A multi-angle photography UAV mapping method

Abstract

The present invention discloses a multi-angle photography unmanned aerial vehicle (UAV) surveying and mapping method, which relates to the technical field of UAV surveying and mapping. By forming a shooting angle scoring set Vgl, a quantitative evaluation of illumination adaptability in the entire posture domain is realized, and the posture combination with the highest score in the scoring set Vgl is selected for image acquisition, which not only improves the imaging quality but also provides a data basis with optimal illumination conditions for modeling. After the image acquisition is completed, a point cloud set Pcd is constructed through the image set Img, and an illumination defect index Lck is calculated to quantitatively evaluate the influence of illumination shading in the model; if the illumination defect index Lck exceeds the illumination shading threshold LckThr, the posture angle candidate space is updated and an iterative optimization process is driven, which effectively solves the problems of large image shadow interference, exposure imbalance and three-dimensional model defects caused by factors such as dynamic changes in natural light, uncontrollable posture and shading of ground objects in existing surveying and mapping UAV methods.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle (UAV) surveying and mapping, and in particular to a multi-angle photography UAV surveying and mapping method. Background Art

[0002] With the in-depth application of remote sensing technology, 3D modeling, and spatial data analysis across multiple industries, the broad field of surveying and mapping science and engineering has experienced rapid development. Within this broad field, aerial and drone surveying, as highly efficient methods for acquiring geographic information, are gradually replacing traditional ground-based surveying methods and becoming a key tool in modern surveying and mapping.

[0003] The invention of Chinese patent application number CN202411523934.3 discloses a surveying and mapping UAV and a surveying and mapping method for reducing surveying and mapping errors. The present invention utilizes the radar device and camera of the UAV to monitor the surrounding environment in real time, determines the position of obstacles through data fusion technology and image recognition algorithm, establishes an obstacle avoidance decision model according to the target surveying and mapping task, introduces the simulated annealing algorithm to optimize the model and formulate an obstacle avoidance strategy, and finally feeds back the obstacle avoidance strategy to the control center. The control center performs route planning and controls the attitude adjustment speed of the UAV in real time to obtain the final route. The present invention uses multiple sensor fusion and optimization algorithms to more accurately detect obstacles, formulate reasonable obstacle avoidance strategies, improve surveying and mapping accuracy, reduce surveying and mapping errors, and ensure the flight safety of the UAV and the accuracy of surveying and mapping data.

[0004] As can be seen, while solutions have been proposed to address the problem of errors caused by obstacles during surveying and mapping, primarily through the combined detection of obstacles using radar and image recognition, and the introduction of a simulated annealing algorithm to optimize obstacle avoidance strategies, thereby improving route safety and efficiency, the external environmental information relied upon primarily focuses on the location and distribution of static obstacles in space, overlooking a crucial variable: the profound impact of natural lighting conditions on image quality and modeling integrity.

[0005] When this technology is applied to multi-angle photogrammetry, because it fails to account for factors such as solar altitude, incident direction, and dynamic lighting changes, flying at different times can result in numerous shadowed areas on the same building or terrain. This is particularly true under low-angle lighting conditions at dawn and dusk, where long, strip-like shadows can easily form, resulting in a significant loss of target structural detail in the captured image, ultimately impacting modeling texture matching and facade reconstruction accuracy. Furthermore, the original solution lacked a perspective adjustment strategy for highly reflective areas, such as glass curtain walls and water surfaces. In these scenarios, strong reflections can easily lead to overexposure, artifacts, or even image failure. In severe cases, this can result in localized defects in the mapping results or interrupted model reconstruction, making it impossible to meet the requirements of high-precision 3D modeling tasks. Summary of the Invention

[0006] In view of the shortcomings of the existing technology, the present invention provides a multi-angle photography UAV surveying and mapping method, which solves the problems mentioned in the background technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: A multi-angle photography UAV surveying and mapping method, comprising the following steps:

[0008] S1, collect the solar incidence parameters of the current UAV operation area and combine them to generate the lighting environment dataset Lenv;

[0009] S2. Extract edge morphological features from the surveyed object image, construct the main structural direction vector Vec, and combine it with the sun incident vector Sun to calculate the angle Spd between the structural direction and the incident light;

[0010] S3. Collect the current UAV's heading angle Yaw and attitude inclination angle Ptc, calculate the heading correction angle Alp with respect to the sun's incident direction, construct the effective illumination projection score II1, and output the illumination adaptability score Gls under the current angle combination;

[0011] S4. Calculate multiple illumination adaptability scores Gls based on different combinations of heading angles Yaw and attitude inclination angles Ptc to form a shooting angle score set Vgl, and select the attitude combination with the highest score in the shooting angle score set Vgl to execute image acquisition.

[0012] S5. After performing image acquisition and shooting, an image set Img is obtained, a three-dimensional model point cloud set Pcd is constructed, and an illumination defect index Lck is calculated;

[0013] S6. When the illumination defect index Lck exceeds the set threshold, a feedback optimization parameter set Fbk is constructed and used to update the pose angle candidate space.

[0014] Preferably, said S1 includes S11 and S12;

[0015] S11. Obtain the longitude and latitude (Lon, Lat) and time Utc using the current geographic location and timestamp information of the drone operation, calculate the solar azimuth angle Azm and solar altitude angle Alt at the current moment, and then construct the unit solar incidence direction vector Dir to represent the direction of the sunlight in the three-dimensional spatial coordinate system under the current time and space conditions. The surface irradiance Irr at the current location is collected in real time through the light intensity sensing sensor mounted on the top of the drone body.

[0016] The unit solar incident direction vector Dir is obtained by the following construction formula:

[0017] Dir=[cos(Alt)*cos(Azm), cos(Alt)*sin(Azm), sin(Alt)], Dir represents the solar incidence direction vector, which is a three-dimensional unit vector, specifically representing the direction of solar radiation, cos represents the cosine function, and sin represents the sine function;

[0018] The solar azimuth angle Azm is obtained by the following calculation formula:

[0019] ;

[0020] Where, SPAAzm represents the solar azimuth angle calculation sub-function based on the NRELSPA model, Lon represents longitude, Lat represents latitude, and Utc represents time;

[0021] The solar altitude angle Alt is obtained by the following calculation formula:

[0022] ;

[0023] Where, SPAAlt represents the solar altitude angle calculation sub-function based on the NRELSPA model;

[0024] S12. Encapsulate and integrate the obtained unit solar incident direction vector Dir, solar altitude angle Alt, solar azimuth angle Azm, and surface irradiance intensity Irr to construct a lighting environment parameter set Lenv, which is used to describe the solar irradiation direction characteristics and radiation intensity state under current natural lighting conditions;

[0025] The specific form of the lighting environment parameter set Lenv is lighting environment parameter set Lenv={Dir, Alt, Azm, Irr}.

[0026] Preferably, said S2 includes S21;

[0027] S21. Based on the image structure recognition algorithm, perform feature recognition processing on the target object in the surveying image, extract the contour edge information of the target object, and combine the image geometric parameters and camera pose information to restore the starting and ending points of the edge segments from pixel coordinates to three-dimensional space coordinates, marking them as the starting point coordinate Pts and the ending point coordinate Pte respectively. After integrating all edge segments, obtain the edge feature set EdgeSet. By normalizing and directional averaging the edge feature set EdgeSet, construct a main structural direction vector Vec representing the main orientation and main facade normal direction of the target object, which is used to represent the main orientation characteristics or main facade normal direction of the target object in the current viewing angle;

[0028] The specific form of the edge feature set EdgeSet is edge feature set EdgeSet={E(1), E(2), ..., E(i)|i∈N}, N represents the total number of edge segments, wherein E(i) represents the edge feature unit of the i-th edge segment, specifically E(i)={Pts, Pte}, Pts represents the spatial coordinates of the starting point of the edge segment (x(1), y(1), z(1)), and Pte represents the spatial coordinates of the end point of the edge segment (x(2), y(2), z(2));

[0029] The main structure direction vector Vec is obtained by the following calculation formula:

[0030] ;

[0031] Where (x(i,1),y(i,1),z(i,1)) represents the spatial coordinates of the starting point of the i-th edge segment, and (x(i,2),y(i,2),z(i,2)) represents the spatial coordinates of the end point of the i-th edge segment.

[0032] Preferably, said S2 includes S22;

[0033] S22, performing direction matching based on the acquired main structure direction vector Vec and the unit solar incidence direction vector Dir, and calculating the angle value Spd between the main structure direction vector Vec and the unit solar incidence direction vector Dir using a three-dimensional space vector angle formula;

[0034] The angle value Spd is obtained by the following calculation formula:

[0035] ;

[0036] Where arccos represents the inverse cosine function, Represents a vector dot product operation.

[0037] Preferably, said S3 includes S31;

[0038] S31. Collect the heading angle Yaw and attitude inclination angle Ptc of the current UAV, and calculate the heading offset angle Alp between the current UAV camera viewing direction and the solar illumination direction in combination with the unit solar incident direction vector Dir, reflecting the difference between the current heading configuration and the main illumination direction. Based on the obtained heading offset angle Alp, and then with the attitude inclination angle Ptc, construct the illumination effective projection score Ill, which is used to quantify the projectable effectiveness of the solar incident light on the target object surface under the current body posture;

[0039] The heading deviation angle Alp is obtained by the following calculation formula:

[0040] ;

[0041] Where Cam represents the three-dimensional unit line of sight vector, which is obtained by converting the heading angle Yaw of the drone camera and the attitude pitch angle Ptc;

[0042] The three-dimensional unit sight line vector Cam is specifically converted by the following calculation formula:

[0043] ;

[0044] In the formula, cos represents the cosine function, and sin represents the sine function;

[0045] The lighting effective projection score 111 is passed Build a formula to get it.

[0046] Preferably, said S3 includes S32;

[0047] S32, using the effective illumination projection score Ill and the angle value Spd as evaluation basic data, and constructing an illumination adaptability evaluation function to calculate and obtain the illumination adaptability score Gls under the current posture angle combination;

[0048] The illumination suitability score Gls is obtained by the following calculation formula:

[0049] ;

[0050] Where γ represents the penalty coefficient, which is used to suppress the scoring deviation when the posture direction is inconsistent with the ground object direction. The specific value is set by the user.

[0051] Preferably, the S4 includes S41;

[0052] S41. Based on the unit solar incident direction vector Dir and the main structure direction vector Vec, a candidate shooting pose parameter set PoseSet is constructed based on a combination of multiple heading angles Yaw and attitude pitch angles Ptc within the controllable angle range of the UAV. A lighting adaptability score Gls is calculated for each combination of poses in the candidate shooting pose parameter set PoseSet. The lighting adaptability scores Gls for each combination of poses in the candidate shooting pose parameter set PoseSet are then integrated to form a score set Vgl.

[0053] The specific form of the candidate shooting posture parameter set PoseSet is the candidate shooting posture parameter set PoseSet={Pose(1), Pose(2), ..., Pose(j)|j∈M}, M represents the total number of combined postures, Pose(j) represents the j-th group of combined postures, specifically Pose(j)={Yaw(j), Ptc(j)}, Yaw(j) and Ptc(j) respectively represent the heading angle Yaw and the posture pitch angle Ptc of the j-th group of combined postures;

[0054] The specific form of the score set Vgl is score set Vgl={Gls(1), Gls(1), ..., Gls(j)|j∈M}, where Gls(j) represents the lighting adaptability score Gls obtained by calculating the j-th group of combined postures.

[0055] Preferably, said S4 includes S42;

[0056] S42. Filter out the pose combination Pose(j) with the largest score value from the constructed score set Vgl, and then extract the heading angle Yaw(j) and the posture pitch angle Ptc(j) of the j-th combination pose from the pose combination Pose(j), wherein the pose combination Pose(j) has the best lighting matching and shooting potential under the current lighting conditions. Generate a pose control instruction for the pose combination Pose(j), which includes the target's heading angle Yaw(j) and the posture pitch angle Ptc(j), and sends the instruction to the execution module through the UAV flight control system, driving the UAV to adjust to the state of the pose combination Pose(j) and then perform the image acquisition task.

[0057] Preferably, the S5 includes S51;

[0058] S51. Based on the image set Img taken when performing the image acquisition task, extract the shadow pixel area with brightness lower than the global average for each image in the image set Img, and mark the pixel position of the shadow pixel area in the image plane. Convert all pixel position coordinates in each image into three-dimensional space coordinates through pixel back projection to form a point cloud set Pcd of the three-dimensional model of each image. Extract the total number of image pixels Ntot of each image based on the point cloud set Pcd, and then extract the total number of shadow pixels Nshd marked as the shadow pixel area. Obtain the illumination incompleteness index Lck by calculating the ratio of the total number of shadow pixels Nshd to the total number of image pixels Ntot.

[0059] Preferably, the S6 includes S61;

[0060] S61. Based on a preset light shielding threshold LckThr, when the light defect index Lck exceeds the light shielding threshold LckThr, it indicates that the current image acquisition posture combination of the UAV is shadow-shielded, which affects the image quality and modeling integrity, and triggers a feedback optimization mechanism;

[0061] The feedback optimization mechanism extracts the pose combination Pose(j), angle value Spd, illumination adaptability score Gls and illumination defect index Lck of the current drone shot, and constructs a feedback optimization parameter set Fbk. The feedback optimization parameter set Fbk is used as the input basis of the optimization strategy to update the pose angle candidate space.

[0062] The updating of the attitude angle candidate space includes expanding the value range of the attitude tilt angle Ptc in the attitude candidate space, and fine-tuning and correcting the heading angle Yaw according to the directional deviation feature of the angle value Spd.

[0063] The present invention provides a multi-angle photography UAV surveying and mapping method, which has the following beneficial effects:

[0064] (1) By forming a shooting angle scoring set Vgl, a quantitative evaluation of illumination adaptability in the full attitude domain is achieved. By selecting the posture combination with the highest score in the scoring set Vgl for image acquisition, not only the imaging quality is improved, but also the data basis for the optimal illumination conditions is provided for modeling. Furthermore, after the image acquisition is completed, the point cloud set Pcd is constructed through the image set Img, and the illumination defect index Lck is calculated to quantitatively evaluate the influence of illumination shading in the model; if the illumination defect index Lck exceeds the illumination shading threshold LckThr, the attitude angle candidate space is updated and the iterative optimization process is driven, effectively solving the problems of large image shadow interference, exposure imbalance and three-dimensional model defects caused by factors such as dynamic changes in natural light, uncontrollable attitude and occlusion of ground objects in existing surveying and mapping UAV methods.

[0065] (2) Calculate the heading offset angle Alp between the current body orientation and the unit solar incident direction vector Dir, and then construct the effective illumination projection score Ill that reflects the efficiency of illumination projection, which comprehensively describes the effectiveness of illumination projection on the target surface under the current attitude. On this basis, the effective illumination projection score Ill and the angle value Spd calculated by the main structure direction vector Vec and the unit solar incident direction vector Dir are input into the illumination adaptability evaluation function to generate the illumination adaptability score Gls, which clearly indicates whether the current attitude angle combination meets the illumination condition requirements for image acquisition. This process not only combines the structural characteristics of the object with the flight attitude, but also introduces natural lighting factors into the attitude angle control logic, significantly improving the adaptive adjustment capability of the body orientation configuration and the judgment accuracy of the image acquisition timing selection when the UAV performs surveying and mapping tasks under complex lighting conditions.

[0066] (3) By taking the unit sun incident direction vector Dir and the main structure direction vector Vec as input references, traversing all heading angles Yaw and attitude pitch angles Ptc combinations within the current controllable angle range of the drone, constructing the candidate shooting posture parameter set PoseSet, and calculating the illumination adaptability score Gls of each posture combination in turn, thus forming a scoring set Vgl, and realizing the global scoring modeling of the responsiveness of multi-angle postures to the current illumination environment. Furthermore, by selecting the posture combination Pose(j) with the largest score value in the scoring set Vgl, it is used as the optimal configuration of the current mapping task under the illumination adaptation dimension, and the flight control system generates attitude control instructions and adjusts the drone's flight attitude in real time, so that the image acquisition task is always carried out around the optimal illumination angle and the target object structure direction. This method not only avoids the shadow occlusion and uneven exposure problems caused by relying on fixed tracks or one-way shooting under traditional paths, but also has high flexibility and real-time performance in actual flight operations, and can dynamically adapt to changes in sun angles, ensuring that each image acquisition process has the maximum imaging advantage under illumination geometry conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 Schematic diagram of the steps of the multi-angle photography UAV surveying and mapping method of the present invention

[0068] Figure 2 Schematic diagram of posture combination scoring and optimal screening mechanism. DETAILED DESCRIPTION

[0069] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0070] Example 1

[0071] The present invention provides a multi-angle photography UAV surveying and mapping method, please refer to Figure 1 , including the following steps:

[0072] S1, collect the solar incidence parameters of the current UAV operation area and combine them to generate the lighting environment dataset Lenv;

[0073] S2. Extract edge morphological features from the surveyed object image, construct the main structural direction vector Vec, and combine it with the sun incident vector Sun to calculate the angle Spd between the structural direction and the incident light;

[0074] S3. Collect the current UAV's heading angle Yaw and attitude inclination angle Ptc, calculate the heading correction angle Alp with respect to the sun's incident direction, construct the effective illumination projection score II1, and output the illumination adaptability score Gls under the current angle combination;

[0075] S4. Calculate multiple illumination adaptability scores Gls based on different combinations of heading angles Yaw and attitude inclination angles Ptc to form a shooting angle score set Vgl, and select the attitude combination with the highest score in the shooting angle score set Vgl to execute image acquisition.

[0076] S5. After performing image acquisition and shooting, an image set Img is obtained, a three-dimensional model point cloud set Pcd is constructed, and an illumination defect index Lck is calculated;

[0077] S6. When the illumination defect index Lck exceeds the set threshold, a feedback optimization parameter set Fbk is constructed and used to update the pose angle candidate space.

[0078] In this embodiment, by generating a lighting environment dataset Lenv, a precise description foundation for the current natural light environment is established; then, in combination with the edge structure recognition algorithm, the main structural direction vector Vec of the target object is extracted, and the angle Spd between the structural direction and the illumination direction is calculated in conjunction with the sun incidence vector Dir, thereby realizing the evaluation of the geometric structure and illumination matching of the photographed object; at the same time, the heading angle Yaw and the attitude inclination angle Ptc of the drone are collected to construct the heading correction angle Alp between the camera orientation and the illumination direction, and based on its matching with the structural features, the effective illumination projection score Ill is constructed, and the illumination adaptability score Gls is output, thereby forming a shooting angle score set Vgl, realizing the quantitative evaluation of illumination adaptability in the full attitude domain. By selecting the posture combination with the highest score in the scoring set Vgl for image acquisition, not only the imaging quality is improved, but also the data foundation with the optimal lighting conditions is provided for modeling. Furthermore, after image acquisition is completed, the point cloud set Pcd is constructed through the image set Img, and the illumination incompleteness index Lck is calculated to quantitatively evaluate the impact of illumination shading in the model; if the illumination incompleteness index Lck exceeds the illumination shading threshold LckThr, the optimal posture combination, the angle value Spd, the illumination adaptability score Gls and the illumination incompleteness index Lck are used to form a feedback optimization parameter set Fbk, update the posture angle candidate space and drive the iterative optimization process, effectively solving the problems of large image shadow interference, exposure imbalance and three-dimensional model defects caused by factors such as dynamic changes in natural light, uncontrollable posture and occlusion of ground objects in existing surveying and mapping UAV methods, realizing illumination-driven UAV image intelligent acquisition optimization, which has wide practicality and engineering adaptability.

[0079] Example 2

[0080] Specifically: S1 includes S11 and S12;

[0081] S11. Obtain the longitude and latitude (Lon, Lat) and time Utc using the current geographic location and timestamp information of the drone operation, calculate the solar azimuth angle Azm and solar altitude angle Alt at the current moment, and then construct the unit solar incidence direction vector Dir to represent the direction of the sunlight in the three-dimensional spatial coordinate system under the current time and space conditions. The surface irradiance Irr at the current location is collected in real time through the light intensity sensing sensor mounted on the top of the drone body.

[0082] The unit solar incident direction vector Dir is obtained by the following construction formula:

[0083] Dir = [cos (Alt) * cos (Azm), cos (Alt) * sin (Azm), sin (Alt)], which represents the solar incidence direction vector. It is a three-dimensional unit vector, specifically indicating the direction of solar radiation. Cos represents the cosine function, and sin represents the sine function.

[0084] The solar azimuth angle Azm is obtained by the following calculation formula:

[0085] ;

[0086] Wherein, SPAAzm represents the solar azimuth angle calculation subfunction based on the NRELSPA model, which is specifically used to calculate the projection direction of the sun on the horizontal plane under the current geographical location and time conditions, Lon represents longitude, Lat represents latitude, and Utc represents time;

[0087] The solar altitude angle Alt is obtained by the following calculation formula:

[0088] ;

[0089] Wherein, SPAAlt represents the solar altitude angle calculation sub-function based on the NRELSPA model, which is specifically used to calculate the elevation angle of the sun's rays relative to the horizon;

[0090] S12. Encapsulate and integrate the obtained unit solar incident direction vector Dir, solar altitude angle Alt, solar azimuth angle Azm, and surface irradiance intensity Irr to construct a lighting environment parameter set Lenv, which is used to describe the solar irradiation direction characteristics and radiation intensity state under current natural lighting conditions. The lighting environment parameter set Lenv serves as standard input data for subsequent steps in multi-angle mapping processes related to lighting, such as ground feature structure direction analysis, attitude adjustment angle derivation, and image acquisition condition judgment.

[0091] The specific form of the lighting environment parameter set Lenv is lighting environment parameter set Lenv={Dir, Alt, Azm, Irr}.

[0092] In this embodiment, an input foundation for dynamic adaptation to the natural environment is established, in which the current geographic location and time information of the drone is specifically used to calculate the solar azimuth angle Azm and the solar altitude angle Alt, and the unit solar incidence direction vector Dir is further constructed, thereby realizing a spatial three-dimensional representation of the solar illumination direction under the current spatiotemporal conditions. At the same time, combined with the surface irradiance Irr obtained in real time by the light intensity sensing sensor on the top of the drone, the system effectively integrates various key physical parameters related to illumination to form a standardized input structural illumination environment parameter set Lenv, which provides a unified, accurate, and quantifiable illumination model input for the entire multi-angle photogrammetry process. This ensures that the core calculation processes in subsequent steps, such as the target object main structure direction analysis, attitude angle adjustment, and illumination matching scoring, have data consistency and spatiotemporal synchronization, significantly improving the system's adaptability under non-ideal natural conditions and the real-time decision-making accuracy of attitude control, and laying a data benchmark guarantee at the illumination level for subsequent modeling reliability and angle optimization.

[0093] Example 3

[0094] Specifically: S2 includes S21;

[0095] S21. Based on the image structure recognition algorithm, perform feature recognition processing on the target object in the surveying image, extract the contour edge information of the target object, and combine the image geometric parameters and camera pose information to restore the starting and ending points of the edge segments from pixel coordinates to three-dimensional space coordinates, marking them as the starting coordinate Pts and the ending coordinate Pte respectively. After integrating all edge segments, obtain the edge feature set EdgeSet. By normalizing and directional averaging the edge feature set EdgeSet, construct a main structural direction vector Vec representing the main orientation and main facade normal direction of the target object. This vector is used to represent the main orientation characteristics or main facade normal direction of the target object at the current viewing angle. The main structural direction vector Vec is the core comparison benchmark for the subsequent sunlight direction deviation analysis.

[0096] The specific form of the edge feature set EdgeSet is edge feature set EdgeSet={E(1), E(2), ..., E(i)|i∈N}, N represents the total number of edge segments, wherein E(i) represents the edge feature unit of the i-th edge segment, specifically E(i)={Pts, Pte}, Pts represents the spatial coordinates of the starting point of the edge segment (x(1), y(1), z(1)), and Pte represents the spatial coordinates of the end point of the edge segment (x(2), y(2), z(2));

[0097] The main structure direction vector Vec is obtained by the following calculation formula:

[0098] ;

[0099] Where, (x(i,1),y(i,1),z(i,1)) represents the spatial coordinates of the starting point of the i-th edge segment, and (x(i,2),y(i,2),z(i,2)) represents the spatial coordinates of the end point of the i-th edge segment; represents the direction vector of the line segment (end point minus start point), Indicates that the direction vector is normalized to obtain a unit vector;

[0100] The main structure direction vector Vec is a unit space direction vector used to represent the main structure orientation or the main facade normal direction of the target object in the surveying image at the current viewing angle. It is obtained by calculating the direction vector of the starting point coordinates Pts and the end point coordinates Pte of each edge segment in the edge feature set EdgeSet extracted from the image and normalizing it. The main structure direction vector Vec can accurately reflect the most significant structural orientation feature of the target object in three-dimensional space. It is an important geometric reference parameter for performing solar radiation direction deviation analysis, posture control and lighting matching assessment, and is used as a key input vector in the subsequent calculation of the angle value Spd.

[0101] Said S2 includes S22;

[0102] S22. Direction matching is performed based on the acquired main structure direction vector Vec and the unit solar incidence direction vector Dir. The angle Spd between the main structure direction vector Vec and the unit solar incidence direction vector Dir is calculated using the three-dimensional space vector angle formula. This angle Spd is used to represent the degree of deviation between the solar illumination direction and the main structure direction of the ground object under the current lighting conditions. The angle Spd is an important indicator reflecting the lighting adaptability. When the angle Spd is small, it indicates that the sunlight illumination direction is close to the main structure surface of the target ground object, and higher lighting sufficiency and less shadow occlusion can be obtained. On the contrary, when the angle Spd is large, there is an obvious backlight or backlight effect, which will affect the image quality and require optimization through posture adjustment or shooting strategy delay.

[0103] The angle value Spd is obtained by the following calculation formula:

[0104] ;

[0105] In the formula, arccos represents the inverse cosine function, which is used to restore the cosine value to the angle value. Represents a vector dot product operation, specifically calculating the directional similarity between two vectors.

[0106] In this embodiment, a quantitative indicator angle value Spd based on the degree of deviation of the illumination incident angle is established through spatial matching of the main structure direction vector Vec and the unit sun incident direction vector Dir. The main structure direction vector Vec is obtained by calculating the spatial starting point coordinates Pts and the end point coordinates Pte of each edge segment in the edge feature set EdgeSet, and has geometric representativeness of structural feature expression and illumination coupling; it not only realizes the spatial relationship modeling between the sun's illumination direction and the main structure of the ground object, but also can dynamically judge whether there are adverse factors such as backlighting, backlighting or sidelighting under the current lighting conditions, and has pre-emptive intervention value for image brightness balance, shadow occlusion degree and imaging geometric accuracy. The setting of this step effectively improves the geometric accuracy of ground object structure direction identification during multi-angle photography, and enhances the rationality and initiative of shooting posture control in scenes with unstable lighting or complex structural orientation, and constitutes an important source of judgment criteria for subsequent posture optimization control.

[0107] Example 4

[0108] Specifically: the S3 includes S31;

[0109] S31. Collect the heading angle Yaw and attitude inclination angle Ptc of the current UAV, and calculate the heading offset angle Alp between the current UAV camera viewing direction and the solar illumination direction in combination with the unit solar incident direction vector Dir, reflecting the difference between the current heading configuration and the main illumination direction. Based on the obtained heading offset angle Alp, and then with the attitude inclination angle Ptc, construct the illumination effective projection score Ill, which is used to quantify the projectable effectiveness of the solar incident light on the target object surface under the current body posture;

[0110] The heading deviation angle Alp is obtained by the following calculation formula:

[0111] ;

[0112] Where Cam represents the three-dimensional unit line of sight vector, which is obtained by converting the heading angle Yaw of the drone camera and the attitude pitch angle Ptc;

[0113] The three-dimensional unit sight line vector Cam is specifically converted by the following calculation formula:

[0114] ;

[0115] In the formula, cos represents the cosine function, and sin represents the sine function;

[0116] The lighting effective projection score 111 is passed Construct a formula to obtain it, where the effective illumination projection score Ill is a score value in the range of [−1, 1]. Usually, the actual focus is on its positive value interval [0, 1]. The closer the score is to 1, the more suitable the camera posture is for the direction of the sun, which is conducive to illumination projection.

[0117] Said S3 includes S32;

[0118] S32: Using the effective illumination projection score Ill and the angle value Spd as basic evaluation data, a lighting adaptability evaluation function is constructed to calculate and obtain the lighting adaptability score Gls under the current posture angle combination, which is used to jointly evaluate whether the current body orientation is suitable for performing the image acquisition task;

[0119] The illumination suitability score Gls is obtained by the following calculation formula:

[0120] ;

[0121] Where γ represents the penalty coefficient, which is used to suppress the scoring deviation when the posture direction is inconsistent with the ground object direction. The specific value is set by the user.

[0122] The illumination adaptability score Gls comprehensively considers the geometric consistency between the current drone attitude angle and the direction of solar incidence, the illumination coordination between the target ground structure surface and the illumination direction, and the spatial synchronization between the camera orientation and the target illumination direction. It is used to quantify the imaging adaptability of the current shooting attitude under the lighting conditions. The higher the value of the illumination adaptability score Gls, the more suitable the current attitude is for performing surveying and mapping image acquisition tasks.

[0123] In this embodiment, a quantitative evaluation of the compatibility between the current UAV body posture and the direction of solar incidence is achieved, effectively solving the problem of reduced image illumination projection efficiency due to unreasonable configuration of the heading angle Yaw and the attitude inclination angle Ptc. Specifically, the current UAV's heading angle Yaw and attitude inclination angle Ptc are first collected, and the heading offset angle Alp between the current body orientation and the unit solar incidence direction vector Dir is calculated, and then an illumination effective projection score Ill reflecting the illumination projection efficiency is constructed to comprehensively characterize the projection effectiveness of illumination on the target object surface under the current posture. On this basis, the illumination effective projection score Ill and the angle value Spd calculated by the main structure direction vector Vec and the unit solar incidence direction vector Dir are jointly input into the illumination adaptability evaluation function to generate the illumination adaptability score Gls, which clearly indicates whether the current posture angle combination meets the illumination condition requirements for image acquisition. This process not only combines the structural characteristics of the terrain with the flight attitude, but also introduces natural lighting factors into the attitude angle control logic, significantly improving the UAV's adaptive adjustment capability of the body orientation configuration and the accuracy of image acquisition timing selection when performing surveying and mapping tasks under complex lighting conditions.

[0124] Example 5

[0125] See also Figure 1 and Figure 2 Specifically: the S4 includes S41;

[0126] S41. Based on the unit solar incident direction vector Dir and the main structure direction vector Vec, a candidate shooting pose parameter set PoseSet is constructed based on a combination of multiple heading angles Yaw and attitude pitch angles Ptc within the controllable angle range of the UAV. A lighting adaptability score Gls is calculated for each combination of poses in the candidate shooting pose parameter set PoseSet. The lighting adaptability scores Gls of each combination of poses in the candidate shooting pose parameter set PoseSet are then integrated to form a score set Vgl, which is used to map to all feasible pose combinations, providing a unified basis for shooting optimization under lighting conditions.

[0127] The specific form of the candidate shooting posture parameter set PoseSet is the candidate shooting posture parameter set PoseSet={Pose(1), Pose(2), ..., Pose(j)|j∈M}, M represents the total number of combined postures, Pose(j) represents the j-th group of combined postures, specifically Pose(j)={Yaw(j), Ptc(j)}, Yaw(j) and Ptc(j) respectively represent the heading angle Yaw and the posture pitch angle Ptc of the j-th group of combined postures;

[0128] The specific form of the score set Vgl is score set Vgl={Gls(1), Gls(1), ..., Gls(j)|j∈M}, where Gls(j) represents the lighting adaptability score Gls obtained by calculating the j-th group of combined postures.

[0129] Said S4 includes S42;

[0130] S42. Filter out the pose combination Pose(j) with the largest score value from the constructed score set Vgl, and then extract the heading angle Yaw(j) and the attitude pitch angle Ptc(j) of the j-th combination pose from the pose combination Pose(j), wherein the pose combination Pose(j) has the best lighting matching and shooting potential under the current lighting conditions, generate a pose control instruction for the pose combination Pose(j), and the instruction includes the heading angle Yaw(j) and the attitude pitch angle Ptc(j) of the target, and send it to the execution module through the UAV flight control system, driving the UAV to adjust to the state of the pose combination Pose(j) and then perform the image acquisition task, thereby ensuring that in a dynamically changing natural light environment, the surveying and mapping image acquisition can always prioritize matching the best lighting conditions and structural angles, thereby improving the image geometric accuracy and visual quality.

[0131] Example of posture combination scoring and optimization:

[0132] Assuming that the unit sun incident direction vector Dir and the main structure direction vector Vec have been obtained in the current lighting environment, the system traverses the range of the heading angle Yaw and attitude pitch angle Ptc allowed by the drone to form the following candidate shooting pose parameter set PoseSet: Table 1: Candidate shooting pose parameter set PoseSet

[0133] Pose(j) Yaw (j) Attitude pitch angle Ptc(j) Lighting effective projection score Ill(j) Angle value Spd(j) Light adaptability score Gls(j) Pose(1) 45° 10° 0.72 12° 0.66 Pose(2) 60° 15° 0.81 8° 0.77 Pose(3) 90° 12° 0.65 20° 0.50 Pose(4) 30° 5° 0.60 25° 0.38

[0134] Combined with the above rating data, the rating set Vgl is formed:

[0135] Rating set Vgl = {Gls(1) = 0.66, Gls(2) = 0.77, Gls(3) = 0.50, Gls(4) = 0.38};

[0136] From the scoring set Vgl, we can see that the posture combination with the highest score is Pose(2).

[0137] In this embodiment, using the unit solar incident direction vector Dir and the main structure direction vector Vec as inputs, the system traverses all heading angle combinations Yaw and attitude pitch angle combinations Ptc within the drone's current controllable angle range to construct a candidate shooting pose parameter set PoseSet. The illumination adaptability score Gls for each pose combination is then calculated, forming a score set Vgl. This achieves a global scoring model for the responsiveness of multi-angle poses to the current lighting environment. Furthermore, the pose combination Pose(j) with the highest score in the score set Vgl is selected as the optimal configuration for the current mapping task under the illumination adaptation dimension. The flight control system generates attitude control commands and adjusts the drone's flight attitude in real time, ensuring that image acquisition tasks are always performed around the optimal illumination angle and target structure orientation. This method not only avoids the shadowing and uneven exposure problems caused by traditional fixed paths or unidirectional shooting, but also offers high flexibility and real-time performance in actual flight operations. It can dynamically adapt to changes in solar angle, ensuring that each image acquisition process maximizes the imaging advantage under the lighting geometry conditions.

[0138] Example 6

[0139] Specifically: the S5 includes S51;

[0140] S51. Based on the image set Img taken when performing the image acquisition task, extract the shadow pixel area with brightness lower than the global average for each image in the image set Img, and mark the pixel position of the shadow pixel area in the image plane. Convert all pixel position coordinates in each image into three-dimensional space coordinates through pixel back projection to form a point cloud set Pcd of the three-dimensional model of each image. Extract the total number of image pixels Ntot of each image based on the point cloud set Pcd, and then extract the total number of shadow pixels Nshd marked as the shadow pixel area. Obtain the illumination incompleteness index Lck by calculating the ratio of the total number of shadow pixels Nshd to the total number of image pixels Ntot.

[0141] Said S6 includes S61;

[0142] S61. Based on a preset light shielding threshold LckThr, when the light defect index Lck exceeds the light shielding threshold LckThr, it indicates that the current image acquisition posture combination of the UAV is shadow-shielded, which affects the image quality and modeling integrity, and triggers a feedback optimization mechanism;

[0143] The feedback optimization mechanism extracts the pose combination Pose(j), angle value Spd, illumination adaptability score Gls and illumination defect index Lck of the current drone shot, and constructs a feedback optimization parameter set Fbk. The feedback optimization parameter set Fbk is used as the input basis of the optimization strategy to update the pose angle candidate space.

[0144] The updating of the attitude angle candidate space includes expanding the value range of the attitude tilt angle Ptc in the attitude candidate space, and fine-tuning and correcting the heading angle Yaw according to the directional deviation feature of the angle value Spd.

[0145] In this embodiment, after acquiring the image set Img, shadow pixel regions are extracted based on image brightness features. A three-dimensional point cloud Pcd is constructed using pixel backprojection. The total number of shadow pixels Nshd and the total number of image pixels Ntot are then counted. The illumination defect index Lck is then calculated to quantitatively characterize the degree of image shadow interference under the current pose combination. Based on this, a feedback optimization mechanism is triggered when the illumination defect index Lck exceeds the threshold, using the illumination occlusion threshold LckThr as a criterion. The feedback optimization parameter set Fbk is constructed by combining the pose combination Pose(j), the angle Spd, the illumination compatibility score Gls, and the illumination defect index Lck corresponding to the current image. This parameter drives dynamic adjustments to the pose candidate space, including expanding the range of the pose tilt angle Ptc and fine-tuning the heading angle Yaw based on directional deviation characteristics. This mechanism not only establishes a closed-loop evaluation system for modeling quality and image lighting adaptability after image acquisition, but also realizes intelligent optimization and updating of posture parameters, providing more lighting-responsive angle scheduling guarantees for subsequent image acquisition and modeling processes, and solving the problem of being unable to perceive image incompleteness under traditional static paths. It has the combined advantages of high adaptability, high stability and strong scene perception capabilities.

[0146] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A multi-angle photography drone mapping method, characterized by: The following steps are involved: S1, collect the solar incidence parameters of the current UAV operation area and combine them to generate the lighting environment dataset Lenv; S2. Extract edge morphological features from the surveyed object image, construct the main structural direction vector Vec, and combine it with the sun incident vector Sun to calculate the angle Spd between the structural direction and the incident light; S3. Collect the current UAV's heading angle Yaw and attitude inclination angle Ptc, calculate the heading correction angle Alp with respect to the sun's incident direction, construct the effective illumination projection score II1, and output the illumination adaptability score Gls under the current angle combination; Said S3 includes S31; S31, collecting the current UAV's heading angle Yaw and attitude inclination angle Ptc, and combining the unit sun incidence direction vector Dir, calculating the heading offset angle Alp between the current UAV camera's viewing angle and the sun's illumination direction, and constructing the illumination effective projection score Ill based on the obtained heading offset angle Alp and the attitude inclination angle Ptc; The lighting effective projection score 111 is passed Construct formula to obtain; Said S3 includes S32; S32, using the effective illumination projection score Ill and the angle value Spd as evaluation basic data, and constructing an illumination adaptability evaluation function to calculate and obtain the illumination adaptability score Gls under the current posture angle combination; The illumination suitability score Gls is obtained by the following calculation formula: ； Where γ represents the penalty coefficient, which is used to suppress the scoring deviation when the posture direction is inconsistent with the ground object direction. The specific value is set by the user. S4. Calculate multiple illumination adaptability scores Gls based on different combinations of heading angles Yaw and attitude inclination angles Ptc to form a shooting angle score set Vgl, and select the attitude combination with the highest score in the shooting angle score set Vgl to execute image acquisition. S5. After performing image acquisition and shooting, an image set Img is obtained, a three-dimensional model point cloud set Pcd is constructed, and an illumination defect index Lck is calculated; The S5 includes S51; S51. Based on the image set Img captured during the image acquisition task, extract shadow pixel regions with brightness lower than the global mean for each image in the image set Img, mark the pixel positions of the shadow pixel regions in the image plane, convert all pixel position coordinates in each image into three-dimensional space coordinates through pixel back-projection, and form a point cloud set Pcd of a three-dimensional model of each image. Based on the point cloud set Pcd, extract the total number of image pixels Ntot for each image, and then extract the total number of shadow pixels Nshd marked as shadow pixel regions. Calculate the ratio of the total number of shadow pixels Nshd to the total number of image pixels Ntot to obtain an illumination defect index Lck. S6. When the illumination defect index Lck exceeds the set threshold, a feedback optimization parameter set Fbk is constructed and used to update the pose angle candidate space.

2. The multi-angle photography drone mapping method according to claim 1, characterized in that: Said S1 includes S11 and S12; S11. Obtain the longitude and latitude (Lon, Lat) and time Utc using the current geographic location and timestamp information of the UAV operation, calculate the solar azimuth angle Azm and solar altitude angle Alt at the current moment, and then construct the unit solar incidence direction vector Dir. The surface irradiance Irr at the current location is collected in real time using the light intensity sensing sensor mounted on the top of the UAV fuselage. The unit solar incident direction vector Dir is obtained by the following construction formula: Dir=[cos(Alt)*cos(Azm), cos(Alt)*sin(Azm), sin(Alt)], Dir represents the solar incidence direction vector, which is a three-dimensional unit vector, specifically representing the direction of solar radiation, cos represents the cosine function, and sin represents the sine function; S12. Encapsulate and integrate the obtained unit solar incident direction vector Dir, solar altitude angle Alt, solar azimuth angle Azm, and surface irradiance intensity Irr to construct a lighting environment parameter set Lenv = {Dir, Alt, Azm, Irr}.

3. The multi-angle photography drone mapping method according to claim 2, characterized in that: Said S2 includes S21; S21. Based on the image structure recognition algorithm, perform feature recognition processing on the target object in the surveying image, extract the contour edge information of the target object, restore the starting and ending points of the edge segments from pixel coordinates to three-dimensional coordinates, mark them as the starting coordinate Pts and the ending coordinate Pte respectively, integrate all edge segments, obtain the edge feature set EdgeSet, and construct the main structural direction vector Vec representing the main orientation and main facade normal direction of the target object by normalizing and directional averaging the edge feature set EdgeSet; The specific form of the edge feature set EdgeSet is edge feature set EdgeSet={E(1), E(2), ..., E(i)|i∈N}, where N represents the total number of edge segments, and E(i) represents the edge feature unit of the i-th edge segment, specifically E(i)={Pts, Pte}, where Pts represents the spatial coordinates of the starting point of the edge segment (x(1), y(1), z(1)), and Pte represents the spatial coordinates of the end point of the edge segment (x(2), y(2), z(2)).

4. The multi-angle photography drone surveying and mapping method according to claim 3, characterized in that: Said S2 includes S22; S22. Direction matching is performed based on the acquired main structure direction vector Vec and the unit solar incidence direction vector Dir, and the angle value Spd between the main structure direction vector Vec and the unit solar incidence direction vector Dir is calculated using a three-dimensional space vector angle formula.

5. The multi-angle photography drone mapping method according to claim 1, characterized in that: Said S4 includes S41; S41. Based on the unit solar incidence direction vector Dir and the main structure direction vector Vec, a candidate shooting pose parameter set PoseSet is constructed based on a combination of multiple heading angles Yaw and attitude pitch angles Ptc within the controllable angle range of the UAV. A lighting adaptability score Gls is calculated for each group of combined poses in the candidate shooting pose parameter set PoseSet. Then, the lighting adaptability score Gls of each group of combined poses in the candidate shooting pose parameter set PoseSet is integrated to form a score set Vgl.

6. The multi-angle photography drone surveying and mapping method according to claim 5, characterized in that: Said S4 includes S42; S42. Filter out the posture combination Pose(j) with the largest score value from the constructed score set Vgl, then extract the heading angle Yaw(j) and the posture pitch angle Ptc(j) of the j-th combination posture from the posture combination Pose(j), generate a posture control instruction for the posture combination Pose(j), and send it to the execution module through the UAV flight control system, driving the UAV to adjust to the posture combination Pose(j) state and then perform the image acquisition task.

7. The multi-angle photography drone mapping method according to claim 1, characterized in that: The S6 includes S61; S61. Based on a preset light shielding threshold LckThr, when the light defect index Lck exceeds the light shielding threshold LckThr, it indicates that the current image acquisition posture combination of the UAV is shadow-shielded, which affects the image quality and modeling integrity, and triggers a feedback optimization mechanism; The feedback optimization mechanism extracts the pose combination Pose(j), angle value Spd, illumination adaptability score Gls, and illumination defect index Lck captured by the current drone, and constructs a feedback optimization parameter set Fbk in combination. The feedback optimization parameter set Fbk is used as the input basis of the optimization strategy to update the pose angle candidate space.

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