Unmanned aerial vehicle airborne sensor and GNSS RTK positioning seamless switching method, system and device

Abstract

The invention discloses an unmanned aerial vehicle airborne sensor and GNSS RTK positioning seamless switching method, system and device, and relates to the technical field of multi-sensor information fusion and navigation positioning, and the method comprises the steps: determining the quality score of a GNSS RTK signal based on the GNSS RTK signal data obtained in real time; judging the quality of the GNSS RTK signal based on the quality score of the GNSS RTK signal by adopting an RTK set threshold value; determining the mass fraction of the airborne sensor signal based on the airborne sensor signal data acquired in real time; determining the quality of the airborne sensor signal based on the quality score of the airborne sensor signal by using an airborne sensor set threshold value; and performing positioning mode switching according to the quality judgment result of the GNSS RTK signal and the quality judgment result of the airborne sensor signal. According to the invention, stable seamless switching of two positioning modes can be realized.

Description

Technical Field

[0001] This application relates to the field of multi-sensor information fusion and navigation positioning technology, and in particular to a method, system and device for seamless switching between UAV airborne sensors and GNSS RTK positioning. Background Technology

[0002] When performing tasks such as inspection, surveying, and emergency rescue, drones typically rely on the Global Navigation Satellite System (GNSS), especially Real-time Kinematic (RTK) technology, to achieve centimeter-level global positioning accuracy. However, GNSS RTK positioning accuracy drops significantly or even completely loses signal in scenarios with severe obstruction or multipath interference, such as urban canyons, forest environments, tunnels, and indoor spaces, rendering the drone unable to continue its autonomous navigation mission.

[0003] To improve the robustness of GNSS RTK technology in signal-constrained environments, related research often employs airborne sensors (such as inertial measurement units (IMUs), lidar, and cameras) for fusion positioning. This type of sensor fusion-based autonomous positioning method can maintain high positioning accuracy for short periods and is independent of external signals. However, due to the lack of global position constraints, these methods inevitably experience cumulative drift over long periods, thus reducing navigation accuracy.

[0004] Most systems combining multi-sensor fusion and GNSS RTK in related research adopt fixed fusion ratios or simple priority switching strategies. When GNSS signal quality fluctuates, problems such as frequent switching, abrupt changes in positioning solutions, or delayed switching can easily occur, leading to discontinuous navigation trajectories and even flight control anomalies. Some systems lack real-time assessment of GNSS signal quality, fusion positioning errors, and environmental characteristics, and cannot intelligently determine the optimal positioning source based on the current mission scenario, resulting in insufficient positioning accuracy and stability in complex environments. Summary of the Invention

[0005] The purpose of this application is to provide a method, system, and device for seamless switching between UAV airborne sensors and GNSS RTK positioning, which may enable a smooth and seamless switching between the two positioning modes.

[0006] To achieve the above objectives, this application provides the following solution: Firstly, this application provides a method for seamless switching between UAV onboard sensors and GNSS RTK positioning, including: Real-time acquisition of GNSS RTK signal data; The quality score of the GNSS RTK signal is determined based on the GNSS RTK signal data; The quality of the GNSS RTK signal is judged based on the quality score of the GNSS RTK signal using an RTK setting threshold, resulting in a GNSS RTK signal quality judgment result; the GNSS RTK signal quality judgment result is RTK Level 1 quality, RTK Level 2 quality, or RTK Level 3 quality. Real-time acquisition of airborne sensor signal data; The quality fraction of the airborne sensor signal is determined based on the airborne sensor signal data. The quality of the airborne sensor signal is judged based on the quality score of the airborne sensor signal by setting a threshold using the airborne sensor, and the quality judgment result of the airborne sensor signal is obtained; the quality judgment result of the airborne sensor signal is level one quality, level two quality, or level three quality. If the quality assessment result of the GNSS RTK signal is RTK Level 1 quality, the UAV uses GNSS RTK positioning mode for positioning; if the quality assessment result of the GNSS RTK signal is RTK Level 2 quality, and the quality assessment result of the airborne sensor signal is Level 3 quality, the UAV uses GNSS RTK positioning mode for positioning. If the quality assessment result of the GNSS RTK signal is RTK level 2 quality, and the quality assessment result of the airborne sensor signal is level 1 or level 2 quality, the UAV uses the GNSS RTK and airborne sensor fusion positioning mode for positioning. If the quality assessment result of the GNSS RTK signal is RTK level 3, the UAV uses the airborne sensor positioning mode for positioning.

[0007] In one embodiment, the GNSS RTK signal data includes: the number of GNSS satellites, position accuracy factor, carrier-to-noise ratio, and RTK solution status; Using formula The quality score of the GNSS RTK signal is determined based on the GNSS RTK signal data; where, This indicates the quality score of the GNSS RTK signal. Indicates the number of GNSS satellites. Indicates the position precision factor. Indicates the carrier-to-noise ratio. The weights represent the different solution states in RTK. This represents normalization based on logical functions.

[0008] In one embodiment, the airborne sensor signal data includes lidar signal data and camera signal data; The quality score of the airborne sensor signals includes the quality score of the lidar signal and the quality score of the camera signal; the quality score of the lidar signal is determined based on lidar signal data; the quality score of the camera signal is determined based on camera signal data.

[0009] In one embodiment, the airborne sensor setting threshold includes a lidar setting threshold and a camera setting threshold; The quality of the airborne sensor signal is judged based on the quality score of the airborne sensor signal using a threshold set by the airborne sensor, resulting in a quality judgment result for the airborne sensor signal, including: The quality of the lidar signal is judged based on the quality score of the lidar signal using a set threshold, and the quality judgment result of the lidar signal is obtained; the quality judgment result of the lidar signal is lidar level 1 quality, lidar level 2 quality, or lidar level 3 quality. The quality of the camera signal is judged based on the quality score of the camera signal using a set threshold, and a quality judgment result of the camera signal is obtained; the quality judgment result of the camera signal is camera level 1 quality, camera level 2 quality, or camera level 3 quality; If the quality assessment result of the lidar signal is lidar level 1 quality, and the quality assessment result of the camera signal is camera level 1 quality, then the quality assessment result of the airborne sensor signal is level 1 quality. If the quality assessment result of the lidar signal is lidar level 1 quality and the quality assessment result of the camera signal is camera level 2 quality, then the quality assessment result of the airborne sensor signal is level 2 quality; if the quality assessment result of the lidar signal is lidar level 2 quality and the quality assessment result of the camera signal is camera level 1 quality, then the quality assessment result of the airborne sensor signal is level 2 quality; if the quality assessment result of the lidar signal is lidar level 2 quality and the quality assessment result of the camera signal is camera level 2 quality, then the quality assessment result of the airborne sensor signal is level 2 quality. If the quality assessment result of the lidar signal is lidar level three quality, or the quality assessment result of the camera signal is camera level three quality, then the quality assessment result of the airborne sensor signal is level three quality.

[0010] In one embodiment, the point cloud matching residual, the consistency score of point cloud registration, and the local geometric anisotropy of the radar point cloud are determined based on lidar signal data. Using formula Determine the quality fraction of the lidar signal; where, This indicates the quality score of the lidar signal. , , These are the weighting coefficients. , This represents the consistency score of point cloud registration. This represents the point cloud matching residual. Representing local geometric anisotropy, This represents normalization based on logical functions.

[0011] In one embodiment, the camera signal data includes image data; based on the image data, the number of feature points, inlier rate, and illumination score of the image are determined; Using formula Determine the quality fraction of the camera signal; where, This indicates the quality score of the camera signal. , , These are the weighting coefficients. , This indicates the number of feature points in the image. This represents the normalization constant for the number of feature points. Represents the interior point ratio. This represents the lighting score of the image.

[0012] In one embodiment, the process of the UAV using a GNSS RTK and airborne sensor fusion positioning mode for positioning includes: The GNSS RTK and airborne sensor are used as independent measurement sources in the same coordinate system. Their poses are acquired separately, and the poses of the GNSS RTK and airborne sensor are fused using an extended Kalman filter to obtain the GNSS RTK and airborne sensor fused positioning result.

[0013] In one embodiment, the process of directly fusing the pose of GNSS RTK and the pose of airborne sensors using extended Kalman filtering includes: Determine whether the quality score of the GNSS RTK signal meets the RTK setting conditions; If the RTK setting conditions are met, the GNSS RTK pose is used first for updating during the extended Kalman filter update stage, and then the pose of the airborne sensor is used for updating, so as to obtain the fusion positioning result of the GNSS RTK and the airborne sensor. If the RTK setting conditions are not met, the pose of the airborne sensor is used first for updating during the extended Kalman filter update stage, and then the pose of the GNSS RTK is used for updating to obtain the fusion positioning result of the GNSS RTK and the airborne sensor.

[0014] Secondly, this application provides a seamless switching system between UAV airborne sensors and GNSS RTK positioning, comprising: The GNSS receiver unit is used to receive GNSS signals in real time and obtain GNSS RTK signal data based on the GNSS signals; Airborne sensor unit, used to acquire airborne sensor signal data in real time; A signal processing unit, connected to both the GNSS receiver unit and the airborne sensor unit, is used to determine the quality score of the GNSS RTK signal based on the GNSS RTK signal data, and also to determine the quality score of the airborne sensor signal based on the airborne sensor signal data. The signal processing unit is further used to judge the quality of the GNSS RTK signal based on the quality score using an RTK set threshold, obtaining a quality judgment result for the GNSS RTK signal; and to judge the quality of the airborne sensor signal based on the quality score using an airborne sensor set threshold, obtaining a quality judgment result for the airborne sensor signal. The positioning mode switching unit is connected to the GNSS receiver unit, the airborne sensor unit, and the signal processing unit, respectively, and is used to switch the positioning mode based on the quality judgment result of the GNSS RTK signal and the quality judgment result of the airborne sensor signal; and to select the GNSS receiver unit and / or the airborne sensor unit for UAV positioning based on the positioning mode.

[0015] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the seamless switching method between UAV airborne sensors and GNSS RTK positioning as described above.

[0016] According to the specific embodiments provided in this application, this application has the following technical effects: This application provides a method, system, and device for seamless switching between UAV airborne sensor and GNSS RTK positioning. By acquiring GNSS RTK signal data and airborne sensor signal data in real time, the quality scores of the GNSS RTK signal and the airborne sensor signal are determined. The quality of the GNSS RTK signal is evaluated in real time, resulting in quality judgments for both the GNSS RTK signal and the airborne sensor signal. Based on these judgments, the required positioning mode is determined, intelligently deciding the optimal positioning mode according to the current mission scenario and data quality, thus improving the positioning accuracy and stability of the UAV in complex environments. Furthermore, the system only switches to the GNSS RTK and airborne sensor fusion positioning mode when the GNSS RTK signal quality judgment result is RTK level 2 and the airborne sensor signal quality judgment result is level 1 or level 2, avoiding instability when switching positioning modes when the airborne sensor signal quality is low. The system only fully switches to airborne sensor positioning mode when the GNSS RTK signal quality assessment result is RTK level 3. This avoids frequent switching caused by fluctuations in GNSS RTK signal quality and achieves a smooth and seamless switching between the two positioning modes under different environmental conditions. This method balances the high accuracy of global positioning with the high robustness of autonomous positioning, thereby ensuring continuous and stable navigation of the UAV in the entire domain. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart of a method for seamless switching between UAV airborne sensors and GNSS RTK positioning in one embodiment of this application; Figure 2 This is a schematic diagram of a positioning mode switching process provided in an embodiment of this application; Figure 3 This is a schematic diagram of a positioning switching method provided in an embodiment of this application; Figure 4 A schematic diagram of a GNSS RTK positioning principle provided in an embodiment of this application; Figure 5 This is a structural diagram of a seamless switching system between an UAV airborne sensor and GNSS RTK positioning provided in an embodiment of this application; Figure 6 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0019] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0020] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0021] In one exemplary embodiment, such as Figure 1 and Figure 2 As shown, a method for seamless switching between UAV onboard sensors and GNSS RTK positioning is provided, the method including: Step 100: Acquire GNSS RTK signal data in real time. Determine the quality score of the GNSS RTK signal based on the GNSS RTK signal data.

[0022] Step 200: Using an RTK-defined threshold, the quality of the GNSS RTK signal is assessed based on its quality score to obtain a quality assessment result. The quality assessment result is RTK Level 1, RTK Level 2, or RTK Level 3.

[0023] Step 300: Acquire airborne sensor signal data in real time. Determine the quality score of the airborne sensor signals based on the airborne sensor signal data.

[0024] Step 400: Using a threshold set by the airborne sensor, the quality of the airborne sensor signal is judged based on its quality score, resulting in a quality judgment result. The quality judgment result of the airborne sensor signal is classified as Level 1, Level 2, or Level 3.

[0025] Step 500: If the GNSS RTK signal quality assessment result is RTK Level 1 quality, the UAV uses GNSS RTK positioning mode for positioning. If the GNSS RTK signal quality assessment result is RTK Level 2 quality, and the airborne sensor signal quality assessment result is Level 3 quality, the UAV uses GNSS RTK positioning mode for positioning. If the GNSS RTK signal quality assessment result is RTK Level 2 quality, and the airborne sensor signal quality assessment result is Level 1 or Level 2 quality, the UAV uses a GNSS RTK and airborne sensor fusion positioning mode for positioning. If the GNSS RTK signal quality assessment result is RTK Level 3 quality, the UAV uses an airborne sensor positioning mode for positioning.

[0026] As an optional implementation, the GNSS RTK signal data includes: the number of GNSS satellites, the Position Dilution of Precision (PDOP), the carrier-to-noise ratio, and the RTK solution status. Based on this, the process of determining the quality score of the GNSS RTK signal based on the GNSS RTK signal data in step 100 provided above in this application includes: using the formula... The quality score of the GNSS RTK signal is determined based on GNSS RTK signal data. Where, This indicates the quality score of the GNSS RTK signal. Indicates the number of GNSS satellites. Indicates the position precision factor. Indicates the carrier-to-noise ratio. The weights represent the different solution states in RTK. Represents normalization based on logical functions ( The smaller, The larger; The larger, The larger (the larger).

[0027] in, , This represents a fixed RTK solution. This represents the RTK floating-point solution. This indicates that there is no RTK differential data or no GNSS satellite signal.

[0028] Quality assessment of airborne sensor signals is a crucial step in ensuring the accuracy of UAV positioning and environmental perception. It typically requires comprehensive analysis and evaluation of both camera and LiDAR data. By monitoring and quantitatively analyzing the quality of these two types of data, a reliable input foundation can be provided for subsequent fusion positioning, improving the UAV's navigation and perception capabilities in complex environments. Therefore, airborne sensor signal data includes LiDAR signal data and camera signal data. The quality score of airborne sensor signals includes the quality score of LiDAR signals and the quality score of camera signals. The quality score of LiDAR signals is determined based on LiDAR signal data. The quality score of camera signals is determined based on camera signal data.

[0029] The quality assessment of lidar signals is mainly determined by the matching residuals of the lidar point cloud, the inlier ratio of the point cloud registration, and local geometric anisotropy. Based on lidar signal data, the matching residuals, the consistency score of the point cloud registration (determined by the inlier ratio), and local geometric anisotropy are determined. This local geometric anisotropy measures the clarity of local point cloud feature structures and is determined through PCA eigenvalues. Specifically, PCA decomposes the local neighborhood covariance matrix, and the difference in eigenvalues ​​quantifies the density of data distribution in each direction; the greater the difference in eigenvalues, the stronger the local geometric anisotropy. The formula used is... Determine the quality fraction of the lidar signal. In the formula, This indicates the quality score of the lidar signal. , , These are the weighting coefficients. , This represents the consistency score of point cloud registration. This represents the point cloud matching residual. Representing local geometric anisotropy, This represents normalization based on logical functions.

[0030] The quality assessment of camera signals is primarily determined by the number of feature points in the image, the inlier-to-inlier ratio of tracked feature points to the total number of tracked feature points, and the illumination score. Camera signal data includes image data; the number of feature points, inlier-to-inlier ratio, and illumination score are determined based on the image data. The formula used is... Determine the quality score of the camera signal. In the formula, This indicates the quality score of the camera signal. , , These are the weighting coefficients. , This indicates the number of feature points in the image. This represents the normalization constant for the number of feature points. Represents the interior point ratio. The illumination score of an image is determined by the image's average brightness and contrast. and adopt The illumination score is obtained by normalization.

[0031] The airborne sensor setting thresholds include lidar setting thresholds and camera setting thresholds. Based on this, the implementation process of step 400 provided above in this application includes: (1) The quality of the lidar signal is judged based on the quality score of the lidar signal by setting a threshold, and the quality judgment result of the lidar signal is obtained. The quality judgment result of the lidar signal is lidar level 1 quality, lidar level 2 quality or lidar level 3 quality.

[0032] (2) The quality of the camera signal is judged based on the quality score of the camera signal using a set threshold, and the quality judgment result of the camera signal is obtained. The quality judgment result of the camera signal is camera level 1 quality, camera level 2 quality, or camera level 3 quality.

[0033] (3) If the quality assessment result of the lidar signal is lidar level 1 quality and the quality assessment result of the camera signal is camera level 1 quality, then the quality assessment result of the airborne sensor signal is level 1 quality. If the quality assessment result of the lidar signal is lidar level 1 quality and the quality assessment result of the camera signal is camera level 2 quality, then the quality assessment result of the airborne sensor signal is level 2 quality. If the quality assessment result of the lidar signal is lidar level 2 quality and the quality assessment result of the camera signal is camera level 1 quality, then the quality assessment result of the airborne sensor signal is level 2 quality. If the quality assessment result of the lidar signal is lidar level 2 quality and the quality assessment result of the camera signal is camera level 2 quality, then the quality assessment result of the airborne sensor signal is level 2 quality. If the quality assessment result of the lidar signal is lidar level 3 quality or the quality assessment result of the camera signal is camera level 3 quality, then the quality assessment result of the airborne sensor signal is level 3 quality.

[0034] The positioning mode switching is primarily guided by the quality assessment results of the GNSS RTK signal, while also referencing the quality assessment results of the airborne sensor signals. Based on this and the implementation process of step 400, the positioning mode switching can be represented as follows: .

[0035] In the formula, , This indicates the RTK setting thresholds (high-quality RTK threshold and low-quality RTK threshold, respectively). , This indicates the camera setting thresholds (high-quality camera threshold and low-quality camera threshold, respectively). , This indicates the LiDAR setting thresholds (high-quality LiDAR threshold and low-quality LiDAR threshold, respectively). Mode 1, 2, and 3 represent GNSS RTK positioning mode, GNSS RTK and airborne sensor fusion positioning mode, and airborne sensor positioning mode, respectively. In this embodiment, the RTK setting threshold, camera setting threshold, and LiDAR setting threshold can be set according to typical UAV operating conditions, or the user can set them according to actual needs. The positioning mode switching process in a certain scenario is as follows: Figure 3 As shown.

[0036] As an optional implementation, in order to simultaneously achieve smooth and robust multi-source pose fusion and ensure its real-time performance, the process of the UAV using the GNSS RTK and airborne sensor fusion positioning mode in step 500 provided above in this application includes: treating GNSS RTK and airborne sensors as independent measurement sources in the same coordinate system, acquiring poses respectively, and using extended Kalman filtering to fuse the poses of GNSS RTK and airborne sensors to obtain the GNSS RTK and airborne sensor fusion positioning result.

[0037] The process of fusing the poses of GNSS RTK and airborne sensors using an Extended Kalman Filter (EKF) includes: determining whether the quality score of the GNSS RTK signal meets the RTK setting conditions. If the RTK setting conditions are met, the GNSS RTK pose is used first for updating during the EKF update phase, followed by the airborne sensor pose, to obtain the fused positioning result of GNSS RTK and airborne sensors. If the RTK setting conditions are not met, the airborne sensor pose is used first for updating during the EKF update phase, followed by the GNSS RTK pose, to obtain the fused positioning result of GNSS RTK and airborne sensors.

[0038] For example, when fusing GNSS RTK pose and airborne sensor pose (determined jointly by IMU, camera, and LiDAR) using Extended Kalman Filter (EKF), both are treated as independent measurement sources in the same coordinate system and introduced separately during the EKF update phase. The prediction part of the filter is driven by the IMU or a simplified motion model, while the update part is corrected using the GNSS RTK pose and the airborne sensor pose, respectively. Based on the quality scores of the GNSS RTK signal, the camera signal, and the LiDAR signal, the noise covariance matrix of the two types of measurements is adaptively adjusted, giving greater weight to the pose measurement of the high-quality signal and reducing the influence of the low-quality measurement (i.e., updating first using the pose of the high-quality signal, then updating using the pose of the low-quality signal). This allows for smooth and robust multi-source pose fusion while ensuring real-time performance.

[0039] The process of extended Kalman filter fusion of poses includes: (1) State prediction model: In the formula, For state vectors, For location, For speed, For attitude quaternions, and The gyroscope and accelerometer are respectively zero-biased. The initial pose is determined by the IMU.

[0040] (2) Measurement model: In the formula, and The position observation vectors are obtained by GNSS RTK and airborne sensors, respectively. and The measurement noise is for GNSS RTK and airborne sensors, respectively.

[0041] (3) Adaptive measurement noise covariance model: The measurement noise covariance is dynamically adjusted based on the quality fraction of the GNSSRTK signal and the quality fraction of the airborne sensor signal. Where, It is a constant, used to prevent the mass fraction from being 0; and The measurement noise covariances for GNSS RTK and airborne sensors are respectively. and These are the covariances of GNSS RTK and airborne sensors, respectively.

[0042] (4) Determine the order of EKF updates for the measurement model based on the quality fraction of the GNSS RTK signal. When First, the pose is updated using GNSS RTK, and then the pose is updated using the airborne sensor; when First, the pose of the airborne sensor is used for updating, and then the pose of the GNSS RTK is used for updating. The EKF update stages are as follows: steps (5) to (7).

[0043] (5) Kalman gain calculation: In the formula, For the first The Kalman gain matrix at time t. In the first Time for the first The covariance matrix of the state at time step. For the first Jacobian matrix at time, For the first Measurement noise covariance at time.

[0044] (6) Status update: In the formula, In the first At that moment, combined with the first The optimal estimate of the system state after the measurement at time t. For the first The measurement residual at time.

[0045] (7) Covariance matrix update: .

[0046] This application provides a method for seamless switching between UAV airborne sensors and GNSS RTK positioning, which includes three positioning modes: GNSS RTK positioning mode, airborne sensor positioning mode, and airborne sensor and GNSS RTK fusion positioning mode. These three modes can automatically switch based on the GNSS RTK signal quality and the airborne sensor signal quality (i.e., sensor status). GNSS RTK Positioning Mode: When GNSS signal is good and RTK calculation is normal, the UAV uses GNSS RTK as the primary positioning source. The IMU provides high-speed inertial calculation, which is fused and corrected with the GNSS RTK pose to output high-precision, high-stability positioning results. In this mode, the UAV can achieve centimeter-level positioning accuracy and is suitable for outdoor environments with good GNSS signal coverage. The principle of GNSS RTK positioning is as follows: Figure 4 As shown.

[0047] Airborne sensor positioning mode: When GNSS signals are interfered with, blocked, or RTK cannot provide reliable solutions, the system switches to airborne sensor mode. In this mode, the UAV relies on a combination of sensors such as IMU, camera, and LiDAR for navigation and positioning. First, the initial pose is predicted using a high-frequency IMU, and then the pose is updated sequentially based on the radar and camera signals. This allows the UAV to continuously acquire its position even indoors or in obstructed environments without GNSS signals.

[0048] GNSS RTK and Airborne Sensor Fusion Positioning Mode: This mode uses Kalman filtering as its core to perform spatiotemporal alignment and optimization fusion of pose information from airborne sensors and GNSS RTK, thereby obtaining high-precision and continuous UAV pose calculation results.

[0049] Through the solutions disclosed in the above embodiments, this application can achieve smooth switching between positioning modes and road binding as output, thereby ensuring that the UAV can obtain high-precision, continuous, and stable pose estimation in various environments.

[0050] Based on the same inventive concept, this application also provides a seamless switching system for UAV onboard sensors and GNSS RTK positioning to implement the aforementioned seamless switching method for UAV onboard sensors and GNSS RTK positioning. The solution provided by this system is similar to the solution described in the above method. Therefore, the specific limitations in the embodiments of the seamless switching system for UAV onboard sensors and GNSS RTK positioning provided below can be found in the limitations of the seamless switching method for UAV onboard sensors and GNSS RTK positioning described above, and will not be repeated here.

[0051] In one exemplary embodiment, such as Figure 5 As shown, a seamless switching system between UAV airborne sensors and GNSS RTK positioning is provided, including: The GNSS receiver unit is used to receive GNSS signals in real time and obtain GNSS RTK signal data based on the GNSS signals; Airborne sensor unit, used to acquire airborne sensor signal data in real time.

[0052] The signal processing unit, connected to both the GNSS receiver unit and the airborne sensor unit, is used to determine the quality score of the GNSS RTK signal based on GNSS RTK signal data, and also to determine the quality score of the airborne sensor signal based on the airborne sensor signal data. The signal processing unit further uses an RTK-defined threshold to judge the quality of the GNSS RTK signal based on its quality score, obtaining a quality judgment result for the GNSS RTK signal. The signal processing unit also uses an airborne sensor-defined threshold to judge the quality of the airborne sensor signal based on its quality score, obtaining a quality judgment result for the airborne sensor signal.

[0053] The positioning mode switching unit, connected to the GNSS receiver unit, airborne sensor unit, and signal processing unit, is used to switch the positioning mode based on the quality judgment results of the GNSS RTK signal and the airborne sensor signal. It selects the GNSS receiver unit and / or the airborne sensor unit for UAV positioning based on the positioning mode.

[0054] The airborne sensor unit includes a camera and a lidar module. The camera acquires camera signal data, and the lidar module acquires lidar signal data. The seamless switching system between the UAV's airborne sensors and GNSS RTK positioning also includes an inertial measurement module to acquire data from the accelerometer and gyroscope. The GNSS receiver, inertial measurement module, and airborne sensor unit provide pose information, GNSS signals, and sensor signal data through a loosely coupled architecture.

[0055] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 6As shown, the computer device includes a processor, memory, input / output interfaces (I / O), and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores data related to a seamless handover method between UAV onboard sensors and GNSS RTK positioning. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a seamless handover method between UAV onboard sensors and GNSS RTK positioning.

[0056] Those skilled in the art will understand that Figure 6 The structures shown are merely block diagrams of some structures related to the present application and do not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than shown in the figures, or combine certain components, or have different component arrangements. In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0057] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0058] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0059] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0060] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0061] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0062] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0063] This document uses specific examples 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. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for seamless switching between UAV airborne sensors and GNSS RTK positioning, characterized in that, include: Real-time acquisition of GNSS RTK signal data; The quality score of the GNSS RTK signal is determined based on the GNSS RTK signal data; The quality of the GNSS RTK signal is judged based on the quality score of the GNSS RTK signal using an RTK setting threshold, and the quality judgment result of the GNSS RTK signal is obtained. The quality assessment result of the GNSS RTK signal is RTK Level 1 quality, RTK Level 2 quality, or RTK Level 3 quality; Real-time acquisition of airborne sensor signal data; The quality fraction of the airborne sensor signal is determined based on the airborne sensor signal data. The quality of the airborne sensor signal is judged based on the quality score of the airborne sensor signal by setting a threshold using the airborne sensor, and the quality judgment result of the airborne sensor signal is obtained; the quality judgment result of the airborne sensor signal is level one quality, level two quality, or level three quality. If the quality assessment result of the GNSS RTK signal is RTK Level 1 quality, the UAV uses GNSS RTK positioning mode for positioning; If the quality assessment result of the GNSS RTK signal is RTK level 2 quality and the quality assessment result of the airborne sensor signal is level 3 quality, the UAV uses GNSS RTK positioning mode for positioning. If the quality assessment result of the GNSS RTK signal is RTK level 2 quality, and the quality assessment result of the airborne sensor signal is level 1 or level 2 quality, the UAV uses the GNSS RTK and airborne sensor fusion positioning mode for positioning. If the quality assessment result of the GNSS RTK signal is RTK level 3, the UAV uses the airborne sensor positioning mode for positioning.

2. The seamless switching method between UAV airborne sensors and GNSS RTK positioning according to claim 1, characterized in that, The GNSS RTK signal data includes: the number of GNSS satellites, position accuracy factor, carrier-to-noise ratio, and RTK solution status; Using formula The quality score of the GNSS RTK signal is determined based on the GNSS RTK signal data; where, This indicates the quality score of the GNSS RTK signal. Indicates the number of GNSS satellites. Indicates the position precision factor. Indicates the carrier-to-noise ratio. The weights represent the different solution states in RTK. This represents normalization based on logical functions.

3. The seamless switching method between UAV airborne sensors and GNSS RTK positioning according to claim 1, characterized in that, Airborne sensor signal data includes lidar signal data and camera signal data; The quality score of the airborne sensor signals includes the quality score of the lidar signal and the quality score of the camera signal; the quality score of the lidar signal is determined based on lidar signal data; the quality score of the camera signal is determined based on camera signal data.

4. The seamless switching method between UAV airborne sensors and GNSS RTK positioning according to claim 3, characterized in that, The airborne sensor setting thresholds include lidar setting thresholds and camera setting thresholds; The quality of the airborne sensor signal is judged based on the quality score of the airborne sensor signal using a threshold set by the airborne sensor, resulting in a quality judgment result for the airborne sensor signal, including: The quality of the lidar signal is judged based on the quality score of the lidar signal using a set threshold, and the quality judgment result of the lidar signal is obtained; the quality judgment result of the lidar signal is lidar level 1 quality, lidar level 2 quality, or lidar level 3 quality. The quality of the camera signal is judged based on the quality score of the camera signal using a set threshold, and a quality judgment result of the camera signal is obtained; the quality judgment result of the camera signal is camera level 1 quality, camera level 2 quality, or camera level 3 quality; If the quality assessment result of the lidar signal is lidar level 1 quality, and the quality assessment result of the camera signal is camera level 1 quality, then the quality assessment result of the airborne sensor signal is level 1 quality. If the quality assessment result of the lidar signal is lidar level 1 quality and the quality assessment result of the camera signal is camera level 2 quality, then the quality assessment result of the airborne sensor signal is level 2 quality; if the quality assessment result of the lidar signal is lidar level 2 quality and the quality assessment result of the camera signal is camera level 1 quality, then the quality assessment result of the airborne sensor signal is level 2 quality; if the quality assessment result of the lidar signal is lidar level 2 quality and the quality assessment result of the camera signal is camera level 2 quality, then the quality assessment result of the airborne sensor signal is level 2 quality. If the quality assessment result of the lidar signal is lidar level three quality, or the quality assessment result of the camera signal is camera level three quality, then the quality assessment result of the airborne sensor signal is level three quality.

5. The seamless switching method between UAV airborne sensors and GNSS RTK positioning according to claim 3, characterized in that, The point cloud matching residual, point cloud registration consistency score, and local geometric anisotropy are determined based on lidar signal data. Using formula Determine the quality fraction of the lidar signal; where, This indicates the quality score of the lidar signal. , , These are the weighting coefficients. , This represents the consistency score of point cloud registration. This represents the point cloud matching residual. Representing local geometric anisotropy, This represents normalization based on logical functions.

6. The seamless switching method between UAV airborne sensors and GNSS RTK positioning according to claim 3, characterized in that, Camera signal data includes image data; based on the image data, the number of feature points, inlier rate, and illumination score of the image are determined; Using formula Determine the quality fraction of the camera signal; where, This indicates the quality score of the camera signal. , , These are the weighting coefficients. , This indicates the number of feature points in the image. This represents the normalization constant for the number of feature points. Represents the interior point ratio. This represents the lighting score of the image.

7. The seamless switching method between UAV airborne sensors and GNSS RTK positioning according to claim 1, characterized in that, The process of a UAV using a GNSS RTK and airborne sensor fusion positioning mode for positioning includes: The GNSS RTK and airborne sensor are used as independent measurement sources in the same coordinate system. Their poses are acquired separately, and the poses of the GNSS RTK and airborne sensor are fused using an extended Kalman filter to obtain the GNSS RTK and airborne sensor fused positioning result.

8. The seamless switching method between UAV airborne sensors and GNSS RTK positioning according to claim 7, characterized in that, The process of directly fusing the pose data of GNSS RTK and airborne sensors using extended Kalman filtering includes: Determine whether the quality score of the GNSS RTK signal meets the RTK setting conditions; If the RTK setting conditions are met, the GNSS RTK pose is used first for updating during the extended Kalman filter update stage, and then the pose of the airborne sensor is used for updating, so as to obtain the fusion positioning result of the GNSS RTK and the airborne sensor. If the RTK setting conditions are not met, the pose of the airborne sensor is used first for updating during the extended Kalman filter update stage, and then the pose of the GNSS RTK is used for updating to obtain the fusion positioning result of the GNSS RTK and the airborne sensor.

9. A seamless switching system for UAV airborne sensors and GNSS RTK positioning, characterized in that, include: The GNSS receiver unit is used to receive GNSS signals in real time and obtain GNSS RTK signal data based on the GNSS signals; Airborne sensor unit, used to acquire airborne sensor signal data in real time; A signal processing unit, connected to both the GNSS receiver unit and the airborne sensor unit, is used to determine the quality score of the GNSS RTK signal based on the GNSS RTK signal data, and also to determine the quality score of the airborne sensor signal based on the airborne sensor signal data. The signal processing unit is further used to judge the quality of the GNSS RTK signal based on the quality score using an RTK set threshold, obtaining a quality judgment result for the GNSS RTK signal; and to judge the quality of the airborne sensor signal based on the quality score using an airborne sensor set threshold, obtaining a quality judgment result for the airborne sensor signal. The positioning mode switching unit is connected to the GNSS receiver unit, the airborne sensor unit, and the signal processing unit, respectively, and is used to switch the positioning mode based on the quality judgment result of the GNSS RTK signal and the quality judgment result of the airborne sensor signal; and to select the GNSS receiver unit and / or the airborne sensor unit for UAV positioning based on the positioning mode.

10. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the seamless switching method between UAV airborne sensors and GNSS RTK positioning as described in any one of claims 1-8.

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