CN115752448A - Laser point cloud air belt adjustment method combining point cloud matching and sensor data - Google Patents
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Abstract
The invention relates to the technical field of flight band parameter correction, and particularly discloses a laser point cloud flight band adjustment method combining point cloud matching and sensor data. The method decodes the original LiDAR file into point cloud data; acquiring the homonymy point pair relation of adjacent flight belts; searching a homonymous point pair of adjacent flight belts, and acquiring coordinate data of the homonymous point pair; solving the POS error correction value; errors from the POS during operation of the onboard LIDAR are corrected. The method comprises the steps of registering adjacent air strip point clouds, finding a plurality of pairs of approximate homonymy point pairs in the registered air strip point clouds, then jointly listing a balance equation according to an unmanned aerial vehicle LiDAR positioning principle formula and original LiDAR information of the approximate homonymy point pairs, then calculating a sensor parameter correction value by utilizing least square solution, and recalculating high-precision point clouds by correcting original POS data of the point clouds, so that the point cloud precision is improved, and the problem of altitude drift between air strips is solved.
Description
Technical Field
The invention belongs to the technical field of flight band parameter correction, and particularly relates to a laser point cloud flight band adjustment method combining point cloud matching and sensor data.
Background
An airborne LiDAR system is a complex multi-sensor integrated system consisting of multiple components such as GNSS, INS, and laser scanners, the accuracy of which is affected by the common influence of each component within the system. Various factors such as surface materials, flight heights, sensors and their platforms, GNSS/INS, observation angles, etc. can cause errors in data. These errors can be classified into both systematic errors and random errors. Random errors mainly come from the signal-to-noise ratio of the received signal, laser beam width, laser divergence, laser wavelength, receiver response, electronic clock accuracy, positioning and orientation accuracy of the platform, system view angle, transparency of air, and terrain coverage type, etc., and are not eliminable, but their effects can be attenuated by using measurement adjustment, repeated observation, etc. The system error mainly comes from the measurement error of each subsystem and the integration error between subsystems. The existence of the system error not only affects the absolute precision of the three-dimensional coordinates of the point cloud, but also more importantly, the system error causes the three-dimensional space offset among the homonymous features of different flight strips. In the operation process of the airborne LiDAR system, due to the limitation of the aerial height and the scanning field angle, the point cloud data of a single aerial zone can only cover a certain ground width. In line-type engineering, it is often necessary to fly multiple routes due to the instability of the route trends and solutions. Under the condition, the systematic error of the point cloud data is mainly reflected in that systematic deviation exists at the joint of the navigation band data after preprocessing and calibration are finished. At present, two main modes for reducing airborne LiDAR system errors are provided, wherein the first mode is system calibration, and the other mode is flight band adjustment.
The manual calibration method for solving the installation angle has the advantages that a calculation formula is simple, calibration can be completed manually, the defects that time is needed for repeating a gradual solving mode, the reliability of a manual measurement result is low, and in the solving process, external orientation elements are not corrected, so that the requirement on the accuracy of the external orientation parameters is high. In view of the disadvantages of time and labor consumption and lack of precision statistical indexes of the manual calibration method, the airborne LiDAR system is used for a rapid emergency response system, and if the manual calibration time is added, the efficiency is greatly reduced, and the requirements of real time and near real time are difficult to meet, so that a faster calibration method with less manual intervention is needed to meet the requirements of the rapid emergency system.
Disclosure of Invention
The embodiment of the invention aims to provide a laser point cloud air belt adjustment method combining point cloud matching and sensor data, and aims to solve the problems in the background technology.
In order to achieve the above purpose, the embodiments of the present invention provide the following technical solutions:
the laser point cloud air belt adjustment method combining point cloud matching and sensor data specifically comprises the following steps:
decoding the original LiDAR file through an airborne LiDAR positioning formula, so that scanning data of the unmanned airborne laser radar is decoded into point cloud data;
processing the point cloud data through a point cloud fine registration algorithm, aligning the point clouds of adjacent strips, and acquiring the homonymy point pair relation of the adjacent strips;
searching homonymous point pairs of adjacent flight belts from homonymous point pair relations of the adjacent flight belts according to a point cloud overlapping area searching algorithm, and acquiring coordinate data of the homonymous point pairs;
listing a balancing model equation, importing the coordinate data into the balancing model equation, and solving to calculate a POS error correction value;
and re-decoding according to the POS error correction value, and correcting the error from the POS in the airborne LIDAR working process.
As a further limitation of the technical solution of the embodiment of the present invention, the onboard LiDAR positioning formula is:
wherein the content of the first and second substances,
the coordinates of the laser scanning point P in the geocentric rectangular coordinate system,
is the coordinate of the IMU center in the earth center rectangular coordinate system,
is a rotation matrix from an IMU coordinate system to a geocentric rectangular coordinate system,
is a component representation of the offset of the Lidar scan center to the IMU navigation center in the IMU coordinate system,
is a rotation matrix from the Lidar coordinate system to the IMU coordinate system,
is the coordinate of the laser scanning point P in the Lidar coordinate system.
As a further limitation of the technical scheme of the embodiment of the invention, the method comprises the following steps
In
Then there are:
if the positioning error of the POS system in one navigation band is stable as B, the following steps are carried out:
as a further limitation of the technical scheme of the embodiment of the invention, the method comprises the following steps
In (1)
Then there are:
when the antisymmetric matrix processing is performed, the following steps are performed:
wherein the content of the first and second substances,
is composed of
And the vectors correspond to an antisymmetric matrix, and phi is a POS angle error vector.
As a further limitation of the technical solution of the embodiment of the present invention, according to the point cloud fine registration algorithm, two overlapped adjacent flight strips have multiple pairs of homonymous points, and for any pair of homonymous points m and n, an equation can be formed:
as a further limitation of the technical solution of the embodiment of the present invention
Determining the coordinate calculation equation of a pair of homonym points m and n:
wherein B (1) represents the POS positioning error of the first navigation band,
represents the POS angle error vector of the first navigation band, B (2) represents the POS positioning error of the second navigation band,
the POS angle error vector for the second flight band is represented.
As a further limitation of the technical solution of the embodiment of the present invention, when there is more than one pair of homologous points in adjacent flight strips, a plurality of equations are adopted:
and performing least square solution on the equation to obtain the position error and the angle error of the adjacent flight band.
Compared with the prior art, the invention has the beneficial effects that:
the embodiment of the invention decodes the original LiDAR file into point cloud data; acquiring the homonymy point pair relation of adjacent flight belts; searching a homonymous point pair of adjacent flight belts, and acquiring coordinate data of the homonymous point pair; solving the POS error correction value; and correcting errors from POS in the working process of the airborne LIDAR. The method comprises the steps of registering adjacent air strip point clouds, finding a plurality of pairs of approximate homonymy point pairs in the registered air strip point clouds, then jointly listing a balance equation according to an unmanned aerial vehicle LiDAR positioning principle formula and original LiDAR information of the approximate homonymy point pairs, then calculating a sensor parameter correction value by utilizing least square solution, and recalculating high-precision point clouds by correcting original POS data of the point clouds, so that the point cloud precision is improved, and the problem of altitude drift between air strips is solved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention.
Fig. 1 shows a flow chart of a method provided by an embodiment of the invention.
FIG. 2 is a schematic diagram illustrating pairs of approximately homonymous points of adjacent streamers in an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
It can be understood that, in the prior art, the manual calibration method has the disadvantages that the time spent on repeating progressive solution is relatively long, the reliability of the manual measurement result is relatively low, and secondly, in the solution process, there is no way to correct the external orientation element, so the precision requirement of the external orientation parameter is relatively high.
In order to solve the problems, the embodiment of the invention constructs an authorized cooperative communication channel between an outsourcer and an insider; sending the basic assistance work data to the inside assistant staff and the outside assistant staff; performing supplementary authorization judgment and simultaneous online judgment; and if the supplementary authorization passes, sending the supplementary authorization to outsourcing personnel through an authorization cooperative communication channel. Can be according to the application of outer cooperation authorization, match interior person of assisting, the cooperation communication channel of authorizing of the person of establishing outer cooperation and interior person of assisting, assist the working data to send simultaneously to interior person of assisting and exterior person of assisting with the basis, and when the person of assisting carries out the supplementary data application, carry out supplementary authorization judgement and online judgement simultaneously, and then according to different situation, the interior person of assisting who supplements the working data examines and send in coordination, or send and authorize the notice not, thereby can be when avoiding the confidential information of enterprise to reveal, can reduce the loaded down with trivial details difficult step of collaborative information application, effectively improve the efficiency of collaborative work.
Fig. 1 shows a flow chart of a method provided by an embodiment of the invention.
Specifically, the laser point cloud air belt adjustment method combining point cloud matching and sensor data specifically comprises the following steps:
step S101, decoding the original LiDAR file through an airborne LiDAR positioning formula, so that scanning data of the unmanned airborne laser radar is decoded into point cloud data.
And S102, processing point cloud data through a point cloud fine registration algorithm, aligning point clouds of adjacent strips, and obtaining the homonymy point pair relation of the adjacent strips.
And S103, searching the homonymous point pairs of the adjacent navigation bands from the homonymous point pair relationship of the adjacent navigation bands according to the point cloud overlapping area searching algorithm, and acquiring the coordinate data of the homonymous point pairs.
And step S104, listing a balancing model equation, importing the coordinate data into the balancing model equation, and calculating the POS error correction value.
And step S105, re-decoding according to the POS error correction value, and correcting the error from the POS in the airborne LIDAR working process.
In the embodiment of the invention, the original LiDAR file is decoded according to an airborne LiDAR positioning formula, and the scanning data of the unmanned airborne laser radar is decoded into point cloud data, so that the subsequent processing is facilitated; aligning the point clouds of adjacent flight zones by a point cloud precise registration algorithm, aiming at obtaining the homonymy point pair relation of the adjacent flight zones; further searching homonymy point pairs of adjacent flight strips by utilizing an algorithm for searching a point cloud overlapping region in PCL (personal computer), and aiming at providing a constraint relation for a subsequent adjustment model; the method comprises the steps of calculating a POS error correction value by using least square solution through listing an equation of a balancing model and substituting coordinate data of the same-name point pair into the equation; and the error correction value of the POS is used and substituted into the LiDAR file decoding link again to correct the error from the POS in the airborne LIDAR working process, so that the aim of resolving the point cloud with higher precision is fulfilled. The specific method comprises the following steps:
unmanned aerial vehicle LiDAR positioning principle formula:
wherein the content of the first and second substances,
the coordinate of the laser scanning point P in a geocentric rectangular coordinate system (e system for short);
coordinates of the IMU center in an e system;
is a rotation matrix from an IMU coordinate system (called I system for short) to an e system;
component expression in line I for the offset of Lidar scanning center to IMU navigation center;
a rotation matrix from a Lidar coordinate system (S system for short) to an I system;
the coordinate of the laser scanning point P in the Lidar coordinate system;
order to
Then the
Can be written as:
if the positioning error of the POS system in one navigation band is stable to be B, then
Can be written as:
wherein, let E be a stable rotation matrix in a navigation band
The angle error vector of (1) is corresponding to an inverse symmetric matrix, such that
Then the
Can be written as:
by the property of an antisymmetric matrix, then
Can be written as:
wherein the content of the first and second substances,
is composed of
The inverse symmetric matrix corresponding to the vector is phi, which is the POS angle error vector corresponding to E;
as fig. 2 shows a schematic diagram of approximately homonymous point pairs of adjacent strips in an embodiment of the present invention, many pairs of homonymous points can be found for two adjacent strips that overlap. For any pair of homologous points (e.g., pair of homologous points m, n), the following equations may be formed:
by
The coordinate calculation equations of two points can be written respectively:
wherein B (1) represents the POS positioning error of the first navigation band,
represents the POS angle error vector of the first navigation band, B (2) represents the POS positioning error of the second navigation band,
a POS angle error vector representing a second flight band;
by mixing
And
by subtraction, one can get:
written in matrix form as:
wherein the content of the first and second substances,
for any pair of homologous points present in adjacent strips, an equation can be formed
If there are multiple pairs of homologous points in adjacent strips, then multiple equations can be formed
And performing least square solution on the equations to obtain the position error and the angle error of the adjacent flight belts.
In summary, in the embodiments of the present invention, the original LiDAR file is decoded by an onboard LiDAR positioning formula, so that the scanning data of the unmanned airborne LiDAR is decoded into point cloud data; processing point cloud data through a point cloud precise registration algorithm, aligning point clouds of adjacent strips, and obtaining homonymy point pair relations of the adjacent strips; searching homonymous point pairs of adjacent flight belts from homonymous point pair relations of the adjacent flight belts according to a point cloud overlapping area searching algorithm, and acquiring coordinate data of the homonymous point pairs; listing a balancing model equation, importing the coordinate data into the balancing model equation, and solving a POS error correction value; and re-decoding according to the POS error correction value, and correcting the error from the POS in the airborne LIDAR working process. The method comprises the steps of registering adjacent air strip point clouds, finding a plurality of pairs of approximate homonymy point pairs in the registered air strip point clouds, then jointly listing a balance equation according to an unmanned aerial vehicle LiDAR positioning principle formula and original LiDAR information of the approximate homonymy point pairs, then calculating a sensor parameter correction value by utilizing least square solution, and recalculating high-precision point clouds by correcting original POS data of the point clouds, so that the point cloud precision is improved, and the problem of altitude drift between air strips is solved.
It should be understood that, although the steps in the flowcharts of the embodiments of the present invention are shown in sequence as indicated by the arrows, the steps are not necessarily performed in sequence as indicated by the arrows. The steps are not limited to being performed in the exact order illustrated and, unless explicitly stated herein, may be performed in other orders. Moreover, at least a portion of the steps in various embodiments may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a non-volatile computer-readable storage medium, and can include the processes of the embodiments of the methods described above when the program is executed. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), rambus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent should be subject to the appended claims.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (7)
1. The laser point cloud air belt adjustment method combining point cloud matching and sensor data is characterized by comprising the following steps:
decoding the original LiDAR file through an airborne LiDAR positioning formula, so that scanning data of the unmanned airborne laser radar is decoded into point cloud data;
processing the point cloud data through a point cloud fine registration algorithm, aligning the point clouds of adjacent strips, and acquiring the homonymy point pair relation of the adjacent strips;
searching homonymous point pairs of adjacent flight belts from homonymous point pair relations of the adjacent flight belts according to a point cloud overlapping area searching algorithm, and acquiring coordinate data of the homonymous point pairs;
listing a balancing model equation, importing the coordinate data into the balancing model equation, and solving a POS error correction value;
and re-decoding according to the POS error correction value, and correcting the error from the POS in the airborne LIDAR working process.
2. The laser point cloud swath adjustment method in combination with point cloud matching and sensor data of claim 1, wherein the onboard LiDAR positioning formula is:
wherein the content of the first and second substances,
the coordinates of the laser scanning point P in the geocentric rectangular coordinate system,
is the coordinate of the IMU center in the earth center rectangular coordinate system,
is a rotation matrix from an IMU coordinate system to a geocentric rectangular coordinate system,
is a component representation of the offset of the Lidar scan center to the IMU navigation center in the IMU coordinate system,
is a rotation matrix from the Lidar coordinate system to the IMU coordinate system,
is the coordinate of the laser scanning point P in the Lidar coordinate system.
3. The laser point cloud swath adjustment method combining point cloud matching and sensor data of claim 2, wherein the method is performed by aligning the laser point cloud swath
In (1)
Then there are:
if the positioning error of the POS system in one navigation band is stable as B, the following steps are carried out:
4. the laser point cloud swath adjustment method combining point cloud matching and sensor data of claim 3, wherein the method is performed by
In (1)
Then there are:
when the antisymmetric matrix processing is performed, the following steps are performed:
wherein, the first and the second end of the pipe are connected with each other,
is composed of
And the vectors correspond to an antisymmetric matrix, and phi is a POS angle error vector.
5. The laser point cloud swath adjustment method combining point cloud matching and sensor data of claim 4 wherein according to the point cloud fine registration algorithm, two adjacent swaths that overlap, have multiple pairs of homonymous points, for any pair of homonymous points m and n, the equation can be formed:
6. the laser point cloud midrange method combining point cloud matching and sensor data of claim 5, wherein the method is based on
Determining the coordinate calculation equation of a pair of homonym points m and n:
wherein B (1) represents the POS positioning error of the first navigation band,
represents the POS angle error vector of the first navigation band, B (2) represents the POS positioning error of the second navigation band,
the POS angle error vector for the second flight strip is represented.
7. The method of laser point cloud fairway leveling in combination with point cloud matching and sensor data of claim 6, wherein when there are more than one pair of homologous points in adjacent fairways, multiple equations are employed:
and performing least square solution on the equation to obtain the position error and the angle error of the adjacent flight band.
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