDAR data.Owing to by laser ranging value and laser light incident angle etc. being
The impact of the system target variable such as variable and target reflectivity, roughness and gradient so that the intensity level of acquisition exists certain
Deviation.Mostly existing method is to first pass through the functional relationship setting up between intensity level and system variable, and then analysis of system variables shadow
The method rung and carry out correcting obtains the attribute information of target surface.Due to manufacturer's secrecy to key parameter, and setting up
Simplification to some variable during functional relationship so that the functional relationship of foundation can not accurately eliminate system variable
Impact.
Summary of the invention
The technical problem to be solved is to provide a kind of LiDAR point cloud intensity correction method based on k neighbour, makes
LiDAR point cloud strength information after must correcting can the attribute information of the most accurate and real reflection surface.
The present invention solves above-mentioned technical problem by the following technical solutions:
The present invention provides a kind of LiDAR point cloud intensity correction method based on k neighbour, including step in detail below:
Step 1, reads the LiDAR point cloud data comprising strength information, obtains number n of cloud data;
Step 2, uses all of some cloud point of K-D tree tissue, calculates the normal vector of each some cloud point simultaneously;
Step 3, travels through all of some cloud point, calculates angle of incidence and the Neighbor Points thereof of each some cloud point, it is thus achieved that Mei Gedian
Intensity level after cloud point correction, specifically comprises the following steps that
301: make i=1;
302: utilize formula 1 to calculate i-th point cloud point piAnd the distance between scanner center:
In formula, RiFor i-th point cloud point piAnd the Euclidean distance between scanner center, and i ∈ (1,2 ..., n), (xi,yi,
zi) it is i-th point cloud point p in the coordinate system with scanner center as initial pointiOriginal coordinates;
303: utilize formula 2 to calculate i-th point cloud point piLaser light incident angle θi:
In formula, θiFor i-th point cloud point piLaser light incident angle, (nxi,nyi,nzi) it is i-th point cloud point piNormal direction
Amount;
304: search i-th point cloud point piK Neighbor Points, calculate the average of the raw intensity values of this k Neighbor Points, should
Average is i-th point cloud point piIntensity level after correction;
305: make i=i+1, if i is < n, then returns step 302, otherwise terminate iteration, thus complete a cloud point
Intensity correction.
As the further prioritization scheme of the present invention, in step 304, the value of k is chosen according to actual needs.
As the further prioritization scheme of the present invention, the value of k is 10.
As the further prioritization scheme of the present invention, the value of k is 15.
As the further prioritization scheme of the present invention, the value of k is 20.
The present invention uses above technical scheme compared with prior art, has following technical effect that what the present invention provided
LiDAR point cloud strength information bearing calibration, based on k nearest neighbor algorithm, is entered by the strength mean value method asking for k Neighbor Points
The correction of row point cloud intensity level, algorithm structure is very simple, and can effectively correct the strength information of a cloud so that after correction
Strength information can the attribute information of the most real reflection surface.
Accompanying drawing explanation
Fig. 1 is the flow chart of the present invention.
Fig. 2 be withFor transverse axis, original LiDAR point cloud intensity level I is the scatterplot of the longitudinal axis.
Fig. 3 be withFor transverse axis, after correction, LiDAR point cloud intensity level I is the scatterplot of the longitudinal axis.
Detailed description of the invention
Below in conjunction with the accompanying drawings technical scheme is described in further detail:
As it is shown in figure 1, a kind of LiDAR point cloud intensity correction method based on k neighbour, comprise the following steps:
Step 1, reads the LiDAR point cloud data comprising strength information, obtains number n=5296 of cloud data.
Step 2, uses all of some cloud point of K-D tree tissue, calculates the normal vector of each some cloud point simultaneously.
Step 3, travels through all of some cloud point, calculates angle of incidence and the Neighbor Points thereof of each some cloud point, it is thus achieved that Mei Gedian
Intensity level after cloud point correction, specifically comprises the following steps that
401: make i=1;
402: utilize formula 1 to calculate i-th point cloud point piAnd the distance between scanner center:
In formula, RiFor i-th point cloud point piAnd the Euclidean distance between scanner center, and i ∈ (1,2 ..., n), (xi,yi,
zi) it is i-th point cloud point p in the coordinate system with scanner center as initial pointiCoordinate;
403: utilize formula 2 to calculate i-th point cloud point piLaser light incident angle θi:
In formula, θiFor i-th point cloud point piLaser light incident angle, (nxi,nyi,nzi) it is i-th point cloud point piNormal direction
Amount;
404: search i-th point cloud point piK Neighbor Points, calculate the average of the observed strength value of this k Neighbor Points, should
Average is i-th point cloud point piIntensity level after correction;
405: make i=i+1, if i is < n, then returns step 402, otherwise terminate iteration, thus complete a cloud point
Intensity correction.
Follow technique scheme of the present invention, the intensity of the laser point cloud after obtaining original laser point cloud intensity level and correcting
Value, as shown in Figures 2 and 3, wherein transverse axis isThe longitudinal axis is the intensity level of laser spots cloud point.
As in figure 2 it is shown, be original laser point cloud point intensity scatterplot.As it is shown on figure 3, for the present invention obtain correction after
Laser point cloud intensity scatterplot.From figures 2 and 3, it will be seen that the laser spots intensity scatterplot of present invention acquisition and original laser
Point cloud intensity scatterplot general trend is the most similar, but the intensity scatterplot after present invention correction is distributed the distribution of relatively green strength scatterplot
More concentrate, illustrate the laser point cloud intensity after present invention correction can not only the attribute information of reflection surface, and more connect
It is bordering on the actual strength value of body surface.
The above, the only detailed description of the invention in the present invention, but protection scope of the present invention is not limited thereto, and appoints
What is familiar with the people of this technology in the technical scope that disclosed herein, it will be appreciated that the conversion expected or replacement, all should contain
Within the scope of the comprising of the present invention, therefore, protection scope of the present invention should be as the criterion with the protection domain of claims.
Claims (5)
1. LiDAR point cloud intensity correction method based on k neighbour, it is characterised in that include step in detail below:
Step 1, reads the LiDAR point cloud data comprising strength information, obtains number n of cloud data;
Step 2, uses all of some cloud point of K-D tree tissue, calculates the normal vector of each some cloud point simultaneously;
Step 3, travels through all of some cloud point, calculates angle of incidence and the Neighbor Points thereof of each some cloud point, it is thus achieved that each some cloud point
Intensity level after correction, specifically comprises the following steps that
301: make i=1;
302: utilize formula 1 to calculate i-th point cloud point piAnd the distance between scanner center:
In formula, RiFor i-th point cloud point piAnd the Euclidean distance between scanner center, and i ∈ (1,2 ..., n), (xi,yi,zi) it is
I-th point cloud point p in the coordinate system with scanner center as initial pointiOriginal coordinates;
303: utilize formula 2 to calculate i-th point cloud point piLaser light incident angle θi:
In formula, θiFor i-th point cloud point piLaser light incident angle, (nxi,nyi,nzi) it is i-th point cloud point piNormal vector;
304: search i-th point cloud point piK Neighbor Points, calculate the average of the raw intensity values of this k Neighbor Points, this average
It is i-th point cloud point piIntensity level after correction;
305: make i=i+1, if i is < n, then returns step 302, otherwise terminate iteration, thus complete the intensity of a cloud point
Correction.
LiDAR point cloud intensity correction method based on k neighbour the most according to claim 1, it is characterised in that step 304
The value of middle k is chosen according to actual needs.
LiDAR point cloud intensity correction method based on k neighbour the most according to claim 2, it is characterised in that the value of k is
10。
LiDAR point cloud intensity correction method based on k neighbour the most according to claim 2, it is characterised in that the value of k is
15。
LiDAR point cloud intensity correction method based on k neighbour the most according to claim 2, it is characterised in that the value of k is
20。
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201610404193.6A CN106097423A (en) | 2016-06-08 | 2016-06-08 | LiDAR point cloud intensity correction method based on k neighbour |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201610404193.6A CN106097423A (en) | 2016-06-08 | 2016-06-08 | LiDAR point cloud intensity correction method based on k neighbour |
Publications (1)
Publication Number | Publication Date |
---|---|
CN106097423A true CN106097423A (en) | 2016-11-09 |
Family
ID=57228343
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201610404193.6A Pending CN106097423A (en) | 2016-06-08 | 2016-06-08 | LiDAR point cloud intensity correction method based on k neighbour |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN106097423A (en) |
Cited By (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN107290734A (en) * | 2017-08-22 | 2017-10-24 | 北京航空航天大学 | A kind of point cloud error calibration method based on the self-control ground laser radar error of perpendicularity |
CN107290735A (en) * | 2017-08-22 | 2017-10-24 | 北京航空航天大学 | A kind of point cloud error calibration method based on self-control ground laser radar verticality error |
CN110208771A (en) * | 2019-07-01 | 2019-09-06 | 南京林业大学 | A kind of point cloud intensity correcting method of mobile two-dimensional laser radar |
CN112816993A (en) * | 2020-12-25 | 2021-05-18 | 北京一径科技有限公司 | Laser radar point cloud processing method and device |
Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN101963666A (en) * | 2010-09-20 | 2011-02-02 | 北京理工大学 | Method for correcting intensity image distortion of streak tube imaging laser radar |
CN103542868A (en) * | 2013-11-05 | 2014-01-29 | 武汉海达数云技术有限公司 | Automatic removing method of vehicle-mounted laser point cloud noisy point based on angle and intensity |
CN104502919A (en) * | 2015-01-13 | 2015-04-08 | 南京大学 | Method for utilizing airborne laser radar point cloud to extract urban vegetation three-dimensional coverage map |
US20150356721A1 (en) * | 2014-06-05 | 2015-12-10 | Weyerhaeuser Nr Company | System and method for differentiating type of vegetation from remotely sensed data |
-
2016
- 2016-06-08 CN CN201610404193.6A patent/CN106097423A/en active Pending
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN101963666A (en) * | 2010-09-20 | 2011-02-02 | 北京理工大学 | Method for correcting intensity image distortion of streak tube imaging laser radar |
CN103542868A (en) * | 2013-11-05 | 2014-01-29 | 武汉海达数云技术有限公司 | Automatic removing method of vehicle-mounted laser point cloud noisy point based on angle and intensity |
US20150356721A1 (en) * | 2014-06-05 | 2015-12-10 | Weyerhaeuser Nr Company | System and method for differentiating type of vegetation from remotely sensed data |
CN104502919A (en) * | 2015-01-13 | 2015-04-08 | 南京大学 | Method for utilizing airborne laser radar point cloud to extract urban vegetation three-dimensional coverage map |
Non-Patent Citations (4)
Title |
---|
WANG NIZHUAN 等: "The K nearest neighbor geometry filter based on spatial domain", 《ICBBE 2011》 * |
程小龙 等: "基于激光强度的建筑立面点云分类及信息提取", 《同济大学学报(自然科学版)》 * |
谭凯,程效军: "地面激光扫描中激光强度值的影响因素及改正方法", 《测绘科学》 * |
谭凯,程效军: "基于强度数据的地面激光点云自适应非监督分类", 《激光与光电子学进展》 * |
Cited By (9)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN107290734A (en) * | 2017-08-22 | 2017-10-24 | 北京航空航天大学 | A kind of point cloud error calibration method based on the self-control ground laser radar error of perpendicularity |
CN107290735A (en) * | 2017-08-22 | 2017-10-24 | 北京航空航天大学 | A kind of point cloud error calibration method based on self-control ground laser radar verticality error |
CN107290734B (en) * | 2017-08-22 | 2020-03-24 | 北京航空航天大学 | Point cloud error correction method based on self-made foundation laser radar perpendicularity error |
CN107290735B (en) * | 2017-08-22 | 2020-03-24 | 北京航空航天大学 | Point cloud error correction method based on self-made foundation laser radar verticality error |
CN110208771A (en) * | 2019-07-01 | 2019-09-06 | 南京林业大学 | A kind of point cloud intensity correcting method of mobile two-dimensional laser radar |
CN110208771B (en) * | 2019-07-01 | 2022-12-30 | 南京林业大学 | Point cloud intensity correction method of mobile two-dimensional laser radar |
CN112816993A (en) * | 2020-12-25 | 2021-05-18 | 北京一径科技有限公司 | Laser radar point cloud processing method and device |
WO2022135172A1 (en) * | 2020-12-25 | 2022-06-30 | 北京一径科技有限公司 | Laser radar point cloud processing method and apparatus, and storage medium and electronic apparatus |
CN112816993B (en) * | 2020-12-25 | 2022-11-08 | 北京一径科技有限公司 | Laser radar point cloud processing method and device |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN106097423A (en) | LiDAR point cloud intensity correction method based on k neighbour | |
CN108335291B (en) | Tunnel disease image preprocessing method | |
CN102721644B (en) | Method and device for processing remote sensing data of water environment | |
US11054503B2 (en) | Radar target spherical projection method for maritime formation | |
CN103729846B (en) | LiDAR point cloud data edge detection method based on triangular irregular network | |
Winstroth et al. | Wind turbine rotor blade monitoring using digital image correlation: a comparison to aeroelastic simulations of a multi-megawatt wind turbine | |
CN107247268A (en) | The bearing calibration of multi-line laser radar system and its horizontal installation angle | |
CN110379011B (en) | Underwater terrain point cloud hole repairing method based on improved cubic B-spline curve | |
CN111060898B (en) | Internal reference calibration method for field end multi-line beam laser radar | |
CN112414403B (en) | Robot positioning and attitude determining method, equipment and storage medium | |
CN107607195A (en) | A kind of beam quality measuring method obtained in real time based on complex amplitude | |
CN106815832B (en) | A kind of steel mesh automatic image registration method and system of surface mounting technology | |
CN115856925A (en) | Multispectral remote sensing image water depth inversion method, medium and equipment based on chart data | |
CN107064934A (en) | A kind of optimized algorithm of SAR wave modes Directional Spectrum of Wind Wave product | |
CN105241395B (en) | For the face shape of space oversize structure and apart from high-precision measuring method | |
CN105701776B (en) | A kind of lens distortion antidote and system for automatic optics inspection | |
CN110232697B (en) | Light spot edge fitting method | |
CN111722202A (en) | Reflector position fitting method and system based on echo intensity | |
CN113777592B (en) | Azimuth calibration method and device | |
CN110296698A (en) | It is a kind of with laser scanning be constraint unmanned plane paths planning method | |
CN111984025A (en) | Unmanned aerial vehicle path planning method for wind turbine generator blades | |
CN104980214A (en) | Coarse and fine scanning method for inter-satellite communication | |
CN106809342A (en) | Ship cross section survey draw a method | |
CN105513071A (en) | Topographic map schema quality evaluation method | |
CN111090130B (en) | Improved algorithm for radar-rain gauge joint precipitation estimation based on minimum functional boundary condition acquisition |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
C06 | Publication | ||
PB01 | Publication | ||
C10 | Entry into substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
WD01 | Invention patent application deemed withdrawn after publication | ||
WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20161109 |