Pan-tilt prism angle error compensation method and device, electronic equipment and storage medium

Abstract

The invention provides a pan-tilt prism angle error compensation method and device, electronic equipment and a storage medium, and relates to the technical field of photoelectric measurement. Controlling a holder to point to different directions of a plurality of total stations under a calibration working condition, jointly resolving angle error parameters based on multi-station observation data to form an error parameter vector, generating compensation pointing according to observation requests of the total stations in an operation working condition, and dynamically updating the error parameter vector based on multi-station error observation quantity; and the updated error parameter vector is used to carry out feedforward compensation on the pointing of the holder, and the updating strategy is adjusted according to the direction residual error. The problems that in the prior art, multi-station data are difficult to fuse, calibration depends on a single station or a fixed direction, error parameters cannot be comprehensively obtained, static updating of the error parameters cannot adapt to dynamic changes in operation, and compensation precision is reduced due to no residual adjustment mechanism can be solved.

Description

Technical Field

[0001] This disclosure relates to the field of photoelectric measurement technology, and in particular to a method and apparatus for compensating for angle error of a gimbal prism, an electronic device, and a storage medium. Background Technology

[0002] The gimbal prism system is a core component of precision photoelectric measurement, widely used in scenarios such as large-scale structural deformation monitoring and moving target tracking. Existing systems use multiple total stations, dual-axis gimbals, and controllers to work together to build a unified control coordinate system. Error parameters (including zero-point deviation, axis angle, encoder scale factor, etc.) are updated offline or within a fixed time window to maintain measurement accuracy.

[0003] However, this type of method has significant limitations: during long-term operation, dynamic factors such as mechanical wear and temperature and humidity changes can easily lead to error parameter drift, making it difficult for existing solutions to adapt in real time; in complex operating conditions such as obstruction or missing stations, parameter update delays can also occur. Furthermore, multi-station observation data often employs equal-weighted fusion or simple averaging, failing to consider differences in observation geometry, ranging distance, and CCD spot quality among different stations; calibration relies on static data from a single station or fixed direction, failing to reflect the dynamic characteristics of the gimbal axis; updates within fixed time windows are prone to lag; and parameter estimations are easily mutually interfered with when observation directions are concentrated. In addition, dynamic compensation lacks a reasonable weighting strategy, affecting recursive stability; and concurrent observation requests from multiple stations lack effective scheduling, weakening the geometric constraints of multi-station collaboration. Summary of the Invention

[0004] This disclosure provides a method and apparatus for compensating for the angle error of a gimbal prism, as well as an electronic device and a storage medium. Its main objective is to at least partially solve one of the technical problems in the related art.

[0005] According to a first aspect of this disclosure, a method for compensating for angle errors of a gimbal prism is provided, comprising: Establish a unified control coordinate system and construct a transformation model between the gimbal axis system and the prism normal direction; Under calibration conditions, the gimbal is controlled to point the prism in different directions of multiple total stations. Based on the multi-station observation data, the angle error parameters of the gimbal axis system are jointly calculated to form an angle error parameter vector. During operation, a compensation pointing is generated based on the observation request of the total station, and the angle error parameter vector is dynamically updated based on the multi-station error observations. The updated angle error parameter vector is used to perform feedforward compensation for the gimbal pointing, and the update strategy is adjusted according to the direction residual.

[0006] Optionally, establishing a unified control coordinate system and constructing a transformation model between the gimbal axis system and the prism normal direction includes: A control coordinate system is defined with the center of gimbal rotation as the origin, where the horizontal axis points in the positive direction of the azimuth axis and the vertical axis is perpendicular to the gimbal mounting plane. Based on the gimbal's mechanical structural parameters and the prism's installation position, the azimuth and pitch axis angles are mapped to a three-dimensional vector of the prism's normal direction in the control coordinate system through coordinate transformation.

[0007] Optionally, the joint calculation of the angle error parameters of the gimbal axis system based on multi-station observation data includes: Control the pan-tilt unit to point at at least three total stations in multiple different azimuth and elevation angles, and acquire the horizontal angle, vertical angle and spot offset data output by each total station; By comparing multi-station observation data with the theoretical optical axis direction, a joint error equation is constructed, and at least some of the parameters in azimuth axis zero deviation, pitch axis zero deviation, shaft system angle deviation, encoder scale factor error, prism eccentricity, and installation tilt angle are solved by the least squares fitting method.

[0008] Optionally, the dynamic updating of the angle error parameter vector based on multi-station error observations includes: Error observations from multiple total stations are collected within a preset sliding time period, and weights are assigned to each error observation based on observation geometry, distance measurement, and spot quality. A recursive estimation algorithm is used to solve the weighted error equation, and the angle error parameter vector and its covariance matrix are updated online.

[0009] Optionally, the step of using the updated angle error parameter vector to perform feedforward compensation for the gimbal pointing includes: When generating the gimbal servo setting angle, the compensation amount calculated based on the angle error parameter vector is superimposed on the nominal pointing angle; Based on the comparison between the directional residual and the preset threshold in the continuous observation period, the weight of the error observation is adaptively adjusted, and the static recalibration process is triggered when the directional residual continues to exceed the limit.

[0010] Optional, also includes: When multiple total stations issue observation requests within the same time interval, they are sorted according to the sensitivity of each request to the angle error parameter based on the station distance, pan-tilt rotation angle, and observation direction. Observation requests with high sensitivity and small pan-tilt rotation are executed first.

[0011] According to a second aspect of this disclosure, a gimbal prism angle error compensation device is provided, comprising: The building unit is used to establish a unified control coordinate system and to build a transformation model between the gimbal axis system and the prism normal direction; The control unit is used to control the gimbal to point the prism to different directions of multiple total stations under calibration conditions. Based on the observation data of multiple stations, the angle error parameters of the gimbal axis system are jointly calculated to form an angle error parameter vector. The update unit is used to generate a compensation pointing according to the observation request of the total station during operation, and to dynamically update the angle error parameter vector based on the multi-station error observations. The adjustment unit is used to perform feedforward compensation of the gimbal pointing using the updated angle error parameter vector, and to adjust the update strategy according to the direction residual.

[0012] Optionally, building blocks are also used for: A control coordinate system is defined with the center of gimbal rotation as the origin, where the horizontal axis points in the positive direction of the azimuth axis and the vertical axis is perpendicular to the gimbal mounting plane. Based on the gimbal's mechanical structural parameters and the prism's installation position, the azimuth and pitch axis angles are mapped to a three-dimensional vector of the prism's normal direction in the control coordinate system through coordinate transformation.

[0013] Optionally, the control unit is also used for: Control the pan-tilt unit to point at at least three total stations in multiple different azimuth and elevation angles, and acquire the horizontal angle, vertical angle and spot offset data output by each total station; By comparing multi-station observation data with the theoretical optical axis direction, a joint error equation is constructed, and at least some of the parameters in azimuth axis zero deviation, pitch axis zero deviation, shaft system angle deviation, encoder scale factor error, prism eccentricity, and installation tilt angle are solved by the least squares fitting method.

[0014] Optionally, the update unit is also used for: Error observations from multiple total stations are collected within a preset sliding time period, and weights are assigned to each error observation based on observation geometry, distance measurement, and spot quality. A recursive estimation algorithm is used to solve the weighted error equation, and the angle error parameter vector and its covariance matrix are updated online.

[0015] Optionally, the adjustment unit is also used for: When generating the gimbal servo setting angle, the compensation amount calculated based on the angle error parameter vector is superimposed on the nominal pointing angle; Based on the comparison between the directional residual and the preset threshold in the continuous observation period, the weight of the error observation is adaptively adjusted, and the static recalibration process is triggered when the directional residual continues to exceed the limit.

[0016] Optional, also includes: The execution unit is used to sort the observation requests issued by multiple total stations within the same time interval according to the sensitivity of the angle error parameter corresponding to the station distance, pan-tilt turning angle and observation direction of each request, and to prioritize the execution of observation requests with high sensitivity and small pan-tilt turning angle.

[0017] According to a third aspect of this disclosure, an electronic device is provided, comprising: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method described in the first aspect above.

[0018] According to a fourth aspect of this disclosure, a non-transitory computer-readable storage medium is provided storing computer instructions, wherein the computer instructions are configured to cause the computer to perform the method described in the first aspect above.

[0019] According to a fifth aspect of this disclosure, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the method described in the first aspect above.

[0020] The gimbal prism angle error compensation method, device, electronic equipment, and storage medium disclosed herein establish a unified control coordinate system and a transformation model between the gimbal axis system and the prism normal direction. Under calibration conditions, the gimbal is controlled to point in different directions of multiple total stations, and angle error parameters are jointly calculated based on multi-station observation data to form an error parameter vector. During operation, a compensation pointing is generated according to the total station observation request, and the error parameter vector is dynamically updated based on the multi-station error observations. Furthermore, the updated error parameter vector is used to perform feedforward compensation on the gimbal pointing and the update strategy is adjusted according to the direction residual. Therefore, it can solve the problems in the prior art caused by the lack of a unified coordinate system and transformation model, which makes it difficult to integrate multi-station data, the reliance on a single station or fixed direction for calibration, the inability to fully obtain error parameters, the inability of static updates of error parameters to adapt to dynamic changes during operation, and the lack of a residual adjustment mechanism leading to a decrease in compensation accuracy. It achieves the technical effects of efficient and unified integration of multi-station observation data, comprehensive and accurate acquisition of angle error parameters, dynamic adaptation of error parameters to operational changes, and stable and reliable gimbal pointing compensation accuracy.

[0021] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0022] The accompanying drawings are provided to better understand this solution and do not constitute a limitation of this disclosure. Wherein: Figure 1 A flowchart illustrating a method for compensating for angle errors of a gimbal prism provided in an embodiment of this disclosure; Figure 2 This is a schematic diagram of the structure of a gimbal prism angle error compensation device provided in an embodiment of the present disclosure; Figure 3 A schematic block diagram of an example electronic device provided for embodiments of this disclosure. Detailed Implementation

[0023] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0024] The following description, with reference to the accompanying drawings, outlines an embodiment of the gimbal prism angle error compensation method, apparatus, electronic device, and storage medium of this disclosure.

[0025] Figure 1 This is a flowchart illustrating a method for compensating for angle errors of a gimbal prism provided in an embodiment of this disclosure.

[0026] like Figure 1 As shown, the method includes the following steps: Step 101: Establish a unified control coordinate system and construct a transformation model between the gimbal axis system and the prism normal direction.

[0027] In the embodiments of this disclosure, a unified control coordinate system is established to provide a consistent spatial reference for the observation data of multiple measuring devices (such as total stations) in the system. This eliminates the problem of inconsistent observation information dimensions caused by differences in the independent coordinate systems of each device, ensuring that subsequent observation data from different stations and at different times can be correlated and fused under the same spatial dimension. Secondly, a transformation model between the gimbal axis system and the prism normal direction is constructed. The core is to establish a definite mapping relationship between the angle parameters of the gimbal axis system (usually including azimuth and pitch axes) and the actual spatial direction of the prism normal. This ensures that changes in the angle of the gimbal axis system accurately correspond to changes in the spatial direction of the prism normal, providing theoretical support for deriving the actual prism direction and calculating angle errors from the gimbal axis system data. As one implementation method, the unified control coordinate system can be a geodetic coordinate system or a custom reference coordinate system adapted to the specific measurement scenario. The construction of the transformation model can combine the mechanical structural parameters of the gimbal (such as the orthogonality of the axis system) and the installation position parameters of the prism on the gimbal, and clarify the correspondence between the gimbal axis angle and the prism normal direction vector through mathematical modeling.

[0028] By establishing a unified control coordinate system, the problem of difficulty in merging multi-station observation data due to inconsistent benchmarks in existing technologies is effectively solved. At the same time, the constructed transformation model provides a necessary theoretical bridge for the subsequent derivation of prism pointing and calculation of angle errors based on gimbal axis data, ensuring the accuracy and logical consistency of subsequent system measurement, error calculation and compensation processes.

[0029] Step 102: Under calibration conditions, control the gimbal to point the prism in different directions of multiple total stations, and jointly calculate the angle error parameters of the gimbal axis system based on the multi-station observation data to form an angle error parameter vector.

[0030] In the embodiments of this disclosure, under calibration conditions, the pan-tilt unit is first controlled to point the prism sequentially in different spatial directions where multiple total stations are located. This process aims to cover multi-directional observation scenarios, ensuring that the acquired observation data can reflect the error characteristics of the pan-tilt unit under different pointing states, and avoiding the limitations of single-direction observation data. Subsequently, joint calculation is performed based on the observation data fed back by multiple total stations. By utilizing the spatial geometric constraints between the multi-station data, angle error parameters related to the pan-tilt unit axis system are comprehensively extracted. Compared with single-station calculation, this effectively avoids the problems of parameter coupling or insufficient accuracy. Finally, the various angle error parameters obtained from the calculation are integrated into an angle error parameter vector, forming the initial benchmark for error compensation in subsequent system operation. As one implementation method, the pan-tilt unit can be controlled to point in the calibration direction of at least three total stations. The total station observation data may include horizontal angle, vertical angle, and CCD spot offset data. The calculated angle error parameters may cover azimuth axis zero-position deviation, pitch axis zero-position deviation, axis system angle deviation, etc., and then form an angle error parameter vector in a fixed order.

[0031] By using multi-directional pointing and multi-station joint calculation, the problem of incomplete error parameter coverage and easy parameter coupling caused by relying on single-station or fixed-direction calibration in the existing technology is solved. The resulting angle error parameter vector can uniformly represent the key error information of the gimbal axis system, providing an accurate and comprehensive initial error benchmark for dynamic error compensation under subsequent system operating conditions, and ensuring the effectiveness of subsequent compensation.

[0032] Step 103: In the operating condition, generate a compensation direction according to the observation request of the total station, and dynamically update the angle error parameter vector based on the multi-station error observations.

[0033] In the embodiments of this disclosure, under operating conditions, the system first responds to the observation request issued by the total station, and calculates and generates a prism target pointing with error compensation (i.e., compensated pointing) based on the existing angle error parameter vector, ensuring that the initial pointing of the gimbal has initially offset the known error, laying the foundation for subsequent accurate observation. Subsequently, based on the error observations fed back after observations by multiple total stations (i.e., data reflecting the deviation between actual observation and theoretical state), the angle error parameter vector is dynamically updated. By utilizing the collaborative constraints of multi-station data, update deviations caused by abnormal or limited data from a single station are avoided. At the same time, "dynamic updating" ensures that the error parameters can adapt in real time to error drift caused by factors such as mechanical wear and environmental changes during operation. As one implementation method, when generating the compensated pointing, the target axis angle can be calculated by combining the station coordinates and the transformation model. The error observations can include the direction difference between the measured and corrected theoretical optical axes and the CCD spot offset. The update process can be implemented using weighted recursive least squares or extended Kalman filtering.

[0034] By generating compensated pointers on demand, the problem of insufficient initial pointer accuracy caused by the lack of targeted compensation in existing technologies is solved. By dynamically updating parameter vectors based on multi-station error observations, the shortcomings of static updates that cannot adapt to error drift during operation and low accuracy of single-station data updates are avoided, thus continuously ensuring the timeliness and accuracy of error parameters and providing support for high-precision measurement.

[0035] Step 104: Use the updated angle error parameter vector to perform feedforward compensation on the gimbal pointing and adjust the update strategy according to the direction residual.

[0036] In the embodiments of this disclosure, the latest updated angle error parameter vector is used to perform feedforward compensation for gimbal pointing. That is, when generating gimbal drive commands, the error compensation amount is incorporated into the gimbal's target pointing control in advance, avoiding the lag inherent in traditional feedback compensation. This ensures that the gimbal can offset the currently known error when performing pointing actions, directly outputting a high-precision pointing state. Secondly, by monitoring the direction residual (i.e., the deviation between the actual observed gimbal pointing and the theoretically compensated pointing), the adaptability of the current error parameter update strategy is analyzed. The update strategy (such as the weight allocation of error observations, the length of the sliding time period, etc.) is then adjusted, allowing the parameter update mechanism to flexibly adapt to the actual error change trend, avoiding the problem of decreased accuracy when the error characteristics change with a fixed update strategy. As one implementation method, feedforward compensation can superimpose the compensated target axis angle into the servo setpoint. If the direction residual continuously exceeds a preset limit, the weight of the error observations within the sliding time period can be adjusted, or static recalibration in the corresponding direction can be triggered to optimize the update strategy.

[0037] By directly eliminating the impact of the current error on the gimbal pointing through feedforward compensation, the problem of insufficient pointing accuracy caused by the lag of traditional feedback compensation is solved. At the same time, the direction residual adjustment and update strategy avoids the defect that the fixed update strategy cannot adapt to the dynamic changes of error, ensuring that the error parameter update always matches the actual error characteristics and guaranteeing the stability of pointing accuracy in long-term system operation.

[0038] The gimbal prism angle error compensation method disclosed herein establishes a unified control coordinate system and a transformation model between the gimbal axis system and the prism normal direction. Under calibration conditions, it controls the gimbal to point in different directions of multiple total stations and jointly calculates angle error parameters based on multi-station observation data to form an error parameter vector. Under operational conditions, it generates a compensation pointing based on the total station's observation requests and dynamically updates the error parameter vector based on multi-station error observations. Furthermore, it uses the updated error parameter vector to perform feedforward compensation on the gimbal pointing and adjusts the update strategy according to the direction residual. Therefore, it can solve the problems in existing technologies, such as the lack of a unified coordinate system and transformation model leading to difficulty in fusion of multi-station data, the reliance on single-station or fixed directions for calibration resulting in incomplete acquisition of error parameters, the inability of static updates of error parameters during operation to adapt to dynamic changes, and the lack of a residual adjustment mechanism leading to decreased compensation accuracy. It achieves the technical effects of efficient and unified fusion of multi-station observation data, comprehensive and accurate acquisition of angle error parameters, dynamic adaptation of error parameters to operational changes, and stable and reliable gimbal pointing compensation accuracy.

[0039] As a specific implementation of this disclosure, based on the basic scheme, the establishment of a unified control coordinate system is further defined, and a transformation model between the gimbal axis system and the prism normal direction is constructed, including: defining a control coordinate system with the gimbal rotation center as the origin, wherein the horizontal axis points to the positive direction of the azimuth axis, and the vertical axis is perpendicular to the gimbal mounting plane; based on the gimbal mechanical structure parameters and the prism mounting position, the azimuth axis angle and pitch axis angle are mapped to a three-dimensional vector of the prism normal direction in the control coordinate system through coordinate transformation relationship.

[0040] Specifically, when establishing a unified control coordinate system, the center of rotation of the gimbal is clearly taken as the origin of the coordinate system, and the direction of each coordinate axis is precisely defined: the horizontal axis (usually set as the X-axis) points to the positive direction of the gimbal's azimuth axis, the vertical axis (usually set as the Z-axis) is perpendicular to the installation plane of the gimbal, and the remaining coordinate axes (usually set as the Y-axis) are determined according to the right-hand coordinate system rules. This forms a unified control benchmark with clear spatial positioning, ensuring that all subsequent measurements and calculations are carried out within this fixed spatial framework. When constructing the transformation model between the gimbal axis system and the prism normal direction, the mechanical structural parameters of the gimbal (including the design orthogonal angles of the azimuth and pitch axes, the reference distance from the intersection of the two axes to the gimbal shell, etc.) and the prism installation position parameters (including the three-dimensional offset of the prism geometric center relative to the gimbal rotation center, the preset angle between the prism and the gimbal platform during installation, etc.) are first obtained through coordinate transformation: the rotation matrix corresponding to the azimuth axis angle (rotating the azimuth axis angle around the Z-axis of the control coordinate system) and the rotation matrix corresponding to the pitch axis angle (rotating the pitch axis angle around the Y-axis of the control coordinate system) are constructed respectively. The two rotation matrices are multiplied in the order of "azimuth first, then pitch" to obtain the joint rotation matrix. Then, combined with the translation vector of the prism installation position, the azimuth axis angle and pitch axis angle of the gimbal are substituted into the transformation formula composed of the joint rotation matrix and the translation vector. Finally, the three-dimensional vector of the prism normal direction in the control coordinate system is calculated (the X, Y, and Z components of this vector correspond to the direction cosines of the normal in the three axes of the control coordinate system, respectively).

[0041] By defining a control coordinate system with the gimbal rotation center as the origin and the coordinate axes directly related to the gimbal axis system, the spatial correspondence between the gimbal axis system parameters and the prism normal direction becomes more intuitive, reducing transformation errors caused by improper definition of the coordinate system origin or axis system. At the same time, based on the actual mechanical parameters and installation position, a transformation model is constructed that can accurately reflect the mapping relationship between the gimbal axis angle and the prism normal, providing a high-precision coordinate transformation basis for subsequent angle error calculation and pointing compensation, further improving the system measurement accuracy.

[0042] As a specific implementation of this disclosure, based on the basic scheme, the method of jointly calculating the angle error parameters of the gimbal axis system based on multi-station observation data is further defined as follows: controlling the gimbal to point at at least three total stations in multiple different azimuth and elevation angle directions, acquiring the horizontal angle, vertical angle and spot offset data output by each total station; comparing the multi-station observation data with the theoretical optical axis direction, constructing a joint error equation, and solving at least some of the parameters in azimuth axis zero position deviation, pitch axis zero position deviation, axis system angle deviation, encoder scale factor error, prism eccentricity and installation tilt angle by least squares fitting method.

[0043] Specifically, following a preset angle sequence, the pan-tilt unit controls the prism to rotate in multiple different azimuth angles (covering at least three discrete angles within the 0°-360° range, such as 0°, 120°, and 240°) and different elevation angles (covering at least two discrete angles within the -30°-60° range, such as 0° and 30°), ensuring that the prism sequentially points to the calibration positions of at least three total stations, and that the observation directions of each total station are not coplanar, thus forming effective spatial geometric constraints. Subsequently, observation data is acquired: each total station, in automatic target recognition mode, synchronously outputs the observed horizontal angle (accuracy up to 0.5″), vertical angle (accuracy up to 0.5″), and spot offset data (including horizontal and vertical pixel offset values, resolution up to 1 pixel), and the pan-tilt controller synchronously records the corresponding azimuth and pitch axis encoder readings. Next, a joint error equation is constructed: First, based on the coordinates of each total station and the nominal installation position of the prism, the theoretical optical axis direction for each observation direction is calculated (represented by a three-dimensional direction vector in the control coordinate system). Then, the horizontal and vertical angles collected from multiple stations are converted into measured optical axis direction vectors. Combined with the spot offset data (converted to angular offset according to pixel size and optical focal length to correct the measured optical axis direction), the deviation between the measured and theoretical optical axis directions is used as an error term. The error terms of all stations are integrated to construct a joint error equation with the angle error parameter as the unknown. Finally, parameter solving is performed: The joint error equation is solved using the least squares fitting method. By minimizing the sum of squares of all error terms, the azimuth axis zero-position deviation (usually within ±5″), pitch axis zero-position deviation (usually within ±5″), axis system angle deviation (usually within ±3″), and encoder scale factor error (usually within ±1×10) are calculated. -6 The solution must include at least some of the parameters in the range of ±0.1mm, prism eccentricity (usually within ±0.1mm), and mounting tilt (usually within ±2″) to ensure that the solution meets the preset residual limit (e.g., the root mean square of the residual is less than 1″).

[0044] By controlling the pan-tilt unit to point at least three total stations at multiple azimuth and elevation angles, the problem of insufficient geometric constraints caused by single-station or few-direction observations is avoided. By combining least-squares fitting to solve multiple types of error parameters, easily coupled error terms such as zero-position deviation, axis angle, and scale factor can be effectively separated, significantly improving the accuracy and completeness of error parameter calculation, providing a more comprehensive and accurate error benchmark for subsequent dynamic compensation, and further reducing system measurement errors.

[0045] As a specific implementation of this disclosure, based on the basic scheme, the dynamic updating of the angle error parameter vector based on multi-station error observations is further defined as follows: collecting error observations from multiple total stations within a preset sliding time period, and assigning weights to each error observation according to the observation geometry, distance measurement, and spot quality; solving the weighted error equation using a recursive estimation algorithm, and updating the angle error parameter vector and its covariance matrix online.

[0046] Specifically, a preset sliding time period is set (the duration can be configured to 5-10 observation cycles according to the observation frequency; for example, if each cycle is 10 seconds, the sliding time period is 50-100 seconds). Error observations from at least two different total stations within this time period are extracted from the error observation buffer. Each error observation includes the direction difference between the measured optical axis and the corrected theoretical optical axis, the CCD spot offset data at the corresponding time, and the time label. This ensures that the observation data covers different azimuth angles to meet geometric constraints. Then, weight allocation is performed: for each error observation, the weights are quantified according to three indicators: observation geometry (if the azimuth interval of the observation direction is greater than 60° and the elevation interval is greater than 30°, the geometry weight is 1.0; otherwise, it is reduced to 0.5 according to the interval ratio), distance measurement (the distance weight is 1.0 when the horizontal distance between the total station and the prism is less than 50m, 0.8 when it is 50-100m, and 0.5 when it is greater than 100m), and spot quality (determined by the CCD spot signal-to-noise ratio; the spot weight is 1.0 when the signal-to-noise ratio is greater than 30dB, 0.7 when it is 20-30dB, and 0.3 when it is less than 20dB). The weights of the three indicators are multiplied to obtain the comprehensive weight of the error observation. Finally, a recursive solution is performed: recursive weighted least squares is used as the recursive estimation algorithm. The angle error parameter vector and the corresponding covariance matrix at the end of the previous update cycle are used as initial values. The error observations in the sliding time period are used to construct a weighted normal equation according to the comprehensive weight. Through the process of "initial covariance matrix participating in equation correction - solving the equation to obtain parameter increment - updating the angle error parameter vector - recalculating the covariance matrix", the updated angle error parameter vector and its covariance matrix are obtained online (the diagonal elements of the covariance matrix reflect the estimation uncertainty of each error parameter).

[0047] By collecting data through a preset sliding time period, the timeliness and representativeness of the error observations involved in the update are ensured. By allocating weights according to multiple indicators, the contribution of reliable observation data can be highlighted and the interference of low-quality data on parameter updates can be reduced. The recursive estimation algorithm is used to realize online parameter updates and simultaneously update the covariance matrix to reflect parameter uncertainty, which effectively improves the accuracy and reliability of angle error parameter vector updates and provides a more realistic error benchmark for subsequent feedforward compensation.

[0048] As a specific implementation of this disclosure, based on the basic scheme, the method of using the updated angle error parameter vector to perform feedforward compensation for gimbal pointing includes: when generating the gimbal servo setting angle, superimposing the compensation amount calculated based on the angle error parameter vector into the nominal pointing angle; adaptively adjusting the weight of the error observation amount according to the comparison result of the direction residual in the continuous observation period and the preset threshold, and triggering the static recalibration process when the direction residual continues to exceed the limit.

[0049] Specifically, when using the updated angle error parameter vector to perform feedforward compensation for gimbal pointing, the compensation amount is first calculated and superimposed: First, based on the coordinates of the total station that initiated the observation request and the coordinates of the target point to be measured, combined with the transformation model of the gimbal axis system and the prism normal, the nominal pointing angles of the gimbal azimuth axis and pitch axis (i.e., the target pointing reference values ​​without error compensation) are calculated; then, the updated angle error parameter vector is called to extract key parameters such as the azimuth axis zero deviation, pitch axis zero deviation, and axis system angle deviation, and the azimuth axis compensation amount (adding the nominal azimuth angle to the azimuth axis zero deviation, and correcting the additional angle error caused by the non-orthogonality of the two axes according to the axis system angle deviation) and the pitch axis compensation amount (adding the nominal pitch angle to the pitch axis zero deviation, and simultaneously correcting the additional pitch direction error caused by the prism eccentricity) are calculated; finally, the azimuth axis compensation amount and the pitch axis compensation amount are superimposed to the corresponding nominal pointing angles to generate the final gimbal servo setting angle and send it to the dual-axis gimbal. In the direction residual processing stage, the preset continuous observation cycle is 3-5 cycles (the duration of each cycle matches the observation frequency of the total station, such as 10 seconds / cycle). The direction residual (i.e., the difference between the actual optical axis direction pointed to by the pan-tilt unit after compensation and the theoretical optical axis direction) is calculated in real time within each cycle and compared with the preset residual threshold (such as ±2″, set according to the system measurement accuracy requirements). If the direction residual of a certain observation direction exceeds the preset threshold for 2 consecutive cycles, the weight of the subsequent error observations generated in that observation direction is reduced by 20%-30% (for example, the original weight is reduced from 0.8 to 0.64-0.56). If the direction residual of that direction exceeds the preset threshold for 4 consecutive cycles, the static recalibration process for the corresponding observation direction is immediately triggered—the pan-tilt unit is controlled to accurately point back to the total station corresponding to that direction, new horizontal angle, vertical angle and spot offset data are collected, and the angle error parameters associated with that direction are recalculated and updated to correct the accumulated error.

[0050] By feeding forward the compensation amount and superimposing it onto the nominal pointing angle, known errors are directly offset during the gimbal servo command generation stage, avoiding the response lag problem of traditional feedback compensation and significantly improving the real-time accuracy of gimbal pointing. At the same time, based on the adaptive adjustment of error observation weights according to the direction residual, the interference of low-precision observation data on parameter updates can be reduced, ensuring the reliability of parameter updates. When the direction residual continuously exceeds the limit, static recalibration is triggered, which can effectively avoid the decline in system accuracy caused by long-term error accumulation, further maintaining the measurement stability of the system in long-term operation.

[0051] As a specific implementation of this disclosure, based on the basic scheme, the embodiments of this disclosure further include: when multiple total stations issue observation requests within the same time interval, the requests are sorted according to the sensitivity of the station distance, gimbal turning angle and observation direction to the angle error parameter, and the observation requests with high sensitivity and small gimbal turning angle are executed first.

[0052] Specifically, observation requests are prioritized based on their sensitivity to angle error parameters, including station spacing, gimbal turning angle, and observation direction. On one hand, prioritizing requests with high sensitivity ensures that observation data that contributes more significantly to the update of angle error parameters participates in the calculation first, effectively improving the accuracy of error parameter updates. On the other hand, prioritizing requests with small gimbal turns can significantly shorten the time spent adjusting the gimbal and reduce mechanical vibration or delay errors caused by large-angle turns, significantly improving the response efficiency of multi-station concurrent observations. At the same time, this prioritization mechanism avoids resource waste caused by random responses, further ensuring the stability and overall measurement efficiency of multi-station collaborative measurements.

[0053] It should be noted that the embodiments of this disclosure may include multiple steps. For ease of description, these steps are numbered, but these numbers are not a limitation on the execution time slots or execution order between the steps; these steps can be implemented in any order, and the embodiments of this disclosure do not limit this.

[0054] Corresponding to the above-described gimbal prism angle error compensation method, this disclosure also proposes a gimbal prism angle error compensation device. Since the device embodiments of this disclosure correspond to the above-described method embodiments, details not disclosed in the device embodiments can be referred to the above-described method embodiments, and will not be repeated here.

[0055] Figure 2 This is a schematic diagram of the structure of a gimbal prism angle error compensation device provided in an embodiment of this disclosure, as shown below. Figure 2 As shown, it includes: Construction unit 21 is used to establish a unified control coordinate system and construct a transformation model between the gimbal axis system and the prism normal direction; Control unit 22 is used to control the gimbal to point the prism to different directions of multiple total stations under calibration conditions, and to jointly calculate the angle error parameters of the gimbal axis system based on multi-station observation data to form an angle error parameter vector. The update unit 23 is used to generate a compensation pointing according to the observation request of the total station during operation, and dynamically update the angle error parameter vector based on the multi-station error observations. The adjustment unit 24 is used to perform feedforward compensation on the gimbal pointing using the updated angle error parameter vector, and to adjust the update strategy according to the direction residual.

[0056] The gimbal prism angle error compensation device disclosed herein establishes a unified control coordinate system and a transformation model between the gimbal axis and the prism normal direction. Under calibration conditions, it controls the gimbal to point in different directions of multiple total stations and jointly calculates angle error parameters based on multi-station observation data to form an error parameter vector. Under operating conditions, it generates a compensation pointing according to the total station observation request and dynamically updates the error parameter vector based on multi-station error observations. It also uses the updated error parameter vector to perform feedforward compensation on the gimbal pointing and adjusts the update strategy according to the direction residual. Therefore, it can solve the problems in the prior art where the lack of a unified coordinate system and transformation model leads to difficulty in fusion of multi-station data, calibration relies on a single station or fixed direction and cannot fully obtain error parameters, static updates of error parameters during operation cannot adapt to dynamic changes, and the lack of a residual adjustment mechanism leads to a decrease in compensation accuracy. It achieves the technical effects of efficient and unified fusion of multi-station observation data, comprehensive and accurate acquisition of angle error parameters, dynamic adaptation of error parameters to operational changes, and stable and reliable gimbal pointing compensation accuracy.

[0057] Furthermore, in one possible implementation of this embodiment, the construction unit 21 is also used for: A control coordinate system is defined with the center of gimbal rotation as the origin, where the horizontal axis points in the positive direction of the azimuth axis and the vertical axis is perpendicular to the gimbal mounting plane. Based on the gimbal's mechanical structural parameters and the prism's installation position, the azimuth and pitch axis angles are mapped to a three-dimensional vector of the prism's normal direction in the control coordinate system through coordinate transformation.

[0058] Furthermore, in one possible implementation of this embodiment, the control unit 22 is also used for: Control the pan-tilt unit to point at at least three total stations in multiple different azimuth and elevation angles, and acquire the horizontal angle, vertical angle and spot offset data output by each total station; By comparing multi-station observation data with the theoretical optical axis direction, a joint error equation is constructed, and at least some of the parameters in azimuth axis zero deviation, pitch axis zero deviation, shaft system angle deviation, encoder scale factor error, prism eccentricity, and installation tilt angle are solved by the least squares fitting method.

[0059] Furthermore, in one possible implementation of this embodiment, the updating unit 23 is also used for: Error observations from multiple total stations are collected within a preset sliding time period, and weights are assigned to each error observation based on observation geometry, distance measurement, and spot quality. A recursive estimation algorithm is used to solve the weighted error equation, and the angle error parameter vector and its covariance matrix are updated online.

[0060] Furthermore, in one possible implementation of this embodiment, the adjustment unit 24 is also used for: When generating the gimbal servo setting angle, the compensation amount calculated based on the angle error parameter vector is superimposed on the nominal pointing angle; Based on the comparison between the directional residual and the preset threshold in the continuous observation period, the weight of the error observation is adaptively adjusted, and the static recalibration process is triggered when the directional residual continues to exceed the limit.

[0061] Furthermore, in one possible implementation of this embodiment, such as Figure 2 As shown, it also includes: The execution unit 25 is used to sort the observation requests issued by multiple total stations within the same time interval according to the sensitivity of the angle error parameter to the corresponding station distance, pan-tilt turning angle and observation direction of each request, and to prioritize the execution of observation requests with high sensitivity and small pan-tilt turning angle.

[0062] It should be noted that the foregoing explanation of the method embodiments also applies to the apparatus of this embodiment, and the principle is the same, so it is not limited in this embodiment.

[0063] According to embodiments of this disclosure, this disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0064] Figure 3 A schematic block diagram of an example electronic device 300 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0065] like Figure 3 As shown, the electronic device 300 includes a computing unit 301, which can perform various appropriate actions and processes based on a computer program stored in ROM (Read-Only Memory) 302 or a computer program loaded from storage unit 308 into RAM (Random Access Memory) 303. The RAM 303 may also store various programs and data required for the operation of the electronic device 300. The computing unit 301, ROM 302, and RAM 303 are interconnected via a bus 304. An I / O (Input / Output) interface 305 is also connected to the bus 304.

[0066] Multiple components in electronic device 300 are connected to I / O interface 305, including: input unit 306, such as keyboard, mouse, etc.; output unit 307, such as various types of displays, speakers, etc.; storage unit 308, such as disk, optical disk, etc.; and communication unit 309, such as network card, modem, wireless transceiver, etc. Communication unit 309 allows electronic device 300 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0067] The computing unit 301 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 301 include, but are not limited to, CPUs (Central Processing Units), GPUs (Graphics Processing Units), various special-purpose AI (Artificial Intelligence) computing chips, various computing units running machine learning model algorithms, DSPs (Digital Signal Processors), and any suitable processor, controller, microcontroller, etc. The computing unit 301 performs the various methods and processes described above, such as the gimbal prism angle error compensation method. For example, in some embodiments, the gimbal prism angle error compensation method can be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as storage unit 308. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 300 via ROM 302 and / or communication unit 309. When the computer program is loaded into RAM 303 and executed by the computing unit 301, one or more steps of the methods described above can be performed. Alternatively, in other embodiments, the computing unit 301 may be configured to perform the aforementioned gimbal prism angle error compensation method by any other suitable means (e.g., by means of firmware).

[0068] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, FPGAs (Field Programmable Gate Arrays), ASICs (Application-Specific Integrated Circuits), ASSPs (Application-Specific Standard Products), SOCs (System-on-Chips), CPLDs (Complex Programmable Logic Devices), computer hardware, firmware, software, and / or combinations thereof. These various implementations may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0069] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0070] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, RAM, ROM, EPROM (Electrically Programmable Read-Only Memory) or flash memory, optical fiber, CD-ROM (Compact Disc Read-Only Memory), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0071] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (Cathode-Ray Tube) or LCD (Liquid Crystal Display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0072] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include LANs (Local Area Networks), WANs (Wide Area Networks), the Internet, and blockchain networks.

[0073] Computer systems can include clients and servers. Clients and servers are generally geographically separated and typically interact via communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. A server can be a cloud server, also known as a cloud computing server or cloud host, a hosting product within the cloud computing service system that addresses the shortcomings of traditional physical hosts and VPS (Virtual Private Server) services, such as high management difficulty and weak business scalability. Servers can also be servers for distributed systems or servers incorporating blockchain technology.

[0074] It's important to note that artificial intelligence (AI) is the study of enabling computers to simulate certain human thought processes and intelligent behaviors (such as learning, reasoning, thinking, and planning). It encompasses both hardware and software technologies. AI hardware technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, and big data processing. AI software technologies primarily include computer vision, speech recognition, natural language processing, machine learning / deep learning, big data processing, and knowledge graph technologies.

[0075] The various numerical designations such as "first," "second," etc., used in this disclosure are merely for ease of description and are not intended to limit the scope of the embodiments of this disclosure, nor do they indicate a sequential order.

[0076] At least one of the features described in this disclosure can also be described as one or more, and multiple features can be two, three, four or more, and this disclosure does not impose any limitations. In the embodiments of this disclosure, for a technical feature, the technical features in that technical feature are distinguished by "first", "second", "third", "A", "B", "C" and "D", etc., and there is no sequential order or size order among the technical features described by "first", "second", "third", "A", "B", "C" and "D".

[0077] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0078] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A method for compensating for angle error of a gimbal prism, characterized in that, include: Establish a unified control coordinate system and construct a transformation model between the gimbal axis system and the prism normal direction; Under calibration conditions, the gimbal is controlled to point the prism in different directions of multiple total stations. Based on the multi-station observation data, the angle error parameters of the gimbal axis system are jointly calculated to form an angle error parameter vector. During operation, a compensation pointing is generated based on the observation request of the total station, and the angle error parameter vector is dynamically updated based on the multi-station error observations. The updated angle error parameter vector is used to perform feedforward compensation for the gimbal pointing, and the update strategy is adjusted according to the direction residual.

2. The method according to claim 1, characterized in that, The establishment of a unified control coordinate system and the construction of a transformation model between the gimbal axis system and the prism normal direction include: A control coordinate system is defined with the center of gimbal rotation as the origin, where the horizontal axis points in the positive direction of the azimuth axis and the vertical axis is perpendicular to the gimbal mounting plane. Based on the gimbal's mechanical structural parameters and the prism's installation position, the azimuth and pitch axis angles are mapped to a three-dimensional vector of the prism's normal direction in the control coordinate system through coordinate transformation.

3. The method according to claim 1, characterized in that, The angle error parameters of the gimbal axis system jointly calculated based on multi-station observation data include: Control the pan-tilt unit to point at at least three total stations in multiple different azimuth and elevation angles, and acquire the horizontal angle, vertical angle and spot offset data output by each total station; By comparing multi-station observation data with the theoretical optical axis direction, a joint error equation is constructed, and at least some of the parameters in azimuth axis zero deviation, pitch axis zero deviation, shaft system angle deviation, encoder scale factor error, prism eccentricity, and installation tilt angle are solved by the least squares fitting method.

4. The method according to claim 1, characterized in that, The dynamic updating of the angle error parameter vector based on multi-station error observations includes: Error observations from multiple total stations are collected within a preset sliding time period, and weights are assigned to each error observation based on observation geometry, distance measurement, and spot quality. A recursive estimation algorithm is used to solve the weighted error equation, and the angle error parameter vector and its covariance matrix are updated online.

5. The method according to claim 1, characterized in that, The step of using the updated angle error parameter vector to perform feedforward compensation for gimbal pointing includes: When generating the gimbal servo setting angle, the compensation amount calculated based on the angle error parameter vector is superimposed on the nominal pointing angle; Based on the comparison between the directional residual and the preset threshold in the continuous observation period, the weight of the error observation is adaptively adjusted, and the static recalibration process is triggered when the directional residual continues to exceed the limit.

6. The method according to claim 1, characterized in that, Also includes: When multiple total stations issue observation requests within the same time interval, they are sorted according to the sensitivity of each request to the angle error parameter based on the station distance, pan-tilt rotation angle, and observation direction. Observation requests with high sensitivity and small pan-tilt rotation are executed first.

7. A gimbal prism angle error compensation device, characterized in that, include: The building unit is used to establish a unified control coordinate system and to build a transformation model between the gimbal axis system and the prism normal direction; The control unit is used to control the gimbal to point the prism to different directions of multiple total stations under calibration conditions. Based on the observation data of multiple stations, the angle error parameters of the gimbal axis system are jointly calculated to form an angle error parameter vector. The update unit is used to generate a compensation pointing according to the observation request of the total station during operation, and to dynamically update the angle error parameter vector based on the multi-station error observations. The adjustment unit is used to perform feedforward compensation of the gimbal pointing using the updated angle error parameter vector, and to adjust the update strategy according to the direction residual.

8. An electronic device, characterized in that, include: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-6.

9. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-6.

10. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method according to any one of claims 1-6.

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