CN108253942B - Method for improving oblique photography measurement space-three quality - Google Patents
Method for improving oblique photography measurement space-three quality Download PDFInfo
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- CN108253942B CN108253942B CN201710426613.5A CN201710426613A CN108253942B CN 108253942 B CN108253942 B CN 108253942B CN 201710426613 A CN201710426613 A CN 201710426613A CN 108253942 B CN108253942 B CN 108253942B
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Abstract
The invention relates to a method for improving oblique photogrammetry space-three quality aiming at oblique image data. The invention discloses a method for improving the aerial three quality of oblique photogrammetry, which comprises the following steps: 1) acquiring inclination image data, GNSS data and IMU data through aerial remote sensing flight; 2) arranging the inclined image data, the GNSS data and the IMU data and converting coordinates to calculate an initial value of the combined navigation data; 3) performing null-three aiming at initial values of the oblique image data and the combined navigation data to obtain corresponding null-three results and null-three reports; 4) and comparing the empty three results and the empty three reports under different conditions, and selecting the optimal result for the live-action three-dimensional modeling. The invention carries out rapid post-processing aiming at the oblique image data, quantitatively analyzes the quality of aerial survey data products, and timely judges and selects an optimized result, thereby improving the working efficiency and reducing the loss.
Description
Technical Field
The invention relates to a method for improving oblique photogrammetry space-three quality aiming at oblique image data, in particular to a method for improving oblique photogrammetry space-three quality.
Background
The method is characterized in that real-scene three-dimensional modeling is carried out by utilizing oblique image data, the key is the quality of aerial triangulation, and if the aerial triangulation quality is poor, the problems of distortion, stretching, blurring, color unevenness, leak and the like can occur in the real-scene three-dimensional modeling at the later stage, so that the operation task fails.
Disclosure of Invention
The invention aims to provide a method for improving oblique photogrammetry aerial three quality, which is used for carrying out aerial triangulation on oblique image data, quantitatively analyzing the quality of aerial survey data products and timely judging and selecting an optimized result, thereby improving the working efficiency and reducing the loss.
In order to achieve the purpose, the invention has the following technical scheme:
the invention relates to a method for improving the quality of oblique photography measurement space and space, which comprises the following steps:
1) acquiring original data comprising inclination image data, GNSS data and IMU data through aerial remote sensing flight;
2) arranging the inclined image data, the GNSS data and the IMU data and converting coordinates to calculate an initial value of the combined navigation data;
3) performing null-third aiming at the initial value of the combined navigation data in the step 2) to obtain a corresponding null-third result and a null-third report;
4) and comparing the empty three results and the empty three reports under different conditions, and selecting the optimal result for the live-action three-dimensional modeling to form clear and accurate three-dimensional geographic information data.
Wherein, the oblique image data in the step 1) comprises color digital image data of five angles of front view, back view, left view, right view and downward view; the GNSS data in the step 1) comprises longitude, latitude and elevation data, and the IMU data in the step 1) comprises angle elements Roll (phi), Pitch (theta) and Heading (psi);
the coordinate conversion in the step 2) is to convert the IMU data into angle elements Roll (Phi), Pitch (theta) and yaw (psi) for describing the sensor attitude into angle elements Omega (Omega), Phi (Phi) and Phi (Phi) for representing the angular orientation of the aerial image in the field of photogrammetry
Kappa(κ)；
Wherein the angle elements omega,
The formula for κ is:
wherein the content of the first and second substances,
wherein the content of the first and second substances,
is a rotation matrix like the space coordinate system (i) to the ground auxiliary coordinate system (m),
is a rotation matrix from the navigation coordinate system (g) to the geocentric coordinate system (E),
is a rotation matrix from the IMU coordinate system (b) to the navigation coordinate system (g),
is a rotation matrix from the sensor coordinate system (c) to the IMU coordinate system (b),
is a rotation matrix like the spatial coordinate system (i) to the sensor coordinate system (c);
the local auxiliary coordinate system is selected as the center (L) of the measuring area0,B0) A rotation matrix formed by the coordinate system of the ellipsoid tangent plane; l, λ is the longitude and latitude of the IMU center at the instant of imaging, Θx,Θy,ΘzIs the IMU axis offset relative to the sensor;
wherein the arrangement in step 2) comprises the following steps:
1) the GNSS data are arranged under the condition that the original data do not have IMU data, the IMU initial value is calculated by combining the inclined image data and importing into photogrammetry software, and the IMU initial value is respectively stored as an angle element Roll (Phi), a Pitch (theta), a Heading (psi) and an angle element Omega (Omega), Phi
Kappa(κ)；
2) The method comprises the steps of sorting GNSS data and IMU data under the condition that original data have IMU data, and directly taking angle elements Roll (phi), Pitch (theta) and header (psi) as initial values of the IMU data;
3) the GNSS data and the IMU data are collated under the condition that the IMU data is contained in the raw data, and the angle elements Roll (Phi), Pitch (theta) and Heading (psi) are converted into angle elements Omega (Omega), Phi (Phi) used for representing the angular orientation of the aerial photography film in the field of photogrammetry
Kappa (. Kappa.) and the corner elements Omega (. Omega.), Phi
Kappa (κ) is used as the initial value of IMU data.
Wherein, still include the following step:
1) under the condition that the original data does not have IMU data, performing null three on the inclination image data, the GNSS data and combined navigation data with angle elements of Roll (phi), Pitch (theta) and Heading (psi) as initial values of the IMU data; for tilt image data, GNSS data and angle elements Omega (Omega), Phi
Performing air-navigation by taking Kappa (Kappa) as combined navigation data of an initial value of IMU data, respectively performing iterative computation on position information and attitude information of the oblique image data, eliminating residual errors and gross errors, and when the optimal iteration times are reached, if the air-navigation results exist that an aerial ray set is not parallel to an X-Y plane, not continuing the iterative computation, and judging that the results fail; if all the aerial sets are parallel to the X-Y plane, the iterative computation is not continued, and the result is judged to be feasible; when the optimal iteration times are not reached, if the three results of the air exist and the aerial sets are not parallel to the X-Y plane, continuously performing iterative computation on position information and posture information of the oblique image data, eliminating residual errors and gross errors until all the aerial sets are parallel to the X-Y plane, and judging that the results are feasible; taking the feasible space-three results as new initial values to continue space-three, iteratively calculating the position information and the attitude information of the oblique image data,removing residual error and gross error, observing the number of connection points after each calculation, when the number of the connection points reaches the maximum, selecting the result of the next time of blank three as a new initial value to continue blank three, iteratively optimizing the position information and the attitude information of the oblique image data, removing the residual error and the gross error, observing the number of the connection points after each optimization, when the number of the connection points reaches the maximum, if the GNSS data does not adopt RTK measurement, finishing the optimization, and obtaining a blank three result and a blank three report; if the GNSS data adopts RTK measurement, selecting the next empty three result as a new initial value to continue empty three, iteratively optimizing the position information and the attitude information of the oblique image data, eliminating residual errors and gross errors, observing the number of connection points after each calculation, selecting the next empty three result as a final result when the number of the connection points reaches the maximum, comparing the final results of the two empty three times, and selecting the result with more connection points and high precision for live-action three-dimensional modeling;
2) performing null-third on the inclination image data, the GNSS data and the combined navigation data taking angle elements of Roll (phi), Pitch (theta) and Heading (psi) as initial values of the IMU data under the condition that the original data contains IMU data; for tilt image data, GNSS data and angle elements Omega (Omega), Phi
Performing air-navigation by taking Kappa (Kappa) as combined navigation data of an initial value of IMU data, respectively performing iterative computation on position information and attitude information of the oblique image data, eliminating residual errors and gross errors, and when the optimal iteration times are reached, if the air-navigation results exist that an aerial ray set is not parallel to an X-Y plane, not continuing the iterative computation, and judging that the results fail; if all the aerial sets are parallel to the X-Y plane, the iterative computation is not continued, and the result is judged to be feasible; when the optimal iteration times are not reached, if the aviation ray set is not parallel to the X-Y plane, if the aviation three results are that the aviation ray set is not parallel to the X-Y plane, continuously performing iterative calculation on the position information and the attitude information of the inclined image data, eliminating residual errors and gross errors until all the aviation ray sets are parallel to the X-Y plane, and judging that the results are feasible; taking the feasible space-three result as a new initial value to continue space-three, and iteratively calculating the position of the oblique image dataSetting information and attitude information, removing residual error and gross error, observing the number of connection points after each calculation, when the number of the connection points reaches the maximum, selecting the result of the next time of blank three as a new initial value to continue blank three, iteratively optimizing the position information and the attitude information of the oblique image data, removing the residual error and the gross error, observing the number of the connection points after each optimization, and when the number of the connection points reaches the maximum, if the GNSS data does not adopt RTK measurement, ending the optimization to obtain a result of blank three and a report of blank three; and if the GNSS data adopts RTK measurement, selecting the next space-three result as a new initial value to continue space-three, iteratively optimizing the position information and the attitude information of the oblique image data, eliminating residual errors and gross errors, observing the number of the connection points after each calculation, selecting the next space-three result as a final result when the number of the connection points reaches the maximum, comparing the final results of the two space-three times, and selecting the result with more connection points and high precision for the live-action three-dimensional modeling.
The iterative calculation for multiple times is used for eliminating residual errors and gross errors, so that the position and the posture of the image are correct, the number of connecting points is sufficient, the number of triangular grids used for modeling is sufficient, and the quality of the three-dimensional model is improved.
Due to the adoption of the technical scheme, the invention has the advantages that:
the method is simple to operate, different strategies can be used for calculation, and then the optimal solution is found from different results, so that the achievement quality is the best.
2 convenient and fast, can quantitative analysis, the precision is high, can improve work efficiency, reduces repetitive work and work loss.
Drawings
FIG. 1 is a flow chart of the present invention when IMU data is available as raw data;
FIG. 2 is a flow chart of the present invention when the original data does not have IMU data;
FIG. 3 is a schematic diagram of the three aerial results of the present invention stored in ray sets not parallel to the X-Y plane;
fig. 4 is a normal diagram of the empty three results.
In the figure, 1, routes are gathered; 2. collecting two aerial lines;
Detailed Description
The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.
Referring to fig. 1-4, a method for improving the quality of oblique photogrammetry space-three according to the present invention comprises the following steps:
1) acquiring original data comprising inclination image data, GNSS data and IMU data through aerial remote sensing flight;
2) arranging the inclined image data, the GNSS data and the IMU data and converting coordinates to calculate an initial value of the combined navigation data;
3) performing null-third aiming at the initial value of the combined navigation data (the combined navigation data refers to GNSS data and IMU data, the combined navigation data is not similar to the inclined image data, the initial value refers to the initial value of the combined navigation data, and the initial value has no relation with the inclined image data) in the step 2) to obtain a corresponding null-third result and a null-third report;
4) and comparing the empty three results and the empty three reports under different conditions, and selecting the optimal result for the live-action three-dimensional modeling to form clear geographic information data.
Wherein, the oblique image data in the step 1) comprises color digital image data of five angles of front view, back view, left view, right view and downward view; the GNSS data in the step 1) comprises longitude, latitude and elevation data, and the IMU data in the step 1) comprises angle elements Roll (phi), Pitch (theta) and Heading (psi);
the coordinate conversion in the step 2) is to convert the IMU data into angle elements Roll (Phi), Pitch (theta) and yaw (psi) for describing the sensor attitude into angle elements Omega (Omega), Phi (Phi) and Phi (Phi) for representing the angular orientation of the aerial image in the field of photogrammetry
Kappa(κ)；
Wherein the angle elements omega,
The formula for κ is:
wherein the content of the first and second substances,
wherein the content of the first and second substances,
is a rotation matrix like the space coordinate system (i) to the ground auxiliary coordinate system (m),
is a rotation matrix from the navigation coordinate system (g) to the geocentric coordinate system (E),
is a rotation matrix from the IMU coordinate system (b) to the navigation coordinate system (g),
is a rotation matrix from the sensor coordinate system (c) to the IMU coordinate system (b),
is a rotation matrix like the spatial coordinate system (i) to the sensor coordinate system (c);
the local auxiliary coordinate system is selected as the center (L) of the measuring area0,B0) A rotation matrix formed by the coordinate system of the ellipsoid tangent plane; l, λ is the longitude and latitude of the IMU center at the instant of imaging, Θx,Θy,ΘzIs the IMU axis offset relative to the sensor;
wherein the arrangement in step 2) comprises the following steps:
1) the GNSS data are arranged under the condition that the original data do not have IMU data, the IMU initial value is calculated by combining the inclined image data and importing into photogrammetry software, and the IMU initial value is respectively stored as an angle element Roll (Phi), a Pitch (theta), a Heading (psi) and an angle element Omega (Omega), Phi
Kappa(κ)；
2) The method comprises the steps of sorting GNSS data and IMU data under the condition that original data have IMU data, and directly taking angle elements Roll (phi), Pitch (theta) and header (psi) as initial values of the IMU data;
3) the GNSS data and the IMU data are collated under the condition that the IMU data is contained in the raw data, and the angle elements Roll (Phi), Pitch (theta) and Heading (psi) are converted into angle elements Omega (Omega), Phi (Phi) used for representing the angular orientation of the aerial photography film in the field of photogrammetry
Kappa (. Kappa.) and the corner elements Omega (. Omega.), Phi
Kappa (κ) is used as the initial value of IMU data.
Wherein, still include the following step:
1) under the condition that the original data does not have IMU data, performing null three on the inclination image data, the GNSS data and combined navigation data with angle elements of Roll (phi), Pitch (theta) and Heading (psi) as initial values of the IMU data; for tilt image data, GNSS data and angle elements Omega (Omega), Phi
Performing air-navigation by taking Kappa (Kappa) as combined navigation data of an initial value of IMU data, respectively performing iterative computation on position information and attitude information of the oblique image data, eliminating residual errors and gross errors, and when the optimal iteration times are reached, if the air-navigation results exist that an aerial ray set is not parallel to an X-Y plane, not continuing the iterative computation, and judging that the results fail; if all the aerial sets are parallel to the X-Y plane, the iterative computation is not continued, and the result is judged to be feasible; when the optimal iteration times are not reached, if the three results of the air exist and the aerial sets are not parallel to the X-Y plane, continuously performing iterative computation on position information and posture information of the oblique image data, eliminating residual errors and gross errors until all the aerial sets are parallel to the X-Y plane, and judging that the results are feasible; continuously performing space-three calculation by taking a feasible space-three result as a new initial value, iteratively calculating position information and attitude information of the oblique image data, removing residual errors and gross errors, observing the number of connection points after each calculation, selecting the space-three result as the new initial value to continuously perform space-three calculation when the number of the connection points reaches the maximum, iteratively optimizing the position information and the attitude information of the oblique image data, removing the residual errors and the gross errors, observing the number of the connection points after each optimization, and finishing the optimization if the GNSS data does not adopt RTK measurement when the number of the connection points reaches the maximum to obtain a space-three result and a space-three report; if the GNSS data adopts RTK measurement, selecting the next empty three result as a new initial value to continue empty three, iteratively optimizing the position information and the attitude information of the oblique image data, eliminating residual errors and gross errors, observing the number of connection points after each calculation, selecting the next empty three result as a final result when the number of the connection points reaches the maximum, comparing the final results of the two empty three times, and selecting the result with more connection points and high precision for live-action three-dimensional modeling;
2) performing null-third on the inclination image data, the GNSS data and the combined navigation data taking angle elements of Roll (phi), Pitch (theta) and Heading (psi) as initial values of the IMU data under the condition that the original data contains IMU data; for tilt image data, GNSS data and angle elements Omega (Omega), Phi
Performing air-navigation by taking Kappa (Kappa) as combined navigation data of an initial value of IMU data, respectively performing iterative computation on position information and attitude information of the oblique image data, eliminating residual errors and gross errors, and when the optimal iteration times are reached, if the air-navigation results exist that an aerial ray set is not parallel to an X-Y plane, not continuing the iterative computation, and judging that the results fail; if all the aerial sets are parallel to the X-Y plane, the iterative computation is not continued, and the result is judged to be feasible; when the optimal iteration times are not reached, if the aviation ray set is not parallel to the X-Y plane, if the aviation three results are that the aviation ray set is not parallel to the X-Y plane, continuously performing iterative calculation on the position information and the attitude information of the inclined image data, eliminating residual errors and gross errors until all the aviation ray sets are parallel to the X-Y plane, and judging that the results are feasible; continuously performing space-three calculation by taking a feasible space-three result as a new initial value, iteratively calculating position information and attitude information of the oblique image data, removing residual errors and gross errors, observing the number of connection points after each calculation, selecting the space-three result as the new initial value to continuously perform space-three calculation when the number of the connection points reaches the maximum, iteratively optimizing the position information and the attitude information of the oblique image data, removing the residual errors and the gross errors, observing the number of the connection points after each optimization, and finishing the optimization if the GNSS data does not adopt RTK measurement when the number of the connection points reaches the maximum to obtain a space-three result and a space-three report; and if the GNSS data adopts RTK measurement, selecting the next space-three result as a new initial value to continue space-three, iteratively optimizing the position information and the attitude information of the oblique image data, eliminating residual errors and gross errors, observing the number of the connection points after each calculation, selecting the next space-three result as a final result when the number of the connection points reaches the maximum, comparing the final results of the two space-three times, and selecting the result with more connection points and high precision for the live-action three-dimensional modeling.
Wherein the optimal number of iterations is 6-10.
The iterative calculation for multiple times is used for eliminating residual errors and gross errors, so that the position and the posture of the image are correct, the number of connecting points is sufficient, the number of triangular grids used for modeling is sufficient, and the quality of the three-dimensional model is improved.
Table 1 is a report table of empty three-precision formed by six iterations in the embodiment of the present invention:
photogrammetry software: photoscan or Pix4D mapper
X-Y plane: is a plane parallel to sea level.
And (4) route collection: is a collection of several oblique images.
Oblique photography: the oblique photography technology is a high and new technology developed in recent years in the international surveying and mapping field, which overturns the limitation that the prior orthoimage can only be shot from a vertical angle, and introduces a user into a real visual world which accords with human vision by carrying a plurality of sensors on the same flight platform and acquiring images from five different angles of one vertical angle, four oblique angles and the like. The aerial oblique image not only can truly reflect the ground object condition, but also embeds accurate geographic information, richer image information and higher user experience by adopting an advanced positioning technology, thereby greatly expanding the application field of the remote sensing image and leading the industry application of the remote sensing image to be deeper. Because the oblique images provide richer geographic information for users and more friendly user experience, the technology is widely applied to the industries of emergency command, homeland security, city management, house tax and the like in developed countries such as Europe and America.
And (3) real scene three-dimensional modeling: it is meant that a high resolution, three-dimensional model with realistic texture maps is automatically generated from a series of two-dimensional photographs, or a set of oblique images. If the oblique shots carry coordinate information, the model's geographic location information is also accurate. The model has vivid effect, comprehensive elements and measurement precision, brings people with personally on-the-scene feeling, can be used for metrology application and is a real reduction of the real world.
An IMU: the inertial measurement unit is a device for measuring the three-axis attitude angle (or angular velocity) and acceleration of an object;
GNSS: i.e. the abbreviation of Global Navigation Satellite System. Beginning in the middle of the 90 s of the 20 th century, the european union has been dedicated to the Global Navigation Satellite System project called Global Navigation Satellite System in order to break the monopoly of the united states in the Satellite positioning, Navigation and time service markets, obtain great market benefits, and increase employment opportunities of european people. The plan is implemented in two steps: the first step is to establish a first generation global navigation satellite system (now called GNSS-1, the later established EGNOS) that makes use of the united states GPS system and russian GLONASS system; the second step is to establish a second generation global navigation satellite system, i.e. the built Galileo satellite navigation positioning system, which is completely independent of the GPS system in the united states and the GLONASS system in russia. Therefore, the GNSS is not a single constellation system but a comprehensive constellation system including GPS, GLONASS and the like as soon as being published; differential GNSS refers to a technique for reducing the positioning error of the GPS system or the GLONASS system by utilizing additional data from a reference GNSS receiver whose position is known.
And (3) combining navigation data: the integrated navigation data is the combined navigation data of satellite navigation data (GNSS data) and inertial navigation data (IMU data), and comprises the position information and the attitude information of a target object.
Connection points (Tiepoints): in the overlapping range of the stereopair, the image construction points of the same object point on different images are called homonymous image points, and a large number of automatically or manually generated homonymous image points are collectively called connection points.
And (3) air separation: the aerial triangulation is a measuring method for encrypting control points indoors according to a small number of field control points to obtain the elevation and the plane position of the encrypted points in the stereo photogrammetry. The main purpose of the method is to provide absolutely directional control points for mapping regions lacking field control points. The aerial triangulation is generally divided into two types, namely simulated aerial triangulation, namely optical mechanical aerial triangulation; and resolving the aerial triangulation, namely commonly called computerised encryption. The simulated aerial triangulation is aerial triangulation performed on an all-purpose stereo measurement instrument (such as a multiplier). It recovers the space model similar to or corresponding to the shooting course on the instrument, selects the encrypted points according to the mapping requirement, and determines the elevation and plane position. The method for encrypting the control point indoors utilizes the intrinsic geometric characteristics of the photo in the aerial photogrammetry. The method is characterized in that aerial camera films which are continuously shot and have certain overlap are utilized, and a corresponding flight path model or area network model (optical or digital) on the same site is established by a photogrammetry method according to a small number of field control points, so that the plane coordinates and the elevation of an encrypted point are obtained. The method is mainly used for measuring the topographic map.
RTK: namely a real-time dynamic differential method. The method is a new common GPS measurement method, the former static, rapid static and dynamic measurements all need to be solved afterwards to obtain centimeter-level precision, the RTK is a measurement method capable of obtaining centimeter-level positioning precision in real time in the field, a carrier phase dynamic real-time difference method is adopted, the method is a major milestone of GPS application, the appearance of the method is project lofting and terrain mapping, new eosin is brought to various control measurements, and the field operation efficiency is greatly improved. The RTK positioning technology is a real-time dynamic positioning technology based on a carrier phase observation value, and can provide a three-dimensional positioning result of a measuring station in a specified coordinate system in real time and achieve centimeter-level precision. In the RTK mode of operation, the base station transmits its observations to the rover station along with the coordinate information of the rover station via the data chain. The rover station not only receives data from the reference station through a data chain, but also acquires GPS observation data, forms differential observation values in the system for real-time processing, and simultaneously gives centimeter-level positioning results for less than one second. The rover can be in a static state and a moving state; the method can be used for initializing on a fixed point and then entering dynamic operation, can also be directly started under a dynamic condition, and can complete the searching and solving of the ambiguity of the whole cycle under a dynamic environment. After the unknown number is fixed in the whole week, each epoch can be processed in real time, and the rover can give centimeter-level positioning results at any time as long as the tracking and necessary geometric figures of more than four satellite phase observed values can be kept.
Roll, Pitch, Heading: roll, pitch and yaw angles, are the systems of angular elements commonly employed by Inertial Measurement Units (IMUs) to describe sensor attitude;
omega, Phi, Kappa: also roll, pitch and yaw, but the field of photogrammetry is used to represent the system of angular elements of the angular orientation of aerial photographs.
It should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. Not all embodiments are exhaustive. All obvious changes and modifications which are obvious to the technical scheme of the invention are covered by the protection scope of the invention.
Claims (3)
1. A method for improving oblique photogrammetry empty three quality is characterized by comprising the following steps:
1) acquiring original data comprising inclination image data, GNSS data and IMU data through aerial remote sensing flight;
2) arranging the inclined image data, the GNSS data and the IMU data and converting coordinates to calculate an initial value of the combined navigation data;
3) performing null-third aiming at the initial value of the combined navigation data in the step 2) to obtain a corresponding null-third result and a null-third report;
4) comparing the empty three results and the empty three reports under different conditions, and selecting the optimal result for real-scene three-dimensional modeling to form clear and accurate three-dimensional geographic information data;
the finishing in the step 2) comprises the following steps:
(1) GNSS data are arranged under the condition that the original data do not have IMU data, the IMU initial value is calculated by combining the inclined image data and importing into photogrammetry software, and the IMU initial value is respectively stored as an angle element Roll (Phi), a Pitch (theta), a Heading (psi) and angle elements Omega (Omega), Phi
Kappa(κ)；
(2) The GNSS data and the IMU data are collated under the condition that the original data have IMU data, and angle elements Roll (phi), Pitch (theta) and Heading (psi) are directly used as initial values of the IMU data;
(3) the GNSS data and IMU data are collated under the condition that the original data comprises IMU data, namely angle elements Roll (Phi), Pitch (theta) and Heading (psi) are converted into angle elements Omega (Omega), Phi (Phi) used for expressing the angular orientation of the aerial photography film in the field of photogrammetry
Kappa (. Kappa.) and the corner elements Omega (. Omega.), Phi
Kappa (Kappa) as an initial value of IMU data;
further comprising the steps of:
(1) under the condition that the original data does not have IMU data, performing null three on the inclination image data, the GNSS data and combined navigation data with angle elements of Roll (phi), Pitch (theta) and Heading (psi) as initial values of the IMU data; for tilt image data, GNSS data and angle elements Omega (Omega), Phi
Performing air-navigation by taking Kappa (Kappa) as combined navigation data of an initial value of IMU data, respectively performing iterative computation on position information and attitude information of the oblique image data, eliminating residual errors and gross errors, and when the optimal iteration times are reached, if the air-navigation results exist that an aerial ray set is not parallel to an X-Y plane, not continuing the iterative computation, and judging that the results fail; if all the aerial sets are parallel to the X-Y plane, the iterative computation is not continued, and the result is judged to be feasible; when the optimal iteration times are not reached, if the three results of the air exist and the aerial sets are not parallel to the X-Y plane, continuously performing iterative computation on position information and posture information of the oblique image data, eliminating residual errors and gross errors until all the aerial sets are parallel to the X-Y plane, and judging that the results are feasible; taking the feasible space-three results as new initial values to continue space-three, iteratively calculating the position information and the posture information of the inclined image data, and rejectingRemoving residual error and gross error, observing the number of connection points after each calculation, when the number of the connection points reaches the maximum, selecting the result of the next time of blank three as a new initial value to continue blank three, iteratively optimizing the position information and the attitude information of the oblique image data, eliminating the residual error and the gross error, observing the number of the connection points after each optimization, when the number of the connection points reaches the maximum, if the GNSS data does not adopt RTK measurement, finishing the optimization, and obtaining a blank three result and a blank three report; if the GNSS data adopts RTK measurement, selecting the next empty three result as a new initial value to continue empty three, iteratively optimizing the position information and the attitude information of the oblique image data, eliminating residual errors and gross errors, observing the number of connection points after each calculation, selecting the next empty three result as a final result when the number of the connection points reaches the maximum, comparing the final results of the two empty three times, and selecting the result with more connection points and high precision for live-action three-dimensional modeling;
(2) performing null-third on the inclination image data, the GNSS data and the combined navigation data taking angle elements of Roll (phi), Pitch (theta) and Heading (psi) as initial values of the IMU data under the condition that the original data contains IMU data; for tilt image data, GNSS data and angle elements Omega (Omega), Phi
Performing air-navigation by taking Kappa (Kappa) as combined navigation data of an initial value of IMU data, respectively performing iterative computation on position information and attitude information of the oblique image data, eliminating residual errors and gross errors, and when the optimal iteration times are reached, if the air-navigation results exist that an aerial ray set is not parallel to an X-Y plane, not continuing the iterative computation, and judging that the results fail; if all the aerial sets are parallel to the X-Y plane, the iterative computation is not continued, and the result is judged to be feasible; when the optimal iteration times are not reached, if the three results of the air exist and the aerial sets are not parallel to the X-Y plane, continuously performing iterative computation on position information and posture information of the oblique image data, eliminating residual errors and gross errors until all the aerial sets are parallel to the X-Y plane, and judging that the results are feasible; taking the feasible space-three results as new initial values to continue space-three, iteratively calculating the position information and the attitude information of the oblique image data,removing residual error and gross error, observing the number of connection points after each calculation, when the number of the connection points reaches the maximum, selecting the result of the next time of blank three as a new initial value to continue blank three, iteratively optimizing the position information and the attitude information of the oblique image data, removing the residual error and the gross error, observing the number of the connection points after each optimization, when the number of the connection points reaches the maximum, if the GNSS data does not adopt RTK measurement, finishing the optimization, and obtaining a blank three result and a blank three report; if the GNSS data adopts RTK measurement, selecting the next empty three result as a new initial value to continue empty three, iteratively optimizing the position information and the attitude information of the oblique image data, eliminating residual errors and gross errors, observing the number of connection points after each calculation, selecting the next empty three result as a final result when the number of the connection points reaches the maximum, comparing the final results of the two empty three times, and selecting the result with more connection points and high precision for live-action three-dimensional modeling;
the iterative calculation for multiple times is used for eliminating residual errors and gross errors, so that the position and the posture of the image are correct, the number of connecting points is sufficient, the number of triangular grids used for modeling is sufficient, and the quality of the three-dimensional model is improved.
2. The method of claim 1, wherein the method comprises: the oblique image data in the step 1) comprises color digital image data of five angles of front view, back view, left view, right view and downward view; the GNSS data in the step 1) comprises longitude, latitude and elevation data, and the IMU data in the step 1) comprises angle elements Roll (phi), Pitch (theta) and Heading (psi).
3. The method of claim 1, wherein the method comprises: the coordinate conversion in the step 2) is to convert the IMU data into angle elements Roll (Phi), Pitch (theta) and yaw (psi) for describing the sensor attitude into angle elements Omega (Omega), Phi (Phi) and Phi (Phi) for representing the angular orientation of the aerial photo in the field of photogrammetry
Kappa(κ)；
Wherein the angle elements omega,
The formula for κ is:
wherein the content of the first and second substances,
wherein the content of the first and second substances,
is a rotation matrix like the space coordinate system (i) to the ground auxiliary coordinate system (m),
is a rotation matrix from the navigation coordinate system (g) to the geocentric coordinate system (E),
is a rotation matrix from the IMU coordinate system (b) to the navigation coordinate system (g),
is a rotation matrix from the sensor coordinate system (c) to the IMU coordinate system (b),
is a rotation matrix like the spatial coordinate system (i) to the sensor coordinate system (c);
the local auxiliary coordinate system is selected as the center (L) of the measuring area0,B0) A rotation matrix formed by the coordinate system of the ellipsoid tangent plane; l, λ is the longitude and latitude of the IMU center at the instant of imaging, Θx,Θy,ΘzIs the IMU axis offset relative to the sensor.
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