Imitation estimation method and device, equipment and storage medium

Abstract

The invention relates to the technical field of unmanned aerial vehicle perception, in particular to a terrain simulation estimation method, device and equipment and a storage medium. The terrain simulation estimation method comprises the following steps: acquiring sensor data of an unmanned aerial vehicle; performing time synchronization processing on the sensor data of the unmanned aerial vehicle; the terrain height observed by the unmanned aerial vehicle at the current position is obtained through a terrain simulation height estimation module; establishing a terrain height prediction model to obtain the predicted terrain height of the unmanned aerial vehicle at the current position; and performing data fusion on the terrain height predicted by the terrain height prediction model and the terrain height observed by the simulated terrain height estimation module by using a Kalman filter to obtain the optimal estimation of the terrain height. According to the invention, the Kalman filter technology is used, the terrain height with high confidence is calculated based on the terrain height predicted by the terrain height prediction model and the terrain height observed by the terrain-imitated height estimation module, and the safety and stability of the terrain-imitated flight of the unmanned aerial vehicle are effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of unmanned aerial vehicle perception technology, and in particular to a terrain estimation method, device, equipment and storage medium. Background Art

[0002] Plant protection UAVs often need to perform terrain-simulating flight operations in complex terrains, such as mountains, orchards, and tall plant fields. Therefore, this requires the UAV's real-time terrain detection technology to be extremely robust and stable.

[0003] Existing technologies usually use millimeter-wave data based on ground-based radar to detect terrain height. In complex terrain, there is a lot of noise in the terrain data obtained by the ground-based radar. Therefore, the smoothness of the terrain height calculated by the ground-based height estimation module is usually poor, which is not conducive to achieving stable ground-based flight of drones. In addition, for drone operation scenarios with obstructions, such as the spreading of particles, the terrain data obtained by the ground-based radar is extremely sparse. In this scenario, the terrain height confidence output by the ground-based height estimation module is low, which seriously reduces the safety of drone ground-based flight operations.

[0004] The information disclosed in this background technology section is only intended to enhance the understanding of the overall background of the invention and should not be regarded as an acknowledgement or any form of suggestion that the information constitutes the prior art already known to ordinary technicians in this field. Summary of the invention

[0005] The purpose of the present invention is to overcome the deficiencies in the prior art and provide a terrain estimation method, device, equipment and storage medium to solve the problems of poor smoothness and low confidence of the terrain height output by the terrain estimation module in the related art.

[0006] In order to solve the above technical problems, the present invention is implemented by adopting the following technical solutions:

[0007] In a first aspect, the present invention provides a terrain estimation method, comprising:

[0008] Collect sensor data from drones;

[0009] Performing time synchronization processing on sensor data of the drone;

[0010] The terrain height observed at the current position of the UAV is obtained through the terrain-simulation height estimation module;

[0011] Establish a terrain height prediction model to obtain the predicted terrain height at the current location of the drone;

[0012] The Kalman filter is used to fuse the terrain height predicted by the terrain height prediction model and the terrain height observed by the terrain simulation height estimation module to obtain the optimal estimate of the terrain height.

[0013] Furthermore, the collecting of sensor data of the drone includes:

[0014] Collect the UAV's real-time dynamic measurement RTK data, inertial measurement unit IMU data and ground-based radar data.

[0015] Furthermore, the time synchronization processing of the sensor data of the drone includes:

[0016] Preprocessing the real-time dynamic measurement RTK data and the inertial measurement unit IMU data to obtain real-time position information data of the UAV;

[0017] Preprocessing the ground-imitation radar data to obtain effective ground-imitation radar data;

[0018] The pre-processed real-time dynamic measurement RTK data, inertial measurement unit IMU data and ground-based radar data are time-synchronized to obtain the position and posture information data corresponding to each frame of ground-based radar data.

[0019] Furthermore, the preprocessing of the ground-based radar data to obtain effective ground-based radar data includes:

[0020] Calculate the distance of each data point relative to the origin of the GPR coordinate system;

[0021] According to the preset distance threshold, data points with a distance less than the threshold are removed from the data set;

[0022] Data points whose distance is greater than or equal to the threshold are retained as valid data.

[0023] Furthermore, obtaining the terrain height observed at the current position of the drone through the terrain-simulating height estimation module includes:

[0024] Acquire ground-based radar data within the target range;

[0025] The terrain-simulating radar data is processed to obtain an observed value of the terrain height at the current position of the UAV.

[0026] If the amount of terrain-simulating radar data meets a set threshold, the obtained terrain height is a valid terrain height, otherwise it is an invalid terrain height.

[0027] Furthermore, the terrain height prediction model is established to obtain the predicted terrain height at the current position of the drone, including:

[0028] Filling qualified terrain-based height determination points into the queue based on the first-in-first-out principle, wherein the terrain-based height determination points are composed of the effective terrain height output by the terrain-based height estimation module and the corresponding UAV position;

[0029] Calculate the weight of all the distances of the simulated height points in the queue to the terrain height prediction of the current position of the drone;

[0030] According to the weights, the weighted average of the terrain heights of all the terrain-simulating height-fixing points in the queue is calculated to obtain a predicted value of the terrain height at the current position of the drone.

[0031] Furthermore, filling the queue with qualified terrain-simulating height-determining points based on the first-in-first-out principle includes:

[0032] If the distance between the latest terrain-based altitude point and the current drone position is greater than the sampling distance, the latest terrain-based altitude point is sampled and put into the queue;

[0033] If the number of sampling points in the queue is greater than the set threshold, the earliest stored terrain-simulated height-determining point in the queue will be removed and the latest terrain-simulated height-determining point will be added.

[0034] In a second aspect, the present invention provides a terrain estimation device, comprising:

[0035] Data acquisition module, used to collect sensor data from drones;

[0036] A data processing module, used for performing time synchronization processing on the sensor data of the drone;

[0037] The data acquisition module obtains the terrain height observed at the current position of the UAV through the terrain height estimation module; establishes a terrain height prediction model to obtain the predicted terrain height at the current position of the UAV;

[0038] The data calculation module uses the Kalman filter to fuse the terrain height predicted by the terrain height prediction model and the terrain height observed by the terrain simulation height estimation module to obtain the optimal estimate of the terrain height.

[0039] In a third aspect, the present invention provides an electronic device, the electronic device comprising:

[0040] at least one processor; and

[0041] a memory communicatively connected to the at least one processor; wherein,

[0042] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the terrain simulation estimation method described in any embodiment of the present invention.

[0043] In a fourth aspect, the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the terrain simulation estimation method described in any embodiment of the present invention when executed.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] The terrain estimation method provided by the present invention utilizes the historical terrain height and UAV position information output by the terrain estimation module, and combines the Gaussian kernel regression method to establish a terrain height prediction model. Finally, the Kalman filter technology is used to calculate the terrain height with high confidence based on the terrain height predicted by the terrain height prediction model and the terrain height observed by the terrain estimation module, thereby effectively improving the safety and stability of the UAV's terrain estimation flight. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0047] Figure 1 is a flow chart of a terrain estimation method provided in Embodiment 1 of the present invention;

[0048] Figure 2 is a structural diagram of a terrain simulation estimation device provided in Embodiment 2 of the present invention;

[0049] Figure 3 It is a structural diagram of an electronic device provided in Embodiment 3 of the present invention. DETAILED DESCRIPTION

[0050] The present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the present invention, rather than to limit the present invention. In addition, the embodiments of the present invention and the features in the embodiments can be combined with each other without conflict. It is also necessary to explain that, for the convenience of description, only the parts related to the present invention are shown in the accompanying drawings, rather than all structures.

[0051] It should be mentioned before discussing the exemplary embodiments in more detail that some exemplary embodiments are described as processes or methods depicted as flow charts. Although the flow charts describe the steps as sequential processes, many of the steps therein can be implemented in parallel, concurrently or simultaneously. In addition, the order of the steps can be rearranged. The process can be terminated when its operation is completed, but can also have additional steps not included in the accompanying drawings. The process can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0052] It should be noted that the concepts such as "first" and "second" mentioned in the embodiments of the present invention are only used to distinguish different devices, modules, units or other objects, and are not used to limit the order or interdependence of the functions performed by these devices, modules, units or other objects.

[0053] Embodiment 1:

[0054] Figure 1 This is a flow chart of a terrain estimation method provided in Embodiment 1 of the present invention. This embodiment can be applied to a terrain estimation test on a UAV. The method can be executed by a terrain estimation device. The terrain estimation device can be implemented in the form of hardware and / or software. The terrain estimation device can be configured in an electronic device.

[0055] The terrain estimation method provided in this embodiment refers to a series of technical means and algorithms used to estimate the height of the drone relative to the ground in real time in order to ensure that the drone maintains a constant safe height above the ground (i.e., "terrain imitation") when performing a low-altitude flight mission.

[0056] like Figure 1 As shown, the terrain estimation method includes:

[0057] Step 1: Collecting sensor data of the drone, wherein collecting sensor data of the drone includes:

[0058] Collect the UAV's real-time dynamic measurement RTK data, inertial measurement unit IMU data and ground-based radar data.

[0059] Specifically, RTK (real-time dynamic measurement), IMU (inertial measurement unit) and ground-following radar are installed on the plant protection UAV, and it is placed in an area with flat ground and open sides for ground-following flight and sowing operations, so as to collect the UAV's real-time dynamic measurement RTK data, inertial measurement unit IMU data and ground-following radar data.

[0060] Step 2: performing time synchronization processing on the sensor data of the drone, the time synchronization processing on the sensor data of the drone includes:

[0061] Preprocessing the real-time dynamic measurement RTK data and the inertial measurement unit IMU data to obtain real-time position information data of the UAV;

[0062] Preprocessing the ground-imitation radar data to obtain effective ground-imitation radar data;

[0063] The pre-processed real-time dynamic positioning RTK data, inertial measurement unit IMU data and ground-based radar data are time-synchronized to obtain the position and posture information data corresponding to each frame of ground-based radar data.

[0064] Specifically, the real-time dynamic measurement RTK data and the inertial measurement unit IMU data are preprocessed to obtain the real-time posture information data of the drone, including:

[0065] Acquire RTK data to obtain centimeter-level accurate location information (latitude, longitude, altitude);

[0066] Get IMU data to get acceleration, angular velocity and possible magnetometer data to calculate the attitude angle (pitch, roll and heading) of the drone.

[0067] Combining the high-precision position information provided by RTK and the attitude information provided by IMU, a real-time six-degree-of-freedom attitude model of the UAV is constructed. The flight control system fuses these two data sources through a built-in algorithm to obtain the real-time attitude information data of the UAV.

[0068] Preprocessing the ground-imitation radar data to obtain effective ground-imitation radar data includes:

[0069] Calculate the distance of each data point relative to the origin of the GPR coordinate system;

[0070] According to the preset distance threshold, data points with a distance less than the threshold are removed from the data set;

[0071] Data points whose distance is greater than or equal to the threshold are retained as valid data.

[0072] The preset distance threshold is determined according to the size of the UAV, the installation position of the ground-following radar and the required flight safety distance.

[0073] It should be noted that for plant protection UAVs, the ground-based radar data near the body is often inaccurate due to the obstruction of the body itself or other factors. Therefore, it is necessary to filter according to the distance from the origin of the ground-based radar coordinate system.

[0074] When the drone is in flight, the ground-following radar will continuously collect ground height data, calculate the distance of each data point relative to the origin of the ground-following radar coordinate system, and then determine a reasonable distance threshold based on the specific model and size of the drone and the installation location of the ground-following radar. This threshold should ensure that the drone will not collide with the ground due to invalid data during flight.

[0075] Invalid GRA data are filtered according to the distance from the origin of the GRA coordinate system. Data beyond the threshold are considered valid, and data points within the threshold that are subject to greater interference are ignored. This filtering method helps to eliminate erroneous measurement results caused by obstruction by the drone body or other close-range interference objects.

[0076] The pre-processed real-time dynamic positioning RTK data, inertial measurement unit IMU data and ground-based radar data are time-synchronized to obtain the pose information data corresponding to each frame of ground-based radar data, including:

[0077] Add accurate timestamps to the pose information provided by RTK and IMU and the distance data of the ground-based radar. For the timestamp of the i-th frame of ground-based radar data, find the two most recent frames of RTK / IMU pose data and use the linear interpolation method to calculate the accurate pose information corresponding to the i-th frame of ground-based radar data.

[0078] Step 3: Obtaining the terrain height observed at the current position of the UAV through the terrain-simulating height estimation module, wherein obtaining the terrain height observed at the current position of the UAV through the terrain-simulating height estimation module includes:

[0079] Acquire millimeter-wave radar data within target range;

[0080] The millimeter wave radar data is processed to obtain an observation value of the terrain height at the current position of the UAV.

[0081] Among them, if the amount of millimeter-wave radar data meets the set threshold, the obtained terrain height is the effective terrain height, otherwise it is an invalid terrain height.

[0082] Specifically, the terrain-simulating height estimation module estimates the terrain height based on the terrain-simulating radar data within a certain radius around the current position of the UAV. Through a series of processing steps, including data smoothing, static point clustering, and sorting by height, it finally outputs a high-confidence valid terrain height or a low-confidence invalid terrain height.

[0083] Among them, data smoothing includes using filtering algorithms to reduce the impact of noise and improve data quality; static point clustering includes identifying and separating static point groups representing ground features and excluding the impact of dynamic objects (such as moving vehicles, pedestrians, etc.); sorting by height includes sorting the clustered point cloud data by height for subsequent analysis.

[0084] The obtained effective terrain height is combined with the XY plane position of the UAV in the ENU coordinate system to form a "terrain-based height point" to establish a terrain height prediction model.

[0085] Step 4: Establishing a terrain height prediction model to obtain the predicted terrain height at the current position of the drone. The establishment of the terrain height prediction model to obtain the predicted terrain height at the current position of the drone includes:

[0086] Filling qualified terrain-based height determination points into the queue based on the first-in-first-out principle, wherein the terrain-based height determination points are composed of the effective terrain height output by the terrain-based height estimation module and the corresponding UAV position;

[0087] Calculate the weight of all the distances of the simulated height points in the queue to the terrain height prediction of the current position of the drone;

[0088] According to the weights, the weighted average of the terrain heights of all the terrain-simulating height-fixing points in the queue is calculated to obtain a predicted value of the terrain height at the current position of the drone.

[0089] Specifically, filling the queue with qualified terrain-simulating height-determining points based on the first-in-first-out principle includes:

[0090] If the distance between the latest terrain-based altitude point and the current drone position is greater than the sampling distance, the latest terrain-based altitude point is sampled and put into the queue;

[0091] If the number of sampling points in the queue is greater than the set threshold, the earliest stored terrain-simulated height-determining point in the queue will be removed and the latest terrain-simulated height-determining point will be added.

[0092] More specifically, the terrain height prediction model predicts the terrain height of the current position of the drone based on the historical effective terrain heights and the corresponding drone positions.

[0093] First, set the required number of terrain-based height-determining point threshold N max and the sampling distance l, and then a queue P based on first-in-first-out is used to store the topographically determined height points.

[0094] Assume that the current terrain imitation height estimation module outputs the effective terrain imitation height z j , the corresponding drone position is (x j ,y j ), if the current drone position is equal to the latest terrain high point p in the queue k (x k ,y k , z k ) is greater than l, then the new topographic high point p j (x j ,y j , z j ) is put into queue P.

[0095] If the number of sampling points in queue P is greater than N max , then first remove the earliest terrain-imitation height point stored in queue P, and then put in the latest terrain-imitation height point.

[0096] By setting thresholds and distances, it can be ensured that the terrain-simulating high points stored in the queue can cover the terrain changes on the drone's flight path. By dynamically adjusting the terrain-simulating high points in the queue, it can be ensured that the prediction model can adapt to terrain changes.

[0097] Then, the terrain height of the current position of the UAV is predicted based on the Gaussian kernel regression model. According to the idea of ​​Gaussian kernel regression, the closer the historical terrain-fixed height point is to the current position of the UAV, the higher its weight is in predicting the terrain height of the current position.

[0098] Specifically, assuming the current position of the drone is (x i ,y i ), the weight of the distance of the historical terrain-fixed height points in the queue P to the terrain height prediction of the current position of the UAV can be calculated by formulas (1) and (2).

[0099]

[0100] According to the weight calculated by formula (2), the weighted average of the terrain heights of all the terrain-simulating height-determining points in queue P is calculated:

[0101]

[0102] The weighted average value calculated by formula (3) is used to obtain the predicted value of the terrain height at the current UAV position.

[0103] Step 5: Use the Kalman filter to fuse the terrain height predicted by the terrain height prediction model and the terrain height observed by the terrain simulation height estimation module to obtain the optimal estimate of the terrain height.

[0104] Specifically, the Kalman filter is a recursive algorithm used to estimate the state of a dynamic system from a series of incomplete and noisy measurements. It optimizes the estimation result through two steps: prediction and update.

[0105] Prediction stage: The prior terrain height is obtained through the terrain height prediction model, and the prior covariance is obtained by combining the current covariance and the process noise covariance.

[0106] Prior terrain height z prior That is, the terrain height at the current position of the UAV output by the terrain height prediction model. This is an estimated value given by the terrain prediction model, that is, the best guess of the terrain height at the current position of the UAV before there is new observation data.

[0107] The prior covariance represents the uncertainty of the prior terrain height estimate. The prior covariance is P prior =P current +Q, where P current is the current covariance. For the first use of the filter, it is equal to the covariance value initially set. Q is the process noise covariance, which is set according to the accuracy of the terrain height prediction model.

[0108] Update phase: According to the observations and confidence of the terrain height estimation module, the Kalman gain is calculated, and the Kalman gain is used to update the terrain height estimation and covariance.

[0109] Terrain height observation z measure That is, the terrain height of the current position of the UAV is output by the terrain-simulating height estimation module, which is the actual terrain height value directly measured by the terrain-simulating height estimation module.

[0110] The observation noise covariance R is dynamically adjusted according to the terrain height confidence output by the terrain height estimation module. The higher the confidence, the smaller the R value; the lower the confidence, the larger the R value, which reflects the degree of trust in the measurement data.

[0111] The calculation formula of Kalman gain is: It determines the weight of new measurements and predictions in updating the estimated value. The posterior terrain height is the final terrain height estimate after the Kalman filter is processed. post =z prior +K·(z measure -z prior ), the posterior covariance is P post =(1-K)·P prior .

[0112] By combining the Kalman filter and the terrain height prediction model, the prior information of the prediction model and the real-time data of the observation module can be effectively utilized to optimize the terrain height estimation.

[0113] The terrain imitation estimation method provided by the Kalman filter in this embodiment can greatly alleviate the problem of poor smoothness of the terrain height output by the terrain imitation height estimation module, and effectively improve the smoothness and stability of the UAV's terrain imitation flight operation.

[0114] In addition, the terrain estimation method based on Kalman filter can still output terrain height with high confidence when the terrain estimation module fails, which can significantly improve the operation efficiency of plant protection UAVs while ensuring the safe operation of plant protection UAVs.

[0115] Embodiment 2:

[0116] Figure 2 A schematic diagram of the structure of a terrain estimation device provided in Embodiment 2 of the present invention.

[0117] like Figure 2 As shown, the device comprises:

[0118] Data acquisition module, used to collect sensor data from drones;

[0119] A data processing module, used for performing time synchronization processing on the sensor data of the drone;

[0120] The data acquisition module obtains the terrain height observed at the current position of the UAV through the terrain height estimation module; establishes a terrain height prediction model to obtain the predicted terrain height at the current position of the UAV;

[0121] The data calculation module uses the Kalman filter to fuse the terrain height predicted by the terrain height prediction model and the terrain height observed by the terrain simulation height estimation module to obtain the optimal estimate of the terrain height.

[0122] The terrain simulation estimation device provided in the embodiment of the present invention can execute the terrain simulation estimation method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0123] Embodiment three:

[0124] Figure 3 A schematic diagram of an electronic device that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0125] like Figure 3 As shown, the electronic device includes at least one processor, and a memory connected to the at least one processor, such as a read-only memory (ROM), a random access memory (RAM), etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) or the computer program loaded from the storage unit to the random access memory (RAM). In the RAM, various programs and data required for the operation of the electronic device can also be stored. The processor, ROM and RAM are connected to each other via a bus. The input / output (I / O) interface is also connected to the bus.

[0126] Multiple components in an electronic device are connected to the I / O interface, including: input units, such as keyboards, mice, etc.; output units, such as various types of displays, speakers, etc.; storage units, such as disks, optical disks, etc.; and communication units, such as network cards, modems, wireless communication transceivers, etc. The communication unit allows the electronic device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunication networks.

[0127] The processor may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of processors include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The processor executes the various methods and processes described above, such as the terrain estimation method.

[0128] In some embodiments, the terrain estimation method can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device via a ROM and / or a communication unit. When the computer program is loaded into the RAM 43 and executed by the processor, one or more steps of the terrain estimation method described above can be performed.

[0129] Alternatively, in other embodiments, the processor may be configured to execute the terrain-simulating estimation method in any other appropriate manner (for example, by means of firmware).

[0130] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0131] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0132] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0133] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0134] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0135] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0136] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0137] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A terrain estimation method, characterized in that: include: Collect sensor data from drones; Performing time synchronization processing on sensor data of the drone; The terrain height observed at the current position of the UAV is obtained through the terrain-simulation height estimation module; Establish a terrain height prediction model to obtain the predicted terrain height at the current location of the drone; The Kalman filter is used to fuse the terrain height predicted by the terrain height prediction model and the terrain height observed by the terrain simulation height estimation module to obtain the optimal estimate of the terrain height.

2. The method according to claim 1, characterized in that The sensor data collected from the drone includes: Collect the UAV's real-time dynamic measurement RTK data, inertial measurement unit IMU data and ground-based radar data.

3. The method according to claim 2, characterized in that The time synchronization processing of the sensor data of the drone includes: Preprocessing the real-time dynamic measurement RTK data and the inertial measurement unit IMU data to obtain real-time position information data of the UAV; Preprocessing the ground-imitation radar data to obtain effective ground-imitation radar data; The pre-processed real-time dynamic measurement RTK data, inertial measurement unit IMU data and ground-based radar data are time-synchronized to obtain the position and posture information data corresponding to each frame of ground-based radar data.

4. The method according to claim 3, characterized in that The preprocessing of the ground-imitation radar data to obtain effective ground-imitation radar data includes: Calculate the distance of each data point relative to the origin of the GPR coordinate system; According to the preset distance threshold, data points with a distance less than the threshold are removed from the data set; Data points whose distance is greater than or equal to the threshold are retained as valid data.

5. The method according to claim 4, characterized in that The terrain height observed at the current position of the UAV obtained by the terrain-simulating height estimation module includes: Acquire ground-based radar data within the target range; Processing the terrain-simulating radar data to obtain an observed value of the terrain height at the current position of the UAV; If the amount of terrain-simulating radar data meets a set threshold, the obtained terrain height is a valid terrain height, otherwise it is an invalid terrain height.

6. The method according to claim 5, characterized in that The terrain height prediction model is established to obtain the predicted terrain height at the current position of the drone, including: Filling qualified terrain-based height determination points into the queue based on the first-in-first-out principle, wherein the terrain-based height determination points are composed of the effective terrain height output by the terrain-based height estimation module and the corresponding UAV position; Calculate the weight of all the distances of the simulated height points in the queue to the terrain height prediction of the current position of the drone; According to the weights, the weighted average of the terrain heights of all the terrain-simulating height-fixing points in the queue is calculated to obtain a predicted value of the terrain height at the current position of the drone.

7. The method according to claim 6, characterized in that The method of filling the queue with qualified terrain-simulating height-determining points based on the first-in-first-out principle includes: If the distance between the latest terrain-based altitude point and the current drone position is greater than the sampling distance, the latest terrain-based altitude point is sampled and put into the queue; If the number of sampling points in the queue is greater than the set threshold, the earliest stored terrain-simulated height-determining point in the queue will be removed and the latest terrain-simulated height-determining point will be added.

8. A terrain height estimation device, characterized in that: include: Data acquisition module, used to collect sensor data from drones; A data processing module, used for performing time synchronization processing on the sensor data of the drone; The data acquisition module obtains the terrain height observed at the current position of the UAV through the terrain imitation height estimation module; establishes a terrain height prediction model to obtain the predicted terrain height at the current position of the UAV; The data calculation module uses the Kalman filter to fuse the terrain height predicted by the terrain height prediction model and the terrain height observed by the terrain simulation height estimation module to obtain the optimal estimate of the terrain height.

9. An electronic device, characterized in that: include: one or more processors; one or more memories; And one or more computer programs, wherein the one or more computer programs are stored in the one or more memories, and the one or more computer programs include instructions, which, when executed by the electronic device, enable the electronic device to perform the method described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the method according to any one of claims 1 to 7.

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