CN115951371B - Method for determining equivalent points of airborne LiDAR sounding navigation belt - Google Patents
Method for determining equivalent points of airborne LiDAR sounding navigation belt Download PDFInfo
- Publication number
- CN115951371B CN115951371B CN202310237959.6A CN202310237959A CN115951371B CN 115951371 B CN115951371 B CN 115951371B CN 202310237959 A CN202310237959 A CN 202310237959A CN 115951371 B CN115951371 B CN 115951371B
- Authority
- CN
- China
- Prior art keywords
- point
- points
- formula
- homonymous
- neighborhood
- 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.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 29
- 238000012876 topography Methods 0.000 claims abstract description 17
- 238000005070 sampling Methods 0.000 claims description 22
- 238000004364 calculation method Methods 0.000 claims description 9
- NAWXUBYGYWOOIX-SFHVURJKSA-N (2s)-2-[[4-[2-(2,4-diaminoquinazolin-6-yl)ethyl]benzoyl]amino]-4-methylidenepentanedioic acid Chemical compound C1=CC2=NC(N)=NC(N)=C2C=C1CCC1=CC=C(C(=O)N[C@@H](CC(=C)C(O)=O)C(O)=O)C=C1 NAWXUBYGYWOOIX-SFHVURJKSA-N 0.000 claims description 3
- 230000001174 ascending effect Effects 0.000 claims description 3
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 abstract description 9
- 238000001514 detection method Methods 0.000 abstract description 4
- 238000012545 processing Methods 0.000 abstract description 4
- 238000005259 measurement Methods 0.000 description 11
- 238000010586 diagram Methods 0.000 description 8
- 230000000694 effects Effects 0.000 description 7
- 230000009466 transformation Effects 0.000 description 5
- 238000002474 experimental method Methods 0.000 description 4
- 238000000605 extraction Methods 0.000 description 4
- 238000005457 optimization Methods 0.000 description 4
- 230000008030 elimination Effects 0.000 description 2
- 238000003379 elimination reaction Methods 0.000 description 2
- 230000004927 fusion Effects 0.000 description 2
- 239000004576 sand Substances 0.000 description 2
- 230000009897 systematic effect Effects 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- 241001489705 Aquarius Species 0.000 description 1
- 230000006978 adaptation Effects 0.000 description 1
- 238000007792 addition Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000012790 confirmation Methods 0.000 description 1
- 238000006073 displacement reaction Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000002245 particle Substances 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Images
Classifications
-
- 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
- Optical Radar Systems And Details Thereof (AREA)
Abstract
The invention discloses a method for determining the same name point of an airborne LiDAR sounding strip, which belongs to the technical field of measuring distance, level or azimuth and adopting the positioning or existence detection of the reflection or reradiation of radio waves, and is used for determining the same name point of the sounding strip and comprises the following steps: based on the acquired airborne LiDAR sounding data, extracting submarine topography characteristics, taking a neighborhood topography complexity multi-index model into consideration, performing homonymous point coarse determination, and removing error point pairs to perform homonymous point fine determination by combining homonymous point spacing constraint and a RANSAC algorithm. The method for accurately determining the homonymous points between the adjacent bands solves the problem that the airborne LiDAR sounding homonymous points in the shallow water sea area are difficult to extract, and provides effective technical support and solution for further improving sounding data processing and application.
Description
Technical Field
The invention discloses a method for determining the same name point of an airborne LiDAR sounding navigation belt, and belongs to the technical field of distance, level or azimuth measurement and positioning or presence detection by adopting reflection or reradiation of radio waves.
Background
The airborne LiDAR sounding system has the characteristics of high measurement accuracy, high measurement point density, high working efficiency, strong maneuverability, measurement continuity and the like, is particularly suitable for rapid detection of complex terrains such as shallow water areas, island reef nearby areas and the like, can realize seamless splicing of underwater terrains on coastline water, and is widely applied to rapid detection of complex terrains in shallow water areas such as coastal zones, island reefs and the like. Because the airborne LiDAR system belongs to the measurement of the navigation belt, a certain degree of overlap between adjacent navigation belts needs to be maintained in order to ensure the full coverage of the measurement. For reasons of systematic errors and the like, the point clouds between adjacent navigation belts often have displacement, and full-coverage and continuous point clouds are required to be obtained through navigation belt splicing. The determination of the homonymy point is a key link for realizing the splicing of the navigation belt. The sea bottom point cloud data are mainly obtained through a multi-beam sounding system and an airborne LiDAR sounding system. The multi-beam sounding system is one of main means of underwater topography measurement, and a homonymous point confirmation method based on characteristics is often adopted for data fusion. The airborne LiDAR sounding system is commonly used for rapidly detecting the topography of shallow water areas such as islands and reefs, but the point cloud density is low, the thickness is large, the homonymous point features are rare and the extraction is difficult, so that the method for determining the homonymous point of the airborne LiDAR sounding seabed point cloud data is less at present.
The existing method mainly comprises the following steps: the method is simple and convenient to operate, but dead zones still exist in rough difference elimination and systematic difference compensation of mass sounding data; the implicit B spline surface fitting method and the least square trend surface method carry out weight distribution on the point clouds of the overlapped area to filter the points with the same name, and the method improves the splicing precision of the navigation belt better, but the filtering effect in the measurement of a large-scale water area is not ideal; the method for obtaining the island region homonymous points is higher in reliability, but lower in accuracy in a water area with unobvious extracted characteristic distinction.
Disclosure of Invention
The invention discloses a method for determining homonymy points of airborne LiDAR sounding strips, which solves the problem that homonymy point features between adjacent airborne LiDAR sounding strips are difficult to extract in the prior art.
A method for determining the same name point of an airborne LiDAR sounding strip comprises the following steps:
step 1: extracting submarine topography features based on the acquired airborne LiDAR sounding data;
step 2: taking the neighborhood terrain complexity multi-index model into consideration, carrying out coarse determination of the same name points;
step 3: and (3) combining homonymy point spacing constraint and a RANSAC algorithm, removing error point pairs and carrying out homonymy point precise determination.
Extracting the submarine topography features includes:
constructing a submarine point cloud local model by using an LM algorithm, wherein the model is shown in a formula (1):
in the formula ：(x，y，z) Is the three-dimensional coordinates of the laser point,a、b、c、d、e、fis the trend surface model coefficient.
Calculating the gradient of the laser point based on the formula (1)φCurvature ofC G Roughness ofK r ；
S1.1. Calculating sampling pointsx，yThe gradient in the direction is as shown in formula (2):
in the formula ：φ x andφ y respectively represent sampling pointsx、ySlope in the direction;
the gradient calculation of the sampling point is shown in the formula (3):
s1.2, selecting Gaussian curvatureC G ) As a topographical feature parameter, it is known that in a curved surface, each point has two radii of curvature, one for each pointR max AndR min and maximum curvatureC max And minimum curvatureC min Presence ofC max =1/R max 、C min =1/R min In whichR max AndR min respectively, in the formula (4) about the radius of curvatureRIs a root of (2):
in the formula ：
；
calculation of Gaussian curvatureC G As shown in formula (5):
s1.3, calculating the terrain roughness as shown in formulas (6) and (7):
in the formula ：S s representing the locally fitted surface area of the sample point,S E and representing the area of the surface horizontal projection corresponding to the local fitting curved surface of the sampling point.
The making of the coarse determination of the homonymy point includes:
s2.1, constructing a neighborhood terrain complexity multi-index model, obtaining the gradient, curvature and roughness of each neighborhood point and the distance between each neighborhood point and a sampling point, determining a neighborhood point set by using a KD tree algorithm, and constructing the neighborhood terrain complexity multi-index model by using an inverse distance weighting method, wherein the model is shown in a formula (8):
in the formula ：ω j =1/r j representing the weight;nis thatP i The number of neighborhood points;j=1,2,…,n；P i in order to be any point in the point cloud,r j representing the neighborhood point distanceP i Is a distance of (2);φ j is thatP i Is the first of (2)jGradient of each neighborhood point;
is thatP i Is the first of (2)jGaussian curvature of each neighborhood point; />
Is thatP i Is the first of (2)jRoughness of the neighboring points;φ NT (P i )、/>
(P i )、/>
(P i ) Respectively representing indexes of complexity gradient, curvature and roughness of neighborhood terrain;
s2.2, threshold constraint, setting a maximum allowable error constraint coefficientεTo realize automatic selection of threshold value, two adjacent navigation bands acquired by the airborne LiDAR sounding system are set as point cloudsA、BFirst, a point set is calculatedA、BThe calculation formula of the average topographic feature parameter of (2) is shown as formula (9):
in the formula ：N A 、N B respectively, point setsA、BThe number of points in (a) is,φ Ai 、φ Bi representing a set of pointsA、BMiddle (f)iSlope of the point;
and />
Representing a set of pointsA、BMiddle (f)iCurvature of the point; />
and />
Representing a set of pointsA、BMiddle (f)iRoughness of the dots;
is a point setAAndBcommon average slope, average curvature, average roughness;
s2.3. obtainingA、BThe absolute difference between the topographic feature value of the point set and the average value calculated in the formula (9) is arranged in ascending order, and then the first is selectedN r The difference of bits being the maximum allowable amount of the topography difference between the correct homonymous points, i.e. the thresholdT 0 、T 1 、T 2 Setting the maximum allowable error constraint coefficient asεThenN r Can be represented by formula (10):
calculating a threshold valueT 0 、T 1 、T 2 As shown in formula (11):
in the formula ：N=N A +N B ，εin percent, where deltaφ 1 、Δφ 2 、Δφ 3 、…、Δφ N Representative ofA、BGradient characteristic differences between pairs of points in the point set,
representative ofA、BCurvature characteristic difference between each point pair in point set, +.>
Representative ofA、BRoughness characteristic difference values between each point pair in the point set;
s2.3, roughly determining the same name points, and defining a same name point roughly determining condition according to the constructed neighborhood terrain complexity multi-index model, wherein the condition is shown in a formula (12):
in the formula ：
is thatA、BGradient difference among corresponding points in point set +.>
Is thatA、BThe difference in curvature between the corresponding points in the set of points,
is thatA、BThe roughness differences between corresponding points in the set of points.
Accurate determination of the homonymy point comprises the following steps:
the distance Li between the potential homonymous point pairs is shown in a formula (13):
in the formula ：i=1,2,3,…,n, wherein nRepresenting the number of pairs of points in the "potential homonymous point set",x Ai 、y Ai 、z Ai representing aerobandACoordinate values of the "potential homonymy points" in (a),x Bi 、y Bi 、z Bi representing aerobandBCoordinate values of the potential homonymous points;
find a set of point pairsSMaximum value of corresponding point-to-distance of (2)L max And minimum value ofL min From the collectionSRandomly selecting a pair of homonymous characteristic points, calculating Euclidean distance differences, counting the Euclidean distance differences of other point pairs and the absolute differences of the Euclidean distance differences between the Euclidean distance differences and the Euclidean distance differences are 0,k*(L max -L min )]the number of the inner points traverses all the point pairs, selects the Euclidean distance approximation with the maximum number of the point pairs as the homonymous point distance, eliminates the wrong homonymous point pairs and generates a new homonymous point setSAnd iterating for a plurality of times until the optimal homonymous point pair is obtained.
The invention has the beneficial effects that: the problem of shallow water sea area airborne LiDAR sounding homonymous point extraction difficulty is solved, and effective technical support and solution are provided for further improving sounding data processing and application.
Drawings
FIG. 1 is a flow chart of a method for determining a depth-measuring homonymy point of an airborne LiDAR taking the complexity of neighborhood terrain into consideration.
FIG. 2 is a schematic diagram of a neighborhood point set for constructing a sampling point Pi of a multi-index model of neighborhood terrain complexity in the invention.
FIG. 3 is a theoretical basis of the RANSAC algorithm of the invention for binding site spacing, where FIG. 3 shows [ ]a) Representing the point cloud set, in fig. 3b) Representing the selection of sampling points, shown in figure 3 @c) Is a polyhedral schematic diagram formed by sampling points and a horizontal projection plane thereof, in fig. 3 @d) Representing the simple transformation of the polyhedron, in figure 3 @e) Representing a schematic diagram of the distance between the same name points after simple transformation.
FIG. 4 is an analysis of the impact of threshold parameters and neighborhood radius on coarse determination accuracy.
Fig. 5 is a diagram of correspondence between precisely defined homonymous points.
Fig. 6 is a graph of ALB band effects based on determined homonymous point pairs prior to stitching.
Fig. 7 is a graph of the effects of the ALB band based on the determined homonymous point pairs after stitching.
Detailed Description
The invention is described in further detail below with reference to the attached drawings and detailed description:
a method for determining the same name point of an airborne LiDAR sounding strip comprises the following steps:
step 1: extracting submarine topography features based on the acquired airborne LiDAR sounding data;
step 2: taking the neighborhood terrain complexity multi-index model into consideration, carrying out coarse determination of the same name points;
step 3: and (3) combining homonymy point spacing constraint and a RANSAC algorithm, removing error point pairs and carrying out homonymy point precise determination.
Extracting the submarine topography features includes:
constructing a submarine point cloud local model by using an LM algorithm, wherein the model is shown in a formula (1):
in the formula ：(x，y，z) Is the three-dimensional coordinates of the laser point,a、b、c、d、e、fis the trend surface model coefficient.
Calculating the gradient of the laser point based on the formula (1)φCurvature ofC G Roughness ofK r ；
S1.1. Calculating sampling pointsx，yThe gradient in the direction is as shown in formula (2):
in the formula ：φ x andφ y respectively represent sampling pointsx、ySlope in the direction;
the gradient calculation of the sampling point is shown in the formula (3):
s1.2, selecting Gaussian curvatureC G ) As a topographical feature parameter, it is known that in a curved surface, each point has two radii of curvature, one for each pointR max AndR min and maximum curvatureC max And minimum curvatureC min Presence ofC max =1/R max 、C min =1/R min In whichR max AndR min respectively, in the formula (4) about the radius of curvatureRIs a root of (2):
in the formula ：
；
calculation of Gaussian curvatureC G As shown in formula (5):
s1.3, calculating the terrain roughness as shown in formulas (6) and (7):
in the formula ：S s representing the locally fitted surface area of the sample point,S E and representing the area of the surface horizontal projection corresponding to the local fitting curved surface of the sampling point.
The making of the coarse determination of the homonymy point includes:
s2.1, constructing a neighborhood terrain complexity multi-index model, obtaining the gradient, curvature and roughness of each neighborhood point and the distance between each neighborhood point and a sampling point, determining a neighborhood point set by using a KD tree algorithm, and constructing the neighborhood terrain complexity multi-index model by using an inverse distance weighting method, wherein the model is shown in a formula (8):
in the formula ：ω j =1/r j representing the weight;nis thatP i The number of neighborhood points;j=1,2,…,n；P i in order to be any point in the point cloud,r j representing the neighborhood point distanceP i Is a distance of (2);φ j is thatP i Is the first of (2)jGradient of each neighborhood point;
is thatP i Is the first of (2)jGaussian curvature of each neighborhood point; />
Is thatP i Is the first of (2)jRoughness of the neighboring points;φ NT (P i )、/>
(P i )、/>
(P i ) Respectively representing indexes of complexity gradient, curvature and roughness of neighborhood terrain;
s2.2, threshold constraint, setting a maximum allowable error constraint coefficientεTo realize automatic selection of threshold value, two adjacent navigation bands acquired by the airborne LiDAR sounding system are set as point cloudsA、BFirst, a point set is calculatedA、BThe calculation formula of the average topographic feature parameter of (2) is shown as formula (9):
in the formula ：N A 、N B respectively, point setsA、BThe number of points in (a) is,φ Ai 、φ Bi representing a set of pointsA、BMiddle (f)iSlope of the point;
and />
Representing a set of pointsA、BMiddle (f)iCurvature of the point; />
and />
Representing a set of pointsA、BMiddle (f)iRoughness of the dots;
is a point setAAndBcommon average slope, average curvature, average roughness;
s2.3. obtainingA、BThe absolute difference between the topographic feature value of the point set and the average value calculated in the formula (9) is arranged in ascending order, and then the first is selectedN r The difference of bits being the maximum allowable amount of the topography difference between the correct homonymous points, i.e. the thresholdT 0 、T 1 、T 2 Setting the maximum allowable error constraint coefficient asεThenN r Can be represented by formula (10):
calculating a threshold valueT 0 、T 1 、T 2 As shown in formula (11):
in the formula ：N=N A +N B ，εin percent, where deltaφ 1 、Δφ 2 、Δφ 3 、…、Δφ N Representative ofA、BGradient characteristic differences between pairs of points in the point set,
representative ofA、BCurvature characteristic difference between each point pair in point set, +.>
Representative ofA、BRoughness characteristic difference values between each point pair in the point set;
s2.3, roughly determining the same name points, and defining a same name point roughly determining condition according to the constructed neighborhood terrain complexity multi-index model, wherein the condition is shown in a formula (12):
in the formula ：
is thatA、BGradient difference among corresponding points in point set +.>
Is thatA、BCurvature difference between corresponding points in the point set +.>
Is thatA、BThe roughness differences between corresponding points in the set of points.
Accurate determination of the homonymy point comprises the following steps:
the distance Li between the potential homonymous point pairs is shown in a formula (13):
in the formula ：i=1,2,3,…,n, wherein nRepresenting the number of pairs of points in the "potential homonymous point set",x Ai 、y Ai 、z Ai representing aerobandACoordinate values of the "potential homonymy points" in (a),x Bi 、y Bi 、z Bi representing aerobandBCoordinate values of the potential homonymous points;
find a set of point pairsSMaximum value of corresponding point-to-distance of (2)L max And minimum value ofL min From the collectionSRandomly selecting a pair of homonymous characteristic points, calculating Euclidean distance differences, counting the Euclidean distance differences of other point pairs and the absolute differences of the Euclidean distance differences between the Euclidean distance differences and the Euclidean distance differences are 0,k*(L max -L min )]the number of the inner points traverses all the point pairs, selects the Euclidean distance approximation with the maximum number of the point pairs as the homonymous point distance, eliminates the wrong homonymous point pairs and generates a new homonymous point setSAnd iterating for a plurality of times until the optimal homonymous point pair is obtained.
The flow of the invention is shown in figure 1, the schematic diagram of the sampling point neighborhood point set is shown in figure 2, the theoretical basis diagram of RANSAC algorithm of the point cloud data combination point distance is shown in figure 3, wherein the graph is shown in the figure 3a) Representing the point cloud set, in fig. 3b) Representing the selection of sampling points, wherea、b、c、d、eAll are sampling points, in FIG. 3c) Namely a polyhedral schematic diagram formed by sampling points and a horizontal projection plane thereof, in figure 3d) Representing the simple transformation of the polyhedron, in figure 3 @e) The schematic diagram showing the distance between the same name points after simple transformation is shown by the graph of the figure 3e) It is known that when a three-dimensional image is simply transformed, the distances between homonymous points thereof tend to coincide. Therefore, according to the similarity of the distances between the homonymous points which are not subjected to coordinate transformation in the adjacent navigation bands, the invention provides a RANSAC algorithm combined with the distance constraint between the points to realize the elimination of error point pairs.
Due to the maximum distanceIs far from the minimum distance, thus introducing a proportionality coefficientkTo adjust the allowable interval. In practice, the correct point-to-point spacing is substantially uniform and will not differ too farkThe value range is set to be 0-1.kThe more the number of the obtained homonymous points is, the higher the point-to-error rate is, and the adjustment can be carried out according to the actual situation of the submarine topography.
In order to verify the performance of the method for determining the homonymy points between the navigation belts of the airborne LiDAR sounding system, the airborne LiDAR sounding data of a certain sea area is adopted for verification. The experiment uses an Optech Aquarius airborne LiDAR sounding system to measure shallow water submarine topography, the laser is green laser with wavelength of 532 nm, the pulse frequency is 70 kHz, the typical sounding capacity is 1 Secchi depth, the aircraft flying height is 300 m, the laser scans the nadir 15 degrees (a larger scanning nadir influences the measurement accuracy, a smaller nadir reduces the measurement efficiency, the scanning nadir is set to 15 degrees according to flight experience on the premise of ensuring the sounding accuracy and the measurement efficiency), the laser emitting angle is 1mrad, and the pulse width is 8.3 ns, so that 14 main measuring lines are obtained. After data pretreatment, a total of 1.8X10 were obtained 7 The density of the sea bottom point cloud is 4 pts/m 2 。
Differential threshold taking into account multiple indicators of neighborhood terrain complexityT 0 、T 1 、T 2And neighborhood radiusrHas a direct influence on the final precision result, and therefore needs to be done before accurate determinationT 0 、T 1 、T 2Andra number of experiments were performed. Since after the external factors are removed,T 0 、T 1 、T 2is equal to the maximum allowable error constraint coefficient onlyεRelated to the following. So only discussεAnd (3) withrAnd (3) obtaining the product. Selectingε=5 to 15% sumrThe coarse determination accuracy was calculated by=0.5 to 3 m, as shown in fig. 4.
As can be seen from fig. 4, whenεIs positioned between 5% and 15%, whenrWhen the distance is less than 2m, the determination accuracy is along withrIs gradually increased in the process ofrWhen=2 m, a maximum value is reached;rwhen the particle size is greater than 2mThe accuracy is along withrThe increase of (2) is slowly reduced, which indicates that when the neighborhood radius is 2m, the number of the neighborhood points is moderate, and the similarity degree of the neighborhood topography of the same name point pair can be effectively expressed. It can be clearly seen from a combination of tables 1 and 2 that, whenε、rWhen the value is smaller, the precision is relatively lower, and the number of homonymous points obtained after coarse determination is too small, so that the subsequent data processing is not facilitated; when (when)ε、rWhen the value is larger, the point-to-point precision is not continuously increased, and the operation efficiency of the whole algorithm is greatly reduced, soε、rThe appropriate value should be selected. As can be seen from a large number of experiments, whenεThe value is 10 percent,r=2m, the coarse determination works best. On the basis, the subsequent accurate determination of the homonymy point is performed, and the efficiency is higher. When (when)εThe neighborhood optimization for different search radii at 10% is shown in table 1.
TABLE 1 whenεNeighborhood optimization case for different search radii at 10%;
when (when)rThe neighborhood optimization for different search radii at=2m is shown in table 2.
TABLE 2 whenrNeighborhood optimization cases of different search radii when=2m;
in order to test the applicability of the method to terrains with different complexity, the test sample is divided into two groups of gentle submarine terrains and complex submarine terrains, and 4 experiments are carried out respectively.
Accurate determination of the same name point: after the characteristics are extracted, the airborne LiDAR sounding homonymy point determination method considering the complexity of the neighborhood terrain is utilized to detect and reject the same-name points, and the homonymy points after accurate determination are obtained. Fig. 5 shows the correspondence of part of the homonymous points after the precise determination, and the homonymous point pairs are found to be uniformly distributed in each area of the strip, namely the homonymous point pairs which can be used for splicing the navigation belt, so that the homonymous point extraction method has higher reliability and practicability.
Accurate precision assessment and analysis of the same name points: for quantitatively analyzing the precisely determined accuracy of the homonymous point pairs, the invention provides an evaluation index, namely distance deviation (Absolute Deviation, AD), namely Euclidean distance between the coordinates of the homonymous points after conversion and the real coordinates, and can reflect the deviation degree of the method on the homonymous point extraction. However, in practice, the airborne laser radar sounding system is affected by factors such as navigation errors, incident angle errors, sea waves and tides, and laser scanning data of the airborne laser radar sounding system are discrete points, so that two point clouds in different time phases do not have identical points in strict sense, and the identical points between adjacent strips have a certain amount of position offset. Taking the above factors into consideration, setting a distance deviation threshold value to be 0.3 m (when the distance deviation is smaller than 0.3 m, the point pair is used for subsequent registration, and the splicing effect is ideal), so as to judge whether the homonymy pair is correct, namely if the AD value of the homonymy point pair is smaller than the threshold value, the point pair can be considered to be correct; if the number is greater than the threshold value, the same-name point pair is considered to be wrong. Meanwhile, the invention selects the root mean square error (Root Mean Square Error, RMSE) value between the homonymous point coordinates and the true coordinates converted by the traditional ICP algorithm as the standard for evaluating the total quality of homonymous points, and finally determines the obtained homonymous point pairs preciselyx、y、zThe distance of the direction, the number of pairs of homonyms, and the accuracy were counted as shown in table 3.
The same name point determination condition of the set of strips in the table 3 8;
from table 3, it can be known that the final determination result is basically correct, the accuracy of determining the same-name points of the submarine gentle terrain area is 92.00%, the accuracy of determining the same-name points of the submarine complex terrain area is 97.45%, and from the overall effect, the submarine complex area with more remarkable characteristics has better accuracy effect, which is also a problem faced by all the current same-name point determination methods. However, the accuracy of the homonymous points obtained by the method exceeds 90% no matter in the submarine topography complex zone with obvious characteristics or in the flat sea area with few characteristics, and the homonymous point pairs which are sufficiently and uniformly distributed are used for subsequent data processing such as navigation belt splicing and the like, so that the robustness, the reliability and the practicability of the algorithm are proved. Based on the determined homonymous point pair, the navigation belt is spliced, the results before and after splicing are respectively shown in fig. 5 and 6, the point cloud data layering phenomenon can be obviously improved, and the data fusion effect is ideal.
It should be understood that the above description is not intended to limit the invention to the particular embodiments disclosed, but to limit the invention to the particular embodiments disclosed, and that the invention is not limited to the particular embodiments disclosed, but is intended to cover modifications, adaptations, additions and alternatives falling within the spirit and scope of the invention.
Claims (2)
1. The method for determining the homonymy point of the airborne LiDAR sounding strip is characterized by comprising the following steps of:
step 1: extracting submarine topography features based on the acquired airborne LiDAR sounding data;
step 2: taking the neighborhood terrain complexity multi-index model into consideration, carrying out coarse determination of the same name points;
step 3: combining homonymy point spacing constraint and a RANSAC algorithm, removing error point pairs and carrying out homonymy point fine determination;
extracting the submarine topography features includes:
constructing a submarine point cloud local model by using an LM algorithm, wherein the model is shown in a formula (1):
in the formula ：(x，y，z) Is the three-dimensional coordinates of the laser point,a、b、c、d、e、fis a trend surface model coefficient;
calculating the gradient of the laser point based on the formula (1)φCurvature ofC G Roughness ofK r ；
S1.1. Calculating sampling pointsx，yThe gradient in the direction is as shown in formula (2):
in the formula ：φ x andφ y respectively represent sampling pointsx、ySlope in the direction;
the gradient calculation of the sampling point is shown in the formula (3):
s1.2, selecting Gaussian curvatureC G ) As a topographical feature parameter, it is known that in a curved surface, each point has two radii of curvature, one for each pointR max AndR min and maximum curvatureC max And minimum curvatureC min Presence ofC max =1/R max 、C min =1/R min In whichR max AndR min respectively, in the formula (4) about the radius of curvatureRIs a root of (2):
in the formula ：
；
calculation of Gaussian curvatureC G As shown in formula (5):
s1.3, calculating the terrain roughness as shown in formulas (6) and (7):
in the formula ：S s representing the locally fitted surface area of the sample point,S E representing the area of the surface horizontal projection corresponding to the local fitting curved surface of the sampling point;
the making of the coarse determination of the homonymy point includes:
s2.1, constructing a neighborhood terrain complexity multi-index model, obtaining the gradient, curvature and roughness of each neighborhood point and the distance between each neighborhood point and a sampling point, determining a neighborhood point set by using a KD tree algorithm, and constructing the neighborhood terrain complexity multi-index model by using an inverse distance weighting method, wherein the model is shown in a formula (8):
in the formula ：ω j =1/r j representing the weight;nis thatP i The number of neighborhood points;j=1,2,…,n；P i in order to be any point in the point cloud,r j representing the neighborhood point distanceP i Is a distance of (2);φ j is thatP i Is the first of (2)jGradient of each neighborhood point;
is thatP i Is the first of (2)jGaussian curvature of each neighborhood point; />
Is thatP i Is the first of (2)jRoughness of the neighboring points;φ NT (P i )、/>
(P i )、/>
(P i ) Respectively representing indexes of complexity gradient, curvature and roughness of neighborhood terrain;
s2.2, threshold constraint, setting a maximum allowable error constraint coefficientεTo realize automatic selection of threshold value, two adjacent navigation bands acquired by the airborne LiDAR sounding system are set as point cloudsA、BFirst, a point set is calculatedA、BThe calculation formula of the average topographic feature parameter of (2) is shown as formula (9):
in the formula ：N A 、N B respectively, point setsA、BThe number of points in (a) is,φ Ai 、φ Bi representing a set of pointsA、BMiddle (f)iSlope of the point;
and
representing a set of pointsA、BMiddle (f)iCurvature of the point; />
and />
Representing a set of pointsA、BMiddle (f)iRoughness of the dots; />
Is a point setAAndBcommon average slope, average curvature, average roughness;
s2.3. obtainingA、BThe absolute difference between the topographic feature value of the point set and the average value calculated in the formula (9) is arranged in ascending order,select the firstN r The difference of bits being the maximum allowable amount of the topography difference between the correct homonymous points, i.e. the thresholdT 0 、T 1 、T 2 Setting the maximum allowable error constraint coefficient asεThenN r Can be represented by formula (10):
calculating a threshold valueT 0 、T 1 、T 2 As shown in formula (11):
in the formula ：N=N A +N B , wherein Δφ 1 、Δφ 2 、Δφ 3 、…、Δφ N Representative ofA、BGradient characteristic differences between pairs of points in the point set,
representative ofA、BCurvature characteristic difference between each point pair in point set, +.>
Representative ofA、BRoughness characteristic difference values between each point pair in the point set;
s2.4, roughly determining the same name points, and defining a same name point roughly determining condition according to the constructed neighborhood terrain complexity multi-index model, wherein the condition is shown in a formula (12):
in the formula ：
is thatA、BPoint concentrationGradient difference between corresponding points->
Is thatA、BThe difference in curvature between the corresponding points in the set of points,
is thatA、BThe roughness differences between corresponding points in the set of points.
2. The method for determining the homonymy point of the airborne LiDAR sounding strip according to claim 1, wherein the accurate determination of the homonymy point comprises the following steps:
the distance Li between the potential homonymous point pairs is shown in a formula (13):
in the formula ：i=1,2,3,…,n, wherein nRepresenting the number of pairs of points in the "potential homonymous point set",x Ai 、y Ai 、z Ai representing aerobandACoordinate values of the "potential homonymy points" in (a),x Bi 、y Bi 、z Bi representing aerobandBCoordinate values of the potential homonymous points;
find a set of point pairsSMaximum value of corresponding point-to-distance of (2)L max And minimum value ofL min From the collectionSRandomly selecting a pair of homonymous characteristic points, calculating Euclidean distance differences, counting the Euclidean distance differences of other point pairs and the absolute differences of the Euclidean distance differences between the Euclidean distance differences and the Euclidean distance differences are 0,k×(L max -L min )]the number of the inner points traverses all the point pairs, selects the Euclidean distance approximation with the maximum number of the point pairs as the homonymous point distance, eliminates the wrong homonymous point pairs and generates a new homonymous point setSIterating for a plurality of times until an optimal homonymy point pair is obtained; wherein,kis a proportionality coefficient.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310237959.6A CN115951371B (en) | 2023-03-14 | 2023-03-14 | Method for determining equivalent points of airborne LiDAR sounding navigation belt |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310237959.6A CN115951371B (en) | 2023-03-14 | 2023-03-14 | Method for determining equivalent points of airborne LiDAR sounding navigation belt |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115951371A CN115951371A (en) | 2023-04-11 |
| CN115951371B true CN115951371B (en) | 2023-06-09 |
Family
ID=85892356
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310237959.6A Active CN115951371B (en) | 2023-03-14 | 2023-03-14 | Method for determining equivalent points of airborne LiDAR sounding navigation belt |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115951371B (en) |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103822616A (en) * | 2014-03-18 | 2014-05-28 | 武汉大学 | Remote-sensing image matching method with combination of characteristic segmentation with topographic inequality constraint |
| CN110335297A (en) * | 2019-06-21 | 2019-10-15 | 华中科技大学 | A kind of point cloud registration method based on feature extraction |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111369436A (en) * | 2020-02-27 | 2020-07-03 | 山东科技大学 | Airborne LiDAR point cloud rarefying method considering multi-terrain features |
| CN113012206B (en) * | 2021-02-07 | 2022-03-25 | 山东科技大学 | Airborne and vehicle-mounted LiDAR point cloud registration method considering eave characteristics |
-
2023
- 2023-03-14 CN CN202310237959.6A patent/CN115951371B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103822616A (en) * | 2014-03-18 | 2014-05-28 | 武汉大学 | Remote-sensing image matching method with combination of characteristic segmentation with topographic inequality constraint |
| CN110335297A (en) * | 2019-06-21 | 2019-10-15 | 华中科技大学 | A kind of point cloud registration method based on feature extraction |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115951371A (en) | 2023-04-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105184250B (en) | A kind of terrain classification method of electric power corridor airborne LiDAR point cloud data | |
| CN108681525A (en) | A kind of road surface point cloud intensity enhancing method based on Vehicle-borne Laser Scanning data | |
| CN110780307B (en) | Method for obtaining road cross section based on storage battery car-mounted laser point cloud mobile measurement system | |
| CN109344812B (en) | Improved cluster-based single photon point cloud data denoising method | |
| CN109118574A (en) | A kind of fast reverse modeling method extracted based on three-dimensional feature | |
| CN105469388A (en) | Building point cloud registration algorithm based on dimension reduction | |
| CN113205486A (en) | Surface defect detection method and device based on three-dimensional point cloud and storage medium | |
| CN111539070B (en) | Wing body butt joint gap distribution control method based on measured data | |
| CN113470090A (en) | Multi-solid-state laser radar external reference calibration method based on SIFT-SHOT characteristics | |
| CN114037706A (en) | Precast beam segment surface flatness detection method based on three-dimensional point cloud model | |
| CN104597449B (en) | A kind of airborne many scanning weather radar target vertical contour reconstruction methods | |
| CN114877838B (en) | Road geometric feature detection method based on vehicle-mounted laser scanning system | |
| CN112666554A (en) | Method for identifying radar amplitude characteristic crack width of asphalt pavement | |
| CN110379011A (en) | A kind of underwater topography dot cloud hole method for repairing and mending based on improvement B-spline Curve | |
| CN115131231A (en) | Complex terrain area point cloud hierarchical filtering method assisted by multi-feature clustering | |
| CN104251662A (en) | Ordered point cloud threshold adaptive noise suppression technology | |
| CN113780085B (en) | Offshore single photon denoising classification method | |
| CN115951371B (en) | Method for determining equivalent points of airborne LiDAR sounding navigation belt | |
| CN115422981A (en) | Land and water classification method and system for single-frequency airborne laser sounding data and application | |
| CN117455970B (en) | Airborne laser sounding and multispectral satellite image registration method based on feature fusion | |
| CN106228521B (en) | A kind of barrier feature extracting method based on thin-plate spline interpolation | |
| CN111142165A (en) | Method for acquiring water level information of aquifer by using ground penetrating radar | |
| CN110927765B (en) | Laser radar and satellite navigation fused target online positioning method | |
| Xu et al. | A method of 3d building boundary extraction from airborne lidar points cloud | |
| CN116977580A (en) | Method for manufacturing mountain area large scale DEM based on airborne 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 |