A camera pose adjustment method, device, equipment and medium

Abstract

The application discloses a camera pose adjustment method and device, equipment and medium, and relates to the technical field of computers. The method comprises the following steps: obtaining an initial value of the geodetic coordinates of the current installation position of the camera by matching the global navigation satellite system positioning device with the electronic map data, and establishing a geodetic coordinate system reference based on the initial value; collecting real-time pose data of the camera; performing noise reduction processing on the real-time pose data, and generating a deviation value of the current installation position of the camera based on the processed real-time pose data and the geodetic coordinate system reference; driving the pose adjustment mechanism to automatically correct based on the deviation value, so as to adjust the installation position and posture of the camera, and feeding back the adjusted real-time pose data in real time during the correction process until the deviation value is less than a preset deviation threshold. Thus, high-precision pose matching of the high-definition camera and the geodetic coordinate system can be realized in a complex dynamic environment, and effectively integrated with the body motion control of the mobile platform.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and in particular to a method, apparatus, device, and medium for adjusting the pose of a camera. Background Technology

[0002] With the increasing application of high-definition camera technology in security monitoring, geographic surveying, and intelligent transportation, the market has placed higher demands on the matching accuracy of camera pose and geodetic coordinate system. This is especially true in dynamic scenarios such as drone aerial photography, vehicle-mounted mobile monitoring, and quadruped robot mobile filming. Cameras need to maintain stable posture and accurate position information in complex environments to meet the high-precision coordinate consistency requirements of tasks such as surveying data stitching and multi-device collaborative monitoring. Existing methods for adjusting the pose of high-definition cameras typically rely on manual calibration or single-sensor positioning. Manual calibration is inefficient and easily influenced by operator experience, resulting in high calibration costs and difficulty in guaranteeing accuracy in remote or complex terrain scenarios. Single GNSS (Global Navigation Satellite System) positioning is susceptible to obstruction and electromagnetic interference, leading to large fluctuations in pose data. Long-term use of a single IMU (Inertial Measurement Unit) can result in cumulative errors, failing to meet the requirements for high-precision pose over extended periods. Furthermore, existing methods lack a dynamic correlation mechanism with the geodetic coordinate system, meaning pose deviations cannot be corrected promptly when the environment changes, affecting camera shooting accuracy and the value of data applications. Furthermore, when applied to mobile platforms such as quadruped robot dogs, the camera's pose is easily affected by factors such as body vibration and terrain changes. Existing technologies cannot effectively integrate camera pose adjustment with the robot dog's motion control, leading to problems such as image shaking and positioning deviation, which in turn affects the accuracy of detection results and user experience.

[0003] As can be seen from the above, how to achieve high-precision pose matching between a high-definition camera and the geodetic coordinate system in complex dynamic environments, and effectively integrate it with the motion control of the mobile platform, is an urgent problem to be solved. Summary of the Invention

[0004] In view of this, the purpose of this invention is to provide a camera pose adjustment method, apparatus, device, and medium that can achieve high-precision pose matching between a high-definition camera and the geodetic coordinate system in complex dynamic environments, and effectively integrate with the motion control of the mobile platform. The specific solution is as follows: Firstly, this application provides a camera pose adjustment method, including: The initial geodetic coordinates of the camera's current installation location are obtained by matching the global navigation satellite system positioning device with electronic map data, and a geodetic coordinate system reference is established based on the initial geodetic coordinates. The camera's real-time pose data is acquired; the real-time pose data includes attitude angles and position coordinates; the attitude angles are acquired through a preset inertial measurement unit; the position coordinates are acquired through the fusion of the global navigation satellite system positioning device and a preset visual positioning device. The Kalman filter algorithm is used to denoise the real-time pose data, and the deviation value of the current installation position of the camera is generated based on the processed real-time pose data and the geodetic coordinate system reference; the deviation value includes position deviation and attitude deviation. The position adjustment mechanism is driven to perform automatic correction based on the deviation value, so as to adjust the installation position and posture of the camera, and to provide real-time feedback of the adjusted position and posture data in real time during the correction process, until the deviation value is less than the preset deviation threshold.

[0005] Optionally, establishing a geodetic coordinate system reference based on the initial geodetic coordinate values ​​includes: Differential positioning calibration is performed on the global navigation satellite system positioning device, and the initial value of the geodetic coordinates is corrected for error through the differential signal sent by the reference station, so as to establish a geodetic coordinate system reference based on the corrected initial value of the geodetic coordinates.

[0006] Optionally, the process of acquiring the location coordinates includes: The preset visual positioning device acquires image information of a preset marker; the preset marker is an optical target with coordinate encoding, and the optical target is pre-associated with the geodetic coordinate system reference. The coordinate code of the optical target is identified based on the image information to determine the relative position of the camera with respect to the optical target; The relative position is fused with the positioning data collected by the global navigation satellite system positioning device to generate the real-time position coordinates of the camera.

[0007] Optionally, the step of generating the deviation value of the camera's current installation position based on the processed real-time pose data and the geodetic coordinate system reference includes: A coordinate transformation formula is established based on the transformation relationship between the geodetic coordinate system and the camera's local coordinate system, and an elevation compensation parameter is introduced into the coordinate transformation formula. The elevation compensation parameter is a parameter obtained by calculating the difference between the actual ground elevation of the camera installation point obtained through electronic map or digital elevation model data and the ellipsoidal elevation collected by the global navigation satellite system positioning device. The coordinate transformation formula, after introducing elevation compensation parameters, generates the deviation value of the camera's current installation position based on the processed real-time pose data.

[0008] Optionally, the camera pose adjustment method further includes: During the automatic correction process driven by the posture adjustment mechanism based on the deviation value, the adjustment resistance of the posture adjustment mechanism is monitored in real time by the torque feedback unit in the posture adjustment mechanism. When the adjustment resistance exceeds the preset resistance threshold, the posture adjustment mechanism is controlled to stop automatic correction and issue a fault warning signal; the fault warning signal is any one or more combinations of sound alarm, light alarm or electronic signal alarm.

[0009] Optionally, the camera pose adjustment method further includes: The geodetic coordinate system reference is updated based on a preset update cycle, and the current real-time pose data is compared with historical pose data during the update process to obtain the cumulative deviation value. If the cumulative deviation value exceeds the preset cumulative deviation threshold, a recalibration process is triggered; the recalibration process includes re-executing the steps of establishing a geodetic coordinate system reference, acquiring real-time pose data, generating deviation values, and driving automatic correction.

[0010] Optionally, when the pose adjustment mechanism is the body of a quadruped robot dog, the automatic correction based on the deviation value driving the pose adjustment mechanism includes: Inverse kinematics is performed based on the deviation value to determine the angle adjustment of each joint of the quadruped robot dog. Adjust the angles of each joint according to the angle adjustment amount to keep the foot position of the quadruped robot dog unchanged, while rotating the body coordinate system of the quadruped robot dog relative to the world coordinate system to maintain the target pose of the camera in the geodetic coordinate system reference. During the movement of the quadruped robot dog, the angles of each joint are continuously adjusted based on the real-time pose data fed back by the camera to achieve automatic correction.

[0011] Secondly, this application provides a camera pose adjustment device, comprising: The benchmark establishment module is used to obtain the initial geodetic coordinates of the current installation location of the camera by matching the positioning device of the global navigation satellite system with electronic map data, and to establish a geodetic coordinate system benchmark based on the initial geodetic coordinates. The data acquisition module is used to acquire the real-time pose data of the camera; the real-time pose data includes attitude angles and position coordinates; the attitude angles are acquired through a preset inertial measurement unit; the position coordinates are acquired by fusing the global navigation satellite system positioning device with a preset visual positioning device. The deviation acquisition module is used to perform noise reduction processing on real-time pose data using the Kalman filter algorithm, and generate a deviation value of the current installation position of the camera based on the processed real-time pose data and the geodetic coordinate system reference; the deviation value includes position deviation and attitude deviation. The correction module is used to drive the pose adjustment mechanism to perform automatic correction based on the deviation value, so as to adjust the installation position and posture of the camera, and to provide real-time feedback of the adjusted pose data in real time during the correction process until the deviation value is less than a preset deviation threshold.

[0012] Thirdly, this application provides an electronic device, comprising: Memory, used to store computer programs; A processor is used to execute the computer program to implement the aforementioned camera pose adjustment method.

[0013] Fourthly, this application provides a computer-readable storage medium for storing a computer program, wherein the computer program, when executed by a processor, implements the aforementioned camera pose adjustment method.

[0014] This application provides a camera pose adjustment method. The method involves using a Global Navigation Satellite System (GNSS) positioning device and electronic map data to obtain the initial geodetic coordinates of the camera's current installation location, and establishing a geodetic coordinate system reference based on these initial geodetic coordinates. Real-time pose data of the camera is collected, including attitude angles and position coordinates. The attitude angles are acquired through a preset inertial measurement unit (INS). The position coordinates are acquired through fusion of the GNSS positioning device and a preset visual positioning device. A Kalman filter algorithm is used to denoise the real-time pose data, and a deviation value for the camera's current installation location is generated based on the processed real-time pose data and the geodetic coordinate system reference. This deviation value includes position deviation and attitude deviation. The method drives a pose adjustment mechanism to automatically correct the camera's installation position and posture based on the deviation value, and provides real-time feedback of the adjusted real-time pose data during the correction process until the deviation value is less than a preset deviation threshold.

[0015] As can be seen from the above, this application establishes a high-precision geodetic coordinate system benchmark by matching the Global Navigation Satellite System with electronic maps, integrates inertial measurement units and visual positioning devices to collect pose data in real time, combines Kalman filtering noise reduction and coordinate transformation to accurately calculate deviations, and drives the pose adjustment mechanism to perform closed-loop automatic correction. This effectively solves the problems of single-sensor positioning being susceptible to interference, large cumulative errors, and low efficiency of manual calibration in existing technologies. Thus, it can achieve high-precision pose matching between a high-definition camera and the geodetic coordinate system in complex dynamic environments and effectively integrate with the motion control of the mobile platform. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0017] Figure 1 This is a flowchart of a camera pose adjustment method disclosed in this invention; Figure 2 This is a schematic diagram of a camera pose adjustment device disclosed in this invention; Figure 3 This is a structural diagram of an electronic device disclosed in this invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] With the increasing application of high-definition camera technology in security monitoring, geographic surveying, and intelligent transportation, the market has placed higher demands on the matching accuracy of camera pose and geodetic coordinate system. This is especially true in dynamic scenarios such as drone aerial photography, vehicle-mounted mobile monitoring, and quadruped robot mobile filming. Cameras need to maintain stable posture and accurate position information in complex environments to meet the high-precision coordinate consistency requirements of tasks such as surveying data stitching and multi-device collaborative monitoring. Existing methods for adjusting the pose of high-definition cameras typically rely on manual calibration or single-sensor positioning. Manual calibration is inefficient and easily influenced by operator experience, resulting in high calibration costs and difficulty in guaranteeing accuracy in remote or complex terrain scenarios. Single GNSS positioning is susceptible to obstruction and electromagnetic interference, leading to large fluctuations in pose data. Long-term use of a single IMU (Inertial Measurement Unit) can generate cumulative errors, failing to meet the requirements for high-precision pose over extended periods. Furthermore, existing methods lack a dynamic correlation mechanism with the geodetic coordinate system, meaning pose deviations cannot be corrected promptly when the environment changes, affecting camera shooting accuracy and the value of data applications. Furthermore, when applied to mobile platforms such as quadruped robot dogs, the camera's pose is easily affected by factors such as body vibration and terrain changes. Existing technologies struggle to effectively integrate camera pose adjustment with the robot dog's motion control, leading to issues like image jitter and positioning deviations, which in turn affect the accuracy of detection results and the user experience. Therefore, this application provides a camera pose adjustment method, apparatus, device, and medium that can achieve high-precision pose matching between a high-definition camera and the geodetic coordinate system in complex dynamic environments, and effectively integrate it with the motion control of the mobile platform.

[0020] See Figure 1 As shown in the figure, this application discloses a camera pose adjustment method, including: Step S11: Use the global navigation satellite system positioning device to match the electronic map data to obtain the initial value of the geodetic coordinates of the current installation location of the camera, and establish a geodetic coordinate system reference based on the initial value of the geodetic coordinates.

[0021] In this embodiment, the initial geodetic coordinates of the current installation location of the high-definition camera are obtained by matching the GNSS positioning module with electronic map data. To improve accuracy, differential positioning calibration is performed on the GNSS positioning module, and the initial coordinate error is corrected using the differential signal sent by the reference station to ensure that the corrected initial coordinate error is ≤0.05m, thus meeting the positioning requirements of high-precision video surveillance. Specifically, establishing a geodetic coordinate system reference based on the initial geodetic coordinates may include: performing differential positioning calibration on the Global Navigation Satellite System positioning device, and correcting the initial geodetic coordinates using the differential signal sent by the reference station, so as to establish a geodetic coordinate system reference based on the corrected initial geodetic coordinates. The GNSS positioning module can be a GNSS receiver, specifically a dual-frequency or multi-frequency receiver, to meet usage requirements. The GNSS positioning module and the electronic map data are transmitted to the computing unit via a data transmission module, which can be a 4G / 5G or data radio, used to receive the GNSS positioning module and the electronic map data, as well as differential data. The aforementioned electronic map data is obtained through an electronic map engine, which supports high-precision maps in multiple coordinate systems, thus ensuring the accuracy of the measured data. GNSS differential reference stations are deployed within a preset range of the camera installation area. These GNSS differential reference stations include fixed or mobile reference stations. The reference stations receive satellite signals in real time and calculate positioning errors, sending differential correction signals to the GNSS module at the camera end via wireless communication, including 4G / 5G or BeiDou short message service. After receiving the correction signals, the camera end dynamically corrects the original coordinates, ensuring that the initial coordinate error is controlled within a minimal range, providing a high-precision reference for subsequent pose adjustments.

[0022] It is worth mentioning that, for scenarios with undulating terrain such as mountainous and hilly areas, elevation compensation parameters are introduced when establishing the benchmark. The actual ground elevation of the camera installation point is obtained through electronic map or digital elevation model data, and the difference is calculated with the ellipsoidal elevation collected by GNSS. This difference is used as the elevation compensation amount and integrated into the geodetic coordinate system benchmark to avoid vertical orientation deviation caused by terrain undulation.

[0023] Step S12: Collect the real-time pose data of the camera.

[0024] In this embodiment, the attitude angle of the high-definition camera is acquired by an IMU (Inertial Measurement Unit), and the position coordinates of the high-definition camera are acquired by fusing the data from a GNSS positioning module and a visual positioning module. The visual positioning module determines the relative position of the camera by recognizing the image information of a preset marker, such as an optical target with coordinate codes. Specifically, the acquisition process of the position coordinates may include: acquiring the image information of a preset marker through the preset visual positioning device; the preset marker is an optical target with coordinate codes, and the optical target is pre-associated with the geodetic coordinate system reference; recognizing the coordinate codes of the optical target based on the image information to determine the relative position of the camera relative to the optical target; fusing the relative position with the positioning data acquired by the GNSS positioning device to generate the real-time position coordinates of the camera. That is, because the optical target is pre-associated with the geodetic coordinate system reference, the attitude angle and position coordinates of the high-definition camera can be acquired synchronously with high precision, ensuring the accuracy of the measured data while improving the efficiency of data acquisition. Furthermore, the collaborative acquisition by multiple sensors solves the problem of error accumulation due to interference from a single sensor, ensuring the reliability of the pose data.

[0025] In one specific implementation, the IMU (Inertial Measurement Unit) is used to acquire the camera's attitude angles in real time, including roll angle α, pitch angle β, and yaw angle γ. The sampling frequency is set to 50 to 100 Hz to ensure the capture of rapid attitude changes, such as the attitude adjustment of a vehicle-mounted drone during bumpy rides. The GNSS positioning module is used to continuously acquire the camera's geodetic coordinates in unobstructed scenes, including x, y, and z coordinates, as a global position reference. The visual positioning module is used to acquire images of preset markers through a high-definition lens. These markers are optical targets with unique coordinate codes, such as QR code targets or infrared coded targets, and the target coordinates have been pre-recorded in the geodetic coordinate system. The visual module analyzes the target position using an image recognition algorithm and calculates the relative coordinates between the camera and the target as local position correction data. The multi-source data fusion logic adopts a master-slave complementary fusion approach. The strategy employs a fusion approach: in GNSS signal-stable scenarios such as open areas, GNSS data is used as the primary source, supplemented by visual data, to correct for minor GNSS fluctuations; in GNSS signal-obstructed scenarios such as tunnels and high-rise buildings, it automatically switches to an IMU-plus-visual fusion mode, constructing a pose data chain through continuous attitude data from the IMU and local position data from the vision system to avoid data interruptions; simultaneously, for dynamic scenarios such as vehicle-mounted aerial photography, additional auxiliary equipment data, such as vehicle-mounted odometers and drone flight controller data, are integrated to further optimize data continuity; environmental adaptability optimization involves selecting appropriate sensors based on the application scenario: in low-temperature scenarios such as high-altitude winter outdoor environments, low-temperature resistant sensors with operating temperatures ranging from -40°C to 85°C are used; in foggy scenarios, visual modules with anti-fog lenses are selected; and in electromagnetic interference scenarios, anti-interference GNSS modules are selected to ensure data acquisition stability in harsh environments.

[0026] Step S13: Use the Kalman filter algorithm to perform noise reduction processing on the real-time pose data, and generate the deviation value of the current installation position of the camera based on the processed real-time pose data and the geodetic coordinate system reference.

[0027] In this embodiment, after denoising the acquired pose data using a Kalman filter algorithm, the position and attitude deviations are calculated using a coordinate transformation formula. The coordinate transformation formula is established based on the transformation relationship between the geodetic coordinate system and the camera's local coordinate system, and an elevation compensation parameter is introduced during the transformation process. Specifically, generating the deviation value of the camera's current installation position based on the processed real-time pose data and the geodetic coordinate system reference can include: establishing a coordinate transformation formula based on the transformation relationship between the geodetic coordinate system and the camera's local coordinate system, and introducing an elevation compensation parameter into the coordinate transformation formula. The elevation compensation parameter is a parameter obtained by calculating the difference between the actual ground elevation of the camera's installation point obtained through electronic maps or digital elevation model data and the ellipsoidal elevation collected by the global navigation satellite system positioning device. The coordinate transformation formula with the elevation compensation parameter is used to generate the deviation value of the camera's current installation position based on the processed real-time pose data. In other words, the Kalman filter algorithm is used to reduce noise in the acquired multi-source pose data. For IMU drift errors, the attitude change trend is predicted through a filtering model, and the predicted values ​​are corrected by combining GNSS visual data. For image noise in the visual data, such as blurring in rainy weather, image recognition results are smoothed through filtering to avoid position misjudgments caused by noise. A conversion formula between the geodetic coordinate system and the camera's local coordinate system is established. Using the geodetic coordinate system reference as the origin, the local position of the camera is converted to geodetic coordinates through a rotation matrix and translation vector. The aforementioned local position is the position relative to the target or mounting point. During the conversion process, elevation compensation parameters are simultaneously substituted to correct coordinate deviations caused by terrain undulations. Deviations are calculated from both position and attitude dimensions. The position deviation Δ(x, y, z) is the difference between the real-time acquired geodetic coordinates and the reference coordinates, and the attitude deviation Δ(α, β, γ) is the difference between the real-time acquired attitude angle and the reference attitude angle, ensuring that deviation quantification covers all key dimensions of the camera's pose.

[0028] Step S14: Drive the pose adjustment mechanism to perform automatic correction based on the deviation value, so as to adjust the installation position and posture of the camera, and provide real-time feedback of the adjusted pose data in real time during the correction process, until the deviation value is less than the preset deviation threshold.

[0029] In this embodiment, the pose adjustment mechanism is automatically corrected based on the deviation value. This mechanism includes a horizontal rotation component, a vertical pitch component, and a displacement compensation component. During the correction process, the adjusted pose data is fed back in real time until the pose data deviation value is less than a preset threshold. Specifically, the horizontal rotation component can use a stepper motor or a servo motor to drive the camera to rotate around the vertical axis, adjusting the yaw angle γ with a control accuracy of 0.1°, adapting to horizontal deviation correction. The vertical pitch component can use a servo motor to drive the camera to pitch around the horizontal axis, adjusting the pitch angle β with a response time of no more than 0.5 seconds, adapting to vertical attitude deviation correction. The displacement compensation component can use an electric push rod or a ball screw structure to achieve small displacements of the camera in the xy-axis direction, with a displacement range of ±50 mm, adjusting the position deviation Δxy with a displacement accuracy of 0.01 mm. Furthermore, the pose adjustment mechanism also integrates a torque feedback unit. When the adjustment resistance exceeds a preset threshold (e.g., 5N),... When the deviation value is exceeded (m), the adjustment is automatically stopped and a fault warning signal is issued. Specifically, the camera pose adjustment method may further include: during the automatic correction process driven by the pose adjustment mechanism based on the deviation value, the torque feedback unit in the pose adjustment mechanism is used to monitor the adjustment resistance of the pose adjustment mechanism in real time; when the adjustment resistance exceeds a preset resistance threshold, the pose adjustment mechanism is controlled to stop automatic correction and a fault warning signal is issued; the fault warning signal is any one or more combinations of sound alarm, light alarm, or electronic signal alarm. This embodiment adopts a closed-loop logic of acquisition, calculation, adjustment, and re-acquisition. After each correction action is performed by the adjustment mechanism, the pose data acquisition module immediately re-acquires the real-time pose, and the deviation calculation module synchronously updates the deviation value; if the deviation value is still greater than the preset threshold, the adjustment mechanism is driven to perform correction until the deviation is less than the threshold, ensuring that the adjustment accuracy meets the standard.

[0030] In this embodiment, the geodetic coordinate system reference is updated periodically. The update cycle is set according to the usage scenario. During the update, historical pose data is compared. If the cumulative deviation exceeds a threshold, a recalibration process is triggered. Specifically, the camera pose adjustment method may further include: updating the geodetic coordinate system reference based on a preset update cycle, and comparing the current real-time pose data with historical pose data during the update process to obtain a cumulative deviation value; if the cumulative deviation value exceeds a preset cumulative deviation threshold, a recalibration process is triggered; the recalibration process includes re-executing the steps of establishing the geodetic coordinate system reference, acquiring real-time pose data, generating deviation values, and driving automatic correction. For example, in one specific implementation, a differentiated update cycle is set according to the application scenario. For fixed monitoring scenarios such as road poles in a stable environment, the update cycle is set to 6 to 24 hours; for mobile scenarios such as vehicle-mounted aerial photography in a rapidly changing environment, the update cycle is set to 1 to 6 hours. Each time the baseline is updated, the cumulative deviation between the current pose data and the historical baseline data is compared. If the cumulative position deviation exceeds 0.05 meters and the cumulative attitude deviation exceeds 0.2 degrees, the recalibration process is automatically triggered, and the entire process of baseline establishment, data acquisition, deviation adjustment, and re-execution is carried out to eliminate long-term cumulative deviations. A pose data storage and traceability system is established to record the time value of each baseline update deviation adjustment, which facilitates subsequent fault diagnosis. For example, if the deviation increases abnormally, the start time of the abnormality can be traced. At the same time, it provides data support for scenario-based optimization, such as adjusting the update cycle of specific scenarios based on historical data.

[0031] As can be seen from the above, the embodiments of this application establish a geodetic coordinate system benchmark by matching GNSS with electronic maps, eliminate initial coordinate errors by combining differential positioning calibration, and introduce multi-sensor fusion to collect pose data: GNSS ensures the accuracy of the position benchmark, IMU captures attitude changes in real time, visual positioning module assists in correcting local deviations, and then Kalman filtering noise reduction and elevation compensation optimize coordinate transformation, forming a full-link accuracy guarantee of accurate benchmark, reliable data and optimized calculation, effectively solving the technical problem of deviation caused by positioning of a single sensor, and ensuring that the matching degree between the camera pose and the geodetic coordinate system meets the requirements of coordinate consistency for high-precision scenes.

[0032] Furthermore, this application discloses a specific camera pose adjustment method applied to a quadruped robot dog, including: The camera is calibrated to determine its intrinsic and extrinsic parameters, such as focal length and distortion parameters, which provides a basis for subsequent coordinate transformation.

[0033] Establish a coordinate system, including a body coordinate system and a world coordinate system. The body coordinate system {b} is established with the center of mass of the quadruped robot dog as the origin; the world coordinate system {s} is established with the earth coordinate system as the origin, and a certain point on the ground where the quadruped robot dog initially stands is established as the origin.

[0034] The camera's rotation matrix is ​​converted into Euler angles to facilitate the generation of attitude commands. Assuming that {s} and {b} coincide in the initial state, the camera's attitude in the geodetic coordinate system is obtained through the transformation of the rotation matrix. Then, based on the target pose of the high-definition camera, the angles that each joint of the quadruped robot dog needs to be adjusted are calculated. Through inverse kinematics, the position of the robot dog's feet remains unchanged, while the body coordinate system rotates relative to the world coordinate system.

[0035] Based on the results of inverse kinematics, the angles of each joint of the quadruped robot dog are adjusted to achieve pose adjustment. During the movement of the quadruped robot dog, the joint angles are continuously adjusted based on real-time pose information fed back by a high-definition camera to maintain the target pose of the camera in the geodetic coordinate system. Specifically, when the pose adjustment mechanism is the body of the quadruped robot dog, the automatic correction driven by the deviation value can include: performing inverse kinematics based on the deviation value to determine the angle adjustment amount of each joint of the quadruped robot dog; adjusting the angles of each joint according to the angle adjustment amount to keep the foot position of the quadruped robot dog unchanged, while rotating the body coordinate system of the quadruped robot dog relative to the world coordinate system to maintain the target pose of the camera in the geodetic coordinate system reference; and continuously adjusting the angles of each joint based on real-time pose data fed back by the camera during the movement of the quadruped robot dog to achieve automatic correction.

[0036] As can be seen from the above, by applying the above-described camera pose adjustment method, the embodiments of this application can achieve stable movement and precise control of the robot dog in complex environments, improve the user experience, and facilitate the promotion and application of the above-described robot dog in different technical fields.

[0037] See Figure 2 As shown in the figure, this application discloses a camera pose adjustment device, including: The reference establishment module 11 is used to obtain the initial geodetic coordinates of the current installation location of the camera by matching the positioning device of the global navigation satellite system with electronic map data, and to establish a geodetic coordinate system reference based on the initial geodetic coordinates. The data acquisition module 12 is used to acquire the real-time pose data of the camera; the real-time pose data includes attitude angles and position coordinates; the attitude angles are acquired through a preset inertial measurement unit; the position coordinates are acquired through the fusion acquisition of the global navigation satellite system positioning device and a preset visual positioning device; The deviation acquisition module 13 is used to perform noise reduction processing on the real-time pose data using the Kalman filter algorithm, and generate a deviation value of the current installation position of the camera based on the processed real-time pose data and the geodetic coordinate system reference; the deviation value includes position deviation and attitude deviation. The correction module 14 is used to drive the pose adjustment mechanism to perform automatic correction based on the deviation value, so as to adjust the installation position and posture of the camera, and to provide real-time feedback of the adjusted pose data in real time during the correction process until the deviation value is less than a preset deviation threshold.

[0038] In some specific embodiments, the benchmark establishment module 11 may specifically include: The differential positioning unit is used to perform differential positioning calibration on the global navigation satellite system positioning device, and to correct the error of the initial geodetic coordinates by using the differential signal sent by the reference station, so as to establish a geodetic coordinate system reference based on the corrected initial geodetic coordinates.

[0039] In some specific embodiments, the data acquisition module 12 may specifically include: An image information acquisition unit is used to acquire image information of a preset marker through the preset visual positioning device; the preset marker is an optical target with coordinate encoding, and the optical target is pre-associated with the geodetic coordinate system reference. A relative position determination unit is used to identify the coordinate code of the optical target based on the image information in order to determine the relative position of the camera relative to the optical target; The position coordinate generation unit is used to fuse the relative position with the positioning data collected by the global navigation satellite system positioning device to generate the real-time position coordinates of the camera.

[0040] In some specific embodiments, the deviation acquisition module 13 may specifically include: The coordinate transformation formula establishment unit is used to establish a coordinate transformation formula based on the transformation relationship between the geodetic coordinate system and the camera's local coordinate system, and to introduce elevation compensation parameters into the coordinate transformation formula; the elevation compensation parameters are parameters obtained by calculating the difference between the actual ground elevation of the camera installation point obtained through electronic map or digital elevation model data and the ellipsoidal elevation collected by the global navigation satellite system positioning device. The deviation value determination unit is used to generate the deviation value of the current installation position of the camera based on the processed real-time pose data by using the coordinate transformation formula after introducing elevation compensation parameters.

[0041] In some specific embodiments, the correction module 14 may specifically include: An angle adjustment amount determination unit is used to determine the angle adjustment amount of each joint of the quadruped robot dog by performing inverse kinematics solution based on the deviation value when the posture adjustment mechanism is the body of the quadruped robot dog. The target pose holding unit is used to adjust the angle of each joint according to the angle adjustment amount, so that the foot position of the quadruped robot dog remains unchanged, and at the same time rotates the body coordinate system of the quadruped robot dog relative to the world coordinate system, so as to maintain the target pose of the camera in the geodetic coordinate system reference. An automatic correction unit is used to continuously adjust the angles of each joint based on the real-time pose data fed back by the camera during the movement of the quadruped robot dog, so as to achieve automatic correction.

[0042] In some specific embodiments, the camera pose adjustment device may further include: An adjustment resistance monitoring unit is used to monitor the adjustment resistance of the posture adjustment mechanism in real time using the torque feedback unit in the posture adjustment mechanism during the automatic correction process driven by the posture adjustment mechanism based on the deviation value. The fault alarm unit is used to control the posture adjustment mechanism to stop automatic correction and issue a fault warning signal when the adjustment resistance exceeds a preset resistance threshold; the fault warning signal is any one or more combinations of sound alarm, light alarm or electronic signal alarm. The cumulative deviation value determination unit is used to update the geodetic coordinate system reference based on a preset update cycle, and compare the current real-time pose data with the historical pose data during the update process to obtain the cumulative deviation value. The recalibration unit is used to trigger a recalibration process if the cumulative deviation value exceeds a preset cumulative deviation threshold. The recalibration process includes re-executing the steps of establishing a geodetic coordinate system reference, acquiring real-time pose data, generating deviation values, and driving automatic correction.

[0043] Furthermore, embodiments of this application also disclose an electronic device, Figure 3 This is a structural diagram of an electronic device 20 according to an exemplary embodiment. The content of the diagram should not be construed as limiting the scope of this application. The electronic device 20 may specifically include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 stores a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the camera pose adjustment method disclosed in any of the foregoing embodiments. Furthermore, the electronic device 20 in this embodiment may specifically be a computer.

[0044] In this embodiment, the power supply 23 is used to provide operating voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and external devices, and the communication protocol it follows can be any communication protocol applicable to the technical solution of this application, and is not specifically limited here; the input / output interface 25 is used to acquire external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs, and is not specifically limited here.

[0045] In addition, the memory 22, as a carrier for resource storage, can be a read-only memory, random access memory, disk or optical disk, etc. The resources stored thereon can include operating system 221, computer program 222, etc., and the storage method can be temporary storage or permanent storage.

[0046] The operating system 221 is used to manage and control the various hardware devices on the electronic device 20 and the computer program 222, which may be Windows Server, Netware, Unix, Linux, etc. In addition to including a computer program capable of performing the camera pose adjustment method executed by the electronic device 20 as disclosed in any of the foregoing embodiments, the computer program 222 may further include a computer program capable of performing other specific tasks.

[0047] Furthermore, this application also discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, it implements the aforementioned camera pose adjustment method. Specific steps of this method can be found in the corresponding content disclosed in the foregoing embodiments, and will not be repeated here.

[0048] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section.

[0049] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0050] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0051] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0052] The technical solutions provided in this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for adjusting the pose of a camera, characterized in that, include: The initial geodetic coordinates of the camera's current installation location are obtained by matching the global navigation satellite system positioning device with electronic map data, and a geodetic coordinate system reference is established based on the initial geodetic coordinates. The camera's real-time pose data is acquired; the real-time pose data includes attitude angles and position coordinates; the attitude angles are acquired through a preset inertial measurement unit; the position coordinates are acquired through the fusion of the global navigation satellite system positioning device and a preset visual positioning device. The Kalman filter algorithm is used to denoise the real-time pose data, and the deviation value of the current installation position of the camera is generated based on the processed real-time pose data and the geodetic coordinate system reference; the deviation value includes position deviation and attitude deviation. The position adjustment mechanism is driven to perform automatic correction based on the deviation value, so as to adjust the installation position and posture of the camera, and to provide real-time feedback of the adjusted position and posture data in real time during the correction process, until the deviation value is less than the preset deviation threshold.

2. The camera pose adjustment method according to claim 1, characterized in that, The establishment of the geodetic coordinate system reference based on the initial geodetic coordinate values ​​includes: Differential positioning calibration is performed on the global navigation satellite system positioning device, and the initial value of the geodetic coordinates is corrected for error through the differential signal sent by the reference station, so as to establish a geodetic coordinate system reference based on the corrected initial value of the geodetic coordinates.

3. The camera pose adjustment method according to claim 1, characterized in that, The process of acquiring the location coordinates includes: The preset visual positioning device acquires image information of a preset marker; the preset marker is an optical target with coordinate encoding, and the optical target is pre-associated with the geodetic coordinate system reference. The coordinate code of the optical target is identified based on the image information to determine the relative position of the camera with respect to the optical target; The relative position is fused with the positioning data collected by the global navigation satellite system positioning device to generate the real-time position coordinates of the camera.

4. The camera pose adjustment method according to claim 1, characterized in that, The deviation value of the camera's current installation position generated based on the processed real-time pose data and the geodetic coordinate system reference includes: A coordinate transformation formula is established based on the transformation relationship between the geodetic coordinate system and the camera's local coordinate system, and an elevation compensation parameter is introduced into the coordinate transformation formula. The elevation compensation parameter is a parameter obtained by calculating the difference between the actual ground elevation of the camera installation point obtained through electronic map or digital elevation model data and the ellipsoidal elevation collected by the global navigation satellite system positioning device. The coordinate transformation formula, after introducing elevation compensation parameters, generates the deviation value of the camera's current installation position based on the processed real-time pose data.

5. The camera pose adjustment method according to claim 1, characterized in that, Also includes: During the automatic correction process driven by the posture adjustment mechanism based on the deviation value, the adjustment resistance of the posture adjustment mechanism is monitored in real time by the torque feedback unit in the posture adjustment mechanism. When the adjustment resistance exceeds the preset resistance threshold, the posture adjustment mechanism is controlled to stop automatic correction and issue a fault warning signal; the fault warning signal is any one or more combinations of sound alarm, light alarm or electronic signal alarm.

6. The camera pose adjustment method according to claim 1, characterized in that, Also includes: The geodetic coordinate system reference is updated based on a preset update cycle, and the current real-time pose data is compared with historical pose data during the update process to obtain the cumulative deviation value. If the cumulative deviation value exceeds the preset cumulative deviation threshold, a recalibration process is triggered; The recalibration process includes re-establishing the geodetic coordinate system benchmark, acquiring real-time pose data, generating deviation values, and driving automatic correction.

7. The camera pose adjustment method according to any one of claims 1 to 6, characterized in that, When the pose adjustment mechanism is the body of a quadruped robot dog, the automatic correction based on the deviation value driving the pose adjustment mechanism includes: Inverse kinematics is performed based on the deviation value to determine the angle adjustment of each joint of the quadruped robot dog. Adjust the angles of each joint according to the angle adjustment amount to keep the foot position of the quadruped robot dog unchanged, while rotating the body coordinate system of the quadruped robot dog relative to the world coordinate system to maintain the target pose of the camera in the geodetic coordinate system reference. During the movement of the quadruped robot dog, the angles of each joint are continuously adjusted based on the real-time pose data fed back by the camera to achieve automatic correction.

8. A camera pose adjustment device, characterized in that, include: The benchmark establishment module is used to obtain the initial geodetic coordinates of the current installation location of the camera by matching the positioning device of the global navigation satellite system with electronic map data, and to establish a geodetic coordinate system benchmark based on the initial geodetic coordinates. The data acquisition module is used to acquire the real-time pose data of the camera; the real-time pose data includes attitude angles and position coordinates; the attitude angles are acquired through a preset inertial measurement unit; the position coordinates are acquired by fusing the global navigation satellite system positioning device with a preset visual positioning device. The deviation acquisition module is used to perform noise reduction processing on real-time pose data using the Kalman filter algorithm, and generate a deviation value of the current installation position of the camera based on the processed real-time pose data and the geodetic coordinate system reference; the deviation value includes position deviation and attitude deviation. The correction module is used to drive the pose adjustment mechanism to perform automatic correction based on the deviation value, so as to adjust the installation position and posture of the camera, and to provide real-time feedback of the adjusted pose data in real time during the correction process until the deviation value is less than a preset deviation threshold.

9. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor for executing the computer program to implement the camera pose adjustment method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, Used to store a computer program, wherein the computer program, when executed by a processor, implements the camera pose adjustment method as described in any one of claims 1 to 7.

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