A far-field target pose visual measurement method based on center normalization

Abstract

The present invention discloses a far-field target posture visual measurement method based on center standardization, including: adjusting the far-field target posture to be measured, extracting far-field target feature points; solving the far-field target posture initial value, establishing an objective function by minimizing the back-projection error of image feature points, performing preliminary nonlinear optimization to obtain the posture initial value by Levenberg-Marquardt algorithm, and realizing parameter pre-optimization; after performing center standardization on the posture parameter Jacobian matrix, Levenberg-Marquardt algorithm is again used for nonlinear optimization to obtain the far-field target posture optimal solution. The present invention has the characteristics of reducing the sensitivity of posture parameters to image noise, reducing external parameter coupling and avoiding falling into local minimum, and is of great significance to improving the high-precision posture visual measurement of far-field targets in fields such as aerospace.

Description

Technical Field

[0001] The present invention relates to the technical field of optical measurement, and in particular to a far-field target posture visual measurement method based on center standardization. Background Art

[0002] As an important means of detecting and perceiving targets at long distances, visual measurement of far-field target poses features long range, large space, and high precision. It is widely used in fields such as space-based early warning, earth observation, and ground-based optical detection. It is of great significance for improving space-based and ground-based far-field target tracking, monitoring, and early warning. Examples include space-based satellite monitoring and early warning of spacecraft, attitude monitoring of cooperative and non-cooperative spacecraft, and real-time tracking and monitoring of the pose of aircraft carriers at sea. However, in far-field target visual measurement systems, the object distance is much greater than the camera focal length, and the target is far away, moving at high speed, and is affected by image noise. This makes the target pose extremely sensitive to image noise when solving. Furthermore, when solving the conventional PnP (Perspective-n-Point) pose directly, the external parameters are coupled with each other, resulting in large errors in the target pose parameters, making it difficult to meet the requirements of scenarios such as precise guidance and strikes.

[0003] The process of visually measuring the target pose based on PnP typically involves two steps: initial pose acquisition and nonlinear optimization. Initial pose acquisition is fundamental to both pose measurement and optimization, and largely determines the convergence speed and quality of the optimization algorithm. Numerous researchers, both domestic and international, have conducted in-depth research on efficiently and accurately acquiring the target pose and have proposed various initial pose acquisition and optimization methods. Roberto Opromolla et al. proposed a pose solution architecture combining PnP with the Levenberg-Marquardt algorithm for geostationary space robot capture operations. Henrik Rehbinder et al. improved the accuracy and robustness of pose estimation by integrating computer vision and inertial navigation techniques. Sumant Sharma et al. used Monte Carlo simulations to examine the performance of various PnP solution methods for initial pose estimation, including PosIt, Coplanar PosIt, EPnP, and the Newton–Raphson method. Michele Maestrini et al. used the EPnP method to obtain the initial pose for spacecraft and resident space objects, then used BB (Bounding Box) information to detect outliers and used RoI (Region of Interest)-based estimation to correct the translation vector for pose optimization. Jin Liu et al. proposed an anchor point prediction (APP) algorithm for remote sensing target pose observation to predict the position coordinates of the four corner points of the target object and ultimately calculate the three-dimensional pose through homography matrix decomposition. Nicolas Bourdis et al. proposed a visual servoing method for real-time monitoring of aerial targets that uses visual information feedback to adjust the camera control strategy, enabling the system to gradually acquire and optimize the precise pose. Dianqi Sun et al. used a time-of-flight (TOF) camera to estimate the relative pose of on-orbit space targets. Ravi Kumar et al. used a preprocessed unscented Kalman filter (PP-UKF) and an integrated unscented Kalman filter (PP-IUKF) to preprocess data, reducing the noise variance in sensor measurements to improve tracking performance for long-range targets in high-noise environments. Kelsey et al. used a reweighted least squares iterative method to match the model edge image with the contour projected from the internal wireframe model, achieving accurate pose estimation during autonomous rendezvous and docking of the aircraft. Laurent Jospin et al., in their UAV pose observation, considered the projected illumination intensity of the target point on the image during image acquisition, as well as the effects of motion blur and non-ideal lens systems. They extracted the target's photometric model based on the projection intensity and iteratively minimized the photometric error to achieve more accurate optimization results.Andrew Lee et al. used the iterative closest point (ICP) algorithm to estimate the pose of an aerial refueling system using a 3D point cloud generated by a stereo vision system. Kenneth Alberto Funes Mora et al., in their study of gaze estimation for far-field three-dimensional objects, added constraints to the experimental observation angles to increase optimization accuracy. Jie Li et al. used an unscented Kalman filter (UKF) combined with the Clohessy-Wiltshire (CW) equation to measure the pose and other motion parameters of an on-orbit spacecraft target. Yinlong Liu et al. used a branch-and-bound (BnB) algorithm to find the globally optimal rotational and translational pose parameters.

[0004] When measuring far-field targets, the above pose estimation methods are subject to severe impacts on accuracy and robustness due to minor image errors or sensor system errors, resulting in reduced accuracy of the final measurement results. Furthermore, the nonlinear optimization of pose parameters typically requires fast convergence, high solution accuracy, and low coupling. However, existing optimization algorithms are limited by simple models and computational resources in optimizing far-field target pose parameters, making it difficult to achieve fast and high-precision solutions. Summary of the Invention

[0005] In order to solve the above technical problems, the main purpose of the present invention is to provide a far-field target pose visual measurement method based on center normalization, focusing on using a monocular telephoto camera to achieve high-precision pose measurement of far-field targets and subsequent efficient optimization. This method has the characteristics of low sensitivity to image noise, weak external parameter coupling and avoidance of falling into local minima, which can effectively improve the accuracy of far-field target pose visual measurement.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] A far-field target pose visual measurement method based on center normalization includes the following steps:

[0008] Step 11: Design a far-field target pose visual measurement system based on a monocular telephoto camera. Fix the monocular telephoto camera on a two-axis turntable, use the far-field target as the far-field target to be measured, adjust the pose of the far-field target to be measured, and use power supply, triggering, data acquisition and processing devices to achieve high-precision pose observation of distant targets.

[0009] Step 12: Using a telephoto optical camera to image the far-field target and accurately extract the far-field target feature points;

[0010] Step 13: Obtain the initial values ​​of the pose parameters of the far-field target by using the non-iterative RPnP method;

[0011] Step 14: Establish an objective function by minimizing the back-projection error of the target image feature points, perform nonlinear pre-optimization using the Levenberg-Marquardt algorithm, and obtain the initial values ​​of the pose parameters for subsequent optimization;

[0012] Step 15: Perform center normalization on the Jacobian matrix of the pose parameters, and use the Levenberg-Marquardt algorithm for nonlinear optimization to obtain the optimal solution for the pose parameters.

[0013] Step 16: Adjust the far-field target pose, collect multiple sets of image feature point images in sequence, and finally substitute them into the above steps to finally solve and obtain multiple sets of pose parameters.

[0014] The beneficial effects of the present invention compared with the prior art are:

[0015] This method uses a close-range, short-focus camera and a long-focus optical camera to synchronously measure the pose of far-field targets. Multiple experiments have verified the effectiveness and accuracy of the method. This method reduces the sensitivity of pose parameters to image noise, minimizes external parameter coupling, and avoids local minima. This method is of great significance for improving high-precision visual pose measurement of far-field targets in fields such as aerospace. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a flow chart of a far-field target pose visual measurement method based on center normalization of the present invention;

[0017] Figure 2 This is a schematic diagram of the principle of vision measurement of far-field target pose by a monocular camera of the present invention; in the figure, Represents the camera coordinate system; Represents the world coordinate system; represents the far-field object coordinate system; Indicates the far field distance in km; Indicates that the far field distance is much greater than the camera focal length; Represents the characteristic points of far-field objects; Represents the projection point of the feature point of the far-field object.

[0018] Figure 3 This is the principle diagram for verifying the accuracy of the far-field target pose visual measurement test; in the figure, Represents the telephoto camera coordinate system; Represents the short-focus camera coordinate system; Represents the far-field target coordinate system; Indicates the pose parameters of the target for pose transformation; Represents the pose parameters of the target relative to the telephoto camera; Represents the pose parameters of the target relative to the short-focus camera.

[0019] Figure 4 The RMS error of the pose parameters is obtained for the simulation experiment.

[0020] Figure 5 is the reprojection RMS error of the far-field target in the simulation experiment. DETAILED DESCRIPTION

[0021] The basic concept of this invention is to use a telephoto optical camera to image and accurately extract the far-field target's feature points. The RPnP method is used to determine the initial pose of the far-field target. The objective function is established by minimizing the back-projection error of the image feature points. A preliminary nonlinear optimization is performed using the Levenberg-Marquardt algorithm to obtain the initial pose value, achieving parameter pre-optimization. The pose parameter Jacobian matrix is ​​then centrally normalized and nonlinearly optimized again using the Levenberg-Marquardt algorithm to obtain the optimal solution for the far-field target's pose. The pose of the far-field target is measured synchronously using a short-focus camera and a telephoto optical camera. Multiple experiments were conducted to verify the effectiveness and accuracy of this invention.

[0022] The present invention is further described in detail below by taking the device and method for fast and high-precision visual measurement of far-field target posture as an example.

[0023] like Figure 1 As shown, a far-field target pose visual measurement method based on center standardization of the present invention mainly includes the following steps:

[0024] Step 11: In a ground laboratory environment, build a long-focus optical camera far-field target pose visual measurement device.

[0025] Fix the monocular telephoto optical camera used in the experiment, adjust the far-field target pose to be measured, and build a far-field target pose visual measurement device and system suitable for the telephoto optical camera.

[0026] Step 12: Accurately extract image feature points, including modeling and extraction.

[0027] A high-precision image feature point extraction method based on the optimal scale is used to achieve high-precision feature point extraction under different scale conditions, including:

[0028] set up is the image grayscale distribution function, any pixel of the image The Hessian matrix is:

[0029] (1)

[0030] in, is the pixel coordinate, , , is the image grayscale distribution function The second-order partial derivative after Gaussian convolution is obtained from the image grayscale distribution function It is obtained by convolving with the second-order derivative convolution kernel of the Gaussian kernel. Denotes the transpose of the matrix. The optimal value of the second-order derivative convolution kernel of the Gaussian kernel is determined according to the size of the feature point image to ensure the accuracy of feature point image positioning.

[0031] The present invention adopts coordinate normalization to obtain the normalization operator :

[0032] (2)

[0033] in, Normalization operator The value of the intermediate parameter , is the spot radius, in pixels. According to the spatial distribution of the normalization operator, it can be concluded that the spot center condition is the normalization operator Greater than 0 and is a local maximum. The actual image is affected by noise, and the precise radius of the light spot cannot be accurately obtained. Therefore, in order to improve the accuracy of the center positioning of the image feature point, different The corresponding maximum value is obtained to determine the optimal radius of the light spot .

[0034] Step 13: Perform nonlinear pre-optimization of pose parameters with the goal of minimizing image positioning deviation. This includes:

[0035] After the high-precision extraction of image feature points in step 12, the constraints are established based on the mapping relationship between the far-field target feature points and the corresponding images, where the target feature point image is back-projected onto the target plane, and the corresponding spatial target point distance is minimized; after the initial values ​​of the far-field target pose parameters are obtained by the RPnP method, target points to establish the objective function by minimizing the back-projection error of the image feature points. To optimize the pose parameters, namely:

[0036] (3)

[0037] in, It is the observation The true projection coordinates of the characteristic target points, is its optimized coordinate. In the objective function middle Represents the pose parameters in the objective function:

[0038] (4)

[0039] in, translation vector, To surround The angles of rotation of the three coordinate axes. In three-dimensional space, the rotation matrix can be rotated around the three angles. The three basic rotations are defined by the product of the three coordinate axis rotations. 、 and To represent, thus defining the rotation matrix .

[0040] The present invention uses the Levenberg-Marquardt algorithm to solve the above nonlinear optimization problem. The Levenberg-Marquardt algorithm is a typical algorithm for solving nonlinear optimization problems. It combines the advantages of the steepest descent method and the Gauss-Newton method. In the nonlinear optimization model using the Levenberg-Marquardt algorithm, the objective function to be minimized is After first-order Taylor expansion, it can be expressed as:

[0041] (5)

[0042] in, Represents the posture parameter, which is , used to indicate surrounding 3 angles of rotation of three coordinate axes With translation vector , Indicates the initial value of the parameter, To optimize the number of iterations, is the parameter change, The Jacobian matrix of the objective function with respect to the pose parameters is:

[0043] (6)

[0044] in, Represents pose parameters No. elements, if is the current optimization target parameter. The parameter change for nonlinear optimization using the Gauss-Newton algorithm is:

[0045] (7)

[0046] The Gauss-Newton algorithm requires the Jacobian matrix The Levenberg-Marquardt algorithm used in this invention adds a positive definite matrix to the Gauss-Newton algorithm. arrive To improve, so Becomes a positive definite matrix. So we can deduce that the parameter change of the Levenberg-Marquardt algorithm is:

[0047] (8)

[0048] in, is the identity matrix. is called the Levenberg-Marquardt parameter. When , the Levenberg-Marquardt algorithm tends to the Gauss-Newton algorithm; when When , the Levenberg-Marquardt algorithm tends to the steepest descent method.

[0049] After obtaining the initial values ​​of the parameters to be optimized, nonlinear optimization can be performed according to the following procedure:

[0050] 1) Enter the initial value. Initial parameters Set to 0.01;

[0051] 2) Calculate the objective function and the Jacobian matrix of the target optimization parameters ;

[0052] 3) Calculate the change in the Levenberg-Marquardt parameter , then update .

[0053] 4) If , then stop the iteration and output the result; otherwise, Set to , then return to procedure 2 to continue iteration;

[0054] 5) If , then Set to , recalculate .

[0055] Step 14: Center normalization of pose parameters, that is, center normalization of the Jacobian matrix of the optimized pose parameters.

[0056] Centralization is used to reduce the disparity in Jacobian matrices, reduce the correlation between parameters, and make iterative optimization results more accurate. Furthermore, since the properties and metrics of the parameters in the Jacobian matrix vary, standardization is still required after centralization to ensure consistent scales between parameters and improve iterative optimization accuracy.

[0057] Step 14.1 Centralization includes:

[0058] In the nonlinear optimization model used in the present invention, formula (3) is the objective function to be minimized After the first-order Taylor expansion, formula (6) represents the optimization step length of Order coefficient Jacobian matrix The elements of the Jacobian matrix typically represent the partial derivatives of the model output with respect to each input parameter. Correlations between these elements can lead to coupling effects during parameter optimization, affecting the accuracy of the optimization results. Centering the Jacobian matrix, by subtracting the mean of each element, can effectively reduce these correlations.

[0059] The present invention first centers each column. Let the Jacobian matrix No. Listed as , the centered column vector is :

[0060] (9)

[0061] in, It is The mean vector of the columns is defined as:

[0062] (10)

[0063] in, Represents the Jacobian matrix OK Column elements. Therefore, the centered Jacobian matrix It can be expressed as:

[0064] (11)

[0065] in, is a length of A column vector of all ones, is a column containing the mean of each column The centralization process helps make the influence of each parameter on the model more independent, thereby reducing the interaction between parameters during the optimization process and improving the accuracy and stability of the overall solution.

[0066] Step 14.2 Standardization includes:

[0067] In nonlinear optimization, each column of the Jacobian matrix corresponds to a different parameter, and these parameters may have significant differences in properties, units, and dimensions. This difference may cause the influence of certain parameters on the model to be amplified or reduced during the optimization process, thereby affecting the convergence and accuracy of the optimization. To solve this problem, the present invention further performs standardization after the Jacobian matrix is ​​centralized. The standardization process adjusts the scale of each parameter to be consistent so that different parameters have the same weight during iterative optimization. This processing method can not only improve the stability of the optimization algorithm, but also accelerate convergence and improve the accuracy of the final solution.

[0068] The Jacobian matrix after centering During the standardization process, the standardized matrix Defined as:

[0069] (12)

[0070] in, and Represents matrices and matrix of OK Column element. yes No. The standard deviation of the column is calculated as:

[0071] (13)

[0072] in, is the mean of the column vectors of the centered Jacobian matrix. The standardized Jacobian matrix The scale differences between parameters are eliminated and the correlation is weakened, thereby improving the convergence and accuracy of the nonlinear optimization algorithm.

[0073] Finally, the Jacobian matrix after center normalization is It can be expressed as:

[0074] (14)

[0075] Since the scale of the Jacobian matrix in the nonlinear optimization model has changed, the optimization step size should also be adjusted accordingly, that is, the optimization step size should be used in Equation (8). To replace the original :

[0076] (15)

[0077] After the step size and Jacobian matrix are both center-normalized, subsequent optimization iterations are performed.

[0078] After center standardization, The matrix is ​​transformed into a symmetric matrix with all elements on the main diagonal set to 1. This results in a more stable solution, effectively reducing the impact of accidental observation errors on the results. Incorporating the concept of central normalization into nonlinear optimization methods can effectively reduce parameter coupling, avoid falling into local minima, accelerate convergence, and improve solution efficiency.

[0079] Step 15: Nonlinear optimization is used to solve the remote target pose parameters, including:

[0080] The Levenberg-Marquardt algorithm is used again for nonlinear optimization to obtain the optimal solution for the pose parameters. Depending on the optimization effect, it can be chosen whether to impose parameter prior constraints on the depth information during the optimization process.

[0081] Step 16: Visual measurement and analysis of multiple sets of far-field target poses, including:

[0082] After successfully setting up the observation device in step 11, adjust the far-field target pose to be measured and proceed sequentially through steps 12-15 to obtain multiple sets of pose parameters. Use a short-focus camera to image and measure the target at close range, and a long-focus camera to image and measure the target at long range. The short-focus camera represents the true value of the target's motion pose, while the long-focus camera represents the measured value. Move the target multiple times and calculate the error in the change in the target pose to verify the effectiveness and accuracy of the present invention.

[0083] Figure 2 This is a schematic diagram of the principle of vision measurement of far-field target pose by a monocular camera of the present invention; in the figure, Represents the camera coordinate system; Represents the world coordinate system; represents the far-field object coordinate system; Indicates the far field distance in km; Indicates that the far field distance is much greater than the camera focal length; Represents the characteristic points of far-field objects; Represents the projection point of the feature point of the far-field object.

[0084] Figure 3 This is the principle diagram for verifying the accuracy of the far-field target pose visual measurement test; in the figure, Represents the telephoto camera coordinate system; Represents the short-focus camera coordinate system; Represents the far-field target coordinate system; Indicates the pose parameters of the target for pose transformation; Represents the pose parameters of the target relative to the telephoto camera; Represents the pose parameters of the target relative to the short-focus camera.

[0085] Example:

[0086] 1. Simulation experiment

[0087] The present invention needs to use simulation experiments to verify the effectiveness of the proposed method. In the process of simulating far-field observation, in order to make the camera image occupy about 2 / 3 of the screen as much as possible to improve the observation accuracy, it is necessary to use different object distances to simulate the observation. , different side lengths The far-field target (assumed to be square) is configured with different camera focal lengths The target parameters are shown in Table 1.

[0088] Table 1 Correspondence table of different object distances, focal lengths, and target side lengths

[0089]

[0090] Corresponding to the different observation targets in Table 1, the far-field target to be measured in the simulation experiment is set to have a side length of of At the same time, in order to ensure the reference value of the simulation experiment, it is necessary to select a certain random posture parameter for the far-field target to be measured: the observed target is rotated around Three rotation angles of the axis Set them to random angles between 5° and 30° respectively; set the translation vector of the observed target Set to arrive A random value between ; the translation vector of the observed target Set to arrive A random value between .

[0091] According to the above content, the camera focal length used in the simulation experiment is It can be obtained according to the different observation targets selected in Table 1. The other internal parameters of the camera are set as follows: the principal point coordinates Pixels, Pixel, radial distortion coefficient , , , tangential distortion coefficient , .

[0092] After determining all camera intrinsic parameters, target dimensions, and pose parameters, Gaussian white noise ranging from 0 to 1 pixel with a 0.1 pixel interval was added to each target point to simulate far-field position error. In the simulation experiment, the target pose measurement results were measured and optimized 100 times for each error level, with noise added again each time to enhance the randomness and reliability of the experiment. Each measurement yielded a set of pose parameter results obtained using different methods. The RMS error and reprojection error of all the results were then calculated and finally displayed and analyzed using charts.

[0093] In order to accurately verify the optimization effect of the invented method, three methods are used in simulation detection, namely:

[0094] Method 1: For the simulated far-field target and the simulated target imaging image, the RPnP method is used to solve the pose parameters (RPnP);

[0095] Method 2: The pose parameters obtained by the RPnP algorithm in Method 1 are used as the initial optimization values, and the Levenberg-Marquardt algorithm is used for nonlinear iterative optimization (PnP-BA).

[0096] Method 3: Treat Method 2 as a pre-optimization method, and use the obtained pose parameters as the initial values ​​for subsequent optimization. The initial values ​​are iteratively optimized using the Levenberg-Marquardt nonlinear optimization algorithm based on the central normalization idea. At the same time, the introduction of appropriate parameter prior constraints (PnP-CSBA) can be selected based on the actual optimization effect.

[0097] Among them, the range of the parameter prior constraints is set based on the pose parameters obtained by pre-optimization in method 2.

[0098] In the simulation process, the three proposed methods are compared with the traditional DLT method and EPnP method at multiple noise levels to verify the effectiveness of the proposed methods and demonstrate their optimization effects. The evaluation indicators are the RMS error of the pose parameters and the reprojection error of the far-field target. The results are as follows: Figure 4 shown.

[0099] Depend on Figure 4 The results show that both PnP+BA and PnP+CSBA significantly outperform the basic RPnP method in reducing the RMS error of angles and translation vectors, and the optimization effect is not significantly affected by increasing noise levels. In particular, the centralized PnP+CSBA method exhibits the best performance under all experimental conditions, demonstrating that the PnP+CSBA method not only performs well in low-noise conditions but also maintains a low error level in high-noise environments, making it the most stable and accurate pose estimation method under various noise conditions.

[0100] 2. Physical experiment

[0101] After completing the internal calibration of the Hikvision camera and telephoto camera, a checkerboard target with a size of 12 rows and 9 columns and a side length of 45 mm per grid will be prepared as a target for subsequent observations.

[0102] After fixing the positions of the near-field Hikvision camera and the far-field telephoto camera, the target's pose and position are adjusted, and the two cameras simultaneously capture the target after each adjustment. After each target adjustment, a set of pose parameters for the target in the camera coordinate system is obtained. Using the real-time transformation (RT) transform, the pose change between the two different poses can be derived. Therefore, two non-overlapping sets of pose parameters can be selected from the observed pose parameters to obtain their pose change. Using these pose changes as pose parameter observations not only improves computational efficiency but also generates multiple sets of data for analysis, thereby enhancing the stability and accuracy of the results. The true values ​​of the target's pose parameters are calculated using the RPnP algorithm from target images captured by the Hikvision camera. The observed values ​​of the target's pose parameters are obtained using three methods: RPnP, PnP-BA, and PnP-CSBA.

[0103] In the experiment, the target's poses were adjusted 20 times. Two sets of pose parameters were randomly selected from these sets without duplication, and their pose variations were calculated as pose parameter observations. Because far-field imaging is susceptible to noise, to minimize the impact of gross errors on the observations, the median absolute deviation (MAD) method was used to identify outliers that differed from the median by more than three times the MAD. After removing these outlier solutions from the observations, the optimization effects of the three methods, RPnP, PnP-BA, and PnP-CSBA, were evaluated for the long-focus camera based on the true values ​​of the short-focus camera's pose parameters. The evaluation metric was the RMS error of the pose parameters. The results are shown in Table 2.

[0104] Table 2 RMS error of pose parameters of physical experiment

[0105]

[0106] According to the pose parameter RMS error data of the three methods in Table 2, the relative error reduction percentage of the PnP-BA method and the PnP-CSBA method can be calculated:

[0107] In the pair In the RMS error evaluation of the parameters, the relative error reduction percentages of the PnP-BA method relative to the RPnP method reached 43.53%, 47.11% and 41.92%, respectively, and the relative error reduction percentages of the PnP-CSBA method relative to the RPnP method reached 43.84%, 48.09% and 42.45%, respectively. Moreover, the PnP-CSBA method reduced the relative error by 0.31%, 0.98% and 0.53% more than the PnP-BA method.

[0108] In the pair In the RMS error evaluation of the parameters, the relative error reduction percentages of the PnP-BA method relative to the RPnP method reached 28.19%, 30.27% and 31.15%, respectively, and the relative error reduction percentages of the PnP-CSBA method relative to the RPnP method reached 28.44%, 31.00% and 31.51%, respectively. Moreover, the PnP-CSBA method reduced the relative error by 0.25%, 0.73% and 0.35% more than the PnP-BA method.

[0109] From the above calculation results, it can be seen that in multiple measurement results, the RMS errors of the pose parameters of the PnP-CSBA method and the PnP-BA method are much smaller than that of the RPnP method, and the RMS error of the pose parameters of the PnP-CSBA method is smaller than that of the PnP-BA method, which verifies the effectiveness of the method proposed in this paper.

[0110] Therefore, after analyzing the results of the actual experiment, the observation accuracy of the pose parameters of the PnP-BA method and the PnP-CSBA method have been greatly improved. At the same time, the observation results of the PnP-CSBA method are more accurate than those of the PnP-BA method.

[0111] In the actual experiment, a telephoto camera with a focal length of 1300mm was selected. The experimental results were consistent with the simulation experimental results of a camera with the same focal length, verifying the effectiveness of the method proposed in the present invention. Figure 5 is the reprojection RMS error of the far-field target in the simulation experiment.

Claims

1. A far-field target pose visual measurement method based on center normalization, characterized in that The implementation steps are as follows: Step 11: Design a far-field target pose visual measurement device based on a monocular telephoto camera. Fix the monocular telephoto camera on a two-axis turntable and connect the data acquisition and processing device to the camera. Use the far-field target as the far-field target to be measured. After powering the device, adjust the far-field target pose to achieve high-precision pose observation of the distant target. Step 12: Use a telephoto optical camera to image the far-field target and accurately extract image feature points; Step 13: Obtain the initial values ​​of the pose parameters of the far-field target by using the non-iterative RPnP method; Step 14: Establish an objective function by minimizing the back-projection error of image feature points, perform nonlinear pre-optimization using the Levenberg-Marquardt algorithm, and obtain initial values ​​for subsequent pose parameter optimization; Step 15: The Jacobian matrix of the initial value of the subsequent pose parameter optimization is centered and normalized, and the Levenberg-Marquardt algorithm is used again for nonlinear optimization to obtain the optimal solution of the pose parameters; Step 16: Adjust the far-field target pose, collect multiple sets of image feature points in sequence, and finally substitute them into steps 12 to 15 to finally solve and obtain multiple sets of pose parameters.

2. The method for visually measuring far-field target pose based on center standardization according to claim 1, characterized in that: In step 11, the rotation angle and position of the far-field target are adjusted, and a monocular telephoto camera is installed on a two-axis turntable in the laboratory for observation, thereby building a far-field target posture visual measurement device and system based on a monocular telephoto camera.

3. The method for visually measuring far-field target pose based on center standardization according to claim 1, wherein: In step 12, an adaptive multi-scale image feature point extraction method is used for the far-field target image feature points to achieve optimal scale extraction of all far-field target image feature points.

4. The method for visually measuring far-field target pose based on center standardization according to claim 3, wherein: The step 14 includes: establishing a mapping equation between the coordinates of the feature points of the far-field target image and their corresponding world coordinates, establishing an objective function with the minimum reprojection error of the image feature points, performing nonlinear pre-optimization through the Levenberg-Marquardt algorithm, and obtaining initial values ​​for subsequent pose parameter optimization.

5. The method for visually measuring far-field target pose based on center normalization according to claim 4, characterized in that: Use a short-focus camera to perform imaging measurement on the target at close range, and a long-focus camera to perform imaging measurement on the target at long range. The former is the true value of the target's pose parameter, and the latter is the measured value. The target pose to be measured is adjusted multiple times, and the change in the target pose parameter to be measured is used as the observation quantity, and the error between the measured value and the true value is calculated.

Images