WO2021087785A1 - 地形检测方法、可移动平台、控制设备、***及存储介质 - Google Patents
地形检测方法、可移动平台、控制设备、***及存储介质 Download PDFInfo
- Publication number
- WO2021087785A1 WO2021087785A1 PCT/CN2019/115827 CN2019115827W WO2021087785A1 WO 2021087785 A1 WO2021087785 A1 WO 2021087785A1 CN 2019115827 W CN2019115827 W CN 2019115827W WO 2021087785 A1 WO2021087785 A1 WO 2021087785A1
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- ground point
- ground
- observation
- target area
- position information
- Prior art date
Links
- 238000001514 detection method Methods 0.000 title claims abstract description 71
- 239000011159 matrix material Substances 0.000 claims abstract description 80
- 238000005070 sampling Methods 0.000 claims abstract description 44
- 238000000034 method Methods 0.000 claims abstract description 29
- 230000006870 function Effects 0.000 claims description 52
- 238000004590 computer program Methods 0.000 claims description 23
- 238000006243 chemical reaction Methods 0.000 claims description 22
- 230000008569 process Effects 0.000 claims description 15
- 238000010276 construction Methods 0.000 claims description 5
- 238000007476 Maximum Likelihood Methods 0.000 claims description 4
- 238000003064 k means clustering Methods 0.000 claims description 4
- 238000000611 regression analysis Methods 0.000 claims description 4
- 238000010586 diagram Methods 0.000 description 9
- 238000012545 processing Methods 0.000 description 7
- RZVHIXYEVGDQDX-UHFFFAOYSA-N 9,10-anthraquinone Chemical compound C1=CC=C2C(=O)C3=CC=CC=C3C(=O)C2=C1 RZVHIXYEVGDQDX-UHFFFAOYSA-N 0.000 description 4
- 238000005259 measurement Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 230000001133 acceleration Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 210000002304 esc Anatomy 0.000 description 1
- 238000011478 gradient descent method Methods 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
- 238000013519 translation Methods 0.000 description 1
- 230000017105 transposition Effects 0.000 description 1
Images
Classifications
-
- 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
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
- G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
- G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
- G01C21/165—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/20—Instruments for performing navigational calculations
-
- 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
Definitions
- This application relates to the technical field of terrain detection, and in particular to a terrain detection method, a movable platform, a control device, a system, and a storage medium.
- the present application provides a terrain detection method, a movable platform, a control device, a system, and a storage medium to detect the terrain of the area that has not been scanned, thereby ensuring the safe operation of the movable platform.
- this application provides a terrain detection method, which includes:
- the terrain information of the target area is determined according to the height position information of each ground point.
- this application also provides a movable platform, which includes a detection device, a memory, and a processor;
- the detection device is used for terrain detection and collecting observation data of the scanning area
- the memory is used to store a computer program
- the processor is configured to execute the computer program and, when executing the computer program, implement the following steps:
- the terrain information of the target area is determined according to the height position information of each ground point.
- the present application also provides a control device, the control device including a memory and a processor;
- the memory is used to store a computer program
- the processor is configured to execute the computer program and, when executing the computer program, implement the steps of the above-mentioned terrain detection method, and send the determined terrain information to the movable platform.
- the present application also provides a control system, the control system includes an aircraft and the control device as described in the third aspect; wherein the movable platform is used to sample a target area to obtain the target area The corresponding ground point set, and the observation data of the scanning area are collected, and the ground point set and the observation data are sent to the control device.
- the present application also provides a computer-readable storage medium that stores a computer program, and when the computer program is executed by a processor, the processor implements the above-mentioned terrain detection method.
- the terrain detection method, movable platform, control equipment, system and storage medium proposed in this application can improve the prediction accuracy of terrain information of unscanned target areas and ensure the safe operation of the movable platform.
- Fig. 1 is a schematic block diagram of a control system provided by an embodiment of the present application
- FIG. 2 is a schematic structural diagram of an aircraft provided by an embodiment of the present application.
- FIG. 3 is a schematic flowchart of steps of a terrain detection method provided by an embodiment of the present application.
- FIG. 4 is a schematic diagram of sampling for uniformly sampling a target area provided by an embodiment of the present application.
- FIG. 5 is a schematic block diagram of a movable platform provided by an embodiment of the present application.
- Fig. 6 is a schematic block diagram of a control device provided by an embodiment of the present application.
- the embodiments of the present application provide a terrain detection method, a movable platform, a control device, a control system, and a storage medium, which are used to predict the terrain of the scanning blind area of the detection device, so as to determine the terrain information in the unscanned target area. To ensure the safe operation of the movable platform.
- the ground point set is obtained by sampling the ground points in the target area, and then the covariance matrix corresponding to the ground point set is constructed according to the observation data corresponding to the scanning area and the ground point set, and according to the The covariance matrix determines the height position information of the ground point to determine the terrain information of the target area according to the height position information of the ground point, realizes the terrain prediction of the scanning blind area of the detection device, and improves the prediction accuracy of the terrain information of the unscanned target area rate.
- control system includes a movable platform and control equipment.
- the movable platform includes an aircraft, a robot, or an autonomous vehicle, etc.
- the movable platform is equipped with a detection device, and the detection device includes a radar, a ranging sensor, etc.
- the detection device includes a radar, a ranging sensor, etc.
- this application uses the detection device as a radar for detailed introduction .
- control equipment includes a remote control, a ground control platform, a mobile phone, a tablet computer, a notebook computer, a PC computer, and the like.
- control system is a terrain detection system
- terrain detection system 100 includes an aircraft 110 and a control device 120.
- the aircraft 110 includes a drone, which includes a rotary-wing drone, such as a four-rotor drone, a hexarotor drone, and an eight-rotor drone. It can also be a fixed-wing drone or a rotary-wing drone. The combination of type and fixed-wing UAV is not limited here.
- FIG. 2 is a schematic structural diagram of an aircraft 110 according to an embodiment of the present specification.
- a rotary wing unmanned aerial vehicle is taken as an example for description.
- the aircraft 110 may include a power system, a flight control system, and a frame.
- the aircraft 110 may communicate with the control device 120 wirelessly, and the control device 120 may display flight information of the aircraft.
- the control device 120 may communicate with the aircraft 110 in a wireless manner for remote control of the aircraft 110.
- the frame may include a fuselage 111 and a tripod 112 (also referred to as a landing gear).
- the fuselage 111 may include a center frame 1111 and one or more arms 1112 connected to the center frame 1111, and the one or more arms 1112 extend radially from the center frame.
- the tripod 112 is connected to the fuselage 111 for supporting the aircraft 110 when the aircraft 110 is landing.
- the power system may include one or more electronic governors (referred to as ESCs for short), one or more propellers 113, and one or more motors 114 corresponding to the one or more propellers 113, where the motors 114 are connected to the electronic governors.
- the motor 114 and the propeller 113 are arranged on the arm 1112 of the aircraft 110; the electronic governor is used to receive the driving signal generated by the flight control system, and provide driving current to the motor according to the driving signal to control the motor 114 speed.
- the motor 114 is used to drive the propeller 113 to rotate, so as to provide power for the flight of the aircraft 110, and the power enables the aircraft 110 to realize movement of one or more degrees of freedom.
- the aircraft 110 may rotate about one or more rotation axes.
- the aforementioned rotation axis may include a roll axis, a yaw axis, and a pitch axis.
- the motor 114 may be a DC motor or an AC motor.
- the motor 114 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 unmanned aerial vehicle, that is, the position information and state information of the aerial vehicle 110 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 sensors such as 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 the Global Positioning System (GPS).
- the flight controller is used to control the flight of the aircraft 110, for example, it can control the flight of the aircraft 110 according to the attitude information measured by the sensing system. It should be understood that the flight controller may control the aircraft 110 according to pre-programmed program instructions, or may control the aircraft 110 by responding to one or more control instructions from the control device 120.
- the tripod 112 of the aircraft 110 is equipped with a radar 115, and the radar 115 is used to realize the function of surveying terrain information.
- the aircraft 110 may include two or more tripods 112, and the radar 115 is mounted on one of the tripods 112.
- the radar mainly includes an RF front-end module and a signal processing module.
- the RF front-end module includes a transmitting antenna and a receiving antenna.
- the signal processing module is responsible for generating modulated signals and processing and analyzing the collected intermediate frequency signals.
- the RF front-end module receives the modulated signal to generate a high-frequency signal whose frequency changes linearly with the modulated signal, and radiates downward through the transmitting antenna.
- the electromagnetic wave encounters the ground, targets or obstacles and is reflected back, and then is received by the receiving antenna and transmitted
- the signal and the intermediate frequency are mixed to obtain an intermediate frequency signal, and the speed information and distance information can be obtained according to the frequency of the intermediate frequency signal.
- the radar When the radar is used to scan in the area to be scanned, the radar encounters a target object by radiating electromagnetic waves in space, and the echo scattered by the target object is received by the radar to detect the target object.
- the radar When the radar is flying with the movable platform, it continuously collects observation data by radiating electromagnetic waves, but the radar will generate a scanning blind area in the area to be scanned, and it is unable to collect the observation data in the target area corresponding to the scanning blind area.
- FIG. 3 is a schematic flowchart of steps of a terrain detection method according to an embodiment of the present application.
- the method can be applied to control equipment or aircraft to predict the terrain in the scan blind area that has not been scanned, and determine the terrain information in the scan blind area to ensure the safe operation of the movable platform.
- the terrain detection method will be introduced in detail below in conjunction with the control system in FIG. 1. It should be understood that the control system in FIG. 1 does not constitute a limitation on the application scenario of the terrain detection method.
- the terrain detection method includes step S101 to step S105.
- the unscanned target area refers to the scan blind area adjacent to the scan area.
- the area to be scanned includes the target area and the scanning area.
- the radar scans the area to be scanned for terrain detection, due to geographical conditions or electromagnetic wave propagation characteristics, there will be a scanning blind area in radar scanning. In order to scan the blind area, that is, it is not scanned. To predict the terrain in the target area, it is necessary to sample the ground points in the target area.
- the step of sampling the ground points of the target area to obtain a set of ground points corresponding to the target area includes: performing multiple random sampling on the target area to obtain multiple ground points, and the multiple ground points constitute the ground Point collection.
- the target area can be randomly and irregularly sampled multiple times in a certain direction to obtain multiple spatial sampling points, and the ground point corresponding to each sampling point is determined as the sampled ground point according to the multiple sampling points. Form a collection of ground points.
- the target area can be randomly and irregularly sampled multiple times in a certain direction according to the varying spatial step length to obtain multiple spatial sampling points.
- the space step refers to the distance between two sampling points in space.
- the step of sampling the ground points of the target area to obtain a set of ground points corresponding to the target area includes: uniformly sampling the target area multiple times at a preset spatial step interval to obtain multiple ground points,
- the multiple ground points constitute a ground point set.
- FIG. 4 it is a sampling schematic diagram for uniformly sampling the target area, where the filled part in the picture is the scanning area, the blank part is the target area, and the intersection points in the picture are the ground points obtained by sampling.
- the target area can be uniformly sampled multiple times in a certain direction according to the preset spatial step interval to obtain multiple spatial sampling points.
- the ground point corresponding to each sampling point is determined as the ground obtained by sampling. Points, which constitute a collection of ground points.
- the space step interval can be 0.2m, with the edge of the target area as the starting sampling point, and the ground points corresponding to the target area are sampled every 0.2m along the direction parallel to the ground until the entire target area is completed. Sampling, respectively determining the ground points corresponding to multiple sampling points, and the obtained multiple ground points constitute a ground point set.
- the target area is uniformly sampled according to a certain spatial step length to obtain ground points, thereby discretizing the entire target area into multiple grids with preset side lengths.
- the side length of the grid is also the preset spatial step interval, thereby improving Determine the accuracy of the terrain information in the target area according to the ground points.
- the observation data includes position information and height position information of the observation point in the scanning area
- the position information includes first position information and second position information.
- the coordinates of the observation point in the radar coordinate system are marked as (x A , y A , z A ), where x A is the first position information of the observation point, y A is the second position information of the observation point, and z A is the height position information of the observation point.
- the first position information, the second position information, and the height position information are perpendicular to each other.
- the first position information includes a depth-of-field detection distance
- the second position information includes a horizontal detection distance.
- the method further includes: performing coordinate conversion on the coordinates of the observation points in the observation data, and clustering the observation points in the observation data after the coordinate conversion according to a clustering algorithm To get rid of the noise.
- the radar mounted on the aircraft may have different observation data obtained by scanning the point due to the change of the flying attitude of the aircraft. Therefore, in order to improve the accuracy of the observation data and reduce the influence of external factors such as the flight attitude of the aircraft, the coordinates of the observation points in the observation data can be converted.
- the step of performing coordinate conversion on the coordinates of the observation point in the observation data includes:
- the detection device is used to detect the scanning area to obtain observation data; according to the posture quaternion, the coordinates of the observation point in the observation data are converted from the first coordinate system to the second coordinate system , Wherein the first coordinate system and the second coordinate system are different.
- the first coordinate system includes a radar coordinate system
- the second coordinate system includes a geodetic coordinate system.
- the geodetic coordinate system used is ENU (East-North-UP coordinate system, northeast sky coordinate system).
- the coordinates of the observation point in the geodetic coordinate system are marked as (x G , y G , z G ), where x G is the upward distance of the observation point relative to the origin of the coordinate, and y G is the observation The distance of the point relative to the origin of the coordinates in the east direction, z G is the distance in the vertical direction of the observation point relative to the origin of the coordinates.
- q 0 , q 1 , q 2 , and q 3 are the four attitudes of the radar respectively.
- x i,j , y i,j and z i,j represent the coordinates of the point (i,j).
- the posture quaternion rotation matrix Specifically:
- the clustering algorithm may include: K-MEANS clustering algorithm, mean shift clustering algorithm, DBSCAN algorithm clustering, maximum expectation clustering, and one of hierarchical clustering algorithms.
- clustering using the DBSCAN algorithm and clustering the observation points in the observation data after coordinate conversion according to the clustering algorithm to remove the clutter includes:
- Clustering based on the DBSCAN algorithm clustering the observation points according to the density of the observation points in the observation data after the coordinate conversion to eliminate the clutter.
- the DBSCAN algorithm is specifically based on a set of neighborhood parameters to describe the tightness of the distribution of observation points, thereby generating clusters.
- the neighborhood parameters include ⁇ and MinPts, where ⁇ represents the minimum distance between two points. If the distance between two observation points is less than or equal to this value, the two observation points are considered adjacent points. In an embodiment, if the distance between two observation points is less than or equal to this value, it is considered that the two observation points belong to the same point cluster.
- MinPts represents the minimum number of points that form a dense area.
- the DBSCAN algorithm first determines the core observation point from all observation points in the observation data according to the neighborhood parameters, and then uses any core observation point as the starting point to find other observation points whose density is reachable. Class clusters until all core observation points have been visited. Points that are not in the cluster are regarded as noise points and eliminated.
- the covariance matrix corresponding to the ground point set is constructed according to the observation data and the ground point set, so as to use the calculated covariance matrix and the height position information of the observation point in the observation data to predict the height position information corresponding to the ground point.
- the step of constructing a covariance matrix corresponding to the ground point set according to the observation data and the ground point set includes:
- a covariance matrix including the position information of the ground point is constructed according to the position information of the observation point in the observation data and the position information of a ground point in the ground point set.
- the covariance matrix corresponding to the ground point set is a set of covariance matrices of each ground point in the ground point set, and the covariance matrix of each ground point belongs to a joint normal distribution.
- the location information includes first location information and second location information, and the first location information is different from the second location information.
- the first location information is the x coordinate in the geodetic coordinate system
- the second location The information is the y coordinate in the geodetic coordinate system.
- the Gaussian process regression includes: determining a kernel function, and determining the hyperparameters of the kernel function.
- the kernel function includes one of spline kernel function, polynomial kernel function, perceptron kernel function and Gaussian kernel function.
- this application takes a Gaussian kernel function as an example for detailed description.
- the Gaussian kernel function formula is as follows:
- l is the scale parameter, which reflects the correlation between the two variables a and b, and ⁇ controls the variance of the overall regression.
- a represents the location information of the observation point A1
- b represents the location information of the observation point B1.
- a and b can be coordinates (x A1 , y A1 , z A1 ), (x B1 , y B1 , z B1 ) in the geodetic coordinate system of the observation point A1 and the observation point B1.
- the step of determining the hyperparameters of the kernel function includes: using a maximum likelihood method to determine the hyperparameters of the kernel function.
- the hyperparameters can be solved by constructing the likelihood function to maximize the posterior distribution of the hyperparameter ⁇ to determine the hyperparameters in the Gaussian kernel function.
- the constructed likelihood function is:
- x, y, z are the first position information, second position information, and height position information of the known observation point
- K is the covariance matrix of the observation point
- z T represents the transposition matrix of matrix z
- n is the Know the number of observation points.
- the gradient descent method can be used to find the optimal value of the hyperparameter ⁇ , as shown below:
- the observation data of some observation points can be randomly given, that is, the values of x, y, and z in the formula to solve the hyperparameter ⁇ .
- the height position information of each ground point in the ground point set is determined, specifically because the height position information of the ground point belongs to a one-dimensional normal distribution, so the normal distribution formula is used to calculate the normal
- the mean value of the state distribution the mean value is recorded as the estimated value of the height position information of the ground point.
- the step of constructing a covariance matrix including the position information of the ground point according to the position information of the observation point in the observation data and the position information of a ground point in the ground point set includes:
- the kernel function for determining the hyperparameters construct the covariance matrix of the observation points in the observation data according to the position information of the observation points in the observation data; according to the covariance matrix of the observation points and the ground point
- the position information of a ground point in the set constructs a covariance matrix including the position information of the ground point.
- the Gaussian process is the position information (x, y) of a given observation point, the height position information z of the observation point is modeled, and it is assumed that the corresponding height position information z obeys a joint normal distribution.
- Z is the height position information of N observation points
- M is the mean value of the joint normal distribution
- K is the variance of the joint normal distribution.
- k sE (X i , X j ) refers to the covariance between the position information (x, y) of the i-th observation point and the position information (x, y) of the j-th observation point.
- the step of determining the height position information of each ground point in the ground point set according to the covariance matrix includes:
- ⁇ * is the mean value of the normal distribution, that is, the Gaussian process regression prediction value of the height position information z * of the ground point (x * , y * ), that is, the height position information of the ground point in the target area.
- a covariance matrix can be constructed for unpredicted ground points based on predicted ground points and observation points in the scanning area to predict the height position information of the unpredicted ground points.
- S105 Determine the terrain information of the target area according to the height and position information of each ground point.
- the terrain information includes one or more of ground height, ground flatness, and ground slope.
- the spatial point corresponding to the ground point can be determined in the three-dimensional space, and the fitting plane can be obtained by fitting multiple spatial points.
- the ground height, ground slope, and ground can be extracted by fitting the plane.
- Terrain information such as flatness.
- the step of determining the terrain information of the target area according to the height position information of each ground point includes:
- Fitting is performed according to the position information and height position information of each ground point in the ground point set to obtain a fitting plane of the target area; and the terrain information of the target area is determined according to the fitting plane.
- the spatial point corresponding to the ground point can be determined in the three-dimensional space.
- the fitting plane of the target area can be obtained, and the fitting plane can be used. Extract terrain information such as ground height, ground slope, and ground flatness of the target area.
- the average value is calculated according to the height position information of multiple spatial points in the fitting plane, and the ground flatness of the scanning area is determined according to the average value.
- the mean value is calculated according to the residuals of the multiple spatial points of the fitting plane, and the ground flatness of the scanning area is determined according to the mean value.
- the slope of the fitting plane is determined according to the height position information of multiple spatial points.
- the slope of the scanning area is determined according to the changing trend of the heights of the multiple spatial points along a certain horizontal direction.
- the terrain detection method further includes: determining the terrain information of the scanning area according to the observation data of the scanning area.
- the observation data of each observation point in the scanning area is fitted to obtain a fitting plane corresponding to the scanning area, and the topographic information of the scanning area can be extracted through the fitting plane.
- the terrain detection method further includes: splicing the observation data of the scanning area with the prediction data of the target area to obtain splicing data; The stitching data is fitted to obtain a fitting plane of the scanning area and the target area, and topographic information of the scanning area and the target area is determined according to the fitting plane.
- the predicted data of the target area includes the predicted value of the height position information of the ground point of the target area and the position information of the ground point.
- the observation data of the scanning area and the prediction data of the target area are spliced to obtain a complete splicing data of the area to be scanned, where the area to be scanned includes the target area and the scanning area.
- the stitched data is fitted to obtain a complete fitting plane of the area to be scanned, so as to determine the topographic information of the area to be scanned according to the fitted plane.
- the observation data of the scanning area and the prediction data of the target area are spliced to obtain the splicing data of the entire area to be scanned, so that the terrain of the entire area to be scanned can be predicted, which improves the continuity and completeness of terrain prediction.
- the above embodiment obtains the ground point set by acquiring the target area and sampling the ground points of the target area, and then constructs the covariance matrix corresponding to the ground point set according to the observation data corresponding to the scanning area and the ground point set, and determines the ground point set according to the covariance matrix
- the height position information of each ground point in the, and finally the terrain information of the target area is determined according to the height position information of each ground point. Realize the prediction of terrain information of the unscanned target area and improve the accuracy of terrain prediction.
- FIG. 5 is a schematic block diagram of a movable platform provided by an embodiment of the present application.
- the mobile platform 11 includes a processor 111, a memory 112, and a detection device 113.
- the processor 111, the memory 112, and the detection device 113 are connected by a bus, such as an I2C (Inter-integrated Circuit) bus or the detection device 113 and the processing device 113.
- the device 111 is connected via the CAN bus.
- the movable platform includes aircraft, robots or autonomous unmanned vehicles.
- the processor 111 may be a micro-controller unit (MCU), a central processing unit (Central Processing Unit, CPU), a digital signal processor (Digital Signal Processor, DSP), or the like.
- MCU micro-controller unit
- CPU Central Processing Unit
- DSP Digital Signal Processor
- the memory 112 may be a Flash chip, a read-only memory (ROM, Read-Only Memory) disk, an optical disk, a U disk, or a mobile hard disk.
- the detection device 113 is used for terrain detection and collecting observation data of the scanning area.
- the processor is used to run a computer program stored in a memory, and implement the following steps when executing the computer program:
- the terrain information of the target area is determined according to the height position information of each ground point.
- the target area is a scan dead zone adjacent to the scan area.
- the method before the processor implements the step of constructing a covariance matrix corresponding to the ground point set according to the observation data and the ground point set, the method includes:
- Coordinate conversion is performed on the coordinates of the observation points in the observation data, and the observation points in the observation data after the coordinate conversion are clustered according to the clustering algorithm to eliminate the miscellaneous points.
- the processor implementing the step of performing coordinate conversion on the coordinates of the observation point in the observation data includes:
- the coordinates of the observation point in the observation data are converted from a first coordinate system to a second coordinate system according to the posture quaternion, wherein the first coordinate system and the second coordinate system are different.
- the first coordinate system includes a radar coordinate system
- the second coordinate system includes a geodetic coordinate system
- the clustering algorithm includes one of K-MEANS clustering algorithm, mean shift clustering algorithm, DBSCAN algorithm clustering, maximum expectation clustering, and hierarchical clustering algorithm.
- the processor implementing the step of clustering the observation points in the coordinate-converted observation data according to the clustering algorithm to eliminate the clutter includes:
- Clustering based on the DBSCAN algorithm clustering the observation points according to the density of the observation points in the observation data after the coordinate conversion to eliminate the clutter.
- the processor implementing the step of constructing a covariance matrix corresponding to the ground point set according to the observation data and the ground point set includes:
- the covariance matrix corresponding to the ground point set is a set of covariance matrices of each ground point in the ground point set, and the covariance matrix of each ground point belongs to a joint normal distribution.
- the Gaussian process regression includes: determining a kernel function, and determining the hyperparameters of the kernel function.
- the determining the hyperparameters of the kernel function includes: using a maximum likelihood method to determine the hyperparameters of the kernel function.
- the processor implements the construction of a covariance matrix including the position information of the ground point based on the position information of the observation point in the observation data and the position information of a ground point in the ground point set
- the steps include:
- a covariance matrix including the position information of the ground point is constructed.
- the kernel function includes one of a spline kernel function, a polynomial kernel function, a perceptron kernel function, and a Gaussian kernel function.
- the processor implementing the step of determining the height position information of each ground point in the ground point set according to the covariance matrix includes:
- the location information includes first location information and second location information, and the first location information and the second location information are different.
- the step of performing ground point sampling on the target area by the processor to obtain a ground point set corresponding to the target area includes:
- the target area is uniformly sampled multiple times at a preset spatial step interval to obtain multiple ground points, and the multiple ground points constitute a ground point set.
- the step of performing ground point sampling on the target area by the processor to obtain a ground point set corresponding to the target area includes:
- Random sampling is performed on the target area multiple times to obtain multiple ground points, and the multiple ground points constitute a ground point set.
- the processor further implements: determining topographic information of the scanning area according to the observation data of the scanning area.
- the processor implementing the step of determining the terrain information of the target area according to the height position information of each ground point includes:
- the terrain information of the target area is determined according to the fitting plane.
- the terrain information includes one or more of ground height, ground flatness, and ground slope.
- the processor further implements:
- FIG. 6 is a schematic block diagram of a control device provided by an embodiment of the present application.
- the control device 12 includes a processor 121 and a memory 122, and the processor 121 and the memory 122 are connected by a bus, such as an I2C (Inter-integrated Circuit) bus.
- I2C Inter-integrated Circuit
- the processor 121 may be a micro-controller unit (MCU), a central processing unit (CPU), a digital signal processor (Digital Signal Processor, DSP), or the like.
- MCU micro-controller unit
- CPU central processing unit
- DSP Digital Signal Processor
- the memory 122 may be a Flash chip, a read-only memory (ROM, Read-Only Memory) disk, an optical disk, a U disk, or a mobile hard disk, etc.
- the memory 122 is used to store computer programs.
- the processor is used to run a computer program stored in a memory, and implement the following steps when executing the computer program:
- the terrain information of the target area is determined according to the height position information of each ground point, and the determined terrain information is sent to a movable platform.
- the embodiment of the present application also provides a control system, which may be, for example, the flight control system shown in FIG. 1.
- the control system includes a movable platform and a control device, and the control device is communicatively connected with the movable platform;
- the movable platform is used to sample a target area to obtain a set of ground points corresponding to the target area, and to collect observation data of the scanning area, and send the set of ground points and the observation data to the control equipment.
- the embodiments of the present application also provide a computer-readable storage medium, the computer-readable storage medium stores a computer program, the computer program includes program instructions, and the processor executes the program instructions to implement the foregoing implementation The steps of the terrain detection method provided in the example.
- the computer-readable storage medium may be the internal storage unit of the removable platform or the control device described in any of the foregoing embodiments, for example, the hard disk or memory of the removable platform.
- the computer-readable storage medium may also be an external storage device of the removable platform, such as a plug-in hard disk equipped on the removable platform, a smart memory card (Smart Media Card, SMC), and Secure Digital (Secure Digital). , SD) card, flash card (Flash Card), etc.
Landscapes
- Engineering & Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Aviation & Aerospace Engineering (AREA)
- Electromagnetism (AREA)
- Computer Networks & Wireless Communication (AREA)
- Radar Systems Or Details Thereof (AREA)
Abstract
一种地形检测方法、飞行器可移动平台、控制设备、***及存储介质,该方法包括:获取目标区域,对目标区域进行采样得到地面点集合(S101);获取扫描区域对应的观测数据(S102);根据观测数据和地面点集合构建协方差矩阵(S103);根据协方差矩阵确定地面点的高度位置信息(S104);根据高度位置信息确定目标区域的地形信息(S105)。
Description
本申请涉及地形检测技术领域,尤其涉及一种地形检测方法、可移动平台、控制设备、***及存储介质。
目前,在无人飞行器、自主作业机器人等自主作业过程中,通常会采用雷达、超声测距或者是机器视觉等方法扫描地面以获取地形信息。但在扫描过程中,一方面受视场角所限,另一方面受到探测距离的限制,对于探测距离范围外的区域无法进行探测,导致在扫描过程中会存在扫描盲区。由于扫描盲区的存在导致地形测量不够完整和准确,因此保证无人飞行器的安全飞行。
发明内容
基于此,本申请提供了一种地形检测方法、可移动平台、控制设备、***及存储介质,以对未扫描到的区域的地形进行检测,进而保证可移动平台的安全运行。
第一方面,本申请提供了一种地形检测方法,所述方法包括:
获取未扫描的目标区域,对所述目标区域进行地面点采样以得到所述目标区域对应的地面点集合;
获取扫描区域对应的观测数据;
根据所述观测数据和所述地面点集合构建所述地面点集合对应的协方差矩阵;
根据所述协方差矩阵,确定所述地面点集合中每个地面点的高度位置信息;以及
根据所述每个地面点的高度位置信息确定所述目标区域的地形信息。
第二方面,本申请还提供了一种可移动平台,所述可移动平台包括检测装 置、存储器和处理器;
所述检测装置用于地形检测并采集扫描区域的观测数据;
所述存储器用于存储计算机程序;
所述处理器,用于执行所述计算机程序并在执行所述计算机程序时,实现如下步骤:
获取未扫描的目标区域,对所述目标区域进行地面点采样以得到所述目标区域对应的地面点集合;
获取扫描区域对应的观测数据;
根据所述观测数据和所述地面点集合构建所述地面点集合对应的协方差矩阵;
根据所述协方差矩阵,确定所述地面点集合中每个地面点的高度位置信息;以及
根据所述每个地面点的高度位置信息确定所述目标区域的地形信息。
第三方面,本申请还提供了一种控制设备,所述控制设备包括存储器和处理器;
所述存储器用于存储计算机程序;
所述处理器,用于执行所述计算机程序并在执行所述计算机程序时,实现如上述地形检测方法的步骤,并将确定的地形信息发送至可移动平台。
第四方面,本申请还提供了一种控制***,所述控制***包括飞行器和如第三方面所述的控制设备;其中,所述可移动平台用于对目标区域进行采样得到所述目标区域对应的地面点集合,以及采集扫描区域的观测数据,并将所述地面点集合和所述观测数据发送至所述控制设备。
第五方面,本申请还提供了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,所述计算机程序被处理器执行时使所述处理器实现上述的地形检测方法。
本申请提出的一种地形检测方法、可移动平台、控制设备、***及存储介质,可提高对未扫描的目标区域的地形信息的预测准确率,确保了可移动平台的安全运行。
应当理解的是,以上的一般描述和后文的细节描述仅是示例性和解释性的,并不能限制本申请。
为了更清楚地说明本申请实施例技术方案,下面将对实施例描述中所需要使用的附图作简单地介绍,显而易见地,下面描述中的附图是本申请的一些实施例,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是本申请一实施例提供的一种控制***的示意性框图;
图2是本申请一实施例提供的飞行器的示意性架构图;
图3是本申请一实施例提供的一种地形检测方法的步骤示意流程图;
图4是本申请一实施例提供的对目标区域进行均匀采样的采样示意图;
图5是本申请一实施例提供的可移动平台的示意性框图;
图6是本申请一实施例提供的控制设备的示意性框图。
下面将结合本申请实施例中的附图,对本申请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申请一部分实施例,而不是全部的实施例。基于本申请中的实施例,本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例,都属于本申请保护的范围。
附图中所示的流程图仅是示例说明,不是必须包括所有的内容和操作/步骤,也不是必须按所描述的顺序执行。例如,有的操作/步骤还可以分解、组合或部分合并,因此实际执行的顺序有可能根据实际情况改变。
下面结合附图,对本申请的一些实施方式作详细说明。在不冲突的情况下,下述的实施例及实施例中的特征可以相互组合。
本申请的实施例提供了一种地形检测方法、可移动平台、控制设备、控制***及存储介质,用于对检测装置的扫描盲区进行地形预测,从而确定未扫描的目标区域内的地形信息,以确保可移动平台的安全运行。
具体来说,在本申请的实施方式中,通过对目标区域的地面点进行采样得到地面点集合,然后根据扫描区域对应的观测数据和地面点集合构建地面点集合对应的协方差矩阵,根据该协方差矩阵确定地面点的高度位置信息,以根据 地面点的高度位置信息确定目标区域的地形信息,实现对检测装置的扫描盲区进行地形预测,提高对未扫描的目标区域的地形信息的预测准确率。
其中,该控制***包括可移动平台和控制设备。
示例性的,可移动平台包括飞行器、机器人或自动驾驶车辆等,可移动平台上搭载有检测装置,检测装置包括雷达、测距传感器等,为便于描述,本申请以检测装置为雷达进行详细介绍。
示例性的,控制设备包括遥控器、地面控制平台、手机、平板电脑、笔记本电脑和PC电脑等。
示例性的,如图1所示,控制***为地形检测***,该地形检测***100包括飞行器110和控制设备120。
飞行器110包括无人机,该无人机包括旋翼型无人机,例如四旋翼无人机、六旋翼无人机、八旋翼无人机,也可以是固定翼无人机,还可以是旋翼型与固定翼无人机的组合,在此不作限定。
图2是根据本说明书的实施例的飞行器110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。
飞行器110可以包括动力***、飞行控制***和机架。飞行器110可以与控制设备120进行无线通信,该控制设备120可以显示飞行器的飞行信息等,控制设备120可以通过无线方式与飞行器110进行通信,用于对飞行器110进行远程操纵。
其中,机架可以包括机身111和脚架112(也称为起落架)。机身111可以包括中心架1111以及与中心架1111连接的一个或多个机臂1112,一个或多个机臂1112呈辐射状从中心架延伸出。脚架112与机身111连接,用于在飞行器110着陆时起支撑作用。
动力***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113以及与一个或多个螺旋桨113相对应的一个或多个电机114,其中电机114连接在电子调速器与螺旋桨113之间,电机114和螺旋桨113设置在飞行器110的机臂1112上;电子调速器用于接收飞行控制***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114的转速。电机114用于驱动螺旋桨113旋转,从而为飞行器110的飞行提供动力,该动力使得飞行器110能够实现一个或多个自由度的运动。
在某些实施例中,飞行器110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114可以是直流电机,也可以交流电机。另外,电机114可以是无刷电机,也可以是有刷电机。
飞行控制***可以包括飞行控制器和传感***。传感***用于测量无人飞行器的姿态信息,即飞行器110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***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***和气压计等传感器中的至少一种。例如,全球导航卫星***可以是全球定位***(Global Positioning System,GPS)。飞行控制器用于控制飞行器110的飞行,例如,可以根据传感***测量的姿态信息控制飞行器110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飞行器110进行控制,也可以通过响应来自控制设备120的一个或多个控制指令对飞行器110进行控制。
如图1所示,飞行器110的脚架112上搭载有雷达115,该雷达115用于实现对地形信息进行勘测的功能。其中,飞行器110可以包括两个或两个以上脚架112,雷达115搭载在其中一个脚架112上。
雷达主要包括射频前端模块和信号处理模块,射频前端模块包括一个发射天线和一个接收天线,信号处理模块负责产生调制信号以及对采集的中频信号进行处理分析。
具体地,射频前端模块接收到调制信号产生频率随调制信号线性变化的高频信号,通过发射天线向下辐射,电磁波遇到地面、目标物或障碍物被反射回来,再被接收天线接收,发射信号与中频进行混频得到中频信号,根据中频信号的频率就可得到速度信息和距离信息。
在使用雷达在待扫描区域内进行扫描时,雷达通过辐射电磁波在空间中传播遇到目标物,由目标物散射回波被雷达接收实现探测目标物。雷达在随可移动平台飞行过程中,通过辐射电磁波不断采集观测数据,但雷达在待扫描区域内会产生扫描盲区,无法采集到扫描盲区对应的目标区域内的观测数据。
在此情况下,需要对扫描盲区的地形进行预测,现有的对地形信息进行预测的方法大多是根据扫描到的地形信息,采用经验模型对扫描点进行拟合,得到拟合平面,利用拟合平面对未扫描区域的空间方位信息进行预测。但经验模 型选用不恰当对导致拟合平面预测的地形与实际结果的偏差较大,并且拟合会平滑地形变化剧烈区域的地形,使得该区域丢失地形特征。对于扫描盲区的地形特征预测不准确会影响可移动平台的作业和飞行安全。因此有必要提高对扫描盲区的地形预测的准确率。
应理解,上述对于飞行器各组成部分的命名仅是出于标识的目的,并不应理解为对本说明书的实施例的限制。
请参阅图3,图3是本申请一实施例提供的一种地形检测方法的步骤示意流程图。该方法可以应用于控制设备或飞行器中,用于对未扫描到的扫描盲区内的地形进行预测,并确定扫描盲区内的地形信息,以确保可移动平台的安全运行。
以下将结合图1中控制***对该地形检测方法进行详细介绍。需知,图1中的控制***并不构成对该地形检测方法的应用场景的限定。
如图3所示,该地形检测方法包括步骤S101至步骤S105。
S101、获取未扫描的目标区域,对所述目标区域进行地面点采样以得到所述目标区域对应的地面点集合。
其中,未扫描的目标区域是指与扫描区域相邻的扫描盲区。待扫描区域包括目标区域和扫描区域,当雷达在对待扫描区域进行扫描以进行地形检测时,由于地理条件或电磁波传播特性等原因,雷达扫描会存在扫描盲区,为了对扫描盲区,也即未扫描的目标区域内的地形进行预测,需要对目标区域进行地面点采样。
示例性的,对目标区域进行地面点采样以得到目标区域对应的地面点集合的步骤,包括:对所述目标区域进行多次随机采样,得到多个地面点,所述多个地面点构成地面点集合。
具体地,可沿某个方向上对目标区域进行多次随机不规则采样,得到多个空间上的采样点,根据多个采样点确定各个采样点对应的地面点为采样得到的地面点,从而构成地面点集合。
示例性的,当然可以按照变化的空间步长沿某个方向上对目标区域进行多次随机不规则采样,得到多个空间上的采样点。其中,空间步长是指空间上两个采样点之间的距离。
例如,以目标区域的边缘为起始采样点,在沿与地面平行的方向上依次以 0.2m、0.3m、0.4m、0.5m的空间步长间隔进行采样,得到共五个采样点,分别确定这五个采样点对应的地面点,五个地面点构成地面点集合。
示例性的,对目标区域进行地面点采样以得到目标区域对应的地面点集合的步骤,包括:以预设的空间步长间隔对所述目标区域进行多次均匀采样,得到多个地面点,所述多个地面点构成地面点集合。
其中,如图4所示,为对目标区域进行均匀采样的采样示意图,其中,图中填充部分为扫描区域,空白部分为目标区域,图中的交叉点为采样得到的地面点。可沿某个方向上按照预设的空间步长间隔对目标区域进行多次均匀采样,得到多个空间上的采样点,根据多个采样点确定各个采样点对应的地面点为采样得到的地面点,从而构成地面点集合。
例如,空间步长间隔可以取0.2m,以目标区域的边缘为起始采样点,在沿与地面平行的方向上每隔0.2m对目标区域对应的地面点进行采样,直至对整个目标区域完成采样,分别确定多个采样点对应的地面点,得到的多个地面点构成地面点集合。
对目标区域按照一定的空间步长进行均匀采样得到地面点,从而将整个目标区域离散为多个预设边长的网格,网格的边长也即预设的空间步长间隔,从而提高根据地面点确定目标区域内的地形信息的准确度。
S102、获取扫描区域对应的观测数据。
其中,所述观测数据包括扫描区域内观测点的位置信息和高度位置信息,位置信息包括第一位置信息和第二位置信息。在本申请的实施例中,为了便于表述,将观测点在雷达坐标系下的坐标记为(x
A,y
A,z
A),其中,x
A即为观测点的第一位置信息,y
A即为观测点的第二位置信息,z
A即为观测点的高度位置信息。其中,第一位置信息、第二位置信息和高度位置信息三者互相垂直。根据本发明的一实施方式,第一位置信息包括景深探测距离,第二位置信息包括水平探测距离。
在一些实施例中,在获取扫描区域对应的观测数据后,还包括:对所述观测数据中观测点的坐标进行坐标转换,根据聚类算法对坐标转换后的观测数据中观测点进行聚类以剔除杂点。
其中,对于大地上的某一点而言,可能由于飞行器飞行姿态的变化,导致搭载在飞行器上的雷达对于该点扫描得到的观测数据有所不同。因此,为了提 高观测数据的准确性,降低飞行器的飞行姿态等外部因素的影响,可以对观测数据中观测点的坐标进行坐标转换。
示例性的,对所述观测数据中观测点的坐标进行坐标转换的步骤,包括:
获取检测装置的姿态四元数,所述检测装置用于检测扫描区域得到观测数据;根据所述姿态四元数将所述观测数据中观测点的坐标从第一坐标系转换成第二坐标系,其中,所述第一坐标系和所述第二坐标系不同。
其中,所述第一坐标系包括雷达坐标系,所述第二坐标系包括大地坐标系。在本申请的实施例中,所采用的大地坐标系为ENU(East-North-UP coordinate system,东北天坐标系)。为了便于表述,将观测点在大地坐标系下的坐标记为(x
G,y
G,z
G),其中,x
G即为观测点相对于坐标原点正北方向上的距离,y
G即为观测点相对于坐标原点正东方向上的距离,z
G即为观测点相对于坐标原点垂直方向上的距离。
在进行坐标转换时,可以利用以下公式进行坐标转换:
其中,
为齐次变换矩阵,
为姿态四元数旋转矩阵,
为雷达的姿态四元数,用于计算雷达当前时刻雷达的姿态信息,q
0,q
1,q
2,q
3分别为雷达的四个姿态。
为雷达坐标系到大地坐标系的平移向量,x
i,j、y
i,j、z
i,j表示点(i,j)的坐标。
由于观测数据中杂点的存在会对最终的地形预测结果产生影响,因此为了 提高对于未扫描的目标区域的地形预测的准确率,需要使用聚类算法对坐标转换后的观测数据中的杂点进行剔除。
其中,聚类算法可以包括:K-MEANS聚类算法、均值漂移聚类算法、DBSCAN算法聚类、最大期望聚类和层次聚类算法中的一种。
示例性的,采用DBSCAN算法聚类,根据聚类算法对坐标转换后的观测数据中观测点进行聚类以剔除杂点的步骤,包括:
基于DBSCAN算法聚类,根据所述坐标转换后的观测数据中观测点的密集程度,对所述观测点进行聚类以剔除杂点。
其中,DBSCAN算法具体是基于一组邻域参数来刻画观测点分布的紧密程度,从而生成聚类簇。其中,邻域参数包括∈和MinPts,其中,∈表示两点之间的最小距离,如果两个观测点之间的距离小于或等于该值,则认为这两个观测点是相邻的点。在一实施方式中,如果两个观测点之间的距离小于或等于该值,则认为这两个观测点归属于同一点簇。MinPts表示形成密集区域的最小点数。
在具体实施过程中,DBSCAN算法首先根据邻域参数从观测数据中所有观测点中确定出核心观测点,然后以任一核心观测点为出发点,找出由其密度可达的其他观测点生成聚类簇,直到所有核心观测点均被访问过为止。未处于聚类簇内的点则被视为杂点进行剔除。
S103、根据所述观测数据和所述地面点集合构建所述地面点集合对应的协方差矩阵。
其中,根据观测数据和地面点集合构建地面点集合对应的协方差矩阵,以便利用计算得到的协方差矩阵,和观测数据中观测点的高度位置信息,对地面点对应的高度位置信息进行预测。
在一些实施例中,根据所述观测数据和所述地面点集合构建所述地面点集合对应的协方差矩阵的步骤,包括:
基于高斯过程回归,根据所述观测数据中观测点的位置信息和所述地面点集合中一个地面点的位置信息构建包括所述地面点的位置信息的协方差矩阵。
其中,所述地面点集合对应的协方差矩阵为所述地面点集合中每个地面点的协方差矩阵的集合,每个所述地面点的协方差矩阵均属于联合正态分布。所述位置信息包括第一位置信息和第二位置信息,所述第一位置信息和第二位置信息不同,在本申请中,第一位置信息为在大地坐标系下的x坐标,第二位置 信息为在大地坐标系下的y坐标。
示例性的,所述高斯过程回归包括:确定核函数,并确定所述核函数的超参数。
其中,核函数包括样条核函数、多项式核函数、感知器核函数和高斯核函数中的一种。为了便于理解,本申请以高斯核函数为例进行详细说明。高斯核函数公式如下:
其中,l为尺度参数,其体现的是两个变量a,b之间的相关性,σ控制整体回归的方差。上式中,a表示观测点A1的位置信息,b表示观测点B1的位置信息。也就是说,a,b可以为观测点A1和观测点B1大地坐标系下的坐标(x
A1,y
A1,z
A1)、(x
B1,y
B1,z
B1)。
由于高斯过程回归的回归效果很大程度取决于核函数的形式,选取了合适的核函数之后,就要对核函数的参数进行估计。为了便于求解高斯核函数中的参数,可以根据高斯核函数中的参数l和σ构建一个超参数θ,其中,超参数θ为参数l和σ的集合,也即超参数θ={l,σ}。
示例性的,所述确定所述核函数的超参数的步骤,包括:利用极大似然法确定所述核函数的超参数。
具体地,可以通过构建似然函数,使得超参数θ的后验分布最大化来求解超参数,以确定高斯核函数中的超参数。
具体地,对于超参数θ,构建的似然函数为:
其中,x,y,z分别为已知观测点的第一位置信息、第二位置信息和高度位置信息,K为观测点的协方差矩阵,z
T表示矩阵z的转置矩阵,n为已知观测点的数量。
对于构建的关于超参数θ的似然函数,可以采用梯度下降法求解超参数θ的最优值,如下所示:
对上述公式进行求解,可以随机给定一些观测点的观测数据,也即式中x,y,z的值,以求解超参数θ。
S104、根据所述协方差矩阵,确定所述地面点集合中每个地面点的高度位置信息。
其中,根据协方差矩阵,确定地面点集合中每个地面点的高度位置信息,具体是由于地面点的高度位置信息隶属于一个一维正态分布,因此,利用正态分布的公式计算该正态分布的均值,均值记为地面点的高度位置信息的估计值。
在一些实施例中,所述根据所述观测数据中观测点的位置信息和所述地面点集合中一个地面点的位置信息构建包括所述地面点的位置信息的协方差矩阵的步骤,包括:
利用确定所述超参数的核函数,根据所述观测数据中的观测点的位置信息构建所述观测数据中的观测点的协方差矩阵;根据所述观测点的协方差矩阵和所述地面点集合中一个地面点的位置信息构建包括所述地面点的位置信息的协方差矩阵。
其中,高斯过程是给定观测点的位置信息(x,y),对观测点的高度位置信息z进行建模,并且假设对应的这些高度位置信息z服从联合正态分布。
也即,对于已知的多个观测点,根据观测点的位置信息有如下的联合正态分布:
令:
其中,Z为N个观测点的高度位置信息,M为该联合正态分布的均值,K为该联合正态分布的方差。其中,k
sE(X
i,X
j)是指第i个观测点的位置信息(x,y)和第j个观测点的位置信息(x,y)之间的协方差。
在确定观测点的协方差矩阵K后,根据目标区域中的地面点(x
*,y
*),对该地面点的高度位置信息z
*进行建模,并且假设z
*与观测点的高度位置信息z属于同一个联合正态分布,则有:
计算每一个地面点和每一个观测点的位置信息之间的样本距离并利用高斯核计算得到上述联合正态分布的协方差矩阵,也即K
*,如下:
在获取到地面点的协方差矩阵K
*后,利用K
*和N个观测点的高度位置信息Z,即可对地面点(x
*,y
*)所对应的高度位置信息z
*进行回归预测,以确定地面点的高度位置信息。
示例性的,所述根据所述协方差矩阵确定所述地面点集合中每个地面点的高度位置信息的步骤,包括:
根据所述观测点的协方差矩阵对所述地面点集合中每个地面点的协方差矩阵进行回归分析,得到每个地面点的高度位置信息。
其中,由于对于地面点的高度位置信息所服从的联合正态分布中的所有参数均为已知,因此,利用公式即可得到z
*属于一个一维的正态分布,参数为:
z
*~N(μ
*,σ
*)
μ
*=K
*K
-1Z
其中,μ
*为该正态分布的均值,也即为地面点(x
*,y
*)的高度位置信息z
*的高斯过程回归预测值,即是目标区域中的地面点的高度位置信息。
需要说明的是,可以根据已经预测的地面点和扫描区域中的观测点对未预测的地面点构建协方差矩阵,以预测该未预测的地面点的高度位置信息。
S105、根据所述每个地面点的高度位置信息确定所述目标区域的地形信息。
其中,地形信息包括地面高度、地面平整度、地面坡度中的一项或多项。根据地面点的高度位置信息即可在三维空间中确定与地面点对应的空间点,通过对多个空间点进行拟合得到拟合平面,通过拟合平面即可提取地面高度、地面坡度、地面平整度等地形信息。
示例性的,所述根据所述每个地面点的高度位置信息确定所述目标区域的地形信息的步骤,包括:
根据所述地面点集合中每个地面点的位置信息和高度位置信息进行拟合,以得到所述目标区域的拟合平面;根据所述拟合平面确定所述目标区域的地形信息。
其中根据地面点的位置信息和高度位置信息即可在三维空间中确定与地面点对应的空间点,通过对多个空间点进行拟合,得到目标区域的拟合平面,通过拟合平面即可提取目标区域的地面高度、地面坡度、地面平整度等地形信息。
示例性的,根据拟合平面中多个空间点的高度位置信息计算均值,根据均值确定扫描区域的地面平整度。在一个实施方式中,根据拟合平面多个空间点的残差计算均值,根据均值确定扫描区域的地面平整度。
示例性的,依据多个空间点的高度位置信息,确定拟合平面的坡度。具体地,在一个实施方式中,依据多个空间点的高度沿某一水平方向的变化趋势,确定扫描区域的坡度。
在一些实施例中,该地形检测方法还包括:根据所述扫描区域的观测数据确定所述扫描区域的地形信息。
其中,对扫描区域各个观测点的观测数据进行拟合,得到扫描区域对应的拟合平面,通过该拟合平面即可提取出扫描区域的地形信息。
在一些实施例中,为了提高得到的地形信息的连续性和完整度,该地形检测方法还包括:将所述扫描区域的观测数据与所述目标区域的预测数据进行拼接,得到拼接数据;对所述拼接数据进行拟合以得到所述扫描区域和目标区域的拟合平面,根据所述拟合平面确定所述扫描区域和目标区域的地形信息。
其中,目标区域的预测数据包括目标区域的地面点的高度位置信息的预测值和地面点的位置信息。将扫描区域的观测数据和目标区域的预测数据进行拼接,得到一个完整的待扫描区域的拼接数据,其中,待扫描区域包括目标区域和扫描区域。然后对拼接数据进行拟合,得到完整的待扫描区域的拟合平面,从而根据该拟合平面确定待扫描区域的地形信息。
将扫描区域的观测数据与目标区域的预测数据进行拼接,得到整个待扫描区域的拼接数据,从而可以对整个待扫描区域的地形进行预测,提高了地形预测的连续性和完整度。
上述实施例通过获取目标区域并对目标区域进行地面点采样得到地面点集合,然后根据扫描区域对应的观测数据和地面点集合构建地面点集合对应的协方差矩阵,根据协方差矩阵确定地面点集合中各个地面点的高度位置信息,最终根据各个地面点的高度位置信息确定目标区域的地形信息。实现对未扫描的目标区域的地形信息的预测,提高地形预测的准确率。
请参阅图5,图5是本申请一实施例提供的可移动平台的示意性框图。该可移动平台11包括处理器111、存储器112和检测装置113,处理器111、存储器112和检测装置113通过总线连接,该总线比如为I2C(Inter-integrated Circuit)总线或者,检测装置113与处理器111通过CAN总线连接。
其中,该可移动平台包括飞行器、机器人或自动无人驾驶车辆等。
具体地,处理器111可以是微控制单元(Micro-controller Unit,MCU)、中央处理单元(Central Processing Unit,CPU)或数字信号处理器(Digital Signal Processor,DSP)等。
具体地,存储器112可以是Flash芯片、只读存储器(ROM,Read-Only Memory)磁盘、光盘、U盘或移动硬盘等。
具体地,检测装置113用于地形检测并采集扫描区域的观测数据。
其中,所述处理器用于运行存储在存储器中的计算机程序,并在执行所述计算机程序时实现如下步骤:
获取未扫描的目标区域,对所述目标区域进行地面点采样以得到所述目标区域对应的地面点集合;
获取扫描区域对应的观测数据;
根据所述观测数据和所述地面点集合构建所述地面点集合对应的协方差矩阵;
根据所述协方差矩阵,确定所述地面点集合中每个地面点的高度位置信息;以及
根据所述每个地面点的高度位置信息确定所述目标区域的地形信息。
在一些实施例中,所述目标区域为所述扫描区域相邻的扫描盲区。
在一些实施例中,所述处理器实现所述根据所述观测数据和所述地面点集合构建所述地面点集合对应的协方差矩阵的步骤之前,包括:
对所述观测数据中观测点的坐标进行坐标转换,根据聚类算法对坐标转换后的观测数据中观测点进行聚类以剔除杂点。
在一些实施例中,所述处理器实现所述对所述观测数据中观测点的坐标进行坐标转换的步骤,包括:
获取检测装置的姿态四元数,所述检测装置用于检测扫描区域得到观测数据;
根据所述姿态四元数将所述观测数据中观测点的坐标从第一坐标系转换成第二坐标系,其中,所述第一坐标系和所述第二坐标系不同。
在一些实施例中,所述第一坐标系包括雷达坐标系,所述第二坐标系包括大地坐标系。
在一些实施例中,所述聚类算法包括K-MEANS聚类算法、均值漂移聚类算法、DBSCAN算法聚类、最大期望聚类和层次聚类算法中的一种。
在一些实施例中,所述处理器实现所述根据聚类算法对坐标转换后的观测数据中观测点进行聚类以剔除杂点的步骤,包括:
基于DBSCAN算法聚类,根据所述坐标转换后的观测数据中观测点的密集程度,对所述观测点进行聚类以剔除杂点。
在一些实施例中,所述处理器实现所述根据所述观测数据和所述地面点集合构建所述地面点集合对应的协方差矩阵的步骤,包括:
基于高斯过程回归,根据所述观测数据中观测点的位置信息和所述地面点 集合中一个地面点的位置信息构建包括所述地面点的位置信息的协方差矩阵;
其中,所述地面点集合对应的协方差矩阵为所述地面点集合中每个地面点的协方差矩阵的集合,每个所述地面点的协方差矩阵均属于联合正态分布。
在一些实施例中,所述高斯过程回归包括:确定核函数,并确定所述核函数的超参数。
在一些实施例中,所述确定所述核函数的超参数,包括:利用极大似然法确定所述核函数的超参数。
在一些实施例中,所述处理器实现所述根据所述观测数据中观测点的位置信息和所述地面点集合中一个地面点的位置信息构建包括所述地面点的位置信息的协方差矩阵的步骤,包括:
利用确定所述超参数的核函数,根据所述观测数据中的观测点的位置信息构建所述观测数据中的观测点的协方差矩阵;
根据所述观测点的协方差矩阵和所述地面点集合中一个地面点的位置信息构建包括地面点的位置信息的协方差矩阵。
在一些实施例中,所述核函数包括样条核函数、多项式核函数、感知器核函数和高斯核函数中的一种。
在一些实施例中,所述处理器实现所述根据所述协方差矩阵确定所述地面点集合中每个地面点的高度位置信息的步骤,包括:
根据所述观测点的协方差矩阵对所述地面点集合中每个地面点的协方差矩阵进行回归分析,得到每个地面点的高度位置信息。
在一些实施例中,所述位置信息包括第一位置信息和第二位置信息,所述第一位置信息和第二位置信息不同。
在一些实施例中,所述处理器实现所述对所述目标区域进行地面点采样以得到所述目标区域对应的地面点集合的步骤,包括:
以预设的空间步长间隔对所述目标区域进行多次均匀采样,得到多个地面点,所述多个地面点构成地面点集合。
在一些实施例中,所述处理器实现所述对所述目标区域进行地面点采样以得到所述目标区域对应的地面点集合的步骤,包括:
对所述目标区域进行多次随机采样,得到多个地面点,所述多个地面点构成地面点集合。
在一些实施例中,所述处理器还实现:根据所述扫描区域的观测数据确定所述扫描区域的地形信息。
在一些实施例中,所述处理器实现所述根据所述每个地面点的高度位置信息确定所述目标区域的地形信息的步骤,包括:
根据所述地面点集合中每个地面点的位置信息和高度位置信息进行拟合,以得到所述目标区域的拟合平面;
根据所述拟合平面确定所述目标区域的地形信息。
在一些实施例中,所述地形信息包括地面高度、地面平整度、地面坡度中的一项或多项。
在一些实施例中,所述处理器还实现:
将所述扫描区域的观测数据与所述目标区域的预测数据进行拼接,得到拼接数据;
对所述拼接数据进行拟合以得到所述扫描区域和目标区域的拟合平面,根据所述拟合平面确定所述扫描区域和目标区域的地形信息。
请参阅图6,图6是本申请一实施例提供的控制设备的示意性框图。该控制设备12包括处理器121和存储器122,处理器121和存储器122通过总线连接,该总线比如为I2C(Inter-integrated Circuit)总线。
具体地,处理器121可以是微控制单元(Micro-controller Unit,MCU)、中央处理单元(Central Processing Unit,CPU)或数字信号处理器(Digital Signal Processor,DSP)等。
具体地,存储器122可以是Flash芯片、只读存储器(ROM,Read-Only Memory)磁盘、光盘、U盘或移动硬盘等,存储器122用于存储计算机程序。
其中,所述处理器用于运行存储在存储器中的计算机程序,并在执行所述计算机程序时实现如下步骤:
获取未扫描的目标区域,对所述目标区域进行地面点采样以得到所述目标区域对应的地面点集合;
获取扫描区域对应的观测数据;
根据所述观测数据和所述地面点集合构建所述地面点集合对应的协方差矩阵;
根据所述协方差矩阵,确定所述地面点集合中每个地面点的高度位置信息; 以及
根据所述每个地面点的高度位置信息确定所述目标区域的地形信息,并将确定的地形信息发送至可移动平台。
本申请的实施例还提供了一种控制***,可例如为图1所示的飞行控制***,所述控制***包括可移动平台和控制设备,所述控制设备与所述可移动平台通信连接;
其中,所述可移动平台用于对目标区域进行采样得到所述目标区域对应的地面点集合,以及采集扫描区域的观测数据,并将所述地面点集合和所述观测数据发送至所述控制设备。
本申请的实施例中还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,所述计算机程序中包括程序指令,所述处理器执行所述程序指令,实现上述实施例提供的地形检测方法的步骤。
其中,所述计算机可读存储介质可以是前述任一实施例所述的可移动平台或控制设备的内部存储单元,例如所述可移动平台的硬盘或内存。所述计算机可读存储介质也可以是所述可移动平台的外部存储设备,例如所述可移动平台上配备的插接式硬盘,智能存储卡(Smart Media Card,SMC),安全数字(Secure Digital,SD)卡,闪存卡(Flash Card)等。
以上所述,仅为本申请的具体实施方式,但本申请的保护范围并不局限于此,任何熟悉本技术领域的技术人员在本申请揭露的技术范围内,可轻易想到各种等效的修改或替换,这些修改或替换都应涵盖在本申请的保护范围之内。因此,本申请的保护范围应以权利要求的保护范围为准。
Claims (43)
- 一种地形检测方法,其特征在于,包括:获取未扫描的目标区域,对所述目标区域进行地面点采样以得到所述目标区域对应的地面点集合;获取扫描区域对应的观测数据;根据所述观测数据和所述地面点集合构建所述地面点集合对应的协方差矩阵;根据所述协方差矩阵,确定所述地面点集合中每个地面点的高度位置信息;以及根据所述每个地面点的高度位置信息确定所述目标区域的地形信息。
- 根据权利要求1所述的地形检测方法,其特征在于,所述目标区域为所述扫描区域相邻的扫描盲区。
- 根据权利要求1所述的地形检测方法,其特征在于,所述根据所述观测数据和所述地面点集合构建所述地面点集合对应的协方差矩阵之前,还包括:对所述观测数据中观测点的坐标进行坐标转换,根据聚类算法对坐标转换后的观测数据中观测点进行聚类以剔除杂点。
- 根据权利要求3所述的地形检测方法,其特征在于,所述对所述观测数据中观测点的坐标进行坐标转换,包括:获取检测装置的姿态四元数,所述检测装置用于检测扫描区域得到观测数据;根据所述姿态四元数将所述观测数据中观测点的坐标从第一坐标系转换成第二坐标系,其中,所述第一坐标系和所述第二坐标系不同。
- 根据权利要求4所述的地形检测方法,其特征在于,所述第一坐标系包括雷达坐标系,所述第二坐标系包括大地坐标系。
- 根据权利要求3所述的地形检测方法,其特征在于,所述聚类算法包括K-MEANS聚类算法、均值漂移聚类算法、DBSCAN算法聚类、最大期望聚类和层次聚类算法中的一种。
- 根据权利要求6所述的地形检测方法,其特征在于,所述根据聚类算法 对坐标转换后的观测数据中观测点进行聚类以剔除杂点,包括:基于DBSCAN算法聚类,根据所述坐标转换后的观测数据中观测点的密集程度,对所述观测点进行聚类以剔除杂点。
- 根据权利要求1至7任一项所述的地形检测方法,其特征在于,所述根据所述观测数据和所述地面点集合构建所述地面点集合对应的协方差矩阵,包括:基于高斯过程回归,根据所述观测数据中观测点的位置信息和所述地面点集合中一个地面点的位置信息构建包括所述地面点的位置信息的协方差矩阵;其中,所述地面点集合对应的协方差矩阵为所述地面点集合中每个地面点的协方差矩阵的集合,每个所述地面点的协方差矩阵均属于联合正态分布。
- 根据权利要求8所述的地形检测方法,其特征在于,所述高斯过程回归包括:确定核函数,并确定所述核函数的超参数。
- 根据权利要求9所述的地形检测方法,其特征在于,所述确定所述核函数的超参数,包括:利用极大似然法确定所述核函数的超参数。
- 根据权利要求9所述的地形检测方法,其特征在于,所述根据所述观测数据中观测点的位置信息和所述地面点集合中一个地面点的位置信息构建包括所述地面点的位置信息的协方差矩阵,包括:利用确定所述超参数的核函数,根据所述观测数据中的观测点的位置信息构建所述观测数据中的观测点的协方差矩阵;根据所述观测点的协方差矩阵和所述地面点集合中一个地面点的位置信息构建包括所述地面点的位置信息的协方差矩阵。
- 根据权利要求9所述的地形检测方法,其特征在于,所述核函数包括样条核函数、多项式核函数、感知器核函数和高斯核函数中的一种。
- 根据权利要求11所述的地形检测方法,其特征在于,所述根据所述协方差矩阵确定所述地面点集合中每个地面点的高度位置信息,包括:根据所述观测点的协方差矩阵对所述地面点集合中每个地面点的协方差矩阵进行回归分析,得到每个地面点的高度位置信息。
- 根据权利要求8所述的地形检测方法,其特征在于,所述位置信息包括第一位置信息和第二位置信息,所述第一位置信息和第二位置信息不同。
- 根据权利要求1所述的地形检测方法,其特征在于,所述对所述目标 区域进行地面点采样以得到所述目标区域对应的地面点集合,包括:以预设的空间步长间隔对所述目标区域进行多次均匀采样,得到多个地面点,所述多个地面点构成地面点集合。
- 根据权利要求1所述的地形检测方法,其特征在于,所述对所述目标区域进行地面点采样以得到所述目标区域对应的地面点集合,包括:对所述目标区域进行多次随机采样,得到多个地面点,所述多个地面点构成地面点集合。
- 根据权利要求1所述的地形检测方法,其特征在于,还包括:根据所述扫描区域的观测数据确定所述扫描区域的地形信息。
- 根据权利要求1所述的地形检测方法,其特征在于,所述根据所述每个地面点的高度位置信息确定所述目标区域的地形信息,包括:根据所述地面点集合中每个地面点的位置信息和高度位置信息进行拟合,以得到所述目标区域的拟合平面;根据所述拟合平面确定所述目标区域的地形信息。
- 根据权利要求1所述的地形检测方法,其特征在于,所述地形信息包括地面高度、地面平整度、地面坡度中的一项或多项。
- 根据权利要求1所述的地形检测方法,其特征在于,所述方法还包括:将所述扫描区域的观测数据与所述目标区域的预测数据进行拼接,得到拼接数据;对所述拼接数据进行拟合以得到所述扫描区域和目标区域的拟合平面,根据所述拟合平面确定所述扫描区域和目标区域的地形信息。
- 一种可移动平台,其特征在于,所述可移动平台包括检测装置、存储器和处理器;所述检测装置用于地形检测并采集扫描区域的观测数据;所述存储器用于存储计算机程序;所述处理器,用于执行所述计算机程序并在执行所述计算机程序时,实现如下步骤:获取未扫描的目标区域,对所述目标区域进行地面点采样以得到所述目标区域对应的地面点集合;获取扫描区域对应的观测数据;根据所述观测数据和所述地面点集合构建所述地面点集合对应的协方差矩阵;根据所述协方差矩阵,确定所述地面点集合中每个地面点的高度位置信息;以及根据所述每个地面点的高度位置信息确定所述目标区域的地形信息。
- 根据权利要求21所述的可移动平台,其特征在于,所述目标区域为所述扫描区域相邻的扫描盲区。
- 根据权利要求21所述的可移动平台,其特征在于,所述处理器实现所述根据所述观测数据和所述地面点集合构建所述地面点集合对应的协方差矩阵的步骤之前,包括:对所述观测数据中观测点的坐标进行坐标转换,根据聚类算法对坐标转换后的观测数据中观测点进行聚类以剔除杂点。
- 根据权利要求23所述的可移动平台,其特征在于,所述处理器实现所述对所述观测数据中观测点的坐标进行坐标转换的步骤,包括:获取检测装置的姿态四元数,所述检测装置用于检测扫描区域得到观测数据;根据所述姿态四元数将所述观测数据中观测点的坐标从第一坐标系转换成第二坐标系,其中,所述第一坐标系和所述第二坐标系不同。
- 根据权利要求24所述的可移动平台,其特征在于,所述第一坐标系包括雷达坐标系,所述第二坐标系包括大地坐标系。
- 根据权利要求23所述的可移动平台,其特征在于,所述聚类算法包括K-MEANS聚类算法、均值漂移聚类算法、DBSCAN算法聚类、最大期望聚类和层次聚类算法中的一种。
- 根据权利要求26所述的可移动平台,其特征在于,所述处理器实现所述根据聚类算法对坐标转换后的观测数据中观测点进行聚类以剔除杂点的步骤,包括:基于DBSCAN算法聚类,根据所述坐标转换后的观测数据中观测点的密集程度,对所述观测点进行聚类以剔除杂点。
- 根据权利要求21至27任一项所述的可移动平台,其特征在于,所述处理器实现所述根据所述观测数据和所述地面点集合构建所述地面点集合对应 的协方差矩阵的步骤,包括:基于高斯过程回归,根据所述观测数据中观测点的位置信息和所述地面点集合中一个地面点的位置信息构建包括所述地面点的位置信息的协方差矩阵;其中,所述地面点集合对应的协方差矩阵为所述地面点集合中每个地面点的协方差矩阵的集合,每个所述地面点的协方差矩阵均属于联合正态分布。
- 根据权利要求28所述的可移动平台,其特征在于,所述高斯过程回归包括:确定核函数,并确定所述核函数的超参数。
- 根据权利要求29所述的可移动平台,其特征在于,所述确定所述核函数的超参数,包括:利用极大似然法确定所述核函数的超参数。
- 根据权利要求29所述的可移动平台,其特征在于,所述处理器实现所述根据所述观测数据中观测点的位置信息和所述地面点集合中一个地面点的位置信息构建包括所述地面点的位置信息的协方差矩阵的步骤,包括:利用确定所述超参数的核函数,根据所述观测数据中的观测点的位置信息构建所述观测数据中的观测点的协方差矩阵;根据所述观测点的协方差矩阵和所述地面点集合中一个地面点的位置信息构建包含所述地面点的位置信息的协方差矩阵。
- 根据权利要求29所述的可移动平台,其特征在于,所述核函数包括样条核函数、多项式核函数、感知器核函数和高斯核函数中的一种。
- 根据权利要求31所述的可移动平台,其特征在于,所述处理器实现所述根据所述协方差矩阵确定所述地面点集合中每个地面点的高度位置信息的步骤,包括:根据所述观测点的协方差矩阵对所述地面点集合中每个地面点的协方差矩阵进行回归分析,得到每个地面点的高度位置信息。
- 根据权利要求28所述的可移动平台,其特征在于,所述位置信息包括第一位置信息和第二位置信息,所述第一位置信息和第二位置信息不同。
- 根据权利要求21所述的可移动平台,其特征在于,所述处理器实现所述对所述目标区域进行地面点采样以得到所述目标区域对应的地面点集合的步骤,包括:以预设的空间步长间隔对所述目标区域进行多次均匀采样,得到多个地面点,所述多个地面点构成地面点集合。
- 根据权利要求21所述的可移动平台,其特征在于,所述处理器实现所述对所述目标区域进行地面点采样以得到所述目标区域对应的地面点集合的步骤,包括:对所述目标区域进行多次随机采样,得到多个地面点,所述多个地面点构成地面点集合。
- 根据权利要求21所述的可移动平台,其特征在于,所述处理器还实现:根据所述扫描区域的观测数据确定所述扫描区域的地形信息。
- 根据权利要求21所述的可移动平台,其特征在于,所述处理器实现所述根据所述每个地面点的高度位置信息确定所述目标区域的地形信息的步骤,包括:根据所述地面点集合中每个地面点的位置信息和高度位置信息进行拟合,以得到所述目标区域的拟合平面;根据所述拟合平面确定所述目标区域的地形信息。
- 根据权利要求21所述的可移动平台,其特征在于,所述地形信息包括地面高度、地面平整度、地面坡度中的一项或多项。
- 根据权利要求21所述的可移动平台,其特征在于,所述处理器还实现:将所述扫描区域的观测数据与所述目标区域的预测数据进行拼接,得到拼接数据;对所述拼接数据进行拟合以得到所述扫描区域和目标区域的拟合平面,根据所述拟合平面确定所述扫描区域和目标区域的地形信息。
- 一种控制设备,其特征在于,所述控制设备包括存储器和处理器;所述存储器用于存储计算机程序;所述处理器,用于执行所述计算机程序并在执行所述计算机程序时,实现如权利要求1-20中任一项所述地形检测方法的步骤,并将确定的地形信息发送至可移动平台。
- 一种控制***,其特征在于,包括可移动平台和如权利要求41所述的控制设备;其中,所述可移动平台用于对目标区域进行采样得到所述目标区域对应的地面点集合,以及采集扫描区域的观测数据,并将所述地面点集合和所述观测数据发送至所述控制设备。
- 一种计算机可读存储介质,其特征在于,所述计算机可读存储介质存 储有计算机程序,所述计算机程序被处理器执行时使所述处理器实现如权利要求1至20中任一项所述的地形检测方法。
Priority Applications (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
PCT/CN2019/115827 WO2021087785A1 (zh) | 2019-11-05 | 2019-11-05 | 地形检测方法、可移动平台、控制设备、***及存储介质 |
CN201980032944.8A CN112154351A (zh) | 2019-11-05 | 2019-11-05 | 地形检测方法、可移动平台、控制设备、***及存储介质 |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
PCT/CN2019/115827 WO2021087785A1 (zh) | 2019-11-05 | 2019-11-05 | 地形检测方法、可移动平台、控制设备、***及存储介质 |
Publications (1)
Publication Number | Publication Date |
---|---|
WO2021087785A1 true WO2021087785A1 (zh) | 2021-05-14 |
Family
ID=73891489
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
PCT/CN2019/115827 WO2021087785A1 (zh) | 2019-11-05 | 2019-11-05 | 地形检测方法、可移动平台、控制设备、***及存储介质 |
Country Status (2)
Country | Link |
---|---|
CN (1) | CN112154351A (zh) |
WO (1) | WO2021087785A1 (zh) |
Cited By (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN115979127A (zh) * | 2023-03-20 | 2023-04-18 | 山东欧诺威数控刀具有限公司 | 一种顶尖精度及钢性检测方法 |
Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN103489218A (zh) * | 2013-09-17 | 2014-01-01 | 中国科学院深圳先进技术研究院 | 点云数据质量自动优化方法及*** |
US20140187272A1 (en) * | 2012-12-27 | 2014-07-03 | Jaroslaw J. Sydir | Cellular network scanning control based on ambient identifiable wireless signal sources |
CN104019803A (zh) * | 2014-05-16 | 2014-09-03 | 东华理工大学 | 基于双船模式的水域、滩涂及岸坡地理空间信息测量平台 |
CN105651202A (zh) * | 2016-02-29 | 2016-06-08 | 内蒙古伊泰煤炭股份有限公司 | 一种用于测量矿山体积的三维扫描方法及装置 |
CN105842676A (zh) * | 2016-06-16 | 2016-08-10 | 成都中科合迅科技有限公司 | 一种雷达盲区分析方法及装置 |
Family Cites Families (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
AUPR301401A0 (en) * | 2001-02-09 | 2001-03-08 | Commonwealth Scientific And Industrial Research Organisation | Lidar system and method |
US10274325B2 (en) * | 2016-11-01 | 2019-04-30 | Brain Corporation | Systems and methods for robotic mapping |
KR101977458B1 (ko) * | 2017-03-06 | 2019-05-10 | 지엠 글로벌 테크놀러지 오퍼레이션스 엘엘씨 | 레이더 센서와 upa 센서를 이용하는 충돌 예측 알고리즘 |
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、***和无人机 |
CN109871602A (zh) * | 2019-01-30 | 2019-06-11 | 西安工程大学 | 一种基于高斯过程回归的临界热流密度预测方法 |
CN109801508B (zh) * | 2019-02-26 | 2021-06-04 | 百度在线网络技术(北京)有限公司 | 路口处障碍物的运动轨迹预测方法及装置 |
-
2019
- 2019-11-05 WO PCT/CN2019/115827 patent/WO2021087785A1/zh active Application Filing
- 2019-11-05 CN CN201980032944.8A patent/CN112154351A/zh active Pending
Patent Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20140187272A1 (en) * | 2012-12-27 | 2014-07-03 | Jaroslaw J. Sydir | Cellular network scanning control based on ambient identifiable wireless signal sources |
CN103489218A (zh) * | 2013-09-17 | 2014-01-01 | 中国科学院深圳先进技术研究院 | 点云数据质量自动优化方法及*** |
CN104019803A (zh) * | 2014-05-16 | 2014-09-03 | 东华理工大学 | 基于双船模式的水域、滩涂及岸坡地理空间信息测量平台 |
CN105651202A (zh) * | 2016-02-29 | 2016-06-08 | 内蒙古伊泰煤炭股份有限公司 | 一种用于测量矿山体积的三维扫描方法及装置 |
CN105842676A (zh) * | 2016-06-16 | 2016-08-10 | 成都中科合迅科技有限公司 | 一种雷达盲区分析方法及装置 |
Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN115979127A (zh) * | 2023-03-20 | 2023-04-18 | 山东欧诺威数控刀具有限公司 | 一种顶尖精度及钢性检测方法 |
CN115979127B (zh) * | 2023-03-20 | 2023-06-30 | 山东欧诺威数控刀具有限公司 | 一种顶尖精度及钢性检测方法 |
Also Published As
Publication number | Publication date |
---|---|
CN112154351A (zh) | 2020-12-29 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
US10878243B2 (en) | Method, device and apparatus for generating electronic map, storage medium, and acquisition entity | |
Kaul et al. | Continuous‐time three‐dimensional mapping for micro aerial vehicles with a passively actuated rotating laser scanner | |
US11829142B2 (en) | Unmanned aerial vehicle adaptable to obstructions | |
US9830411B2 (en) | System and method for locating impacts on an external surface | |
JP6029446B2 (ja) | 自律飛行ロボット | |
WO2020103049A1 (zh) | 旋转微波雷达的地形预测方法、装置、***和无人机 | |
JP2019504418A (ja) | 移動物体の位置を判定するための方法およびシステム | |
WO2021087701A1 (zh) | 起伏地面的地形预测方法、装置、雷达、无人机和作业控制方法 | |
WO2021081958A1 (zh) | 地形检测方法、可移动平台、控制设备、***及存储介质 | |
US20200380667A1 (en) | Method and system for servicing an object | |
JP7436657B2 (ja) | 飛行撮影システム及び方法 | |
WO2021087702A1 (zh) | 坡地的地形预测方法、装置、雷达、无人机和作业控制方法 | |
Dill et al. | Seamless indoor-outdoor navigation for unmanned multi-sensor aerial platforms | |
WO2021087785A1 (zh) | 地形检测方法、可移动平台、控制设备、***及存储介质 | |
WO2022126397A1 (zh) | 传感器的数据融合方法、设备及存储介质 | |
CN112313535A (zh) | 距离检测方法、距离检测设备、自主移动平台和存储介质 | |
WO2022094962A1 (zh) | 飞行器的悬停方法、飞行器及存储介质 | |
CN112394744A (zh) | 一体化无人机*** | |
WO2022095061A1 (zh) | 基于雷达的喷洒评估方法、设备及存储介质 | |
CN115166686B (zh) | 卫星拒止环境下多无人机分布式协同定位与建图方法 | |
WO2022087991A1 (zh) | 旋转观测装置的安装校准方法、飞行器及存储介质 | |
WO2021087784A1 (zh) | 可移动平台悬停方法、可移动平台及计算机可读存储介质 | |
CN118393493A (zh) | 一种搭载毫米波雷达的四旋翼无人机的感知定位方法 | |
CN117557654A (zh) | 外参标定方法、装置、电子设备及存储介质 | |
Khoshelham et al. | Positioning and Navigation |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
121 | Ep: the epo has been informed by wipo that ep was designated in this application |
Ref document number: 19951449 Country of ref document: EP Kind code of ref document: A1 |
|
NENP | Non-entry into the national phase |
Ref country code: DE |
|
122 | Ep: pct application non-entry in european phase |
Ref document number: 19951449 Country of ref document: EP Kind code of ref document: A1 |