WO2019119184A1 - 地形预测方法、设备、系统和无人机 - Google Patents

地形预测方法、设备、系统和无人机 Download PDF

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Publication number
WO2019119184A1
WO2019119184A1 PCT/CN2017/116862 CN2017116862W WO2019119184A1 WO 2019119184 A1 WO2019119184 A1 WO 2019119184A1 CN 2017116862 W CN2017116862 W CN 2017116862W WO 2019119184 A1 WO2019119184 A1 WO 2019119184A1
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Prior art keywords
ranging data
ranging
ground
radar
determining
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PCT/CN2017/116862
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English (en)
French (fr)
Inventor
王石荣
王春明
王俊喜
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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 KR1020197035994A priority Critical patent/KR20200003897A/ko
Priority to EP17935388.3A priority patent/EP3705912A4/en
Priority to JP2020533200A priority patent/JP2021509710A/ja
Priority to PCT/CN2017/116862 priority patent/WO2019119184A1/zh
Priority to CN201780025630.6A priority patent/CN109073744A/zh
Publication of WO2019119184A1 publication Critical patent/WO2019119184A1/zh
Priority to US16/868,044 priority patent/US20200265730A1/en

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    • 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
    • G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
    • G01S13/06—Systems determining position data of a target
    • G01S13/08—Systems for measuring distance only
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B64—AIRCRAFT; AVIATION; COSMONAUTICS
    • B64D—EQUIPMENT FOR FITTING IN OR TO AIRCRAFT; FLIGHT SUITS; PARACHUTES; ARRANGEMENT OR MOUNTING OF POWER PLANTS OR PROPULSION TRANSMISSIONS IN AIRCRAFT
    • B64D47/00—Equipment not otherwise provided for
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B64—AIRCRAFT; AVIATION; COSMONAUTICS
    • B64U—UNMANNED AERIAL VEHICLES [UAV]; EQUIPMENT THEREFOR
    • B64U10/00—Type of UAV
    • B64U10/10—Rotorcrafts
    • B64U10/13—Flying platforms
    • B64U10/16—Flying platforms with five or more distinct rotor axes, e.g. octocopters
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B64—AIRCRAFT; AVIATION; COSMONAUTICS
    • B64U—UNMANNED AERIAL VEHICLES [UAV]; EQUIPMENT THEREFOR
    • B64U20/00—Constructional aspects of UAVs
    • B64U20/80—Arrangement of on-board electronics, e.g. avionics systems or wiring
    • B64U20/83—Electronic components structurally integrated with aircraft elements, e.g. circuit boards carrying loads
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B64—AIRCRAFT; AVIATION; COSMONAUTICS
    • B64U—UNMANNED AERIAL VEHICLES [UAV]; EQUIPMENT THEREFOR
    • B64U30/00—Means for producing lift; Empennages; Arrangements thereof
    • B64U30/20—Rotors; Rotor supports
    • B64U30/21—Rotary wings
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B64—AIRCRAFT; AVIATION; COSMONAUTICS
    • B64U—UNMANNED AERIAL VEHICLES [UAV]; EQUIPMENT THEREFOR
    • B64U60/00—Undercarriages
    • B64U60/20—Undercarriages specially adapted for uneven terrain
    • 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
    • G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
    • G01S13/06—Systems determining position data of a target
    • G01S13/42—Simultaneous measurement of distance and other co-ordinates
    • G01S13/426—Scanning radar, e.g. 3D radar
    • 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
    • G01S13/885—Radar or analogous systems specially adapted for specific applications for ground probing
    • 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
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/02—Systems using the reflection of electromagnetic waves other than radio waves
    • G01S17/06—Systems determining position data of a target
    • G01S17/42—Simultaneous measurement of distance and other co-ordinates
    • 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
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/88—Lidar systems specially adapted for specific applications
    • G01S17/93—Lidar systems specially adapted for specific applications for anti-collision purposes
    • G01S17/933—Lidar systems specially adapted for specific applications for anti-collision purposes of aircraft or spacecraft
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G5/00—Traffic control systems for aircraft, e.g. air-traffic control [ATC]
    • G08G5/0047—Navigation or guidance aids for a single aircraft
    • G08G5/0069—Navigation or guidance aids for a single aircraft specially adapted for an unmanned aircraft
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G5/00—Traffic control systems for aircraft, e.g. air-traffic control [ATC]
    • G08G5/0073—Surveillance aids
    • G08G5/0086—Surveillance aids for monitoring terrain
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G5/00—Traffic control systems for aircraft, e.g. air-traffic control [ATC]
    • G08G5/06—Traffic control systems for aircraft, e.g. air-traffic control [ATC] for control when on the ground
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B64—AIRCRAFT; AVIATION; COSMONAUTICS
    • B64U—UNMANNED AERIAL VEHICLES [UAV]; EQUIPMENT THEREFOR
    • B64U2101/00—UAVs specially adapted for particular uses or applications
    • B64U2101/30—UAVs specially adapted for particular uses or applications for imaging, photography or videography
    • B64U2101/32—UAVs specially adapted for particular uses or applications for imaging, photography or videography for cartography or topography
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B64—AIRCRAFT; AVIATION; COSMONAUTICS
    • B64U—UNMANNED AERIAL VEHICLES [UAV]; EQUIPMENT THEREFOR
    • B64U2101/00—UAVs specially adapted for particular uses or applications
    • B64U2101/40—UAVs specially adapted for particular uses or applications for agriculture or forestry operations
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B64—AIRCRAFT; AVIATION; COSMONAUTICS
    • B64U—UNMANNED AERIAL VEHICLES [UAV]; EQUIPMENT THEREFOR
    • B64U2201/00—UAVs characterised by their flight controls
    • B64U2201/20—Remote controls

Definitions

  • Embodiments of the present invention relate to the field of drone technology, and in particular, to a terrain prediction method, device, system, and drone.
  • drones can be applied to a variety of scenarios. Taking the agricultural industry as an example, drones can cultivate land, spread, spray pesticides and harvest crops, which brings great benefits to the agricultural field. In these operating scenarios, most of the drones need to fly near the ground and avoid hitting the ground when climbing. On a relatively flat ground, based on Global Positioning System (GPS) and Inertial Measurement Unit (IMU) data, the drone can perform the above tasks smoothly; in the more rugged terrain, no one The machine needs to adjust the movement in advance, and carry out operations such as climbing, downhill, deceleration, braking, etc., to achieve near-ground flight and even contour flight; this will enable the drone to better perform the above operations.
  • GPS Global Positioning System
  • IMU Inertial Measurement Unit
  • the drone needs to first predict the condition of the terrain of the ground on which it operates.
  • a general vehicle is driven to the ground, and a relative change in acceleration is generated by contact between the vehicle and the ground during the passage, and then the terrain of the ground is estimated based on the amount of change in acceleration.
  • the contact between the car and the ground generates high-frequency noise, which affects the amount of change in acceleration, which in turn affects the accuracy of terrain prediction.
  • Embodiments of the present invention provide a terrain prediction method, device, system, and drone for improving the accuracy of terrain prediction.
  • an embodiment of the present invention provides a terrain prediction method, including:
  • N first ranging data obtained by the radar for ground ranging during the rotation, wherein the N first ranging data is obtained by the rotation angle of the radar being within a preset angle interval, wherein the N Is an integer greater than 1;
  • an embodiment of the present invention provides a terrain prediction apparatus, including: a memory and a processor;
  • the memory is configured to store program code
  • the processor calls the program code to perform the following operations when the program code is executed:
  • N first ranging data obtained by the radar for ground ranging during the rotation, wherein the N first ranging data is obtained by the rotation angle of the radar being within a preset angle interval, wherein the N Is an integer greater than 1;
  • an embodiment of the present invention provides a drone, including: a radar and a terrain prediction device, where the terrain prediction device is connected to the radar;
  • the terrain prediction device is the terrain prediction device according to the second aspect of the present invention.
  • an embodiment of the present invention provides a terrain prediction system, including: a drone and a control terminal, wherein the drone is communicatively connected to the control terminal; and the control terminal is configured to control the drone;
  • the UAV is equipped with a radar; the control terminal includes the terrain prediction device according to the second aspect of the present invention.
  • an embodiment of the present invention provides a chip, including: a memory and a processor;
  • the memory is configured to store program code
  • the processor calls the program code to perform the following operations when the program code is executed:
  • N first ranging data obtained by the radar for ground ranging during the rotation, wherein the N first ranging data is obtained by the rotation angle of the radar being within a preset angle interval, wherein the N Is an integer greater than 1;
  • an embodiment of the present invention provides a readable storage medium, where the readable storage medium stores a computer program; when the computer program is executed, implementing the terrain according to the first aspect of the present invention. method of prediction.
  • the terrain prediction method, the device, the system, and the drone provided by the embodiment of the present invention acquire a plurality of first ranging data obtained by ground ranging within a preset angle interval during the rotation, and then according to multiple A ranging data is used to determine terrain parameters of the ground, such as slope, integrity, and the like. Since each first ranging data reflects the distance of the radar from the ground ranging point when rotating to a corresponding rotation angle, since the plurality of first ranging data can reflect the topographical variation of the ground, thereby predicting the ground Slope, integrity, etc.
  • the ranging data is obtained by the radar, and the radar does not need to be in direct contact with the ground, thereby avoiding the noise interference generated by the direct contact. Therefore, the prediction accuracy of the ground terrain is higher in this embodiment.
  • FIG. 1 is a schematic architectural diagram of an agricultural drone 100 in accordance with an embodiment of the present invention.
  • FIG. 2 is a flowchart of a terrain prediction method according to an embodiment of the present invention.
  • FIG. 3 is a schematic diagram of radar ranging according to an embodiment of the present invention.
  • FIG. 4 is a schematic structural diagram of a terrain prediction device according to an embodiment of the present invention.
  • FIG. 5 is a schematic structural diagram of a drone according to an embodiment of the present invention.
  • FIG. 6 is a schematic structural diagram of a terrain prediction system according to an embodiment of the present invention.
  • Embodiments of the present invention provide terrain prediction methods, apparatus, systems, and drones.
  • the drone may be an agricultural drone, such as a rotorcraft, for example, a multi-rotor aircraft propelled by air by a plurality of pushing devices, and embodiments of the present invention are not limited thereto.
  • FIG. 1 is a schematic architectural diagram of an agricultural drone 100 in accordance with an embodiment of the present invention. This embodiment is described by taking a rotorcraft unmanned aerial vehicle as an example.
  • the agricultural drone 100 can include a power system, a flight control system, and a rack.
  • the agricultural drone 100 can perform wireless communication with the control terminal, the control terminal can display flight information of the agricultural drone, etc., and the control terminal can communicate with the agricultural drone 100 wirelessly for the agricultural drone 100. Perform remote manipulation.
  • the frame may include a fuselage 110 and a stand 120 (also referred to as a landing gear).
  • the fuselage 110 can include a center frame 111 and one or more arms 112 coupled to the center frame 111, one or more arms 112 extending radially from the center frame.
  • the tripod 120 is connected to the fuselage 110 for supporting when the agricultural drone 100 is landing, and the foot frame 120 is further equipped with a liquid storage tank 130 for storing the chemical liquid or water;
  • the end of the arm 112 is also equipped with a head 140, and the liquid in the reservoir 130 is pumped into the head 140 by a pump, and is sprayed out by the 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 machines 160 corresponding to one or more propellers 150, wherein the electric machine 160 is coupled to the electronic tune
  • the electronic governor is configured to receive a driving signal generated by the flight control system, and provide a driving current to the motor according to the driving signal.
  • the motor 160 is used to drive the rotation of the propeller 150 to power the flight of the agricultural drone 100, which enables the agricultural drone 100 to achieve one or more degrees of freedom of motion.
  • the agricultural drone 100 can be rotated about one or more axes of rotation.
  • the above-described rotating shaft may include a roll axis, a yaw axis, and a pitch axis.
  • the motor 160 can be a DC motor or an AC motor.
  • the motor 160 may be a brushless motor or a brushed motor.
  • the flight control system can include a flight controller and a sensing system.
  • the sensing system is used to measure the attitude information of the unmanned aerial vehicle, that is, the position information and state information of the agricultural drone 100 in space, for example, three-dimensional position, three-dimensional angle, three-dimensional speed, 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 can be a Global Positioning System (GPS).
  • the flight controller is used to control the flight of the agricultural drone 100, for example, the flight of the agricultural drone 100 can be controlled based on the attitude information measured by the sensing system. It should be understood that the flight controller may control the agricultural drone 100 in accordance with pre-programmed program instructions, or may control the agricultural drone 100 in response to one or more control commands from the control terminal.
  • the agricultural vehicle can also be equipped with a radar 170 on the stand 120.
  • the radar 170 is a rotating radar, and the radar 170 can be used for ranging, but is not limited to ranging.
  • FIG. 2 is a flowchart of a terrain prediction method according to an embodiment of the present invention. As shown in FIG. 2, the method in this embodiment may include:
  • the ground can be measured by the radar to obtain the distance of the radar from the ground, wherein the radar can rotate, and when the radar rotates at different angles, the ranging point of the radar to the ground is different. Therefore, the distance detected by the radar from the ground may also be different, as shown in Figure 3.
  • the plurality of first ranging data obtained by rotating to the rotation angle is within a preset angle interval, where the first ranging data is N, and N is greater than An integer equal to 2.
  • Each first ranging data reflects the distance from the ground when the radar rotates to a corresponding rotation angle.
  • the distance between the radar and the ground is low, if the measurement The distance from the ground where the point is located is large, and the distance between the radar and the ground is large; for example, if the ground is high and low, the flatness of the ground is low.
  • the distance between the radar and the ground is small, it means that the slope of the ground where the plurality of ranging points are located is high, and if the distance between the radar and the ground is large, the multiple The slope of the ground where the ranging point is located is low.
  • the embodiment can determine the terrain parameters of the ground according to the plurality of first ranging data obtained by the plurality of ranging points, and the terrain parameters include: ground Slope, the flatness of the ground.
  • the preset angle interval is 60 degrees to 120 degrees, corresponding to the terrain parameter of the ground directly below the radar; the preset angle interval is -30 degrees to 30 degrees, corresponding to determining the terrain parameters of the ground in front of the radar;
  • the preset angle interval is 150 degrees to 210 degrees, and the terrain parameter of the ground behind the radar can be determined correspondingly.
  • the present embodiment is for illustrative purposes, and the preset angle interval may be based on actual conditions. Need to set. If the preset angle interval of the embodiment is 60 degrees to 120 degrees, the first ranging data obtained by the radar at a rotation angle of 60 degrees for ground ranging can be obtained at 60.6 degrees for ground ranging.
  • the first ranging data, the first ranging data obtained by the ground ranging at 61.2 degrees, and the first ranging data obtained by the ground ranging at 61.8 degrees are analogous, and are not described herein again.
  • each first ranging data reflects the distance of the radar from the ground ranging point when rotating to a corresponding rotation angle
  • the plurality of first ranging data can reflect the topographical variation of the ground, thereby predicting the ground Slope, integrity, etc.
  • the ranging data is obtained by the radar, and the radar does not need to be in direct contact with the ground, thereby avoiding the noise interference generated by the direct contact. Therefore, the prediction accuracy of the ground terrain is higher in this embodiment.
  • the first ranging data includes: a horizontal distance of the radar from the ground ranging point, and a vertical distance of the radar from the ground ranging point. Due to the different rotation angles of the radar, the radar signal transmission direction is different, which results in different ground ranging points, so the ground ranging point varies with the rotation angle of the radar.
  • the first ranging data in this embodiment includes the horizontal distance. And a vertical distance, wherein the horizontal distance and the vertical distance may be obtained according to a distance between the radar and the ground ranging point and a rotation angle of the radar corresponding to the ground ranging point.
  • the slope of the ground can be considered higher, if the radar is at the level of the ground ranging point. The smaller the distance and the larger the vertical distance, the lower the slope of the ground can be considered.
  • a possible implementation manner of the foregoing S201 may include the following steps A and B;
  • Step A acquiring M second ranging data for the ground ranging in the rotation process of the radar; the M second ranging data is that the rotation angle of the radar is within a preset angle interval to the ground ranging For all ranging data, the M is an integer greater than or equal to N.
  • M second ranging data M is an integer greater than or equal to the above N.
  • step A may include: step A1 and step A2.
  • Step A1 Acquire all second ranging data of the ground ranging for one rotation of the radar and the rotation angle of the radar corresponding to each second ranging data.
  • Step A2 Obtain, according to the preset angle interval, that the second ranging data corresponding to the rotation angle of the radar in the preset angle interval is the M second ranging data.
  • the radar rotates one revolution, and the radar is rotated by an angle of 360 degrees.
  • the rotation of the radar by 0.6 degrees means that the radar rotates to a corresponding light grid, and then triggers a ranging, so that 600 ranging data can be obtained, and the embodiment also The rotation angle of the radar corresponding to each ranging data is recorded.
  • the principle of the ranging of the radar can be referred to the related description in the prior art, and details are not described herein again. Then, according to the preset angle interval, the second ranging data obtained by the rotation angle of the radar in the preset angle interval is obtained.
  • the preset angle interval is 60-120 degrees, 60, 60.6, 61.2, ..., 118.8, 119.4, and 120 degrees respectively correspond to the second ranging data, where a total of 100 second ranging data can be obtained, and M is equal to 100.
  • Step B Acquire the N first ranging data according to the M second ranging data.
  • the second ranging data is data obtained by actual radar ranging, and after obtaining the M second ranging data, acquiring the foregoing for performing terrain prediction according to the M second ranging data.
  • N first ranging data, N is an integer less than or equal to M.
  • step B above may include step B1.
  • Step B1 Determine the N first ranging data according to the M second ranging data and the effective ranging condition.
  • the effective ranging condition includes: a preset maximum distance less than or equal to and greater than or equal to a preset minimum distance.
  • the validity of each ranging data is judged, and the radar has a blind zone and a far-distance ranging distance in a short range. Therefore, an effective ranging condition is set, and the effective ranging condition can be expressed as [d Min , d max ], that is, the effective second ranging data should be greater than or equal to d min and less than or equal to d max . Therefore, in this embodiment, the N first ranging data determined according to the M second ranging data and the effective ranging condition are used to predict the ground terrain, and the error of the ranging data is avoided to improve the ground terrain prediction. The accuracy rate.
  • step B1 may include step B11 and step B12.
  • Step B11 Determine, from the M second ranging data, that the second ranging data that meets the effective ranging condition is N second ranging data.
  • all second ranging data that is less than or equal to a preset maximum distance and less than or equal to a preset minimum distance is determined from the M second ranging data, and the second ranging data is N second ranging data. data.
  • Step B11 Determine, according to the N second ranging data, the N first ranging data.
  • the N first ranging data are determined according to the N second ranging data that meet the determined effective ranging condition.
  • 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 pieces of second ranging data are smoothed to obtain the N first ranging data.
  • the N pieces of second ranging data are sorted according to the order of the rotation angles of the radars corresponding to the second ranging data, for example, the first second ranging data is: the second ranging data corresponding to 60 degrees d 1 .
  • the second second ranging data is: a 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 the Nth second ranging data is the Nth second ranging data, that is, D N is equal to d N .
  • the j-1th second ranging data eg, d j-1
  • the jth second ranging data eg, d j
  • the j+1th second ranging data eg, d j+1
  • D j is not limited to the average value of d j and one of the left and right adjacent ones, and may be an average value of d j and two adjacent left and right, and correspondingly, the first and second first
  • the ranging data is 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 and Nth second ranging data.
  • the embodiment may also adopt three or four adjacent to the left and right, and the scheme is similar, and details are not described herein again.
  • the above d j may be a value, that is, the distance between the radar and the ground ranging point, and the embodiment may obtain the corresponding first ranging data according to the rotation angle of the corresponding radar after performing the smoothing process.
  • the horizontal distance x j and the vertical distance y j may be a value, that is, the distance between the radar and the ground ranging point, and the embodiment may obtain the corresponding first ranging data according to the rotation angle of the corresponding radar after performing the smoothing process.
  • the above d j may include two values, that is, a horizontal distance and a vertical distance between the radar and the ground ranging point, and the embodiment may perform smoothing processing on the horizontal distance to obtain a horizontal distance in the first ranging data. It is also possible to perform smoothing processing for the vertical distance to obtain the vertical distance of the first ranging data.
  • a possible implementation manner of the foregoing S202 may include the following steps C and D;
  • Step C Perform straight line fitting on the N first ranging data by a least squares method to obtain a straight line function.
  • a, b, and c are temporarily unknown.
  • the slope and the intercept in the straight line function are then determined based on the N first ranging data, the straight line function, and the least squares method.
  • each of the first ranging data includes a horizontal distance and a vertical distance of the radar and the corresponding ground ranging point, and the known values of the N sets of x and y are substituted.
  • the slope (for example, a) and the intercept (for example, b) in the straight line function are determined by the least squares method.
  • the present embodiment is not limited to the above-described least squares method, and a filtering method may also be employed.
  • Step D Determine a terrain parameter of the ground according to the straight line function.
  • the slope of the ground may be determined according to the slope of the straight line function. For example, the larger the slope is, the larger the slope of the ground is, and the smaller the slope is, the smaller the slope of the ground is; and/or according to the straight line function.
  • the slope and intercept determine the flatness of the ground.
  • the following describes how to determine the slope and the intercept in the straight line function based on the N first ranging data, the straight line function, and the least squares method.
  • e y i -ax i -b
  • y i is the vertical distance in the i-th first ranging data
  • x i is the horizontal distance in the i-th first ranging data.
  • Q represents the weighted squared sum of the residuals
  • w i represents the weighting coefficient of the residual corresponding to the i-th first ranging data
  • the value of n is equal to the value of N.
  • the value of the slope of the straight line function and the value of the intercept are determined according to the weighted square sum of the residuals.
  • the first derivative of the slope is equal to the first preset value according to the weighted square sum of the residuals, and the weighted square sum of the residuals is equal to the second pre-derivative of the intercept.
  • a value is set to determine the value of the slope of the straight line function and the value of the intercept.
  • the values of a and b are optimal, and the first preset value and the second preset value may be set to zero.
  • the weighted sum of squares (Q) of the residuals is equal to 0 for the first derivative of the slope (a) and the weighted sum of squares (Q) of the residuals is equal to 0 for the first derivative of the intercept (b), For example, as shown in Equation 3:
  • This embodiment can As the value of the slope a, and will As the value of the intercept b.
  • one possible implementation manner of determining the flatness of the ground is to determine the weighted sum of squares of the residuals according to the value of the determined slope and the value of the intercept determined above. Value; for example: the value of a above (as above And the value of b above (as above ), substituting into the above formula 2 to obtain the value of Q. Then, according to the value of the weighted square sum of the residuals, the flatness of the ground is determined; for example, if the value of Q is larger, the ground is more uneven, and if the value of Q is smaller, the ground is flatter.
  • the embodiment may pre-store the formulas 4 and 2 in the above, and substitute the obtained N first ranging data into the pre-stored formula 4 to obtain as well as according to Determine the slope of the ground. Then will get as well as Substituting into the pre-stored formula 2 to obtain Q, the flatness of the ground is determined 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 the values of i are different, 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 ranging data obtained by radar ranging since the ranging data obtained by radar ranging has an error that increases as the distance increases, it is necessary to perform weight distribution on the corresponding first ranging data according to the rotation angle of the radar.
  • the weighting coefficient of the residual corresponding to each of the first ranging data is a trigonometric function of a rotation angle of the radar corresponding to the first ranging data, for example, as shown in Equation 5:
  • k min is the minimum value of the preset angle interval
  • k max is the maximum value of the preset angle interval
  • k i is the rotation angle of the radar corresponding to the i-th first ranging data.
  • the weighting coefficient of the residual is, for example, as shown in Equation 6. :
  • the weighting coefficient of the residual corresponding to each of the first ranging data is a Gaussian function about a rotation angle of the radar corresponding to the first ranging data, for example, as shown in Equation 7. :
  • k i is the rotation angle of the radar corresponding to the i-th first ranging data
  • ⁇ represents the variance
  • ⁇ represents the intermediate value of the preset angle interval
  • the shape of the function may be adjusted according to the value of the variance.
  • the weighting coefficient of the residual is as shown in Equation 8. :
  • the flatness can be used in the fixed height and obstacle avoidance scheme of the drone.
  • the embodiment determines the slope of the ground by the above embodiments the slope can be used to guide the subsequent actions of the drone.
  • the radar involved in each of the above embodiments may be an electromagnetic wave radar, or may be a laser radar.
  • a computer storage medium is also provided in the embodiment of the present invention.
  • the computer storage medium stores program instructions, and the program may include some or all of the steps of the terrain prediction method in FIG. 2 and its corresponding embodiments.
  • the terrain prediction device 400 of this embodiment may include: a memory 401 and a processor 402.
  • the foregoing memory 401 and processor 402 pass Bus connection.
  • Memory 401 can include read only memory and random access memory and provides instructions and data to processor 402.
  • a portion of the memory 401 may also include a non-volatile random access memory.
  • the memory 401 is configured to store program code
  • the processor 402 calls the program code to perform the following operations when the program code is executed:
  • N first ranging data obtained by the radar for ground ranging during the rotation, wherein the N first ranging data is obtained by the rotation angle of the radar being within a preset angle interval, wherein the N Is an integer greater than 1;
  • the first ranging data comprises: a horizontal distance and a vertical distance of the radar from a ground ranging point; wherein the ground ranging point differs depending on a rotation angle of the radar.
  • the processor 402 is specifically configured to:
  • a terrain parameter of the ground is determined based on the straight line function.
  • the processor 402 is specifically configured to:
  • the processor 402 determines, when determining the slope and the intercept in the straight line function according to the N first ranging data, the straight line function, and the least squares method, specifically for: And determining, by the N first ranging data and the straight line function, a residual in the straight line function corresponding to each first ranging data; wherein, the residual corresponding to each first ranging data is a function of a slope and an intercept in the straight line function; and determining, according to a residual corresponding to each of the first ranging data and a weighting coefficient of the residual, corresponding to the N first ranging data a weighted sum of squares of the residuals; determining a value of a slope of the straight line function and a value of an intercept according to a weighted sum of squares of the residuals;
  • the processor 402 is specifically configured to:
  • a first derivative of the slope is equal to a first preset value
  • a weighted sum of squares of the residuals is equal to a second preset value of the intercept
  • the first preset value and the second preset value are 0.
  • the weighting coefficients of the residuals corresponding to each of the first ranging data are equal; or
  • the weighting coefficient of the residual corresponding to each of the first ranging data is a trigonometric function or a Gaussian function with respect to a rotation angle of the radar corresponding to the first ranging data.
  • the sum of the weighting coefficients of the residuals corresponding to the N first ranging data is equal to one.
  • the processor 402 is specifically configured to:
  • the M second ranging data is all ranging data of the ground ranging within the preset angle interval of the radar rotation angle , the M is an integer greater than or equal to N;
  • the processor 402 is specifically configured to:
  • the effective ranging condition includes: a preset maximum distance less than or equal to and greater than or equal to a preset minimum distance.
  • the processor 402 is specifically configured to:
  • the processor 402 is specifically configured to:
  • the N second ranging data is the N first ranging data
  • the processor 402 is specifically configured to:
  • the Nth second ranging data is the Nth first ranging data
  • j is an integer greater than or equal to 2 and less than or equal to the N-1.
  • the processor 402 is specifically configured to:
  • the second ranging data corresponding to the rotation angle of the radar in the preset angle interval is the M second ranging data.
  • the terrain prediction device 400 may be a radar, or may be a drone, or may be a control terminal of the drone.
  • the drone may be an agricultural drone.
  • the device in this embodiment may be used to implement the technical solution of the foregoing method embodiment of the present invention, and the implementation principle and the technical effect are similar, and details are not described herein again.
  • FIG. 5 is a schematic structural diagram of a drone according to an embodiment of the present invention.
  • the drone 500 of the embodiment includes a radar 501 and a terrain prediction device 502.
  • the terrain prediction device 502 is communicatively coupled to the radar 501.
  • the terrain prediction device 502 can adopt the structure of the embodiment shown in FIG. 4, and correspondingly, the technical solution of FIG. 2 and its corresponding embodiment can be executed, and the implementation principle and technical effects are similar, and details are not described herein again.
  • the drone 500 also includes other components, which are not shown here.
  • FIG. 6 is a schematic structural diagram of a terrain prediction system according to an embodiment of the present invention.
  • the terrain prediction system 600 of the present embodiment includes: a drone 601 and a control terminal 602.
  • the drone 601 is communicatively coupled to the control terminal 602; the control terminal 602 is configured to control the drone 601.
  • the UAV 601 is equipped with an upper radar 601a; the control terminal 602 includes a terrain prediction device 602a.
  • the terrain prediction device 602a may adopt the structure of the embodiment shown in FIG. 4, and correspondingly, the technical solution of FIG. 2 and its corresponding embodiment may be performed, and the implementation principle and technical effects thereof are similar, and details are not described herein again. It should be noted that the drone 601 and the control terminal 602 also include other components, which are not shown here.
  • the foregoing program may be stored in a computer readable storage medium, and the program is executed when executed.
  • the foregoing storage medium includes: read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk, and the like, which can store program codes. Medium.

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Abstract

一种地形预测方法，包括：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多个第一测距数据，然后根据多个第一测距数据，确定地面的地形参数，例如坡度、完整度等。由于每个第一测距数据反映了雷达在旋转至对应的旋转角度时与地面测距点的距离，多个第一测距数据可以反映出地面的地形变化，从而据此可以预测地面的坡度、完整度等。此方法通过雷达获得测距数据，并不需要雷达与地面直接接触，避免了直接接触产生的噪声干扰，因此，对地面地形的预测准确率更高。还提供一种应用此方法的设备、系统和无人机。

Description

地形预测方法、设备、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，尤其涉及一种地形预测方法、设备、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，无人机需要先预测其作业的地面的地形的状况。现有技术中，一般驾驶汽车通行地面，在通行过程中通过汽车与地面的接触产生加速度的相对变化，然后根据加速度的变化量估计地面的地形。但是，汽车与地面的接触会产生高频噪声，从而对加速度的变化量造成影响，进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地形预测方法、设备、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地形预测方法，包括：
获取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；
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。
第二方面，本发明实施例提供一种地形预测设备，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；
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。
第三方面，本发明实施例提供一种无人机，包括：雷达以及地形预测设备，所述地形预测设备与所述雷达通信连接；
所述地形预测设备为如第二方面本发明实施例所述的地形预测设备。
第四方面，本发明实施例提供一种地形预测系统，包括：无人机和控制终端，所述无人机与所述控制终端通信连接；所述控制终端用于控制所述无人机；
所述无人机上搭载上雷达；所述控制终端包括如第二方面本发明实施例所述的地形预测设备。
第五方面，本发明实施例提供一种芯片，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；
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。
第六方面，本发明实施例提供一种可读存储介质，所述可读存储介质上存储有计算机程序；所述计算机程序在被执行时，实现如第一方面本发明实施例所述的地形预测方法。
本发明实施例提供的地形预测方法、设备、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多个第一测距数据，然后根据多个第一测距数据，确定地面的地形参数，例如坡度、完整度等。由于每个第一测距数据反映了雷达在旋转至对应的旋转角度时与地面测距点的距离，因为多个第一测距数据可以反映出地面的地形变化，从而据此可以预测地面的坡度、完整度等。本实施例通过雷达获得测距数据，并不需要雷达与地面直接接触，避免了直接接触产生的噪声干扰，因此，本实施例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地形预测方法的流程图；
图3为本发明一实施例提供的雷达测距的一种示意图；
图4为本发明实施例提供的地形预测设备的一种结构示意图；
图5为本发明实施例提供的无人机的一种结构示意图；
图6为本发明实施例提供的地形预测系统的一种结构示意图。
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地形预测方法、设备、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上还可以搭载雷达170，该雷达170为旋转雷达，该雷达170可以用于测距，但不限于测距。
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雷达的旋转角度处于预设角度区间内获得的。
S202、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。
本实施例中，可以通过雷达可以对地面进行测距，以获得该雷达相距地面的距离，其中雷达可以旋转，当雷达旋转不同的角度时，雷达对地面进行测距的测距点也不相同，因此雷达检测到的与地面的距离也可能不相同，如图3所示。本实施例获取雷达在旋转过程对地面测距时，旋转至旋转角度处于预设角度区间内，获得的多个第一测距数据，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，则雷达与地面的距离低，若该测距点所在的地面低，则雷达与地面的距离大；例如：若地面一块高一块低，则说明地面的平整度低。对于相同的多个测距点，若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。由于地面是一个面，由于多个点可以确定一个面，因此，本实施例根据多个测距点获得的多个第一测距数据，可以确定地面的地形参数，该地形参数包括：地面的坡度、地面的平整度。
例如：该预设角度区间为60度至120度，对应的可以确定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，则本实施例可以在雷达在旋转角度为60度对地面测距获得的第一测距数据，在60.6度对地面测距获得的第一测距数据，在61.2度对地面测距获得的第一测距数据，在61.8度对地面测距获得的第一测距数据，以类推类，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多个第一测距数据，然后根据多个第一测距数据，确定地面的地形参数，例如坡度、完整度等。由于每个第一测距数据反映了雷达在旋转至对应的旋转角度时与地面测距点的距离，因为多个第一测距数据可以反映出地面的地形变化，从而据此可以预测地面的坡度、完整度等。本实施例通过雷达获得测距数据，并不需要雷达与地面直接接触，避免了直接接触产生的噪声干扰，因此，本实施例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：该雷达距地面测距点的水平距离，以及该雷达距地面测距点的垂直距离。由于雷达的旋转角度不同，雷达的信号发射方向不同，从而造成地面测距点不同，所以地面测距点随雷达的旋转角度不同而不同。本实施例中为了避免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雷达与地面测距点之间的距离以及该地面测距点对应的雷达的旋转角度获得。例如：对于相同的雷达与地面测距点之间的距离，若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、获取雷达在旋转过程中对地面测距的M个第二测距数据；所述M个第二测距数据为所述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M为大于等于N的整数。
本实施例中，获取雷达在旋转过程对地面测距，且，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M个第二测距数据，M为大于等于上述N的整数。
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雷达的旋转角度所对应的第二测距数据为所述M个第二测距数据。
本实施例中，雷达旋转一周，对应雷达一共旋转了360度的角度。例如：雷达旋转一周对应600个光栅格，则雷达每旋转0.6度即表示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雷达的旋转角度；其中，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，M即等于100。
步骤B、根据所述M个第二测距数据，获取所述N个第一测距数据。
本实施例中，该第二测距数据是雷达实际测距获得的数据，在获得上述M个第二测距数据之后，根据该M个第二测距数据，获取用于进行地形预测的上述N个第一测距数据，N为小于等于M的整数。
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M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，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M个第二测距数据和有效测距条件，确定的N个第一测距数据用于预测地面地形，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M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M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且小于等于所述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以及左右相邻一个三者的平均值，也可以是d j以及左右相邻两个的平均值，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，再根据对应的雷达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。
另外，上述d j可以包括两个值，即雷达与地面测距点之间的水平距离和垂直距离，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。
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和D；
步骤C、对所述N个第一测距数据通过最小二乘法进行直线拟合，获得直线函数。
其中，构建雷达与地面测距点的垂直距离关于雷达与地面测距点的水平距离的直线函数，该直线函数例如如公式一所示：y＝ax+b+e，其中，y为雷达与地面测距点的垂直距离，x为雷达与地面测距点的水平距离，此时a、b、c暂时未知。然后根据所述N个第一测距数据、所述直线函数以及最小二乘法，确定所述直线函数中的斜率和截距。其中，N个第一测距数据是已知的，而且每个第一测距数据包括雷达与对应地面测距点的水平距离和垂直距离，将这N组x与y的已知值，代入上述公式一中，再通过最小二乘法，来确定该直线函数中的斜率(例如a)和截距(例如b)。
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D、根据所述直线函数，确定所述地面的地形参数。
本实施例中，可以根据该直线函数的斜率，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；和/或，根据该直线函数的斜率和截距，确定地面的平整度。
下面对如何根据所述N个第一测距数据、所述直线函数以及最小二乘法，确定所述直线函数中的斜率和截距进行描述。
在一种可能的实现方式中，根据所述N个第一测距数据和所述直线函数，确定每个第一测距数据对应的所述直线函数中的残差；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，y i为第i个第一测距数据中的垂直距离，x i为第i个第一测距数据中的水平距离。然后根据所述每个第一测距数据对应的残差以及所述残差的加权系数，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二所示：
Figure PCTCN2017116862-appb-000001
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，n的数值等于N的数值。
本实施例中，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，可以例如如公式三所示：
Figure PCTCN2017116862-appb-000002
Figure PCTCN2017116862-appb-000003
根据上述公式三可以获得a的估计值
Figure PCTCN2017116862-appb-000004
和b的估计值
Figure PCTCN2017116862-appb-000005
分别如下所示公式四：
Figure PCTCN2017116862-appb-000006
Figure PCTCN2017116862-appb-000007
本实施例可以将
Figure PCTCN2017116862-appb-000008
作为斜率a的值，以及将
Figure PCTCN2017116862-appb-000009
作为截距b的值。
相应地，根据该直线函数的斜率和截距，确定地面的平整度的一种可能的实现方式为：根据上述确定的斜率的值和上述确定的截距的值，确定残差的加权平方和的值；例如：将上述a的值(如上述
Figure PCTCN2017116862-appb-000010
)和上述b的值(如上述
Figure PCTCN2017116862-appb-000011
)，代入上述公式二中，从而获得Q的值。然后根据所述残差的加权平方和的值，确定所述地面的平整度；例如：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四和公式二，将获得的N个第一测距数据代入预先存储的公式四中，可获得
Figure PCTCN2017116862-appb-000012
以及
Figure PCTCN2017116862-appb-000013
根据
Figure PCTCN2017116862-appb-000014
确定地面的坡度。然后将获得的
Figure PCTCN2017116862-appb-000015
以及
Figure PCTCN2017116862-appb-000016
代入预先存储的公式二中，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雷达的旋转角度的三角函数，例如如公式五所示：
Figure PCTCN2017116862-appb-000017
其中，k min为预设角度区间的最小值，k max为预设角度区间的最大值，k i为第i个第一测距数据对应的雷达的旋转角度。
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六所示：
Figure PCTCN2017116862-appb-000018
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雷达的旋转角度的高斯函数，例如如公式七所示：
Figure PCTCN2017116862-appb-000019
其中，k i为第i个第一测距数据对应的雷达的旋转角度，σ表示方差，μ表示预设角度区间的中间值。
其中，可根据方差的值调节上述函数的形状，方差越小，预设角度区间的中间值的权重越大；方差越大，预设角度区间的中间值的权重越小；该方差的值可以实际需要预先设定。若预设角度区间为60-120度，则μ为90度。
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八 所示：
Figure PCTCN2017116862-appb-000020
本实施例通过上述各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。本实施例通过上述各实施例确定地面的坡度之后，该坡度可以用于指导无人机后续要采取的动作。
可选地，上述各实施例中涉及的雷达可以为电磁波雷达，或者，也可以为激光雷达。
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地形预测方法的部分或全部步骤。
图4为本发明实施例提供的地形预测设备的一种结构示意图，如图4所示，本实施例的地形预测设备400可以包括：存储器401和处理器402；上述存储器401和处理器402通过总线连接。存储器401可以包括只读存储器和随机存取存储器，并向处理器402提供指令和数据。存储器401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401，用于存储程序代码；
所述处理器402，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；
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。
在一些实施例中，所述第一测距数据包括：所述雷达距地面测距点的水平距离与垂直距离；其中，地面测距点随所述雷达的旋转角度不同而不同。
在一些实施例中，所述处理器402，具体用于：
对所述N个第一测距数据通过最小二乘法进行直线拟合，获得直线函数；
根据所述直线函数，确定所述地面的地形参数。
在一些实施例中，所述处理器402，具体用于：
构建雷达与地面测距点的垂直距离关于雷达与地面测距点的水平距离的直线函数；
根据所述N个第一测距数据、所述直线函数以及最小二乘法，确定所述直线函数中的斜率和截距；
根据所述直线函数据中的斜率，确定所述地面的坡度；和/或，根据所述直线函数中的斜率和截距，确定所述地面的平整度。
在一些实施例中，所述处理器402在根据所述N个第一测距数据、所述直线函数以及最小二乘法，确定所述直线函数中的斜率和截距时，具体用于：根据所述N个第一测距数据和所述直线函数，确定每个第一测距数据对应的所述直线函数中的残差；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；以及根据所述每个第一测距数据对应的残差以及所述残差的加权系数，确定所述N个第一测距数据对应的所述残差的加权平方和；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；
所述处理器402在根据所述直线函数的斜率和截距，确定所述地面的平整度时，具体用于：根据所述斜率的值和所述截距的值，确定所述残差的加权平方和的值；以及根据所述残差的加权平方和的值，确定所述地面的平整度。
在一些实施例中，所述处理器402，具体用于：
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和截距。
在一些实施例中，所述第一预设值、所述第二预设值为0。
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；或者，
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雷达的旋转角度的三角函数或者高斯函数。
在一些实施例中，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，所述处理器402，具体用于：
获取雷达在旋转过程中对地面测距的M个第二测距数据；所述M个第二测距数据为所述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M为大于等于N的整数；
根据所述M个第二测距数据，获取所述N个第一测距数据。
在一些实施例中，所述处理器402，具体用于：
根据所述M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。
在一些实施例中，所述处理器402，具体用于：
从所述M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。
在一些实施例中，所述处理器402，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。
在一些实施例中，所述处理器402，具体用于：
根据第二测距数据对应的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且小于等于所述N-1的整数。
在一些实施例中，所述处理器402，具体用于：
获取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雷达的旋转角度所对应的第二测距数据为所述M个第二测距数据。
可选地，上述地形预测设备400可以是雷达，或者可以是无人机，或者可以是无人机的控制终端。可选地，该无人机可以是农业无人机。
本实施例的设备，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5为本发明实施例提供的无人机的一种结构示意图，如图5所示，本实施例的无人机500包括：雷达501和地形预测设备502。所述地形预测设备502与所述雷达501通信连接。其中，地形预测设备502可以采用图4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。需要说明的是，无人机500还包括其它部件，此处未示出。
图6为本发明实施例提供的地形预测系统的一种结构示意图，如图6所示，本实施例的地形预测系统600包括：无人机601和控制终端602。其中，无人机601与所述控制终端602通信连接；所述控制终端602用于控制所述无人机601。
所述无人机601上搭载上雷达601a；所述控制终端602包括地形预测设备602a。其中，地形预测设备602a可以采用图4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。需要说明的是，无人机601和控制终端602还包括其它部件，此处未示出。
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 (33)

  1. 一种地形预测方法，其特征在于，包括：
    获取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；
    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。
  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，所述根据所述N个第一测距数据，确定所述地面的地形参数，包括：
    对所述N个第一测距数据通过最小二乘法进行直线拟合，获得直线函数；
    根据所述直线函数，确定所述地面的地形参数。
  4. 根据权利要求3所述的方法，其特征在于，所述对所述N个第一测距数据通过最小二乘法进行直线拟合，获得直线函数，包括：
    构建雷达与地面测距点的垂直距离关于雷达与地面测距点的水平距离的直线函数；
    根据所述N个第一测距数据、所述直线函数以及最小二乘法，确定所述直线函数中的斜率和截距；
    所述根据所述直线函数，确定所述地面的地形参数，包括：
    根据所述直线函数据中的斜率，确定所述地面的坡度；和/或，
    根据所述直线函数中的斜率和截距，确定所述地面的平整度。
  5. 根据权利要求4所述的方法，其特征在于，所述根据所述N个第一测距数据、所述直线函数以及最小二乘法，确定所述直线函数中的斜率和截距，包括：
    根据所述N个第一测距数据和所述直线函数，确定每个第一测距数据对应的所述直线函数中的残差；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；
    根据所述每个第一测距数据对应的残差以及所述残差的加权系数，确定 所述N个第一测距数据对应的所述残差的加权平方和；
    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；
    根据所述直线函数的斜率和截距，确定所述地面的平整度，包括：
    根据所述斜率的值和所述截距的值，确定所述残差的加权平方和的值；
    根据所述残差的加权平方和的值，确定所述地面的平整度。
  6. 根据权利要求5所述的方法，其特征在于，所述根据所述残差的加权平方和，确定所述直线函数的斜率和截距，包括：
    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和截距。
  7. 根据权利要求6所述的方法，其特征在于，所述第一预设值、所述第二预设值为0。
  8. 根据权利要求5-7任意一项所述的方法，其特征在于，每个第一测距数据对应的残差的加权系数均相等；或者，
    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雷达的旋转角度的三角函数或者高斯函数。
  9. 根据权利要求8所述的方法，其特征在于，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。
  10. 根据权利要求1-9任意一项所述的方法，其特征在于，所述获取雷达在旋转过程中对地面测距的N个第一测距数据，包括：
    获取雷达在旋转过程中对地面测距的M个第二测距数据；所述M个第二测距数据为所述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M为大于等于N的整数；
    根据所述M个第二测距数据，获取所述N个第一测距数据。
  11. 根据权利要求10所述的方法，其特征在于，根据所述M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M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。
  12. 根据权利要求11所述的方法，其特征在于，所述根据所述M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M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。
  13. 根据权利要求12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。
  14. 根据权利要求13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且小于等于所述N-1的整数。
  15. 根据权利要求10-14任意一项所述的方法，其特征在于，所述获取雷达在旋转过程中对地面测距的M个第二测距数据，包括：
    获取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雷达的旋转角度所对应的第二测距数据为所述M个第二测距数据。
  16. 一种地形预测设备，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；
    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。
  17. 根据权利要求16所述的设备，其特征在于，所述第一测距数据包括：所述雷达距地面测距点的水平距离与垂直距离；其中，地面测距点随所述雷达的旋转角度不同而不同。
  18. 根据权利要求16或17所述的设备，其特征在于，所述处理器，具体用于：
    对所述N个第一测距数据通过最小二乘法进行直线拟合，获得直线函数；
    根据所述直线函数，确定所述地面的地形参数。
  19. 根据权利要求18所述的设备，其特征在于，所述处理器，具体用于：
    构建雷达与地面测距点的垂直距离关于雷达与地面测距点的水平距离的直线函数；
    根据所述N个第一测距数据、所述直线函数以及最小二乘法，确定所述直线函数中的斜率和截距；
    根据所述直线函数据中的斜率，确定所述地面的坡度；和/或，根据所述直线函数中的斜率和截距，确定所述地面的平整度。
  20. 根据权利要求19所述的设备，其特征在于，所述处理器在根据所述N个第一测距数据、所述直线函数以及最小二乘法，确定所述直线函数中的斜率和截距时，具体用于：根据所述N个第一测距数据和所述直线函数，确定每个第一测距数据对应的所述直线函数中的残差；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；以及根据所述每个第一测距数据对应的残差以及所述残差的加权系数，确定所述N个第一测距数据对应的所述残差的加权平方和；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；
    所述处理器在根据所述直线函数的斜率和截距，确定所述地面的平整度时，具体用于：根据所述斜率的值和所述截距的值，确定所述残差的加权平方和的值；以及根据所述残差的加权平方和的值，确定所述地面的平整度。
  21. 根据权利要求20所述的设备，其特征在于，所述处理器，具体用于：
    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和截距。
  22. 根据权利要求21所述的设备，其特征在于，所述第一预设值、所述第二预设值为0。
  23. 根据权利要求20-22任意一项所述的设备，其特征在于，每个第一测距数据对应的残差的加权系数均相等；或者，
    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雷达的旋转角度的三角函数或者高斯函数。
  24. 根据权利要求23所述的设备，其特征在于，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。
  25. 根据权利要求16-24任意一项所述的设备，其特征在于，所述处理器，具体用于：
    获取雷达在旋转过程中对地面测距的M个第二测距数据；所述M个第二测距数据为所述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M为大于等于N的整数；
    根据所述M个第二测距数据，获取所述N个第一测距数据。
  26. 根据权利要求25所述的设备，其特征在于，所述处理器，具体用于：
    根据所述M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。
  27. 根据权利要求26所述的设备，其特征在于，所述处理器，具体用于：
    从所述M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。
  28. 根据权利要求27所述的设备，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。
  29. 根据权利要求28所述的设备，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且小于等于所述N-1的整数。
  30. 根据权利要求25-29任意一项所述的设备，其特征在于，所述处理器，具体用于：
    获取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雷达的旋转角度所对应的第二测距数据为所述M个第二测距数据。
  31. 根据权利要求16-30任意一项所述的设备，其特征在于，所述设备为雷达，或者，所述设备为无人机，或者，所述设备为无人机的控制终端。
  32. 一种无人机，其特征在于，包括：雷达以及地形预测设备，所述地形预测设备与所述雷达通信连接；
    所述地形预测设备为如权利要求16-30任意一项所述的地形预测设备。
  33. 一种地形预测系统，其特征在于，包括：无人机和控制终端，所述无人机与所述控制终端通信连接；所述控制终端用于控制所述无人机；
    所述无人机上搭载上雷达；所述控制终端包括如权利要求16-30任意一项所述的地形预测设备。
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