WO2020041959A1 - 连续波雷达的地形预测方法、装置、系统和无人机 - Google Patents

连续波雷达的地形预测方法、装置、系统和无人机 Download PDF

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Publication number
WO2020041959A1
WO2020041959A1 PCT/CN2018/102628 CN2018102628W WO2020041959A1 WO 2020041959 A1 WO2020041959 A1 WO 2020041959A1 CN 2018102628 W CN2018102628 W CN 2018102628W WO 2020041959 A1 WO2020041959 A1 WO 2020041959A1
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WIPO (PCT)
Prior art keywords
ranging data
ranging
ground
wave radar
continuous wave
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PCT/CN2018/102628
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English (en)
French (fr)
Inventor
祝煌剑
高迪
王春明
谭洪仕
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深圳市大疆创新科技有限公司
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Application filed by 深圳市大疆创新科技有限公司 filed Critical 深圳市大疆创新科技有限公司
Priority to PCT/CN2018/102628 priority Critical patent/WO2020041959A1/zh
Priority to CN201880040244.9A priority patent/CN110892355A/zh
Publication of WO2020041959A1 publication Critical patent/WO2020041959A1/zh
Priority to US17/183,315 priority patent/US20210199798A1/en

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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/10—Simultaneous control of position or course in three dimensions
    • G05D1/101—Simultaneous control of position or course in three dimensions specially adapted for aircraft
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/88—Radar or analogous systems specially adapted for specific applications
    • G01S13/93—Radar or analogous systems specially adapted for specific applications for anti-collision purposes
    • G01S13/933—Radar or analogous systems specially adapted for specific applications for anti-collision purposes of aircraft or spacecraft
    • G01S13/935—Radar or analogous systems specially adapted for specific applications for anti-collision purposes of aircraft or spacecraft for terrain-avoidance
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/88—Radar or analogous systems specially adapted for specific applications
    • G01S13/89—Radar or analogous systems specially adapted for specific applications for mapping or imaging
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/10—Simultaneous control of position or course in three dimensions
    • G05D1/101—Simultaneous control of position or course in three dimensions specially adapted for aircraft
    • G05D1/106—Change initiated in response to external conditions, e.g. avoidance of elevated terrain or of no-fly zones

Definitions

  • Embodiments of the present invention relate to the technical field of unmanned aerial vehicles, and in particular, to a method, a device, a system, and an unmanned aerial vehicle for terrain prediction of a continuous wave radar.
  • drones can be applied to a variety of scenarios. Taking the agricultural industry as an example, drones can cultivate land, spread seeds, spray pesticides, and harvest crops, which brings great benefits to the agricultural field. In these operating scenarios, most drones need to fly near the ground, and avoid accidentally hitting the ground when climbing. On relatively flat ground, based on Global Positioning System (GPS) and Inertial Measurement Unit (IMU) data, drones can successfully complete the above tasks; in rough terrain, no one The aircraft needs to adjust its actions in advance to perform operations such as climbing, downhill, deceleration, braking, etc., to achieve near-ground flight or even contour flight; this can make the drone better complete the above operations. Therefore, it is necessary to first predict the terrain information of the ground where the drone operates.
  • GPS Global Positioning System
  • IMU Inertial Measurement Unit
  • continuous distance radar rotation is generally used to measure multiple distances from the ground, and these distances are converted into coordinates on a coordinate system with the ranging sensor as the coordinate origin, and then a straight line is fitted using these coordinates. The terrain information is obtained from the fitted straight line.
  • the continuous wave radar due to the internal and external interference of the continuous wave radar, there will be outliers in the distance measured by the continuous wave radar, which will affect the accuracy of the terrain prediction.
  • Embodiments of the present invention provide a terrain prediction method, device, system, and unmanned aerial vehicle for continuous wave radar, which are used to improve the accuracy of terrain prediction.
  • an embodiment of the present invention provides a continuous wave radar terrain prediction method, including:
  • N first ranging data obtained by measuring the ground during continuous rotation of the continuous wave radar, wherein the N first ranging data are obtained when the rotation angle of the continuous wave radar is within a preset angle interval , Where N is an integer greater than 1.
  • Outliers are eliminated from the N first ranging data to obtain M first ranging data, where M is a positive integer less than N.
  • terrain parameters of the ground are determined, and the terrain parameters include at least one of the following: slope, flatness, and the height value of the continuous wave radar directly below the ground.
  • an embodiment of the present invention provides a control system for a continuous wave radar, including: a memory and a processor.
  • the memory is configured to store program code.
  • the processor calls the program code, and when the program code is executed, is used to perform the following operations:
  • N first ranging data obtained by measuring the ground during continuous rotation of the continuous wave radar, wherein the N first ranging data are obtained when the rotation angle of the continuous wave radar is within a preset angle interval , Where N is an integer greater than 1.
  • Outliers are eliminated from the N first ranging data to obtain M first ranging data, where M is a positive integer less than N.
  • terrain parameters of the ground are determined, and the terrain parameters include at least one of the following: slope, flatness, and the height value of the continuous wave radar directly below the ground.
  • an embodiment of the present invention provides a radar detection device, which includes a continuous wave radar and a control system of the continuous wave radar, and the control system of the continuous wave radar is communicatively connected with the continuous wave radar.
  • the control system of the continuous wave radar is the control system of the continuous wave radar according to the second aspect of the embodiment of the present invention.
  • an embodiment of the present invention provides an unmanned aerial vehicle, including: a chassis, a flight control system, and the radar detection device according to the embodiment of the third aspect of the present invention, wherein the continuous wave radar is mounted on the aircraft Shelf.
  • the flight control system is communicatively connected with the radar detection device to obtain the terrain parameters, and the flight control system controls the drone according to the terrain parameters.
  • an embodiment of the present invention provides a computer-readable storage medium.
  • the computer-readable storage medium stores a computer program, where the computer program includes at least one piece of code, and the at least one piece of code can be executed by a computer to control all
  • the computer executes the first aspect of the continuous wave radar terrain prediction method according to the embodiment of the present invention.
  • an embodiment of the present invention provides a computer program for implementing the terrain prediction method of a continuous wave radar according to the first aspect of the present invention when the computer program is executed by a computer.
  • the terrain prediction method, device, system and unmanned aerial vehicle of the continuous wave radar provided by the embodiments of the present invention obtain the N first ranging data obtained by rotating the ground to a preset angle range during the rotation process, and then Eliminate outliers from the N first ranging data to obtain M first ranging data, and then determine the terrain topographic parameters, such as slope, integrity, and the continuous wave, based on the M first ranging data.
  • FIG. 1 is a schematic architecture diagram of an agricultural drone 100 according to an embodiment of the present invention.
  • FIG. 2 is a flowchart of a terrain prediction method for a continuous wave radar according to an embodiment of the present invention
  • FIG. 3 is a schematic diagram of continuous wave radar ranging provided by an embodiment of the present invention.
  • FIG. 4 is a schematic diagram of a range measurement by a continuous wave radar in a prediction angle interval according to an embodiment of the present invention
  • 5A-5F are schematic diagrams of eliminating outliers according to an embodiment of the present invention.
  • FIG. 6A is a schematic diagram of a fitted straight line obtained from N first ranging data without culling outliers in the prior art
  • 6B is a schematic diagram of a fitting obtained from M first ranging data after excluding outliers according to an embodiment of the present invention
  • FIG. 7 is a schematic structural diagram of a control system for a continuous wave radar according to an embodiment of the present invention.
  • FIG. 8 is a schematic structural diagram of a radar detection device according to an embodiment of the present invention.
  • FIG. 9 is a schematic structural diagram of an unmanned aerial vehicle provided by an embodiment of the present invention.
  • Embodiments of the present invention provide a terrain prediction method, device, system, and unmanned aerial vehicle for continuous wave radar.
  • the drone may be an agricultural drone, such as a rotorcraft, for example, a multi-rotor aircraft propelled by multiple propulsion devices through air, and embodiments of the present invention are not limited thereto.
  • FIG. 1 is a schematic architecture diagram of an agricultural drone 100 according to an embodiment of the present invention. This embodiment is described by taking a rotary wing unmanned aerial vehicle as an example.
  • the agricultural drone 100 may include a power system, a flight control system, and a rack.
  • the agricultural drone 100 can communicate with the control terminal wirelessly.
  • the control terminal can display the flight information of the agricultural drone, etc.
  • the control terminal can communicate with the agricultural drone 100 wirelessly. Perform remote manipulation.
  • the rack may include a fuselage 110 and a tripod 120 (also referred to as a landing gear).
  • the fuselage 110 may include a center frame 111 and one or more arms 112 connected to the center frame 111. One or more arms 112 extend radially from the center frame.
  • the tripod 120 is connected to the fuselage 110 and is used to support the agricultural drone 100 when landing.
  • a liquid storage tank 130 is mounted between the tripod 120 and the liquid storage tank is used for storing medicinal liquid or water;
  • a spray head 140 is also mounted at the end of the arm 112, and the liquid in the liquid storage tank 130 is pumped into the spray head 140 by a pump, and is sprayed out by the spray head 140.
  • the power system may include one or more electronic governors (referred to as ESCs), one or more propellers 150, and one or more electric motors 160 corresponding to the one or more propellers 150, where the electric motors 160 are connected to the electronic governor.
  • ESCs electronic governors
  • the motor 160 and the propeller 150 are arranged on the arm 112 of the agricultural drone 100; the electronic governor is used to receive the driving signal generated by the flight control system, and provides the driving current to the motor according to the driving signal.
  • the motor 160 is used to drive the propeller 150 to rotate, so as to provide power for the flight of the agricultural drone 100, and the power enables the agricultural drone 100 to achieve one or more degrees of freedom of movement.
  • the agricultural drone 100 may rotate about one or more rotation axes.
  • the rotation axis may include a roll axis, a yaw axis, and a pitch axis.
  • the motor 160 may be a DC motor or an AC motor.
  • the motor 160 may be a brushless motor or a brushed motor.
  • the flight control system may include a flight controller and a sensing system.
  • the sensing system is used to measure the attitude information of the UAV, that is, the position information and status information of the agricultural UAV 100 in space, such as three-dimensional position, three-dimensional angle, three-dimensional velocity, three-dimensional acceleration, and three-dimensional angular velocity.
  • the sensing system may include, for example, at least one of a gyroscope, an ultrasonic sensor, an electronic compass, an Inertial Measurement Unit (IMU), a vision sensor, a global navigation satellite system, and a barometer.
  • the global navigation satellite system may be a Global Positioning System (Global Positioning System, GPS).
  • the flight controller is used to control the flight of the agricultural drone 100.
  • the flight controller may control the flight of the agricultural drone 100 according to the attitude information measured by the sensing system. It should be understood that the flight controller may control the agricultural drone 100 according to a pre-programmed program instruction, and may also control the agricultural drone 100 by responding to one or more control instructions from a control terminal.
  • a continuous wave radar 170 can also be mounted on the tripod 120 of the agricultural drone.
  • the continuous wave radar 170 is a rotating continuous wave radar.
  • the continuous wave radar 170 can be used for distance measurement, but is not limited to distance measurement.
  • the agricultural drone may include two or more tripods 170, and the continuous wave radar 170 is mounted on one of the tripods 170.
  • FIG. 2 is a flowchart of a continuous wave radar terrain prediction method according to an embodiment of the present invention. As shown in FIG. 2, the method in this embodiment may include:
  • N first ranging data obtained by the continuous wave radar during ground ranging during rotation where the N first ranging data is that the rotation angle of the continuous wave radar is within a preset angle interval acquired.
  • S203 Determine the terrain parameters of the ground according to the N first ranging data, where the terrain parameters include at least one of the following: slope, flatness, and a height value of the ground directly below the continuous wave radar.
  • the continuous wave radar can be used to measure the ground to obtain the distance between the continuous wave radar and the ground.
  • the continuous wave radar can rotate. When the continuous wave radar rotates at different angles, the continuous wave radar faces the ground. The distance measurement points for distance measurement are also different, so the distance detected by the continuous wave radar to the ground may also be different, as shown in Figure 3.
  • the continuous wave radar obtains a plurality of first ranging data when the ground is measured by the rotation process and the rotation angle of the continuous wave radar is within a preset angle interval. For example, as shown in FIG.
  • the first ranging data is N, and N is an integer of 2 or more. Each first ranging data reflects the distance between the continuous wave radar and the ground when it is rotated to the corresponding rotation angle.
  • the distance between the continuous wave radar and the ground is low
  • the distance between the continuous wave radar and the ground is large; for example, if the distance difference between the continuous wave radar and the different ranging points on the ground is large, it means that the ground is flat.
  • the distance between the continuous wave radar and the ground is small, it means that the slope of the ground where the multiple ranging points are located is high. If the distance between the continuous wave radar and the ground is large, then This shows that the slope of the ground where the multiple ranging points are located is low.
  • outliers are eliminated from the N first ranging data to obtain M first ranging data, where M is a positive integer less than N.
  • the terrain parameters of the location of the plurality of ranging points can be determined.
  • the terrain parameters include: the slope of the ground, the flatness of the ground, and the continuous wave radar distance. The height value of the ground below.
  • the preset angle interval is 60 degrees to 120 degrees, which can determine the terrain parameters of the ground directly below the continuous wave radar; the preset angle interval is -30 degrees to 30 degrees, which can determine the ground in front of the continuous wave radar. Terrain parameters; the preset angle range is 150 degrees to 210 degrees, and the corresponding terrain parameters of the ground behind the continuous wave radar can be determined. It should be noted that this is for the purpose of illustration and is not limited to this embodiment.
  • the angle interval can be set according to actual needs. If the preset angle interval of this embodiment is 60 degrees to 120 degrees, in this embodiment, the first ranging data can be obtained by measuring the ground with the continuous wave radar at a rotation angle of 60 degrees, and obtained with the ground ranging at 60.6 degrees. For the first ranging data, the first ranging data is obtained from the ground ranging at 61.2 degrees, the first ranging data is obtained from the ground ranging at 61.8 degrees, and so on, and details are not described herein again.
  • N first ranging data obtained by rotating the ground to a preset angle interval during the rotation process are acquired, and then outliers are eliminated from the N first ranging data to obtain M First ranging data, and then according to the M first ranging data, the terrain parameters such as the slope, the completeness, and the height of the ground directly below the continuous wave radar distance are determined. Since the outliers in the obtained ranging data are removed first and then terrain prediction is performed in this embodiment, the interference received by the continuous wave radar is cleared, so that the accuracy of the continuous wave radar's prediction of the ground terrain is higher.
  • Each first ranging data includes: the horizontal distance of the continuous wave radar from the ground ranging point, and the vertical distance of the continuous wave radar from the ground ranging point. Because the rotation angle of the continuous wave radar is different, the signal transmission direction of the continuous wave radar is different, which results in different ground ranging points. Therefore, the ground ranging point varies with the rotation angle of the continuous wave radar.
  • the first ranging data in this embodiment includes the above Horizontal distance and vertical distance, where the above-mentioned horizontal distance and vertical distance can be obtained according to the distance between the continuous wave radar and the ground ranging point and the rotation angle of the continuous wave radar corresponding to the ground ranging point.
  • the horizontal distance of the continuous wave radar from the ground ranging point is larger and the vertical distance is small, it can be considered that the slope of the ground is higher. The smaller the horizontal distance of the ground ranging point and the larger the vertical distance, the lower the slope of the ground can be considered.
  • Step A Obtain T second ranging data of the continuous wave radar on the ground during the rotation process; the T second ranging data is that the rotation angle of the continuous wave radar is within a preset angle interval
  • the H is an integer greater than or equal to N.
  • T second Ranging data all the ranging data obtained by the continuous wave radar during the rotation to the ground are obtained, and the rotation angle of the continuous wave radar is within a preset angle interval. These ranging data are referred to herein as T second Ranging data.
  • step A may include: step A1 and step A2.
  • Step A1 Obtain all second ranging data of the ground ranging performed by the continuous wave radar for one revolution and the rotation angle of the continuous wave radar corresponding to each second ranging data.
  • Step A2 According to the preset angle interval, obtain second ranging data corresponding to the rotation angle of the continuous wave radar located in the preset angle interval as the T second ranging data.
  • the continuous wave radar rotates once, corresponding to the continuous wave radar rotated a total of 360 degrees. For example: if the continuous wave radar rotates for 600 light grids per revolution, every 0.6 degree rotation of the continuous wave radar means that the continuous wave radar rotates to a corresponding light grid, and then triggers a ranging, so that 600 ranging data can be obtained.
  • the rotation angle of the continuous wave radar corresponding to each ranging data is also recorded.
  • the ranging principle of the continuous wave radar can refer to the related description in the prior art, and will not be repeated here. Then according to the preset angle interval, obtain the second ranging data corresponding to the rotation angle of the continuous wave radar within the preset angle interval.
  • the preset angle interval is 60-120 degrees
  • you can filter out 60 The second ranging data corresponding to 60.6, 61.2, ..., 118.8, 119.4, and 120 degrees respectively.
  • a total of 100 second ranging data can be obtained, and H is equal to 100.
  • Step B Acquire the N first ranging data according to the T second ranging data.
  • the second ranging data is data obtained by actual ranging of the continuous wave radar. After obtaining the T second ranging data, according to the T second ranging data, the N first ranging data is obtained. Ranging data.
  • a possible implementation manner of the foregoing step B may include step B1.
  • Step B1 Determine the N first ranging data according to the T second ranging data and valid ranging conditions.
  • the effective ranging conditions include: less than or equal to a preset maximum distance and greater than or equal to a preset minimum distance.
  • the validity of each ranging data is judged.
  • the continuous wave radar has a blind zone and the longest ranging distance within a short range. Therefore, an effective ranging condition is set, and the effective ranging condition can be expressed as [d min , d max ] means that the valid second ranging data should be greater than or equal to d min and less than or equal to d max . Therefore, this embodiment will determine the above-mentioned N first ranging data according to the T second ranging data and effective ranging conditions, avoiding errors of the ranging data, and improving the accuracy of the terrain prediction.
  • a possible implementation manner of the foregoing step B1 may include steps B11 and B12.
  • Step B11 From the T second ranging data, determine that the second ranging data satisfying the effective ranging condition is N second ranging data.
  • all second ranging data less than or equal to a preset maximum distance and less than or equal to a preset minimum distance are determined from the T second ranging data, and the second ranging data are N second rangings. data.
  • Step B11 Determine the N first ranging data according to the N second ranging data.
  • the N first ranging data are determined according to the N second ranging data that meets the valid ranging conditions determined above.
  • the N second ranging data may be determined as the N first ranging data, that is, the first ranging data is equal to the second ranging data.
  • the N second ranging data is smoothed to obtain the N first ranging data.
  • the N second ranging data is sorted according to the sequence of the rotation angle of the continuous wave radar corresponding to the second ranging data, such as: the first second ranging data is: the second ranging corresponding to 60 degrees Data d 1 , the second second ranging data is: second ranging data d 2 corresponding to 60.6 degrees, and so on; and then determining that the first second ranging data is the first first ranging data, That is, D 1 is equal to d 1 , and it is determined that the N-th second ranging data is the N-th second ranging data, that is, D N is equal to d N.
  • D j and d j is not limited to a horizontally adjacent, respectively (i.e. three) is calculated, and d j may be respectively two laterally adjacent (i.e., five persons) the average value, respectively ,
  • the first and second first ranging data are equal to the first and second second ranging data, respectively, and the N-1th and Nth first ranging data are respectively equal to the N-1th, The Nth second ranging data.
  • three, four, and so on, which are adjacent to each other, may also be adopted. The solutions are similar, and details are not described herein again.
  • the above d j may be a value, that is, the distance between the continuous wave radar and the ground ranging point.
  • a corresponding first measurement may be obtained according to the corresponding rotation angle of the continuous wave radar.
  • the continuous wave radar rotation center is taken as the origin (0, 0) of the coordinate system XOY
  • the forward direction of the continuous wave radar is taken as the positive direction of the X axis
  • the vertical downward direction is taken as the positive direction of the Y axis.
  • Vertical distance, x can be positive or negative.
  • the above d j may include two values, that is, the horizontal distance x j and the vertical distance y j between the continuous wave radar and the ground ranging point.
  • the horizontal distance may be smoothed to obtain the first ranging.
  • the horizontal distance in the data can also be smoothed for the vertical distance to obtain the vertical distance of the first ranging data.
  • the continuous wave radar data ranging (L i ) and its corresponding light grid (G i ) are converted into the first ranging data, that is, the coordinate values in the coordinate system established above:
  • G0 is the grating scale directly below the continuous wave radar, and Z is the angle value corresponding to a single light grid.
  • a possible implementation manner of the above S202 may include the following steps C to E.
  • Step C Obtain at least two first ranging data from the N first ranging data.
  • Step D Perform a straight line fitting according to the at least two first ranging data to obtain a first straight line function.
  • Step E Remove outliers from the N first ranging data according to the first straight line function to obtain M first ranging data.
  • At least two first ranging data may be randomly obtained from N first ranging data (where FIG. 5A is a distribution of the N first ranging data in the XOY coordinate system), and then according to the A straight line fitting is performed on at least two first ranging data to obtain a straight line function of vertical distance and horizontal distance in the first ranging data, and the straight line function is called a first straight line function.
  • the first straight line function is as follows:
  • the outlier value is first ranging data whose distance between straight lines corresponding to the first straight line function is greater than a preset distance. That is, this embodiment first determines the distance between each first ranging data and the established straight line (as shown in FIG. 5C), and then determines whether the distance is greater than a preset distance. If the distance is less than or equal to the preset distance, If the distance is set, it is determined that the first ranging data corresponding to the distance belongs to the M first ranging data. If the distance is greater than the preset distance, the first ranging data corresponding to the distance is greatly different, and the distance is determined. The corresponding first ranging data is outlier, and the first ranging data corresponding to the distance is eliminated.
  • the distance P i between the i-th first ranging data (x i , y i ) and the established straight line is shown below.
  • a possible implementation manner of the above S202 may include the following steps C 'to F'.
  • Step C ' Obtain at least two first ranging data from the N first ranging data K times, and the at least two first ranging data acquired each time are not completely the same.
  • Step D ' For at least two first ranging data acquired each time, perform a straight line fitting according to the at least two first ranging data acquired this time to obtain a first straight line function.
  • Step E ' Remove outliers from the N first ranging data according to the first straight line function to obtain a set of first ranging data.
  • Step F ' Obtain the M first ranging data according to the obtained K group first ranging data.
  • two first ranging data are acquired from N first ranging data each time as an example.
  • First obtain (for example, randomly obtain) two first ranging data from N first ranging data for the first time, as shown in FIG. 5B, and perform straight line simulation based on the two first ranging data obtained for the first time.
  • obtain a first first straight line function and then according to the first straight line function, remove outliers from N first ranging data to obtain a first set of first ranging data
  • the set of first ranging data may include multiple first ranging data.
  • the two first ranging data acquired for the second time are not exactly the same as the two first ranging data acquired for the first time.
  • the above process may be shown in FIG. 5E, for example.
  • the two first ranging data acquired at the third time are not exactly the same as the two first ranging data acquired at the first time, and are not completely the same as the two first ranging data acquired at the second time.
  • the above process can be shown, for example, in FIG. 5F.
  • K is equal to 3, that is, when the number of times of obtaining two first ranging data is greater than or equal to 3 in this embodiment, this embodiment stops obtaining two first ranging data from N first ranging data.
  • a ranging data is equal to 3.
  • the M first sets of first ranging data, the second set of first ranging data, and the third set of first ranging data are used to obtain the M first Ranging data.
  • a set of first ranging data including the largest number of first ranging data is determined Is the above-mentioned M first ranging data.
  • the first set of first ranging data includes 20 first ranging data
  • the second set of first ranging data includes 30 first ranging data
  • the third set of first ranging data includes 25 first ranging data Range data
  • this embodiment determines that the 30 first ranging data in the second set of first ranging data are the above-mentioned M first ranging data, where M is equal to 30.
  • outliers are eliminated from the N first ranging data to obtain a set of first ranging data.
  • the implementation process may be: For example, first determine the distance between each first ranging data and the straight line corresponding to any of the first straight line functions, and then determine whether the distance is greater than a preset distance. If the distance is less than or equal to a preset distance, determine The first ranging data corresponding to the distance belongs to the above-mentioned group of first ranging data. If the distance is greater than a preset distance, it indicates that the first ranging data corresponding to the distance is greatly different, and the first A ranging data is outlier.
  • the value of M is smaller than the first preset value. If the value of M is greater than or equal to the first preset value, then M is described.
  • the first ranging data has a sufficient amount of data for terrain prediction, and then the terrain parameters of the ground are determined according to the M first ranging data. If the value of M is less than the first preset value, it means that the M first ranging data is not enough for terrain prediction. In order to avoid inaccurate terrain prediction, this embodiment determines that the ranging data measured by the continuous wave radar is invalid.
  • a possible implementation manner of determining the terrain parameters of the ground according to the M first ranging data may include the following steps G and H;
  • Step G Perform a straight line fitting on the M first ranging data to obtain a second straight line function.
  • a straight line function may be performed on the M first ranging data by a least square method to obtain a straight line function, and the straight line function is referred to as a second straight line function.
  • the first ranging data includes a horizontal distance and a vertical distance.
  • a second linear function of the vertical distance between the continuous wave radar and the ground ranging point with respect to the horizontal distance between the continuous wave radar and the ground ranging point is constructed.
  • a and b are temporarily unknown. Then, the slope and intercept of the second straight line function are determined according to the M first ranging data, the second straight line function, and a least square method.
  • each first ranging data includes the horizontal distance and vertical distance between the continuous wave radar and the corresponding ground ranging point, and the known values of the M groups x and y Substitute into the above formula 1, and then determine the slope (for example, a) and intercept (for example, b) of the second straight line function by the least square method.
  • the above a and b can be determined by the Clem method, as shown below, where (x i , y i ) is any ranging data among the above M first ranging data.
  • this embodiment is not limited to the above-mentioned least square method, and a filtering method may also be adopted.
  • Step H Determine the terrain parameters of the ground according to the second linear function.
  • this embodiment may determine the slope of the ground according to the slope of the second straight line function. For example, the larger the slope, the greater the slope of the ground, and the smaller the slope, the slope of the ground The smaller.
  • the arc tangent of the slope may be determined as the slope of the ground.
  • the slope of the ground can be used to guide subsequent actions to be taken by the drone.
  • this embodiment determines the height value of the continuous wave radar from the ground directly below the intercept based on the second linear function, for example The intercept of the second straight line function may be determined as the height value of the ground directly below the continuous wave radar distance.
  • the height value of the continuous wave radar directly below the ground can be used for UAV obstacle avoidance, for example, to avoid collision with ground crops.
  • it can also be used for precise spraying of UAV, because when spraying, it is necessary to determine High spraying.
  • this embodiment may determine each first ranging of the M first ranging data according to the M first ranging data and the second straight line function. The residuals in the second straight line function corresponding to the data; and then the flatness of the ground is determined according to the residuals in the second straight line function corresponding to the M first ranging data respectively.
  • the residual in the second straight line function corresponding to each first ranging data can be obtained by the following formula.
  • e i y i -y i '
  • e i the residual in the second straight line function corresponding to the ith first ranging data in the M first ranging data
  • y i is the M first measuring
  • the vertical distance in the i-th first ranging data in the distance data, y i ' is the horizontal distance x i in the i-th first ranging data in the M first ranging data, which is obtained by substituting the variable x into the second straight line function
  • the sum of squares of the residuals in the second linear function corresponding to the M first ranging data, respectively may be determined as the flatness of the ground. If the sum of squared residuals is larger, the ground is more uneven, and if the sum of squared residuals is smaller, the ground is more flat.
  • the flatness of the ground is:
  • the flatness can be used in the height-fixing and obstacle avoidance scheme of the drone.
  • this embodiment may determine a median vertical distance according to a vertical distance from the continuous wave radar to a ranging point corresponding to each of the M first ranging data. . That is, from y 1 , y 2 , y 3 , ..., y M-2 , y M-1 , y M , the median of these values is determined. This median can also be called the vertical distance of the median. . For example: Take M equal to 7 as an example, y 1 , y 2 , y 3 , y 4 , y 5 , y 6 , y 7 are sorted in order of size: 1.2, 1.3, 1.3, 1.5, 1.6, 1.7, 1.8, Then 1.5 is the median.
  • step G it is judged whether the difference between the intercept of the second straight line function and the vertical distance from the median is smaller than a second preset value, and if the difference is smaller than the second preset value, step G is performed. If the difference is greater than or equal to the second preset value, the foregoing step G is not performed, indicating that the ranging data measured by the continuous wave radar is not suitable for predicting terrain.
  • the N first ranging data including outliers are subjected to a least squares straight line fitting.
  • the obtained fitted straight line is, for example, as shown in the figure.
  • FIG. 6A as shown in FIG. 6A, the terrain parameters obtained from the fitted straight line are not accurate.
  • a least-squares straight line fitting is performed according to the first tracking data after removing outliers, and the fitted straight line is obtained.
  • FIG. 6B as shown in FIG. 6B, the terrain parameters obtained from the fitted straight line are more accurate.
  • the outliers are not removed, but a weighted least squares straight line is performed on the N first ranging data. After fitting, a third straight line function is obtained, and then the terrain parameters of the ground are determined according to the third straight line function. Therefore, in this embodiment, the weighted least squares method can be used to eliminate the interference that the continuous wave radar receives when obtaining ranging data, thereby improving the accuracy of straight line fitting and further improving the accuracy of terrain prediction.
  • a weighted least squares straight line fitting is performed on the N first ranging data, and a possible implementation manner of obtaining a third straight line function is:
  • y i ′ corresponding to x i may be determined, where y i ′ is a value obtained by substituting x i as a variable x into the third straight line function. Value (that is, the fitted vertical distance), and x i is the horizontal distance of the ith first ranging data in the N first ranging data.
  • a weighted sum of squares of the residuals corresponding to the N ranging data is determined, such as, for example, as Equation three shows:
  • Q represents the weighted square sum of the residuals
  • w i represents the weighting coefficient of the residuals corresponding to the i-th first ranging data.
  • the value of the slope and the intercept of the linear function are determined according to the weighted square sum of the residuals.
  • the first derivative of the weighted squared sum of the residuals to the slope is equal to a first preset value
  • the first derivative of the weighted squared sum of the residuals to the intercept is equal to a second Set the value to determine the value of the slope and the value of the intercept of the linear function.
  • the first preset value and the second preset value may be set to 0. Accordingly, the first derivative of the weighted sum of squared residuals (Q) to the slope (a) is equal to 0 and the first derivative of the weighted sum of squared residuals (Q) to the intercept (b) is equal to 0, This can be shown, for example, in Equation 4 below:
  • the estimated value of a can be obtained according to the above formula four
  • the estimated values of b and b ⁇ are as follows:
  • the flatness of the ground is determined according to the slope a of the third straight line function.
  • terrain parameters of the ground include: the height value of the continuous wave radar from the ground directly below, determine the height value of the continuous wave radar from the ground directly below the intercept of the third straight line function.
  • the smoothness of the ground is determined according to the value of Q above. For example: the value of a above (as above ) And the value of b above (as above ), Which is substituted into the above formula 2 to obtain the value of Q. If the value of Q is larger, the ground is more uneven, and if the value of Q is smaller, the ground is more flat.
  • formulas 3 and 5 described above may be stored in advance, and the N first ranging data obtained may be substituted into the formula 5 stored in advance to obtain as well as according to Determine the slope of the ground. Then will get as well as Substitute it into the formula 3 stored in advance to obtain Q, and determine the flatness of the ground according to the value of Q.
  • the weighting coefficients of the residuals corresponding to each of the first ranging data are equal, and even if i has different values, w i is the same, for example, w i is equal to 1. Or, for example, w i is equal to 1 / N, which means that the sum of the weighting coefficients of the residuals corresponding to the N first ranging data is equal to 1.
  • the distance measurement data obtained through continuous wave radar ranging has an error that increases with distance, it is necessary to perform weight allocation on the corresponding first ranging data according to the rotation angle of the continuous wave radar. .
  • the weighting coefficient of the residual corresponding to each first ranging data is a trigonometric function about the rotation angle of the continuous wave radar corresponding to the first ranging data, for example, as shown in formula 6.
  • k mid represents the median value of the preset angle interval
  • k min represents the minimum value of the preset angle interval
  • k max represents the maximum value of the preset angle interval
  • k i represents the continuous wave corresponding to the i-th first ranging data.
  • the rotation angle of the radar For example: the preset angle interval is [-60 °, 60 °] for a total of 120 ° data, the value of k corresponding to -60 ° is 1, the value of k for -59 ° is 2, and so on, where k max is 120, k mid is 60 or 61, and k min is 1.
  • the weighting coefficients of the residuals are, for example, as shown in Formula 7. :
  • the weighting coefficient of the residual corresponding to each first ranging data is a Gaussian function about the rotation angle of the continuous wave radar corresponding to the first ranging data, such as formula 8 As shown:
  • x i is the horizontal distance of the ith first ranging data among N first ranging data
  • ⁇ and ⁇ are constants
  • ⁇ represents the average value of x 1 to x N
  • ⁇ represents the variance of x 1 to x N .
  • the shape of the function can be adjusted according to the value of the variance; the value of the variance can be set in advance according to actual needs.
  • the weighting coefficients of the residuals are, for example, as shown in Formula Nine :
  • the weighting coefficient of the residual corresponding to each first ranging data is an error function about the rotation angle of the continuous wave radar corresponding to the first ranging data, such as, for example, a formula Ten are shown:
  • the smaller the error the larger the weight coefficient; the larger the error, the smaller the weight coefficient.
  • the weighting coefficients of the residuals are, for example, as shown in formula XI Show:
  • the continuous wave radar involved in the foregoing embodiments may be an electromagnetic wave continuous wave radar, or may also be a laser continuous wave radar.
  • An embodiment of the present invention also provides a computer storage medium.
  • the computer storage medium stores program instructions, and the program execution may include a part of the terrain prediction method of a continuous wave radar as shown in FIG. 2 and its corresponding embodiments. Or all steps.
  • FIG. 7 is a schematic structural diagram of a continuous wave radar control system according to an embodiment of the present invention.
  • the continuous wave radar control system 700 of this embodiment may include: a memory 701 and a processor 702; It is connected to the processor 702 through a bus.
  • the memory 701 may include a read-only memory and a random access memory, and provides instructions and data to the processor 702.
  • a part of the memory 701 may further include a non-volatile random access memory.
  • the memory 701 is configured to store program code.
  • the processor 702 calls the program code, and when the program code is executed, is used to perform the following operations:
  • the first ranging data includes: a horizontal distance and a vertical distance of the continuous wave radar from a ground ranging point; wherein the ground ranging point varies with a rotation angle of the continuous wave radar.
  • the processor 702 is specifically configured to: obtain at least two first ranging data from the N first ranging data; and perform a straight line fitting according to the at least two first ranging data To obtain a first straight line function; according to the first straight line function, remove outliers from the N first ranging data to obtain M first ranging data.
  • the processor 702 is specifically configured to: obtain at least two first ranging data from the N first ranging data K times, and obtain at least two first ranging data each time Not exactly the same; for at least two first ranging data acquired each time, a straight line fitting is performed according to the at least two first ranging data acquired this time to obtain a first straight line function; according to the first straight line function, Outliers are eliminated from the N first ranging data to obtain a set of first ranging data; and the M first ranging data is obtained according to the obtained K group of first ranging data.
  • the processor 702 is specifically configured to determine, from the K first ranging data, a group of first ranging data including the largest number of first ranging data as the M first rangings data.
  • the outlier value is first ranging data whose distance between straight lines corresponding to the first straight line function is greater than a preset distance.
  • the processor 702 is specifically configured to: when the value of M is greater than or equal to a first preset value, determine the terrain parameters of the ground according to the M first ranging data.
  • the processor 702 is specifically configured to: perform a straight line fitting on the M first ranging data to obtain a second straight line function; and determine the terrain parameter of the ground according to the second straight line function .
  • the processor 702 is specifically configured to determine a median position according to a vertical distance from the continuous wave radar to a ranging point corresponding to each of the M first ranging data. If the difference between the intercept of the second straight line function and the vertical distance of the median is less than a second preset value, determining the terrain parameters of the ground according to the second straight line function .
  • the processor 702 is specifically configured to determine a slope of the ground according to a slope in the second straight line function.
  • the processor 702 is specifically configured to determine an arc tangent value of the slope as a slope of the ground.
  • the processor 702 is specifically configured to determine the continuous according to the intercept of the second straight line function The height of the radar directly below the ground.
  • the processor 702 is specifically configured to determine the M first based on the M first ranging data and the second straight line function The residuals in the second straight line function corresponding to each of the first ranging data in the ranging data are determined according to the residuals in the second straight line function respectively corresponding to the M first ranging data.
  • the flatness of the ground is specifically configured to determine the M first based on the M first ranging data and the second straight line function The residuals in the second straight line function corresponding to each of the first ranging data in the ranging data.
  • the processor 702 is specifically configured to determine a sum of residuals in the second linear function corresponding to the M first ranging data respectively as the flatness of the ground.
  • the processor 702 is specifically configured to: obtain T second ranging data of the continuous wave radar ranging on the ground during rotation; the T second ranging data is the continuous wave radar All the ranging data of the ground ranging within the preset angle interval, where T is an integer greater than or equal to N; and the N first ranging data is obtained according to the T second ranging data .
  • the processor 702 is specifically configured to determine the N first ranging data according to the T second ranging data and valid ranging conditions;
  • the effective ranging conditions include: less than or equal to a preset maximum distance and greater than or equal to a preset minimum distance.
  • the processor 702 is specifically configured to determine, from the T second ranging data, that the second ranging data that satisfies the effective ranging condition is N second ranging data;
  • the N second ranging data is described, and the N first ranging data is determined.
  • the processor 702 is specifically configured to: determine the N second ranging data as the N first ranging data; or perform smooth processing on the N second ranging data To obtain the N first ranging data.
  • the processor 702 is specifically configured to: sort the N second ranging data according to the sequence of the rotation angle of the continuous wave radar corresponding to the second ranging data; determine the first second ranging The data is the first first ranging data and the Nth second ranging data is the Nth first ranging data; the j-1th second ranging data and the jth second ranging data are determined The average value of the three j + 1th second ranging data is the jth first ranging data; wherein j is an integer greater than or equal to 2 and less than or equal to N-1.
  • the processor 702 is specifically configured to: obtain all second ranging data of the ground ranging performed by the continuous wave radar for one revolution and the rotation angle of the continuous wave radar corresponding to each second ranging data; According to the preset angle interval, the second ranging data corresponding to the rotation angle of the continuous wave radar located in the preset angle interval is the T second ranging data.
  • control system of the continuous wave radar of this embodiment may be used to implement the technical solution of the foregoing method embodiment of the present invention.
  • the implementation principles and technical effects thereof are similar, and are not repeated here.
  • FIG. 8 is a schematic structural diagram of a radar detection device according to an embodiment of the present invention.
  • the radar detection device 800 of this embodiment includes a continuous wave radar 801 and a continuous wave radar control system 802.
  • the control system 802 of the continuous wave radar is communicatively connected with the continuous wave radar 801.
  • the control system 802 of the continuous wave radar may adopt the structure of the embodiment shown in FIG. 7, and correspondingly, the technical solution shown in FIG. 2 and the corresponding embodiment may be implemented.
  • the implementation principles and technical effects are similar, and are not described here. To repeat.
  • FIG. 9 is a schematic structural diagram of a drone according to an embodiment of the present invention.
  • the drone 900 of this embodiment includes a rack (not shown in the figure), a flight control system 901, and a radar.
  • Detection device 902 wherein the radar detection device 902 may adopt the structure of the embodiment shown in FIG. 8, and correspondingly, the technical solution shown in FIG. 2 and its corresponding embodiments may be implemented. The implementation principles and technical effects are similar. More details.
  • the continuous wave radar in the radar detection device 902 is mounted on the frame.
  • the flight control system 901 is communicatively connected with the radar detection device 902 to obtain terrain parameters, and the flight control system 901 controls the drone 900 according to the terrain parameters.
  • the flight control system 901 may control subsequent actions of the drone 900 according to the slope of the ground.
  • the flight control system 901 may control the setting of the drone 900 and / or the obstacle avoidance of the drone 900 according to the flatness of the ground.
  • the flight control system 901 may perform obstacle avoidance based on the height value of the continuous wave radar from the ground directly below, for example:
  • the man-machine 900 hits the ground crops.
  • the drone 900 can also be controlled for precise spraying, because the spraying needs to be performed at a fixed height.
  • the foregoing program may be stored in a computer-readable storage medium.
  • the program is executed, the program is executed.
  • the foregoing storage medium includes: a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc. The medium.

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Abstract

一种连续波雷达的地形预测方法、装置、系统和无人机，该方法包括：获取连续波雷达在旋转过程中对地面测距获得的N个第一测距数据，其中，该N个第一测距数据为连续波雷达的旋转角度处于预设角度区间内获得的（S201）；从N个第一测距数据中剔除野值，获得M个第一测距数据（S202）；根据M个第一测距数据，确定地面的地形参数，该地形参数包括以下至少一种：坡度、平整度、连续波雷达距离正下方地面的高度值（S203）。该方法将获得的测距数据中的野值先剔除然后再进行地形预测，清除了连续波雷达受到的干扰，使得连续波雷达对地面地形的预测准确率更高。

Description

连续波雷达的地形预测方法、装置、系统和无人机 技术领域
本发明实施例涉及无人机技术领域，尤其涉及一种连续波雷达的地形预测方法、装置、系统和无人机。
背景技术
目前无人机可以应用于多种场景，以农行业为例，无人机可以耕地、撒播、喷洒农药和收割庄稼等，给农业领域带来了极大的好处。在这些作业场景下，无人机大多需要近地飞行，并且要避免爬坡时误撞地面。在较平坦的地面上，基于全球定位系统(Global Positioning System，GPS)及惯性测量单元(Inertial Measurement Unit，IMU)数据，无人机可以较顺利地完成上述任务；在较为崎岖的地形，无人机需要提前进行动作调整，进行爬坡、下坡、减速、刹车等操作，实现近地飞行甚至等高飞行；这样才能使得无人机更好地完成上述作业。因此，需要先预测无人机作业的地面的地形信息。
现有技术中，一般通过连续波雷达旋转来测量与地面的多个距离，将这些距离分别转换为以测距传感器为坐标原点的坐标系上的坐标，然后利用这些坐标拟合出一条直线，根据拟合得到的直线获取地面的地形信息。但是，在实际情况中，由于连续波雷达内部、外部环境的干扰，会导致连续波雷达测量到的距离中存在野值，从而影响地形预测的准确率。
发明内容
本发明实施例提供一种连续波雷达的地形预测方法、装置、系统和无人机，用于提高地形预测的准确率。
第一方面，本发明实施例提供一种连续波雷达的地形预测方法，包括：
获取连续波雷达在旋转过程中对地面测距获得的N个第一测距数据，其中，所述N个第一测距数据为所述连续波雷达的旋转角度处于预设角度区间内获得的，所述N为大于1的整数。
从所述N个第一测距数据中剔除野值，获得M个第一测距数据，所述M 为小于N的正整数。
根据所述M个第一测距数据，确定所述地面的地形参数，所述地形参数包括以下至少一种：坡度、平整度、所述连续波雷达距离正下方地面的高度值。
第二方面，本发明实施例提供一种连续波雷达的控制系统，包括：存储器和处理器。
所述存储器，用于存储程序代码。
所述处理器，调用所述程序代码，当程序代码被执行时，用于执行以下操作：
获取连续波雷达在旋转过程中对地面测距获得的N个第一测距数据，其中，所述N个第一测距数据为所述连续波雷达的旋转角度处于预设角度区间内获得的，所述N为大于1的整数。
从所述N个第一测距数据中剔除野值，获得M个第一测距数据，所述M为小于N的正整数。
根据所述M个第一测距数据，确定所述地面的地形参数，所述地形参数包括以下至少一种：坡度、平整度、所述连续波雷达距离正下方地面的高度值。
第三方面，本发明实施例提供一种雷达探测装置，包括：连续波雷达以及连续波雷达的控制系统，所述连续波雷达的控制系统与所述连续波雷达通信连接。
所述连续波雷达的控制系统为如第二方面本发明实施例所述的连续波雷达的控制系统。
第四方面，本发明实施例提供一种无人机，包括：机架、飞行控制系统和以及如第三方面本发明实施例所述的雷达探测装置，所述连续波雷达搭载在所述机架上。
所述飞行控制系统与所述雷达探测装置通信连接，以获取所述地形参数，所述飞行控制系统根据所述地形参数控制所述无人机。
第五方面，本发明实施例提供一种计算机可读存储介质，所述计算机可读存储介质存储有计算机程序，所述计算机程序包含至少一段代码，所述至少一段代码可由计算机执行，以控制所述计算机执行第一方面本发明实施例 所述的连续波雷达的地形预测方法。
第六方面，本发明实施例提供一种计算机程序，当所述计算机程序被计算机执行时，用于实现第一方面本发明实施例所述的连续波雷达的地形预测方法。
本发明实施例提供的连续波雷达的地形预测方法、装置、系统和无人机，通过获取在旋转过程中旋转至预设角度区间内对地面测距获得的N个第一测距数据，然后从所述N个第一测距数据中剔除野值，获得M个第一测距数据，再根据M个第一测距数据，确定地面的地形参数，例如坡度、完整度、所述连续波雷达距离正下方地面的高度值等。由于本实施例将获得的测距数据中的野值先剔除然后再进行地形预测，所以清除了连续波雷达受到的干扰，使得连续波雷达对地面地形的预测准确率更高。
附图说明
为了更清楚地说明本发明实施例或现有技术中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作一简单地介绍，显而易见地，下面描述中的附图是本发明的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。
图1是根据本发明的实施例的农业无人机100的示意性架构图；
图2为本发明一实施例提供的连续波雷达的地形预测方法的流程图；
图3为本发明一实施例提供的连续波雷达测距的一种示意图；
图4为本发明一实施例提供的连续波雷达在预测角度区间内测距的一种示意图；
图5A-图5F为本发明一实施例提供的剔除野值的一种示意图；
图6A为现有技术中根据未剔除野值的N个第一测距数据获得的拟合直线的一种示意图；
图6B为本发明一实施例提供的根据剔除野值后的M个第一测距数据获得的拟合的一种示意图；
图7本发明实施例提供的连续波雷达的控制系统的一种结构示意图；
图8为本发明实施例提供的雷达探测装置的一种结构示意图；
图9为本发明实施例提供的无人机的一种结构示意图。
具体实施方式
为使本发明实施例的目的、技术方案和优点更加清楚，下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有作出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。
本发明的实施例提供了连续波雷达的地形预测方法、装置、系统和无人机。无人机可以是农业无人机，如旋翼飞行器(rotorcraft)，例如，由多个推动装置通过空气推动的多旋翼飞行器，本发明的实施例并不限于此。
图1是根据本发明的实施例的农业无人机100的示意性架构图。本实施例以旋翼无人飞行器为例进行说明。
农业无人机100可以包括动力系统、飞行控制系统和机架。农业无人机100可以与控制终端进行无线通信，该控制终端可以显示农业无人机的飞行信息等，控制终端可以通过无线方式与农业无人机100进行通信，用于对农业无人机100进行远程操纵。
其中，机架可以包括机身110和脚架120(也称为起落架)。机身110可以包括中心架111以及与中心架111连接的一个或多个机臂112，一个或多个机臂112呈辐射状从中心架延伸出。脚架120与机身110连接，用于在农业无人机100着陆时起支撑作用，另外脚架120之间还搭载有储液箱130，该储液箱用于存储药液或者水；而且机臂112的末端还搭载有喷头140，储液箱130中的液体通过泵泵入至喷头140，由喷头140喷散出去。
动力系统可以包括一个或多个电子调速器(简称为电调)、一个或多个螺旋桨150以及与一个或多个螺旋桨150相对应的一个或多个电机160，其中电机160连接在电子调速器与螺旋桨150之间，电机160和螺旋桨150设置在农业无人机100的机臂112上；电子调速器用于接收飞行控制系统产生的驱动信号，并根据驱动信号提供驱动电流给电机，以控制电机160的转速。电机160用于驱动螺旋桨150旋转，从而为农业无人机100的飞行提供动力，该动力使得农业无人机100能够实现一个或多个自由度的运动。在某些实施例中，农业无人机100可以围绕一个或多个旋转轴旋转。例如，上述旋转轴 可以包括横滚轴、偏航轴和俯仰轴。应理解，电机160可以是直流电机，也可以交流电机。另外，电机160可以是无刷电机，也可以是有刷电机。
飞行控制系统可以包括飞行控制器和传感系统。传感系统用于测量无人飞行器的姿态信息，即农业无人机100在空间的位置信息和状态信息，例如，三维位置、三维角度、三维速度、三维加速度和三维角速度等。传感系统例如可以包括陀螺仪、超声传感器、电子罗盘、惯性测量单元(Inertial Measurement Unit，IMU)、视觉传感器、全球导航卫星系统和气压计等传感器中的至少一种。例如，全球导航卫星系统可以是全球定位系统(Global Positioning System，GPS)。飞行控制器用于控制农业无人机100的飞行，例如，可以根据传感系统测量的姿态信息控制农业无人机100的飞行。应理解，飞行控制器可以按照预先编好的程序指令对农业无人机100进行控制，也可以通过响应来自控制终端的一个或多个控制指令对农业无人机100进行控制。
如图1所示，农业无人机的脚架120上还可以搭载连续波雷达170，该连续波雷达170为旋转连续波雷达，该连续波雷达170可以用于测距，但不限于测距。其中，农业无人机可以包括两个或两个以上脚架170，连续波雷达170搭载在其中一个脚架170上。
应理解，上述对于农业无人机各组成部分的命名仅是出于标识的目的，并不应理解为对本发明的实施例的限制。
图2为本发明一实施例提供的连续波雷达的地形预测方法的流程图，如图2所示，本实施例的方法可以包括：
S201、获取连续波雷达在旋转过程中对地面测距获得的N个第一测距数据，其中，所述N个第一测距数据为所述连续波雷达的旋转角度处于预设角度区间内获得的。
S202、从所述N个第一测距数据中剔除野值，获得M个第一测距数据。
S203、根据所述N个第一测距数据，确定所述地面的地形参数，所述地形参数包括以下至少一种：坡度、平整度、所述连续波雷达距离正下方地面的高度值。
本实施例中，可以通过连续波雷达可以对地面进行测距，以获得该连续波雷达相距地面的距离，其中连续波雷达可以旋转，当连续波雷达旋转不同的角度时，连续波雷达对地面进行测距的测距点也不相同，因此连续波雷达 检测到的与地面的距离也可能不相同，如图3所示。本实施例中，连续波雷达在旋转过程对地面测距时并且该连续波雷达的旋转角度处于预设角度区间内时获得多个第一测距数据，例如如图4所示，此处称第一测距数据为N个，N为大于等于2的整数。每个第一测距数据反映了连续波雷达在旋转至对应的旋转角度时与地面的距离，对于同一测距点，若该测距点所在的地面高，则连续波雷达与地面的距离低，若该测距点所在的地面低，则连续波雷达与地面的距离大；例如：若连续波雷达与地面的不同测距点之间的距离差距较大，则说明地面的平整度低。对于相同的多个测距点，若连续波雷达与地面的距离均较小，则说明该多个测距点所在的地面的坡度较高，若连续波雷达与地面的距离均较大，则说明该多个测距点所在的地面的坡度较低。
但是，由于而实际情况中，由于连续波雷达内部、外部环境的干扰，会导致连续波雷达测量到的距离中存在野值，例如：对于一测距点，实际上该测距点与连续波雷达之间的距离较大，但由连续波雷达受到干扰，从而导致获得的第一测距数据较小，进而会导致测得的地形的坡度与实际坡度存在较大误差。尤其是在诸如农田、茶山等复杂应用场景中，野值的存在会导致地形预测不准确。
因此，本实施例中，从所述N个第一测距数据中剔除野值，获得M个第一测距数据，M为小于N的正整数。然后根据剔除野值后的多个第一测距数据，可以确定该多个测距点所在地面的地形参数，该地形参数包括：地面的坡度、地面的平整度、所述连续波雷达距离正下方地面的高度值。
例如：该预设角度区间为60度至120度，对应的可以确定连续波雷达正下方地面的地形参数；该预设角度区间为-30度至30度，对应的可以确定连续波雷达前方地面的地形参数；该预设角度区间为150度至210度，对应的可以确定连续波雷达后方地面的地形参数，需要说明的是，此处是为了举例说明，并不限定本实施例，该预设角度区间可以根据实际需要来设定。若本实施例的预设角度区间为60度至120度，则本实施例可以在连续波雷达在旋转角度为60度对地面测距获得第一测距数据，在60.6度对地面测距获得第一测距数据，在61.2度对地面测距获得第一测距数据，在61.8度对地面测距获得第一测距数据，以此类推，此处不再赘述。
本实施例中，通过获取在旋转过程中旋转至预设角度区间内对地面测距 获得的N个第一测距数据，然后从所述N个第一测距数据中剔除野值，获得M个第一测距数据，再根据M个第一测距数据，确定地面的地形参数，例如坡度、完整度、所述连续波雷达距离正下方地面的高度值等。由于本实施例将获得的测距数据中的野值先剔除然后再进行地形预测，所以清除了连续波雷达受到的干扰，使得连续波雷达对地面地形的预测准确率更高。
其中，每个第一测距数据包括：该连续波雷达距地面测距点的水平距离，以及该连续波雷达距地面测距点的垂直距离。由于连续波雷达的旋转角度不同，连续波雷达的信号发射方向不同，从而造成地面测距点不同，所以地面测距点随连续波雷达的旋转角度不同而不同。本实施例中为了避免连续波雷达与地面测距点之间的距离值相同时，但是地面的地形不同，而造成后续预测地形不准确的情况，本实施例中的第一测距数据包括上述水平距离和垂直距离，其中，上述水平距离和垂直距离可以根据连续波雷达与地面测距点之间的距离以及该地面测距点对应的连续波雷达的旋转角度获得。例如：对于相同的连续波雷达与地面测距点之间的距离，若连续波雷达距地面测距点的水平距离越大且垂直距离小，可以认为地面的坡度越高，若连续波雷达距地面测距点的水平距离越小且垂直距离大，可以认为地面的坡度越低。
在一些实施例中，上述S201的一种可以的实现方式中，可以包括如下步骤A和B；
其中，步骤A、获取连续波雷达在旋转过程中对地面测距的T个第二测距数据；所述T个第二测距数据为所述连续波雷达的旋转角度处于预设角度区间内对地面测距的所有测距数据，所述H为大于等于N的整数。
本实施例中，获取连续波雷达在旋转过程对地面测距，且，连续波雷达的旋转角度处于预设角度区间内获得的所有测距数据，这些测距数据此处称为T个第二测距数据。
在一些实施例中，步骤A的一种可能的实现方式可以包括：步骤A1和步骤A2。
步骤A1、获取连续波雷达旋转一周对地面测距的所有第二测距数据以及每个第二测距数据对应的所述连续波雷达的旋转角度。
步骤A2、根据所述预设角度区间，获取位于所述预设角度区间内所述连续波雷达的旋转角度所对应的第二测距数据为所述T个第二测距数据。
本实施例中，连续波雷达旋转一周，对应连续波雷达一共旋转了360度的角度。例如：连续波雷达旋转一周对应600个光栅格，则连续波雷达每旋转0.6度即表示连续波雷达旋转到一个对应的光栅格，然后触发一次测距，这样可以获得600个测距数据，另外本实施例还记录每个测距数据对应的连续波雷达的旋转角度；其中，连续波雷达的测距原理可以参见现有技术中的相关描述，此处不再赘述。然后根据预设角度区间，获取连续波雷达的旋转角度位于该预设角度区间内所对应获得的第二测距数据，例如：预设角度区间为60-120度，则可以从中筛选出60、60.6、61.2、…、118.8、119.4和120度分别对应的第二测距数据，此处共可以获得100个第二测距数据，H即等于100。
步骤B、根据所述T个第二测距数据，获取所述N个第一测距数据。
本实施例中，该第二测距数据是连续波雷达实际测距获得的数据，在获得上述T个第二测距数据之后，根据该T个第二测距数据，获取上述N个第一测距数据。
在一些实施例中，上述步骤B的一种可能的实现方式可以包括步骤B1。
步骤B1、根据所述T个第二测距数据和有效测距条件，确定所述N个第一测距数据。其中，有效测距条件包括：小于等于预设最大距离且大于等于预设最小距离。
本实施例中，对每次测距数据判断其有效性，连续波雷达存在近距离范围内的盲区及最远测距距离，因此，设置有有效测距条件，该有效测距条件可以表示为[d min,d max]，即表示有效的第二测距数据应大于等于d min且小于等于d max。因此，本实施例将根据所述T个第二测距数据和有效测距条件，确定上述的N个第一测距数据，避免了测距数据的误差，以提高地面地形预测的准确率。
在一些实施例中，上述步骤B1的一种可能的实现方式可以包括步骤B11和步骤B12。
步骤B11、从所述T个第二测距数据中确定满足所述有效测距条件的第二测距数据为N个第二测距数据。
本实施例中，从该T个第二测距数据中确定小于等于预设最大距离且小于等于预设最小距离的所有第二测距数据，这些第二测距数据为N个第二测 距数据。
步骤B11、根据所述N个第二测距数据，确定所述N个第一测距数据。
本实施例再根据上述确定出的满足有效测距条件的N个第二测距数据，确定上述N个第一测距数据。
在一种可能的实现方式中，可以将该N个第二测距数据确定为该N个第一测距数据，即第一测距数据等于第二测距数据。
在另一种可能的实现方式中，对所述N个第二测距数据进行平滑处理，获得所述N个第一测距数据。例如：根据第二测距数据对应的连续波雷达的旋转角度的顺序对所述N个第二测距数据排序，如：第1个第二测距数据为：60度对应的第二测距数据d 1，第2个第二测距数据为：60.6度对应的第二测距数据d 2，以此类推；然后确定第1个第二测距数据为第1个第一测距数据，即D 1等于d 1，以及确定第N个第二测距数据为第N个第二测距数据，即D N等于d N。以及确定第j-1个第二测距数据(例如d j-1)、第j个第二测距数据(例如d j)、第j+1个第二测距数据(例如d j+1)三者的平均值为所述第j个第一测距数据，其中，所述j为大于等于2且小于等于N-1的整数。即D j＝[d j-1+d j+d j+1]/3。
需要说明的是，D j也不限于d j以及左右相邻分别一个(即三者)的平均值，也可以是d j以及左右相邻分别两个(即五者)的平均值，相应地，第1个、第2个第一测距数据分别等于第1个、第2个第二测距数据，第N-1个、第N个第一测距数据分别等于第N-1个、第N个第二测距数据。另外，本实施例也可以采用左右相邻分别三个、四个等，方案类似，此处不再赘述。
另外，上述d j可以为一个值，即连续波雷达与地面测距点之间的距离，则本实施例可以在进行平滑处理之后，再根据连续波雷达对应的旋转角度获得对应的第一测距数据中的水平距离x j和垂直距离y j。其中，以连续波雷达旋转中心为坐标系XOY的原点(0，0)，连续波雷达旋转正前方向作为X轴正方向，垂直向下方向作为Y轴正方向，x表示水平距离，y表示垂直距离，x可以为正值或者负值。
另外，上述d j可以包括两个值，即连续波雷达与地面测距点之间的水平距离x j和垂直距离y j，则本实施例可以针对水平距离进行平滑处理，获得第一测距数据中的水平距离，也可以针对垂直距离进行平滑处理，获得第一测 距数据的垂直距离。
其中，若连续波雷达测到的是连续波雷达与地面测距点之间的直线距离，在获得连续波雷达与地面测距点之间的直线距离L i后，将连续波雷达测距数据(L i)及其对应光栅格(G i)转化为第一测距数据，即上述建立的坐标系中的坐标值：
x i＝L i*sin((G0–G i)/Z)
y i＝L i*cos((G0–G i)/Z)
其中G0为连续波雷达的正下方光栅刻度，Z为单个光栅格对应的角度值。
在上述各实施例的基础上，在一些实施例中，上述S202的一种可以的实现方式中，可以包括如下步骤C至步骤E。
步骤C、从所述N个第一测距数据中获取至少两个第一测距数据。
步骤D、根据所述至少两个第一测距数据进行直线拟合，获取第一直线函数。
步骤E、根据所述第一直线函数，从所述N个第一测距数据中剔除野值，获得M个第一测距数据。
本实施例中，从N个第一测距数据(其中，图5A为N个第一测距数据在XOY坐标系中的分布)中可以随机获取至少两个第一测距数据，然后根据该至少两个第一测距数据进行直线拟合，获得第一测距数据中垂直距离关于水平距离的直线函数，该直线函数称为第一直线函数。
其中，如图5B所示，从N个第一测距数据中获取两个第一测距数据(x 1，y 1)和(x 2，y 2)，建立经过这两个第一测距据的直线，以获得第一直线函数。其中，第一直线函数如下所示：
Figure PCTCN2018102628-appb-000001
在根据该第一测距数据获得第一直线函数后，根据该第一直线函数，从N个第一测距数据中剔除野值，获得M个第一测距数据。可选地，所述野值为与所述第一直线函数对应的直线之间的距离大于预设距离的第一测距数据。也就是，本实施例先确定每个第一测距数据到上述建立的直线之间的距离(如图5C所示)，然后再判断该距离是否大于预设距离，若该距离小于或等于预设距离，则确定该距离对应的第一测距数据属于M个第一测距数据，若该距离大于预设距离，则说明该距离对应的第一测距数据相差较大，并确定该距 离对应的第一测距数据为属于野值，并剔除掉该距离对应的第一测距数据。
其中，第i个第一测距数据(x i，y i)到上述建立的直线之间的距离P i如下所示。
Figure PCTCN2018102628-appb-000002
在上述各实施例的基础上，在另一些实施例中，上述S202的一种可以的实现方式中，可以包括如下步骤C’至步骤F’。
步骤C’、从所述N个第一测距数据中K次分别获取至少两个第一测距数据，每次获取的至少两个第一测距数据不完全相同。
步骤D’、针对每次获取的至少两个第一测距数据，根据该次获取的至少两个第一测距数据进行直线拟合，获取第一直线函数。
步骤E’、根据第一直线函数，从所述N个第一测距数据中剔除野值，获得一组第一测距数据。
步骤F’、根据获得的K组第一测距数据，获得所述M个第一测距数据。
本实施例以每次从N个第一测距数据中获取两个第一测距数据为例。
先从N个第一测距数据中第一次获取(例如随机获取)两个第一测距数据，如图5B所示，并根据第一次获取的两个第一测距数据进行直线拟合(如图5C所示)，获取第一个第一直线函数，再根据该第一直线函数，从N个第一测距数据是剔除野值，获得第一组第一测距数据(如图5D所示)，该组第一测距数据中可以包括多个第一测距数据。
再从N个第一测距数中第二次获取(例如随机获取)两个第一测距数据，并根据第二次获取的两个第一测距数据进行直线拟合，获取第二个第一直线函数，再根据该第一直线函数，从N个第一测距数据是剔除野值，获得第二组第一测距数据，该组第一测距数据中可以包括多个第一测距数据。其中，第二次获取的两个第一测距数据与第一次获取的两个第一测距数据不完全相同。上述的过程可以例如图5E所示。
再从N个第一测距数中第三次获取(例如随机获取)两个第一测距数据，并根据第三次获取的两个第一测距数据进行直线拟合，获取第三个第一直线函数，再根据该第一直线函数，从N个第一测距数据是剔除野值，获得第三 组第一测距数据，该组第一测距数据中可以包括多个第一测距数据。其中，第三次获取的两个第一测距数据与第一次获取的两个第一测距数据不完全相同，也与第二次获取的两个第一测距数据不完全相同。上述的过程可以例如图5F所示。
本实施例中以K等于3，也就是，当本实施例中获取两个第一测距数据的次数大于或等于3时，本实施例停止从N个第一测距数据中获取两个第一测距数据。
本实施例在获取上述三组第一测距数据之后，根据第一组第一测距数据、第二组第一测距数据、第三组第一测距数据，获得所述M个第一测距数据。可选地，从第一组第一测距数据、第二组第一测距数据、第三组第一测距数据中，确定包括第一测距数据数量最多的一组第一测距数据为上述M个第一测距数据。例如：第一组第一测距数据包括20个第一测距数据，第二组第一测距数据包括30个第一测距数据，第三组第一测距数据包括25个第一测距数据，则本实施例确定第二组第一测距数据中的30个第一测距数据为上述M个第一测距数据，此处的M等于30。
可选地，本实施例中，根据上述任一第一直线函数，从所述N个第一测距数据中剔除野值，获得一组第一测距数据的实现过程可以是：本实施例先确定每个第一测距数据到上述任一第一直线函数对应的直线之间的距离，然后再判断该距离是否大于预设距离，若该距离小于或等于预设距离，则确定该距离对应的第一测距数据属于上述的一组第一测距数据，若该距离大于预设距离，则说明该距离对应的第一测距数据相差较大，并确定该距离对应的第一测距数据为属于野值。
在一些实施例中，在通过上述各实施方式获得M个第一测距数据之后，判断该M的值是否小于第一预设值，若M的值大于等于第一预设值，则说明M个第一测距数据具有足够的数据量用于进行地形预测，然后再根据M个第一测距数据，确定所述地面的地形参数。若M的值小于第一预设值，则说明M个第一测距数据不够用来进行地形预测，为了避免地形预测不准确，本实施例确定上述连续波雷达测到的测距数据无效。
在上述各实施例的基础上，在一些实施例中，上述根据M个第一测距数据，确定地面的地形参数的一种可能的实现方式可以包括如下步骤G和H；
步骤G、对所述M个第一测距数据进行直线拟合，获得第二直线函数。
本实施例中，可以对所述M个第一测距数据通过最小二乘法进行直线拟合，获得一直线函数，该直线函数称为第二直线函数。第一测距数据包括水平距离和垂直距离。
其中，构建连续波雷达与地面测距点的垂直距离关于连续波雷达与地面测距点的水平距离的第二直线函数，该第二直线函数例如如公式一所示：y＝ax+b，其中，y为连续波雷达与地面测距点的垂直距离，x为连续波雷达与地面测距点的水平距离，此时a、b暂时未知。然后根据所述M个第一测距数据、所述第二直线函数以及最小二乘法，确定所述第二直线函数的斜率和截距。其中，M个第一测距数据是已知的，而且每个第一测距数据包括连续波雷达与对应地面测距点的水平距离和垂直距离，将这M组x与y的已知值，代入上述公式一中，再通过最小二乘法，来确定该第二直线函数的斜率(例如a)和截距(例如b)。
可选地，上述a和b可以通过克莱姆法来确定，如下所示，其中，(x i，y i)为上述M个第一测距数据中的任一测距数据。
Figure PCTCN2018102628-appb-000003
Figure PCTCN2018102628-appb-000004
需要说明的是，本实施例并不限于上述最小二乘法，也可以采用滤波法。
步骤H、根据所述第二直线函数，确定所述地面的地形参数。
若地面的地形参数包括地面的坡度，则本实施例可以根据该第二直线函数的斜率，确定地面的坡度，例如：斜率越大，则地面的坡度越大，斜率越小，则地面的坡度越小。可选地，可以将所述斜率的反正切值确定为地面的坡度。
可选地，该地面的坡度可以用于指导无人机后续要采取的动作。
若地面的地形参数包括：所述连续波雷达距离正下方地面的高度值，则本实施例根据所述第二直线函数的截距，确定所述连续波雷达距离正下方地 面的高度值，例如可以将第二直线函数的截距，确定为所述连续波雷达距离正下方地面的高度值。
可选地，该连续波雷达距离正下方地面的高度值可用于无人机避障，例如：以避免碰撞到地面农作物，另外，还可以用于无人机精确喷洒，因为喷洒时，需要定高喷洒。
若地面的地形参数包括地面的平整度，本实施例可以根据所述M个第一测距数据和所述第二直线函数，确定所述M个第一测距数据中每个第一测距数据对应的所述第二直线函数中的残差；然后根据所述M个第一测距数据分别对应的所述第二直线函数中的残差，确定所述地面的平整度。
其中，每个第一测距数据对应的第二直线函数中的残差可以通过如下公式获得。
e i＝y i–y i’，其中，e i为M个第一测距数据中第i个第一测距数据对应的第二直线函数中的残差，y i为M个第一测距数据中第i个第一测距数据中的垂直距离，y i’为M个第一测距数据中第i个第一测距数据中水平距离x i作为变量x代入第二直线函数得到y的值，即y i’＝ax i+b。
可选地，本实施例可以将所述M个第一测距数据分别对应的所述第二直线函数中的残差的平方和，确定为所述地面的平整度。若残差的平方和越大，则说明地面越不平整，若残差的平方和越小，则说明地面越平整。例如：地面的平整度为：
Figure PCTCN2018102628-appb-000005
可选地，本实施例确定地面的平整度之后，该平整度可以用于无人机的定高和避障方案中。
在一些实施例中，本实施例可以根据所述M个第一测距数据中每个第一测距数据对应的所述连续波雷达到测距点的垂直距离，确定中位数的垂直距离。即从y 1、y 2、y 3、……、y M-2、y M-1、y M中，确定这些值的中位数，该中位数也可称为中位数的垂直距离。例如：以M等于7为例，y 1、y 2、y 3、y 4、y 5、y 6、y 7按大小顺序排序后为：1.2、1.3、1.3、1.5、1.6、1.7、1.8，则1.5为中位数。然后判断所述第二直线函数的截距与所述中位数的垂直距离的差值是否小于第二预设值，若所述差值小于第二预设值，则执行上述步骤G。若所述差值大于等于第二预设值，则不执行上述步骤G，说明连续波雷达测 得的测距数据不适合用于预测地形。
综上所述，若不剔除N个第一测距数据中的野值，则将包括野值的该N个第一测距数据进行最小二乘法直线拟合，获得的拟合直线例如如图6A所示，如图6A所示，通过该拟合直线获得的地面的地形参数不准确。而采用本发明上述各实施例的方案，在剔除N个第一测距数据中的野值之后，根据剔除野值后的第一测跟数据进行最小二乘法直线拟合，获得的拟合直线例如如图6B所示，如图6B所示，通过该拟合直线获得的地面的地形参数更准确。
在另一些实施例中，与上述各实施例不同的是，在获取上述N个第一测距数据之后，不剔除野值，而是对该N个第一测距数据进行加权最小二乘法直线拟合，获得第三直线函数，然后根据第三直线函数，确定地面的地形参数。因此，本实施例中可以利用加权最小二乘法来消除连续波雷达在获得测距数据时受到的干扰，从而可以提高直线拟合精度，进而提高地形预测的准确率。
其中，对该N个第一测距数据进行加权最小二乘法直线拟合，获得第三直线函数的一种可能的实现方式为：
构建连续波雷达与地面测距点的垂直距离关于连续波雷达与地面测距点的水平距离的第三直线函数，该第三直线函数例如如公式二所示：y＝ax+b，其中，y表示连续波雷达与地面测距点的垂直距离，x表示连续波雷达与地面测距点的水平距离，此时a、b暂时未知。然后根据所述N个第一测距数据、所述第三直线函数，可以确定x i对应的y i’，其中，y i’为x i作为变量x代入第三直线函数中获得的y的值(即拟合垂直距离)，x i为N个第一测距数据中第i个第一测距数据的水平距离。
在确定N个第一测距数据中每个第一测距数据中水平距离对应的拟合垂直距离之后，确定每个第一测距数据对应的所述第三直线函数中的残差；其中，所述每个第一测距数据对应的残差是关于所述直线函数中的斜率与截距的函数，例如：e＝y i-ax i-b。然后根据所述每个第一测距数据对应的残差以及该残差的加权系数，确定所述N个测距数据对应的所述残差的加权平方和，残差的加权平方和例如如公式三所示：
Figure PCTCN2018102628-appb-000006
其中，Q表示残差的加权平方和，w i表示第i个第一测距数据对应的残差的加权系数。
本实施例在获得残差的加权平方和之后，根据所述残差的加权平方和，确定所述直线函数的斜率的值和截距的值。具体可以为：根据所述残差的加权平方和对所述斜率的一阶导数等于第一预设值，以及所述残差的加权平方和对所述截距的一阶导数等于第二预设值，确定所述直线函数的斜率的值和截距的值。
为了令Q的值最小，a与b的值最优，可以将第一预设值和第二预设值设为0。相应地，残差的加权平方和(Q)对所述斜率(a)的一阶导数等于0以及残差的加权平方和(Q)对所述截距(b)的一阶导数等于0，可以例如如下公式四所示：
Figure PCTCN2018102628-appb-000007
Figure PCTCN2018102628-appb-000008
根据上述公式四可以获得a的估计值
Figure PCTCN2018102628-appb-000009
和b的估计值b^，分别如下所示公式五：
Figure PCTCN2018102628-appb-000010
Figure PCTCN2018102628-appb-000011
本实施例可以将
Figure PCTCN2018102628-appb-000012
作为第三直线函数的斜率a的值，以及将
Figure PCTCN2018102628-appb-000013
作为第三直线函数的截距b的值。
可选地，若地面的地形参数包括地面的平整度，则根据该第三直线函数的斜率a，确定地面的平整度。
若地面的地形参数包括：所述连续波雷达距离正下方地面的高度值，则根据所述第三直线函数的截距，确定所述连续波雷达距离正下方地面的高度值。
若地面的地形参数包括地面的平整度，则根据上述Q的值，克腚地面的平整度。例如：将上述a的值(如上述
Figure PCTCN2018102628-appb-000014
)和上述b的值(如上述
Figure PCTCN2018102628-appb-000015
)，代入上述公式二中，从而获得Q的值。若Q的值越大，则说明地面越不平整， 若Q的值越小，则说明地面越平整。
在一种可替换的方案中，本实施例可以预先存储有如上述公式三和公式五，将获得的N个第一测距数据代入预先存储的公式五中，可获得
Figure PCTCN2018102628-appb-000016
以及
Figure PCTCN2018102628-appb-000017
根据
Figure PCTCN2018102628-appb-000018
确定地面的坡度。然后将获得的
Figure PCTCN2018102628-appb-000019
以及
Figure PCTCN2018102628-appb-000020
代入预先存储的公式三中，从而获得Q，根据Q的值，确定地面的平整度。
在一些实施例中，每个第一测距数据对应的残差的加权系数均相等，即使i的取值不同，则w i均相同，例如：w i均等于1。或者，例如：w i均等于1/N，这表示所述N个第一测距数据对应的残差的加权系数之和等于1。
在一些实施例中，由于通过连续波雷达测距获得的测距数据，其误差随距离增大而变大，因此，需要根据连续波雷达的旋转角度对对应的第一测距数据进行权重分配。
在一种可能的实现方式中，所述每个第一测距数据对应的残差的加权系数是关于该第一测距数据对应的连续波雷达的旋转角度的三角函数，例如如公式六所示：
Figure PCTCN2018102628-appb-000021
其中，k mid表示预设角度区间的中值，k min表示预设角度区间的最小值，k max表示预设角度区间的最大值，k i表示第i个第一测距数据对应的连续波雷达的旋转角度。例如：预设角度区间为[-60°，60°]共120°的数据，-60°对应的k的值为1，-59°的k的值为2，依次类推，其中，k max为120，k mid为60或61，k min为1。
可选地，所述N个第一测距数据对应的残差的加权系数之和等于1，则需要对上述三角函数进行归一化处理，因此，残差的加权系数例如如公式七所示：
Figure PCTCN2018102628-appb-000022
在另一种可能的实现方式中，所述每个第一测距数据对应的残差的加权系数是关于该第一测距数据对应的连续波雷达的旋转角度的高斯函数，例如如公式八所示：
Figure PCTCN2018102628-appb-000023
其中，x i为N个第一测距数据中第i个第一测距数据的水平距离，σ、μ为常数，μ表示x 1至x N的均值，σ表示x 1至x N的方差。
其中，可根据方差的值调节上述函数的形状；该方差的值可以根据实际需要预先设定。
可选地，所述N个第一测距数据对应的残差的加权系数之和等于1，则需要对上述高斯函数进行归一化处理，因此，残差的加权系数例如如公式九所示：
Figure PCTCN2018102628-appb-000024
在另一种可能的实现方式中，所述每个第一测距数据对应的残差的加权系数是关于该第一测距数据对应的连续波雷达的旋转角度的误差函数，例如如如公式十所示：
Figure PCTCN2018102628-appb-000025
其中，e i＝y i–y i’，其中，e i为N个第一测距数据中第i个第一测距数据对应的第三直线函数中的残差，y i为N个第一测距数据中第i个第一测距数据中的垂直距离，y i’为N个第一测距数据中第i个第一测距数据中水平距离x i作为变量x代入第三直线函数得到y的值，即y i’＝ax i+b。
其中，误差越小，权重系数越大；误差越大，权重系数越小。
可选地，所述N个第一测距数据对应的残差的加权系数之和等于1，则需要对上述误差函数进行归一化处理，因此，残差的加权系数例如如公式十一所示：
Figure PCTCN2018102628-appb-000026
可选地，上述各实施例中涉及的连续波雷达可以为电磁波连续波雷达，或者，也可以为激光连续波雷达。
本发明实施例中还提供了一种计算机存储介质，该计算机存储介质中存储有程序指令，所述程序执行时可包括如图2及其对应实施例中的连续波雷 达的地形预测方法的部分或全部步骤。
图7本发明实施例提供的连续波雷达的控制系统的一种结构示意图，如图7所示，本实施例的连续波雷达的控制系统700可以包括：存储器701和处理器702；上述存储器701和处理器702通过总线连接。存储器701可以包括只读存储器和随机存取存储器，并向处理器702提供指令和数据。存储器701的一部分还可以包括非易失性随机存取存储器。
所述存储器701，用于存储程序代码。
所述处理器702，调用所述程序代码，当程序代码被执行时，用于执行以下操作：
可选地，所述第一测距数据包括：所述连续波雷达距地面测距点的水平距离与垂直距离；其中，地面测距点随所述连续波雷达的旋转角度不同而不同。
可选地，所述处理器702，具体用于：从所述N个第一测距数据中获取至少两个第一测距数据；根据所述至少两个第一测距数据进行直线拟合，获取第一直线函数；根据所述第一直线函数，从所述N个第一测距数据中剔除野值，获得M个第一测距数据。
可选地，所述处理器702，具体用于：从所述N个第一测距数据中K次分别获取至少两个第一测距数据，每次获取的至少两个第一测距数据不完全相同；针对每次获取的至少两个第一测距数据，根据该次获取的至少两个第一测距数据进行直线拟合，获取第一直线函数；根据第一直线函数，从所述N个第一测距数据中剔除野值，获得一组第一测距数据；根据获得的K组第一测距数据，获得所述M个第一测距数据。
可选地，所述处理器702，具体用于：从K组第一测距数据中，确定包括第一测距数据数量最多的一组第一测距数据为所述M个第一测距数据。
可选地，所述野值为与所述第一直线函数对应的直线之间的距离大于预设距离的第一测距数据。
可选地，所述处理器702，具体用于：在所述M的值大于等于第一预设值时，根据所述M个第一测距数据，确定所述地面的地形参数。
可选地，所述处理器702，具体用于：对所述M个第一测距数据进行直线拟合，获得第二直线函数；根据所述第二直线函数，确定所述地面的地形 参数。
可选地，所述处理器702，具体用于：根据所述M个第一测距数据中每个第一测距数据对应的所述连续波雷达到测距点的垂直距离，确定中位数的垂直距离；若所述第二直线函数的截距与所述中位数的垂直距离的差值小于第二预设值，则根据所述第二直线函数，确定所述地面的地形参数。
可选地，若所述地形参数包括：坡度，则所述处理器702，具体用于：根据所述第二直线函数中的斜率，确定所述地面的坡度。
可选地，所述处理器702，具体用于：将所述斜率的反正切值确定为所述地面的坡度。
可选地，若所述地形参数包括：所述连续波雷达距离正下方地面的高度值，则所述处理器702，具体用于：根据所述第二直线函数的截距，确定所述连续波雷达距离正下方地面的高度值。
可选地，若所述地形参数包括：平整度，则所述处理器702，具体用于：根据所述M个第一测距数据和所述第二直线函数，确定所述M个第一测距数据中每个第一测距数据对应的所述第二直线函数中的残差；根据所述M个第一测距数据分别对应的所述第二直线函数中的残差，确定所述地面的平整度。
可选地，所述处理器702，具体用于：将所述M个第一测距数据分别对应的所述第二直线函数中的残差之和，确定为所述地面的平整度。
可选地，所述处理器702，具体用于：获取连续波雷达在旋转过程中对地面测距的T个第二测距数据；所述T个第二测距数据为所述连续波雷达的旋转角度处于预设角度区间内对地面测距的所有测距数据，所述T为大于等于N的整数；根据所述T个第二测距数据，获取所述N个第一测距数据。
可选地，所述处理器702，具体用于：根据所述T个第二测距数据和有效测距条件，确定所述N个第一测距数据；
其中，有效测距条件包括：小于等于预设最大距离且大于等于预设最小距离。
可选地，所述处理器702，具体用于：从所述T个第二测距数据中确定满足所述有效测距条件的第二测距数据为N个第二测距数据；根据所述N个第二测距数据，确定所述N个第一测距数据。
可选地，所述处理器702，具体用于：确定所述N个第二测距数据为所述N个第一测距数据；或者，对所述N个第二测距数据进行平滑处理，获得所述N个第一测距数据。
可选地，所述处理器702，具体用于：根据第二测距数据对应的连续波雷达的旋转角度的顺序对所述N个第二测距数据排序；确定第1个第二测距数据为第1个第一测距数据，以及第N个第二测距数据为第N个第一测距数据；确定第j-1个第二测距数据、第j个第二测距数据、第j+1个第二测距数据三者的平均值为所述第j个第一测距数据；其中，所述j为大于等于2且小于等于N-1的整数。
可选地，所述处理器702，具体用于：获取连续波雷达旋转一周对地面测距的所有第二测距数据以及每个第二测距数据对应的所述连续波雷达的旋转角度；根据所述预设角度区间，获取位于所述预设角度区间内所述连续波雷达的旋转角度所对应的第二测距数据为所述T个第二测距数据。
本实施例的连续波雷达的控制系统，可以用于执行本发明上述方法实施例的技术方案，其实现原理和技术效果类似，此处不再赘述。
图8为本发明实施例提供的雷达探测装置的一种结构示意图，如图8所示，本实施例的雷达探测装置800包括：连续波雷达801和连续波雷达的控制系统802。所述连续波雷达的控制系统802与所述连续波雷达801通信连接。其中，连续波雷达的控制系统802可以采用图7所示实施例的结构，其对应地，可以执行如图2及其对应实施例的技术方案，其实现原理和技术效果类似，此处不再赘述。
图9为本发明实施例提供的无人机的一种结构示意图，如图9所示，本实施例的无人机900包括：机架(图中未示出)、飞行控制系统901和雷达探测装置902，其中，雷达探测装置902可以采用图8所示实施例的结构，其对应地，可以执行如图2及其对应实施例的技术方案，其实现原理和技术效果类似，此处不再赘述。其中，雷达探测装置902中的连续波雷达搭载在所述机架上。所述飞行控制系统901与所述雷达探测装置902通信连接，以获取地形参数，所述飞行控制系统901根据所述地形参数控制所述无人机900。
可选地，若地面的地形参数包括地面的坡度，则飞行控制系统901可以根据地面的坡度控制无人机900后续的动作。
可选地，若地面的地形参数包括地面的平整度，则飞行控制系统901可以根据地面的平整度控制无人机900的定高和/或控制无人机900避障。
可选地，若地面的地形参数包括：所述连续波雷达距离正下方地面的高度值，则飞行控制系统901可以根据连续波雷达距离正下方地面的高度值，进行避障，例如：避免无人机900碰撞到地面农作物，另外，还可以控制无人机900进行精确喷洒，因为喷洒时，需要定高喷洒。
本领域普通技术人员可以理解：实现上述方法实施例的全部或部分步骤可以通过程序指令相关的硬件来完成，前述的程序可以存储于一计算机可读取存储介质中，该程序在执行时，执行包括上述方法实施例的步骤；而前述的存储介质包括：只读内存(Read-Only Memory，ROM)、随机存取存储器(Random Access Memory，RAM)、磁碟或者光盘等各种可以存储程序代码的介质。
最后应说明的是：以上各实施例仅用以说明本发明的技术方案，而非对其限制；尽管参照前述各实施例对本发明进行了详细的说明，本领域的普通技术人员应当理解：其依然可以对前述各实施例所记载的技术方案进行修改，或者对其中部分或者全部技术特征进行等同替换；而这些修改或者替换，并不使相应技术方案的本质脱离本发明各实施例技术方案的范围。

Claims (43)

  1. 一种连续波雷达的地形预测方法，其特征在于，包括：
    获取连续波雷达在旋转过程中对地面测距获得的N个第一测距数据，其中，所述N个第一测距数据为所述连续波雷达的旋转角度处于预设角度区间内获得的，所述N为大于1的整数；
    从所述N个第一测距数据中剔除野值，获得M个第一测距数据，所述M为小于N的正整数；
    根据所述M个第一测距数据，确定所述地面的地形参数，所述地形参数包括以下至少一种：坡度、平整度、所述连续波雷达距离正下方地面的高度值。
  2. 根据权利要求1所述的方法，其特征在于，所述第一测距数据包括：所述连续波雷达距地面测距点的水平距离与垂直距离；其中，地面测距点随所述连续波雷达的旋转角度不同而不同。
  3. 根据权利要求1或2所述的方法，其特征在于，所述从所述N个第一测距数据中剔除野值，获得M个第一测距数据，包括：
    从所述N个第一测距数据中获取至少两个第一测距数据；
    根据所述至少两个第一测距数据进行直线拟合，获取第一直线函数；
    根据所述第一直线函数，从所述N个第一测距数据中剔除野值，获得M个第一测距数据。
  4. 根据权利要求1或2所述的方法，其特征在于，所述从所述N个第一测距数据中剔除野值，获得M个第一测距数据，包括：
    从所述N个第一测距数据中K次分别获取至少两个第一测距数据，每次获取的至少两个第一测距数据不完全相同；
    针对每次获取的至少两个第一测距数据，根据该次获取的至少两个第一测距数据进行直线拟合，获取第一直线函数；
    根据第一直线函数，从所述N个第一测距数据中剔除野值，获得一组第一测距数据；
    根据获得的K组第一测距数据，获得所述M个第一测距数据。
  5. 根据权利要求4所述的方法，其特征在于，所述根据获得的K组第一测距数据，获得所述M个第一测距数据，包括：
    从K组第一测距数据中，确定包括第一测距数据数量最多的一组第一测距数据为所述M个第一测距数据。
  6. 根据权利要求3-5任一项所述的方法，其特征在于，所述野值为与所述第一直线函数对应的直线之间的距离大于预设距离的第一测距数据。
  7. 根据权利要求1-6任一项所述的方法，其特征在于，所述根据所述M个第一测距数据，确定所述地面的地形参数，包括：
    在所述M的值大于等于第一预设值时，根据所述M个第一测距数据，确定所述地面的地形参数。
  8. 根据权利要求1-7任一项所述的方法，其特征在于，所述根据所述M个第一测距数据，确定所述地面的地形参数，包括：
    对所述M个第一测距数据进行直线拟合，获得第二直线函数；
    根据所述第二直线函数，确定所述地面的地形参数。
  9. 根据权利要求8所述的方法，其特征在于，所述根据所述第二直线函数，确定所述地面的地形参数，包括：
    根据所述M个第一测距数据中每个第一测距数据对应的所述连续波雷达到测距点的垂直距离，确定中位数的垂直距离；
    若所述第二直线函数的截距与所述中位数的垂直距离的差值小于第二预设值，则根据所述第二直线函数，确定所述地面的地形参数。
  10. 根据权利要求8或9所述的方法，其特征在于，若所述地形参数包括：坡度，则所述根据所述第二直线函数，确定所述地面的地形参数，包括：
    根据所述第二直线函数中的斜率，确定所述地面的坡度。
  11. 根据权利要求10所述的方法，其特征在于，所述根据所述第二直线函数的斜率，确定所述地面的坡度，包括：
    将所述斜率的反正切值确定为所述地面的坡度。
  12. 根据权利要求8或9所述的方法，其特征在于，若所述地形参数包括：所述连续波雷达距离正下方地面的高度值，则所述根据所述第二直线函数，确定所述地面的地形参数，包括：
    根据所述第二直线函数的截距，确定所述连续波雷达距离正下方地面的高度值。
  13. 根据权利要求8或9所述的方法，其特征在于，若所述地形参数包 括：平整度，则所述根据所述第二直线函数，确定所述地面的地形参数，包括：
    根据所述M个第一测距数据和所述第二直线函数，确定所述M个第一测距数据中每个第一测距数据对应的所述第二直线函数中的残差；
    根据所述M个第一测距数据分别对应的所述第二直线函数中的残差，确定所述地面的平整度。
  14. 根据权利要求13所述的方法，其特征在于，所述根据所述M个第一测距数据分别对应的所述第二直线函数中的残差，确定所述地面的平整度，包括：
    将所述M个第一测距数据分别对应的所述第二直线函数中的残差之和，确定为所述地面的平整度。
  15. 根据权利要求1-14任意一项所述的方法，其特征在于，所述获取连续波雷达在旋转过程中对地面测距的N个第一测距数据，包括：
    获取连续波雷达在旋转过程中对地面测距的T个第二测距数据；所述T个第二测距数据为所述连续波雷达的旋转角度处于预设角度区间内对地面测距的所有测距数据，所述T为大于等于N的整数；
    根据所述T个第二测距数据，获取所述N个第一测距数据。
  16. 根据权利要求15所述的方法，其特征在于，根据所述T个第二测距数据，获取所述N个第一测距数据，包括：
    根据所述T个第二测距数据和有效测距条件，确定所述N个第一测距数据；
    其中，有效测距条件包括：小于等于预设最大距离且大于等于预设最小距离。
  17. 根据权利要求16所述的方法，其特征在于，所述根据所述T个第二测距数据和有效测距范围，确定所述N个第一测距数据，包括：
    从所述T个第二测距数据中确定满足所述有效测距条件的第二测距数据为N个第二测距数据；
    根据所述N个第二测距数据，确定所述N个第一测距数据。
  18. 根据权利要求17所述的方法，其特征在于，所述根据所述N个第二测距数据，确定所述N个第一测距数据，包括：
    确定所述N个第二测距数据为所述N个第一测距数据；或者，
    对所述N个第二测距数据进行平滑处理，获得所述N个第一测距数据。
  19. 根据权利要求18所述的方法，其特征在于，所述对所述N个第二测距数据进行平滑处理，获得所述N个第一测距数据，包括：
    根据第二测距数据对应的连续波雷达的旋转角度的顺序对所述N个第二测距数据排序；
    确定第1个第二测距数据为第1个第一测距数据，以及第N个第二测距数据为第N个第一测距数据；
    确定第j-1个第二测距数据、第j个第二测距数据、第j+1个第二测距数据三者的平均值为所述第j个第一测距数据；
    其中，所述j为大于等于2且小于等于N-1的整数。
  20. 根据权利要求15-19任意一项所述的方法，其特征在于，所述获取连续波雷达在旋转过程中对地面测距的T个第二测距数据，包括：
    获取连续波雷达旋转一周对地面测距的所有第二测距数据以及每个第二测距数据对应的所述连续波雷达的旋转角度；
    根据所述预设角度区间，获取位于所述预设角度区间内所述连续波雷达的旋转角度所对应的第二测距数据为所述T个第二测距数据。
  21. 一种连续波雷达的控制系统，其特征在于，包括：存储器和处理器；
    所述存储器，用于存储程序代码；
    所述处理器，调用所述程序代码，当程序代码被执行时，用于执行以下操作：
    获取连续波雷达在旋转过程中对地面测距获得的N个第一测距数据，其中，所述N个第一测距数据为所述连续波雷达的旋转角度处于预设角度区间内获得的，所述N为大于1的整数；
    从所述N个第一测距数据中剔除野值，获得M个第一测距数据，所述M为小于N的正整数；
    根据所述M个第一测距数据，确定所述地面的地形参数，所述地形参数包括以下至少一种：坡度、平整度、所述连续波雷达距离正下方地面的高度值。
  22. 根据权利要求21所述的系统，其特征在于，所述第一测距数据包括： 所述连续波雷达距地面测距点的水平距离与垂直距离；其中，地面测距点随所述连续波雷达的旋转角度不同而不同。
  23. 根据权利要求21或22所述的系统，其特征在于，所述处理器，具体用于：
    从所述N个第一测距数据中获取至少两个第一测距数据；
    根据所述至少两个第一测距数据进行直线拟合，获取第一直线函数；
    根据所述第一直线函数，从所述N个第一测距数据中剔除野值，获得M个第一测距数据。
  24. 根据权利要求21或22所述的系统，其特征在于，所述处理器，具体用于：
    从所述N个第一测距数据中K次分别获取至少两个第一测距数据，每次获取的至少两个第一测距数据不完全相同；
    针对每次获取的至少两个第一测距数据，根据该次获取的至少两个第一测距数据进行直线拟合，获取第一直线函数；
    根据第一直线函数，从所述N个第一测距数据中剔除野值，获得一组第一测距数据；
    根据获得的K组第一测距数据，获得所述M个第一测距数据。
  25. 根据权利要求24所述的系统，其特征在于，所述处理器，具体用于：
    从K组第一测距数据中，确定包括第一测距数据数量最多的一组第一测距数据为所述M个第一测距数据。
  26. 根据权利要求23-25任一项所述的系统，其特征在于，所述野值为与所述第一直线函数对应的直线之间的距离大于预设距离的第一测距数据。
  27. 根据权利要求21-26任一项所述的系统，其特征在于，所述处理器，具体用于：
    在所述M的值大于等于第一预设值时，根据所述M个第一测距数据，确定所述地面的地形参数。
  28. 根据权利要求21-27任一项所述的系统，其特征在于，所述处理器，具体用于：
    对所述M个第一测距数据进行直线拟合，获得第二直线函数；
    根据所述第二直线函数，确定所述地面的地形参数。
  29. 根据权利要求28所述的系统，其特征在于，所述处理器，具体用于：
    根据所述M个第一测距数据中每个第一测距数据对应的所述连续波雷达到测距点的垂直距离，确定中位数的垂直距离；
    若所述第二直线函数的截距与所述中位数的垂直距离的差值小于第二预设值，则根据所述第二直线函数，确定所述地面的地形参数。
  30. 根据权利要求28或29所述的系统，其特征在于，若所述地形参数包括：坡度，则所述处理器，具体用于：
    根据所述第二直线函数中的斜率，确定所述地面的坡度。
  31. 根据权利要求30所述的系统，其特征在于，所述根据所述第二直线函数的斜率，确定所述地面的坡度，包括：
    将所述斜率的反正切值确定为所述地面的坡度。
  32. 根据权利要求28或29所述的系统，其特征在于，若所述地形参数包括：所述连续波雷达距离正下方地面的高度值，则所述处理器，具体用于：
    根据所述第二直线函数的截距，确定所述连续波雷达距离正下方地面的高度值。
  33. 根据权利要求28或29所述的系统，其特征在于，若所述地形参数包括：平整度，则所述处理器，具体用于：
    根据所述M个第一测距数据和所述第二直线函数，确定所述M个第一测距数据中每个第一测距数据对应的所述第二直线函数中的残差；
    根据所述M个第一测距数据分别对应的所述第二直线函数中的残差，确定所述地面的平整度。
  34. 根据权利要求33所述的系统，其特征在于，所述处理器，具体用于：
    将所述M个第一测距数据分别对应的所述第二直线函数中的残差之和，确定为所述地面的平整度。
  35. 根据权利要求31-34任意一项所述的系统，其特征在于，所述处理器，具体用于：
    获取连续波雷达在旋转过程中对地面测距的T个第二测距数据；所述T个第二测距数据为所述连续波雷达的旋转角度处于预设角度区间内对地面测距的所有测距数据，所述T为大于等于N的整数；
    根据所述T个第二测距数据，获取所述N个第一测距数据。
  36. 根据权利要求35所述的系统，其特征在于，所述处理器，具体用于：
    根据所述T个第二测距数据和有效测距条件，确定所述N个第一测距数据；
    其中，有效测距条件包括：小于等于预设最大距离且大于等于预设最小距离。
  37. 根据权利要求36所述的系统，其特征在于，所述处理器，具体用于：
    从所述T个第二测距数据中确定满足所述有效测距条件的第二测距数据为N个第二测距数据；
    根据所述N个第二测距数据，确定所述N个第一测距数据。
  38. 根据权利要求37所述的系统，其特征在于，所述处理器，具体用于：
    确定所述N个第二测距数据为所述N个第一测距数据；或者，
    对所述N个第二测距数据进行平滑处理，获得所述N个第一测距数据。
  39. 根据权利要求38所述的系统，其特征在于，所述处理器，具体用于：
    根据第二测距数据对应的连续波雷达的旋转角度的顺序对所述N个第二测距数据排序；
    确定第1个第二测距数据为第1个第一测距数据，以及第N个第二测距数据为第N个第一测距数据；
    确定第j-1个第二测距数据、第j个第二测距数据、第j+1个第二测距数据三者的平均值为所述第j个第一测距数据；
    其中，所述j为大于等于2且小于等于N-1的整数。
  40. 根据权利要求35-39任意一项所述的系统，其特征在于，所述处理器，具体用于：
    获取连续波雷达旋转一周对地面测距的所有第二测距数据以及每个第二测距数据对应的所述连续波雷达的旋转角度；
    根据所述预设角度区间，获取位于所述预设角度区间内所述连续波雷达的旋转角度所对应的第二测距数据为所述T个第二测距数据。
  41. 根据权利要求21-40任意一项所述的系统，其特征在于，所述系统为连续波雷达，或者，所述系统为无人机，或者，所述系统为无人机的控制终端。
  42. 一种雷达探测装置，其特征在于，包括：连续波雷达以及权利要求 21-40任意一项所述的控制系统，所述控制系统与所述连续波雷达通信连接。
  43. 一种无人机，其特征在于，包括：机架、飞行控制系统和以及权利要求42所述的雷达探测装置，所述连续波雷达搭载在所述机架上，
    所述飞行控制系统与所述雷达探测装置通信连接，以获取所述地形参数，所述飞行控制系统根据所述地形参数控制所述无人机。
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Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2023082255A1 (zh) * 2021-11-15 2023-05-19 深圳市大疆创新科技有限公司 无人飞行器的控制方法、无人飞行器及存储介质

Citations (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5736957A (en) * 1995-06-30 1998-04-07 The Johns Hopkins University Delay compensated doppler radar altimeter
CN102819023A (zh) * 2012-07-27 2012-12-12 中国地质大学（武汉） 基于LiDAR的复杂地质背景区滑坡识别的方法及系统
CN103339525A (zh) * 2010-12-21 2013-10-02 塔莱斯公司 用于监测地势变化的方法和设备
CN103954970A (zh) * 2014-05-08 2014-07-30 天津市勘察院 一种地形要素采集方法
CN104515988A (zh) * 2014-12-16 2015-04-15 中国安全生产科学研究院 一种基于地基合成孔径雷达的边坡安全监测预警方法
CN104730523A (zh) * 2015-03-04 2015-06-24 中国商用飞机有限责任公司 基于气象雷达显示地形信息的方法
CN106716285A (zh) * 2016-06-30 2017-05-24 深圳市大疆创新科技有限公司 农业无人机作业方法、系统及农业无人机
CN107688177A (zh) * 2017-07-21 2018-02-13 成都中电锦江信息产业有限公司 一种用于无人机目标探测的组网雷达系统和探测方法
CN107783107A (zh) * 2016-08-25 2018-03-09 大连楼兰科技股份有限公司 植保旋翼无人机的毫米波雷达高度表
CN108152808A (zh) * 2017-11-23 2018-06-12 安徽四创电子股份有限公司 一种基于毫米波雷达的周界智能预测预警方法
JP2018119858A (ja) * 2017-01-25 2018-08-02 日本信号株式会社 Ｆｍｃｗ方式レーダ
CN109073744A (zh) * 2017-12-18 2018-12-21 深圳市大疆创新科技有限公司 地形预测方法、设备、系统和无人机

Family Cites Families (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4665401A (en) * 1980-10-10 1987-05-12 Sperry Corporation Millimeter wave length guidance system
US6665063B2 (en) * 2001-09-04 2003-12-16 Rosemount Aerospace Inc. Distributed laser obstacle awareness system
US20060169904A1 (en) * 2005-01-31 2006-08-03 Grobmyer Joseph E Active polarization-resolving infrared imager
CN101770026B (zh) * 2009-01-07 2012-01-25 中国科学院电子学研究所 极化干涉合成孔径雷达数据估计地形的方法
CN101915912B (zh) * 2010-07-02 2013-03-20 武汉大学 一种全面的激光测高回波仿真方法
KR101403357B1 (ko) * 2013-04-10 2014-06-05 삼성탈레스 주식회사 고도 적응형 전파고도계를 이용하는 정밀지형 참조 항법장치
CN104777833B (zh) * 2014-08-14 2018-07-27 北京中科涌泉科技发展有限公司 基于arm和dsp处理器的无人船船载控制系统
CN104950313B (zh) * 2015-06-11 2017-11-07 同济大学 一种路面提取及道路坡度识别方法
CN105761312B (zh) * 2016-02-06 2018-11-30 中国农业大学 一种微地形表面重建方法
CN107783549B (zh) * 2016-08-25 2020-12-08 大连楼兰科技股份有限公司 基于多传感器信息融合技术的单旋翼植保无人机避障系统
CN107783133B (zh) * 2016-08-25 2021-07-16 大连楼兰科技股份有限公司 毫米波雷达的固定翼无人机防撞系统及防撞方法
CN106354157B (zh) * 2016-11-28 2019-05-14 中山市昌源模型有限公司 一种无人机自主飞行系统
CN107504871B (zh) * 2017-08-28 2019-06-07 西京学院 一种常规试验靶场多站协同测试三段式接力引控方法

Patent Citations (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5736957A (en) * 1995-06-30 1998-04-07 The Johns Hopkins University Delay compensated doppler radar altimeter
CN103339525A (zh) * 2010-12-21 2013-10-02 塔莱斯公司 用于监测地势变化的方法和设备
CN102819023A (zh) * 2012-07-27 2012-12-12 中国地质大学（武汉） 基于LiDAR的复杂地质背景区滑坡识别的方法及系统
CN103954970A (zh) * 2014-05-08 2014-07-30 天津市勘察院 一种地形要素采集方法
CN104515988A (zh) * 2014-12-16 2015-04-15 中国安全生产科学研究院 一种基于地基合成孔径雷达的边坡安全监测预警方法
CN104730523A (zh) * 2015-03-04 2015-06-24 中国商用飞机有限责任公司 基于气象雷达显示地形信息的方法
CN106716285A (zh) * 2016-06-30 2017-05-24 深圳市大疆创新科技有限公司 农业无人机作业方法、系统及农业无人机
CN107783107A (zh) * 2016-08-25 2018-03-09 大连楼兰科技股份有限公司 植保旋翼无人机的毫米波雷达高度表
JP2018119858A (ja) * 2017-01-25 2018-08-02 日本信号株式会社 Ｆｍｃｗ方式レーダ
CN107688177A (zh) * 2017-07-21 2018-02-13 成都中电锦江信息产业有限公司 一种用于无人机目标探测的组网雷达系统和探测方法
CN108152808A (zh) * 2017-11-23 2018-06-12 安徽四创电子股份有限公司 一种基于毫米波雷达的周界智能预测预警方法
CN109073744A (zh) * 2017-12-18 2018-12-21 深圳市大疆创新科技有限公司 地形预测方法、设备、系统和无人机

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