CN112154351A - Terrain detection method, movable platform, control device, system and storage medium - Google Patents
Terrain detection method, movable platform, control device, system and storage medium Download PDFInfo
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- 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
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Abstract
A terrain detection method, an aircraft movable platform, a control device, a system and a storage medium, the method comprising: acquiring a target area, and sampling the target area to obtain a ground point set (S101); acquiring observation data corresponding to a scanning area (S102); constructing a covariance matrix according to the observation data and the ground point set (S103); determining altitude position information of the ground point according to the covariance matrix (S104); topographic information of the target area is determined from the altitude position information (S105).
Description
Technical Field
The present disclosure relates to the field of terrain detection technologies, and in particular, to a terrain detection method, a movable platform, a control device, a system, and a storage medium.
Background
At present, in an autonomous operation process of an unmanned aerial vehicle, an autonomous operation robot and the like, methods such as radar, ultrasonic ranging or machine vision and the like are generally adopted to scan the ground to obtain topographic information. However, in the scanning process, the field angle is limited on one hand, and the detection distance is limited on the other hand, so that the region outside the detection distance range cannot be detected, and a scanning blind area exists in the scanning process. Due to the fact that the scanning blind area exists, terrain measurement is incomplete and accurate enough, and therefore safe flight of the unmanned aerial vehicle is guaranteed.
Disclosure of Invention
Based on the above, the application provides a terrain detection method, a movable platform, a control device, a system and a storage medium, so as to detect the terrain of an area which is not scanned, and further ensure the safe operation of the movable platform.
In a first aspect, the present application provides a terrain detection method, the method comprising:
acquiring an unscanned target area, and performing ground point sampling on the target area to obtain a ground point set corresponding to the target area;
acquiring observation data corresponding to a scanning area;
constructing a covariance matrix corresponding to the ground point set according to the observation data and the ground point set;
determining the height position information of each ground point in the ground point set according to the covariance matrix; and
and determining the terrain information of the target area according to the height position information of each ground point.
In a second aspect, the present application also provides a movable platform comprising a detection device, a memory, and a processor;
the detection device is used for terrain detection and acquisition of observation data of a scanning area;
the memory is used for storing a computer program;
the processor is configured to execute the computer program and, when executing the computer program, implement the following steps:
acquiring an unscanned target area, and performing ground point sampling on the target area to obtain a ground point set corresponding to the target area;
acquiring observation data corresponding to a scanning area;
constructing a covariance matrix corresponding to the ground point set according to the observation data and the ground point set;
determining the height position information of each ground point in the ground point set according to the covariance matrix; and
and determining the terrain information of the target area according to the height position information of each ground point.
In a third aspect, the present application further provides a control device comprising a memory and a processor;
the memory is used for storing a computer program;
the processor is configured to execute the computer program and, when executing the computer program, implement the steps of the above-described terrain detection method and send the determined terrain information to the movable platform.
In a fourth aspect, the present application further provides a control system comprising an aircraft and a control device as described in the third aspect; the movable platform is used for sampling a target area to obtain a ground point set corresponding to the target area, acquiring observation data of a scanning area, and sending the ground point set and the observation data to the control equipment.
In a fifth aspect, the present application further provides a computer-readable storage medium storing a computer program which, when executed by a processor, causes the processor to implement the terrain detection method described above.
According to the terrain detection method, the movable platform, the control device, the system and the storage medium, the prediction accuracy of the terrain information of the unscanned target area can be improved, and the safe operation of the movable platform is ensured.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a schematic block diagram of a control system provided in an embodiment of the present application;
FIG. 2 is a schematic architectural diagram of an aircraft provided by an embodiment of the present application;
FIG. 3 is a flowchart illustrating steps of a terrain inspection method according to an embodiment of the present application;
FIG. 4 is a sampling schematic diagram for uniform sampling of a target region according to 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 in an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some, but not all, embodiments of the present application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The flow diagrams depicted in the figures are merely illustrative and do not necessarily include all of the elements and operations/steps, nor do they necessarily have to be performed in the order depicted. For example, some operations/steps may be decomposed, combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.
Some embodiments of the present application will be described in detail below with reference to the accompanying drawings. The embodiments described below and the features of the embodiments can be combined with each other without conflict.
The embodiment of the application provides a terrain detection method, a movable platform, a control device, a control system and a storage medium, which are used for carrying out terrain prediction on a scanning blind area of a detection device, so that terrain information in an unscanned target area is determined, and the safe operation of the movable platform is ensured.
Specifically, in the embodiment of the present application, a ground point set is obtained by sampling ground points of a target area, a covariance matrix corresponding to the ground point set is constructed from observation data corresponding to a scanned area and the ground point set, height position information of the ground points is determined from the covariance matrix, and terrain information of the target area is determined from the height position information of the ground points, so that terrain prediction is performed on a scanning blind area of a detection device, and the accuracy of prediction on the terrain information of the target area which is not scanned is improved.
Wherein the control system comprises a movable platform and a control device.
For convenience of description, the present application will be described in detail with reference to the detection device as a radar.
Illustratively, the control device includes a remote controller, a ground control platform, a mobile phone, a tablet computer, a notebook computer, a PC computer and the like.
Illustratively, as shown in FIG. 1, the control system is a terrain detection system, and the terrain detection system 100 includes an aircraft 110 and a control device 120.
Fig. 2 is a schematic architecture diagram of an aircraft 110 in accordance with an embodiment of the present description. The present embodiment is described by taking a rotor unmanned aerial vehicle as an example.
The aircraft 110 may include a power system, a flight control system, and a airframe. The aircraft 110 may be in wireless communication with a control device 120, the control device 120 may display flight information for the aircraft, etc., and the control device 120 may be in wireless communication with the aircraft 110 for remote maneuvering of the aircraft 110.
The airframe may include, among other things, an airframe 111 and a foot rest 112 (also referred to as a landing gear). The fuselage 111 may include a central frame 1111 and one or more arms 1112 coupled to the central frame 1111, the one or more arms 1112 extending radially from the central frame 1111. A foot rest 112 is connected to the fuselage 111 for support during landing of the aircraft 110.
The power system may include one or more electronic governors (referred to simply as electric governors), 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 between the electronic governors and the propellers 113, the motors 114 and the propellers 113 being disposed on a horn 1112 of the aircraft 110; the electronic governor is configured to receive a drive signal generated by the flight control system and provide a drive current to the motor based on the drive signal to control the rotational speed of the motor 114. The motor 114 is used to drive the propeller 113 to rotate, thereby providing power for the flight of the aircraft 110, which power enables the aircraft 110 to achieve motion in one or more degrees of freedom.
In certain embodiments, the aerial vehicle 110 may rotate about one or more axes of rotation. For example, the above-mentioned rotation axes may include a roll axis, a yaw axis, and a pitch axis. It should be understood that the motor 114 may be a dc motor or an ac motor. The motor 114 may be a brushless motor or a brush motor.
The flight control system may include a flight controller and a sensing system. The sensing system is used to measure attitude information of the unmanned aerial vehicle, i.e., position information and state information of the aircraft 110 in space, such as a three-dimensional position, a three-dimensional angle, a three-dimensional velocity, a three-dimensional acceleration, a three-dimensional angular velocity, and the like. 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. For example, the Global navigation satellite System may be a Global Positioning System (GPS). The flight controller is used to control the flight of the aircraft 110, for example, the flight of the aircraft 110 may be controlled based on attitude information measured by the sensing system. It should be appreciated that the flight controller may control the aircraft 110 according to preprogrammed instructions, or may control the aircraft 110 in response to one or more control instructions from the control device 120.
As shown in fig. 1, a radar 115 is mounted on a foot rest 112 of an aircraft 110, and the radar 115 is used to perform a function of surveying topographic information. The aircraft 110 may include two or more foot rests 112, and the radar 115 may be mounted on one of the foot rests 112.
The radar mainly comprises a radio frequency front end module and a signal processing module, wherein the radio frequency front end module comprises a transmitting antenna and a receiving antenna, and the signal processing module is responsible for generating a modulation signal and processing and analyzing an acquired intermediate frequency signal.
Specifically, the radio frequency front end module receives a modulation signal to generate a high-frequency signal of which the frequency changes linearly along with the modulation signal, the high-frequency signal is radiated downwards through the transmitting antenna, electromagnetic waves meet the ground, a target object or an obstacle and are reflected back and received by the receiving antenna, the transmitting signal and the intermediate frequency are mixed to obtain an intermediate frequency signal, and speed information and distance information can be obtained according to the frequency of the intermediate frequency signal.
When the radar is used for scanning in an area to be scanned, the radar meets a target object through the propagation of radiated electromagnetic waves in space, and the target object scatters echoes to be received by the radar to realize the detection of the target object. In the process that the radar flies along with the movable platform, observation data are continuously collected through radiation electromagnetic waves, but a scanning blind area can be generated in a region to be scanned by the radar, and the observation data in a target region corresponding to the scanning blind area cannot be collected.
In this case, the terrain of the scanning blind area needs to be predicted, and most of the existing methods for predicting the terrain information adopt an empirical model to fit scanning points according to the scanned terrain information to obtain a fitting plane, and predict the spatial azimuth information of the non-scanned area by using the fitting plane. However, the empirical model is selected to have a large deviation of the terrain predicted by the fitting plane from the actual result due to improper matching, and the terrain in the area with severe terrain change is fitted smoothly, so that the terrain features in the area are lost. Inaccurate terrain feature prediction for scanning blind areas can affect the operation and flight safety of the movable platform. It is therefore necessary to improve the accuracy of terrain prediction for scan blind areas.
It should be understood that the above-described nomenclature for the aircraft components is for identification purposes only, and should not be construed as limiting the embodiments of the present description.
Referring to fig. 3, fig. 3 is a schematic flowchart illustrating steps of a terrain detection method according to an embodiment of the present application. The method can be applied to control equipment or an aircraft and used for predicting the terrain in the scanning blind area which is not scanned and determining the terrain information in the scanning blind area so as to ensure the safe operation of the movable platform.
The terrain detection method will be described in detail below in conjunction with the control system of fig. 1. It should be noted that the control system in fig. 1 does not constitute a limitation to the application scenario of the terrain detection method.
As shown in fig. 3, the terrain detection method includes steps S101 to S105.
S101, obtaining an unscanned target area, and carrying out ground point sampling on the target area to obtain a ground point set corresponding to the target area.
The unscanned target area refers to a scanning blind area adjacent to the scanning area. The area to be scanned comprises a target area and a scanning area, when the radar scans the area to be scanned to detect the terrain, due to geographical conditions or electromagnetic wave propagation characteristics and other reasons, a scanning blind area exists in radar scanning, and ground point sampling needs to be carried out on the target area in order to predict the scanning blind area, namely the terrain in the unscanned target area.
Exemplarily, the step of performing ground point sampling on the target region to obtain a ground point set corresponding to the target region includes: and carrying out multiple random sampling on the target area to obtain a plurality of ground points, wherein the ground points form a ground point set.
Specifically, random irregular sampling may be performed on the target region for multiple times along a certain direction to obtain multiple spatial sampling points, and the ground points corresponding to the sampling points are determined as the ground points obtained by sampling according to the multiple sampling points, so as to form a ground point set.
For example, it is needless to say that multiple random irregular samplings can be performed on the target region along a certain direction according to the changing spatial step length, so as to obtain multiple spatial sampling points. The spatial step length refers to a distance between two sampling points in space.
For example, the edge of the target area is used as a starting sampling point, sampling is sequentially performed at spatial step intervals of 0.2m, 0.3m, 0.4m and 0.5m in a direction parallel to the ground, so as to obtain five sampling points in total, ground points corresponding to the five sampling points are respectively determined, and the five ground points form a ground point set.
Exemplarily, the step of performing ground point sampling on the target region to obtain a ground point set corresponding to the target region includes: and uniformly sampling the target area for multiple times at preset space step intervals to obtain a plurality of ground points, wherein the ground points form a ground point set.
As shown in fig. 4, the sampling diagram is a schematic diagram of uniformly sampling a target region, where a filled portion in the diagram is a scanning region, a blank portion is the target region, and an intersection point in the diagram is a ground point obtained by sampling. The target area can be uniformly sampled for a plurality of times along a certain direction according to a preset space step interval to obtain a plurality of sampling points in space, and the ground points corresponding to the sampling points are determined to be the ground points obtained by sampling according to the plurality of sampling points, so that a ground point set is formed.
For example, the spatial step interval may be 0.2m, the edge of the target region is used as a start sampling point, the ground points corresponding to the target region are sampled every 0.2m in the direction parallel to the ground until the whole target region is sampled, the ground points corresponding to the plurality of sampling points are respectively determined, and the obtained plurality of ground points form a ground point set.
The target area is uniformly sampled according to a certain spatial step length to obtain ground points, so that the whole target area is dispersed into a plurality of grids with preset side lengths, the side lengths of the grids are also the preset spatial step length intervals, and the accuracy of determining the topographic information in the target area according to the ground points is improved.
And S102, acquiring observation data corresponding to the scanning area.
The observation data comprises position information and height position information of observation points in the scanning area, and the position information comprises first position information and second position information. In the embodiments of the present application, for convenience of description, the coordinates of the observation point in the radar coordinate system are denoted as (x)A,yA,zA) Wherein x isAI.e. the first position information of the observation point, yAI.e. the second position information of the observation point, zANamely the height position information of the observation point. The first position information, the second position information and the height position information are mutually vertical. According to an embodiment of the invention, the first position information comprises depth of field detection distances and the second position information comprises horizontal detection distances.
In some embodiments, after acquiring the observation data corresponding to the scanning area, the method further includes: and carrying out 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 remove the miscellaneous points.
In this case, for a certain point on the ground, the observation data scanned by the radar mounted on the aircraft for the point may be different due to the change of the flight 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 point in the observation data may be subjected to coordinate conversion.
Illustratively, the step of performing coordinate transformation on the coordinates of the observation point in the observation data includes:
acquiring an attitude quaternion of a detection device, wherein the detection device is used for detecting a scanning area to obtain observation data; and converting the coordinates of the observation point in the observation data from a first coordinate system to a second coordinate system according to the attitude quaternion, wherein the first coordinate system and the second coordinate system are different.
Wherein the first coordinate system comprises a radar coordinate system and the second coordinate system comprises a geodetic coordinate system. In the embodiment of the present application, the geodetic coordinate system used is ENU (East-North-UP coordinate system). For convenience of description, the coordinates of the observation point in the geodetic coordinate system are expressed as (x)G,yG,zG) Wherein x isGI.e. the distance of the observation point in the direction due to the north relative to the origin of coordinates, yGI.e. the distance of the observation point in the east-ward direction with respect to the origin of coordinates, zGI.e. the distance of the observation point in the perpendicular direction with respect to the origin of coordinates.
In the coordinate conversion, the coordinate conversion may be performed using the following formula:
wherein,
in order to transform the matrix in a homogeneous way,
is an attitude quaternion rotation matrix,
is attitude quaternion of radar for calculating attitude information of radar at current moment of radar, q0,q1,q2,q3Respectively the four attitudes of the radar.
Is a translation vector, x, from the radar coordinate system to the geodetic coordinate systemi,j、yi,j、zi,jThe coordinates of point (i, j) are indicated.
Specifically, the attitude quaternion rotation matrix
The method specifically comprises the following steps:
since the existence of the outlier in the observation data affects the final terrain prediction result, in order to improve the accuracy of the terrain prediction of the unscanned target area, the outlier in the observation data after the coordinate transformation needs to be removed by using a clustering algorithm.
Wherein, the clustering algorithm may include: one of a K-MEANS clustering algorithm, a mean shift clustering algorithm, a DBSCAN algorithm clustering, a maximum expected clustering and a hierarchical clustering algorithm.
Exemplarily, clustering by using a DBSCAN algorithm, and clustering observation points in observation data after coordinate conversion according to a clustering algorithm to remove miscellaneous points, includes:
based on clustering of a DBSCAN algorithm, clustering the observation points according to the density degree of the observation points in the observation data after the coordinate conversion so as to eliminate the miscellaneous points.
The DBSCAN algorithm particularly describes the compactness of the distribution of the observation points based on a group of neighborhood parameters, so that a cluster is generated. Wherein the neighborhood parameters include e and MinPts, where e represents the minimum distance between two points, and two observation points are considered to be neighboring points if the distance between the two observation points is less than or equal to the value. In an embodiment, two observation points are considered to belong to the same point cluster if the distance between the two observation points is less than or equal to the value. MinPts represents the minimum number of points forming a dense area.
In a specific implementation process, the DBSCAN algorithm firstly determines core observation points from all observation points in observation data according to neighborhood parameters, and then finds out other observation points with reachable density by taking any core observation point as a starting point to generate a cluster until all the core observation points are visited. And points which are not in the cluster are regarded as the miscellaneous points to be removed.
S103, constructing a covariance matrix corresponding to the ground point set according to the observation data and the ground point set.
And constructing a covariance matrix corresponding to the ground point set according to the observation data and the ground point set so as to predict height position information corresponding to the ground point by using the calculated covariance matrix and the height position information of the observation point in the observation data.
In some embodiments, the step of constructing a covariance matrix corresponding to the set of ground points from the observation data and the set of ground points includes:
and constructing a covariance matrix comprising the position information of the ground points according to the position information of the observation points in the observation data and the position information of one ground point in the ground point set based on Gaussian process regression.
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 matrices of each ground point belong to joint normal distribution. The position information includes first position information and second position information, and the first position information and the second position information are different.
Illustratively, the gaussian process regression includes: determining a kernel function and determining a hyper-parameter of the kernel function.
Wherein the kernel function comprises one of a spline kernel function, a polynomial kernel function, a perceptron kernel function and a gaussian kernel function. For the convenience of understanding, the present application will be described in detail with a gaussian kernel function as an example. The gaussian kernel function formula is as follows:
where l is a scale parameter, which represents the correlation between two variables a, b, σ controlling the variance of the overall regression. In the above equation, a represents positional information of observation point a1, and B represents positional information of observation point B1. That is, a, B may be coordinates (x) in the geodetic coordinate system of observation point a1 and observation point B1A1,yA1,zA1)、(xB1,yB1,zB1)。
Since the regression effect of the gaussian process regression depends greatly on the form of the kernel function, the parameters of the kernel function are estimated after the selection of the appropriate kernel function. In order to solve the parameters in the gaussian kernel function, a hyper-parameter θ can be constructed according to the parameters l and σ in the gaussian kernel function, where the hyper-parameter θ is a set of the parameters l and σ, that is, the hyper-parameter θ is { l, σ }.
Illustratively, the step of determining the hyper-parameter of the kernel function includes: and determining the hyperparameters of the kernel function by utilizing a maximum likelihood method.
In particular, the hyperparameters may be solved by constructing a likelihood function such that a posterior distribution of the hyperparameters θ is maximized to determine the hyperparameters in the gaussian kernel function.
Specifically, for the hyper-parameter θ, the constructed likelihood function is:
wherein x, y and z are respectively the first position information, the second position information and the height position information of the known observation point, K is the covariance matrix of the observation point, and z is the covariance matrix of the observation pointTRepresenting the transpose of the matrix z, n being the number of known observation points.
For the constructed likelihood function about the hyper-parameter theta, a gradient descent method can be adopted to solve the optimal value of the hyper-parameter theta, as follows:
the above formula is solved, and the observed data of some observation points, namely the values of x, y and z in the formula, can be randomly given to solve the hyper-parameter theta.
And S104, determining the height position information of each ground point in the ground point set according to the covariance matrix.
The height position information of each ground point in the ground point set is determined according to the covariance matrix, specifically, the height position information of the ground points belongs to a one-dimensional normal distribution, so that a mean value of the normal distribution is calculated by using a formula of the normal distribution, and the mean value is recorded as an estimated value of the height position information of the ground points.
In some embodiments, the step of constructing a covariance matrix including location information of the ground points based on the location information of the observation points in the observation data and the location information of one of the set of ground points includes:
constructing a covariance matrix of observation points in the observation data according to the position information of the observation points in the observation data by using a kernel function for determining the hyper-parameters; and constructing a covariance matrix comprising the position information of the ground points according to the covariance matrix of the observation points and the position information of one ground point in the ground point set.
Wherein the gaussian process is given the position information (x, y) of an observation point, the altitude position information z of the observation point is modeled, and it is assumed that these corresponding altitude position information z obey a joint normal distribution.
That is, for a plurality of known observation points, the following joint normal distribution exists from the position information of the observation points:
order:
wherein Z is height position information of the N observation points, M is a mean value of the joint normal distribution, and K is a variance of the joint normal distribution. Wherein k isSE(Xi,Xj) Refers to the covariance between the positional information (x, y) of the ith observation point and the positional information (x, y) of the jth observation point.
After determining the covariance matrix K of the observation points, the method is based on the ground points (x) in the target region*,y*) Height position information z for the ground point*Modeling was performed and z was assumed*The height position information z and the observation point belong to the same joint normal distribution, and then:
calculating the sample distance between each ground point and the position information of each observation point and obtaining the covariance matrix of the joint normal distribution by using Gaussian kernel calculation, namely K*The following are:
obtaining covariance matrix K of ground point*Then, use K*And height position information Z of N observation points, i.e. to ground point (x)*,y*) Corresponding height position information z*Regression prediction is performed to determine height location information for ground points.
Illustratively, the step of determining the elevation location information for each ground point in the set of ground points based on the covariance matrix includes:
and carrying out regression analysis on the covariance matrix of each ground point in the ground point set according to the covariance matrix of the observation points to obtain the height position information of each ground point.
Wherein, because all parameters in the joint normal distribution obeyed by the height position information of the ground point are known, the z can be obtained by using a formula*Belonging to a one-dimensional normal distribution, and the parameters are:
z*～N(A*,σ*)
μ*＝K*K-1Z
wherein, mu*Is the mean of the normal distribution, i.e. the ground point (x)*,y*) Height position information z of*The predicted value is the height position information of the ground point in the target area.
It should be noted that a covariance matrix may be constructed from the predicted ground points and observation points in the scanning area to the unpredicted ground points to predict the height position information of the unpredicted ground points.
And S105, determining the terrain information of the target area according to the height position information of each ground point.
Wherein the terrain information comprises one or more of ground height, ground flatness, and ground slope. According to the height position information of the ground points, the space points corresponding to the ground points can be determined in the three-dimensional space, a fitting plane is obtained by fitting the plurality of space points, and the terrain information such as the ground height, the ground slope, the ground flatness and the like can be extracted through the fitting plane.
Illustratively, the step of determining the terrain information of the target area according to the altitude position information of each ground point includes:
fitting according to the position information and the height position information of each ground point in the ground point set to obtain a fitting plane of the target area; and determining the terrain information of the target area according to the fitting plane.
According to the position information and height position information of the ground points, space points corresponding to the ground points can be determined in the three-dimensional space, a fitting plane of the target area is obtained by fitting the space points, and topographic information such as ground height, ground gradient and ground flatness of the target area can be extracted through the fitting plane.
Illustratively, a mean value is calculated according to the height position information of a plurality of space points in the fitting plane, and the ground flatness of the scanning area is determined according to the mean value. In one embodiment, a mean value is calculated from the residuals of the plurality of spatial points of the fitted plane, and the ground flatness of the scanned area is determined from the mean value.
Illustratively, the slope of the fitted plane is determined from altitude position information of a plurality of spatial points. Specifically, in one embodiment, the slope of the scanning area is determined according to the trend of the height of a plurality of spatial points along a certain horizontal direction.
In some embodiments, the terrain detection method further comprises: and determining the topographic information of the scanning area according to the observation data of the scanning area.
The method comprises the steps of fitting observation data of observation points in a scanning area to obtain a fitting plane corresponding to the scanning area, and extracting topographic information of the scanning area through the fitting plane.
In some embodiments, to improve continuity and integrity of the obtained terrain information, the terrain detection method further comprises: splicing the observation data of the scanning area with the prediction data of the target area to obtain spliced data; and fitting the spliced data to obtain a fitting plane of the scanning area and the target area, and determining the terrain information of the scanning area and the target area according to the fitting plane.
The prediction data of the target area comprises a prediction value of height position information of ground points of the target area and position information of the ground points. And splicing the observation data of the scanning area and the prediction data of the target area to obtain complete spliced data of the area to be scanned, wherein the area to be scanned comprises the target area and the scanning area. And then fitting the spliced data to obtain a complete fitting plane of the area to be scanned, so that the topographic information of the area to be scanned is determined according to the fitting plane.
The observation data of the scanning area and the prediction data of the target area are spliced to obtain the spliced data of the whole area to be scanned, so that the terrain of the whole area to be scanned can be predicted, and the continuity and the integrity of terrain prediction are improved.
In the embodiment, the target area is obtained, the ground point sampling is performed on the target area to obtain the ground point set, 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, the height position information of each ground point in the ground point set is determined according to the covariance matrix, and finally the terrain information of the target area is determined according to the height position information of each ground point. The method and the device realize the prediction of the terrain information of the unscanned target area and improve the accuracy of the terrain prediction.
Referring to fig. 5, fig. 5 is a schematic block diagram of a movable platform according to an embodiment of the present application. The movable platform 11 includes a processor 111, a memory 112, and a detection device 113, wherein the processor 111, the memory 112, and the detection device 113 are connected via a bus, such as an I2C (Inter-integrated Circuit) bus, or the detection device 113 and the processor 111 are connected via a CAN bus.
Wherein the movable platform comprises an aircraft, a robot or an automated unmanned vehicle, etc.
Specifically, the Processor 111 may be a Micro-controller Unit (MCU), a Central Processing Unit (CPU), a Digital Signal Processor (DSP), or the like.
Specifically, the Memory 112 may be a Flash chip, a Read-Only Memory (ROM) magnetic disk, an optical disk, a usb disk, or a removable hard disk.
In particular, the detection device 113 is used for terrain detection and acquisition of observation data of the scanned area.
Wherein the processor is configured to run a computer program stored in the memory and to implement the following steps when executing the computer program:
acquiring an unscanned target area, and performing ground point sampling on the target area to obtain a ground point set corresponding to the target area;
acquiring observation data corresponding to a scanning area;
constructing a covariance matrix corresponding to the ground point set according to the observation data and the ground point set;
determining the height position information of each ground point in the ground point set according to the covariance matrix; and
and determining the terrain information of the target area according to the height position information of each ground point.
In some embodiments, the target region is a scanning shadow region adjacent to the scanning region.
In some embodiments, before the step of constructing the covariance matrix corresponding to the set of ground points according to the observation data and the set of ground points by the processor, the method includes:
and carrying out 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 remove the miscellaneous points.
In some embodiments, the processor performs the step of coordinate transforming the coordinates of the observation point in the observation data, including:
acquiring an attitude quaternion of a detection device, wherein the detection device is used for detecting a scanning area to obtain observation data;
and converting the coordinates of the observation point in the observation data from a first coordinate system to a second coordinate system according to the attitude quaternion, wherein the first coordinate system and the second coordinate system are different.
In some embodiments, the first coordinate system comprises a radar coordinate system and the second coordinate system comprises a geodetic coordinate system.
In some embodiments, the clustering algorithm comprises one of a K-MEANS clustering algorithm, a mean shift clustering algorithm, a DBSCAN algorithm clustering, a maximum expected clustering, and a hierarchical clustering algorithm.
In some embodiments, the processor implements the step of clustering observation points in the observation data after coordinate transformation according to a clustering algorithm to remove the outliers, including:
based on clustering of a DBSCAN algorithm, clustering the observation points according to the density degree of the observation points in the observation data after the coordinate conversion so as to eliminate the miscellaneous points.
In some embodiments, the step of constructing a covariance matrix corresponding to the set of ground points from the observation data and the set of ground points by the processor comprises:
based on Gaussian process regression, constructing a covariance matrix comprising position information of the ground points according to the position information of the observation points in the observation data and the position information of one 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 matrices of each ground point belong to joint normal distribution.
In some embodiments, the gaussian process regression comprises: determining a kernel function and determining a hyper-parameter of the kernel function.
In some embodiments, the determining the hyperparameters of the kernel function includes determining the hyperparameters of the kernel function using a maximum likelihood method.
In some embodiments, the processor implements the step of constructing a covariance matrix including location information for the ground points based on the location information for observation points in the observation data and location information for one of the set of ground points, comprising:
constructing a covariance matrix of observation points in the observation data according to the position information of the observation points in the observation data by using a kernel function for determining the hyper-parameters;
and constructing a covariance matrix comprising the position information of the ground points according to the covariance matrix of the observation points and the position information of one ground point in the ground point set.
In some embodiments, the kernel function comprises one of a spline kernel function, a polynomial kernel function, a perceptron kernel function, and a gaussian kernel function.
In some embodiments, the processor implementing the step of determining elevation location information for each ground point in the set of ground points from the covariance matrix includes:
and carrying out regression analysis on the covariance matrix of each ground point in the ground point set according to the covariance matrix of the observation points to obtain the height position information of each ground point.
In some embodiments, the location information includes first location information and second location information, the first location information and the second location information being different.
In some embodiments, the processor implements the step of performing ground point sampling on the target region to obtain a ground point set corresponding to the target region, including:
and uniformly sampling the target area for multiple times at preset space step intervals to obtain a plurality of ground points, wherein the ground points form a ground point set.
In some embodiments, the processor implements the step of performing ground point sampling on the target region to obtain a ground point set corresponding to the target region, including:
and carrying out multiple random sampling on the target area to obtain a plurality of ground points, wherein the ground points form a ground point set.
In some embodiments, the processor further implements: and determining the topographic information of the scanning area according to the observation data of the scanning area.
In some embodiments, the processor implements the step of determining terrain information for the target area from the elevation location information for each ground point, comprising:
fitting according to the position information and the height position information of each ground point in the ground point set to obtain a fitting plane of the target area;
and determining the terrain information of the target area according to the fitting plane.
In some embodiments, the terrain information comprises one or more of ground height, ground flatness, ground slope.
In some embodiments, the processor further implements:
splicing the observation data of the scanning area with the prediction data of the target area to obtain spliced data;
and fitting the spliced data to obtain a fitting plane of the scanning area and the target area, and determining the terrain information of the scanning area and the target area according to the fitting plane.
Referring to fig. 6, fig. 6 is a schematic block diagram of a control device according to 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.
Specifically, the Processor 121 may be a Micro-controller Unit (MCU), a Central Processing Unit (CPU), a Digital Signal Processor (DSP), or the like.
Specifically, the Memory 122 may be a Flash chip, a Read-Only Memory (ROM) magnetic disk, an optical disk, a usb disk, or a removable hard disk, and the Memory 122 is used for storing a computer program.
Wherein the processor is configured to run a computer program stored in the memory and to implement the following steps when executing the computer program:
acquiring an unscanned target area, and performing ground point sampling on the target area to obtain a ground point set corresponding to the target area;
acquiring observation data corresponding to a scanning area;
constructing a covariance matrix corresponding to the ground point set according to the observation data and the ground point set;
determining the height position information of each ground point in the ground point set according to the covariance matrix; and
and determining the terrain information of the target area according to the height position information of each ground point, and sending the determined terrain information to the movable platform.
Embodiments of the present application also provide a control system, which may be, for example, a flight control system as shown in fig. 1, including a movable platform and a control device communicatively coupled to the movable platform;
the movable platform is used for sampling a target area to obtain a ground point set corresponding to the target area, acquiring observation data of a scanning area, and sending the ground point set and the observation data to the control equipment.
In an embodiment of the present application, a computer-readable storage medium is further provided, where a computer program is stored in the computer-readable storage medium, where the computer program includes program instructions, and the processor executes the program instructions to implement the steps of the terrain detection method provided in the foregoing embodiment.
The computer readable storage medium may be an internal storage unit of the removable platform or the control device described in any previous embodiment, for example, a hard disk or a 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, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), etc. provided on the removable platform.
While the invention has been described with reference to specific embodiments, the scope of the invention is not limited thereto, and those skilled in the art can easily conceive various equivalent modifications or substitutions within the technical scope of the invention. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (43)
1. A terrain detection method, comprising:
acquiring an unscanned target area, and performing ground point sampling on the target area to obtain a ground point set corresponding to the target area;
acquiring observation data corresponding to a scanning area;
constructing a covariance matrix corresponding to the ground point set according to the observation data and the ground point set;
determining the height position information of each ground point in the ground point set according to the covariance matrix; and
and determining the terrain information of the target area according to the height position information of each ground point.
2. The terrain detection method of claim 1, wherein the target area is a scanning shadow area adjacent to the scanning area.
3. The terrain detection method of claim 1 wherein prior to said constructing a covariance matrix corresponding to the set of ground points from the observation data and the set of ground points, further comprising:
and carrying out 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 remove the miscellaneous points.
4. The terrain detection method according to claim 3, wherein the coordinate conversion of the coordinates of the observation point in the observation data includes:
acquiring an attitude quaternion of a detection device, wherein the detection device is used for detecting a scanning area to obtain observation data;
and converting the coordinates of the observation point in the observation data from a first coordinate system to a second coordinate system according to the attitude quaternion, wherein the first coordinate system and the second coordinate system are different.
5. A terrain detection method as claimed in claim 4, characterized in that the first coordinate system comprises a radar coordinate system and the second coordinate system comprises a geodetic coordinate system.
6. A terrain detection method as claimed in claim 3, characterized in that the clustering algorithm comprises one of a K-MEANS clustering algorithm, a mean shift clustering algorithm, a DBSCAN algorithm clustering, a maximum expected clustering and a hierarchical clustering algorithm.
7. The terrain detection method of claim 6, wherein the clustering observation points in the observation data after coordinate transformation according to a clustering algorithm to eliminate miscellaneous points comprises:
based on clustering of a DBSCAN algorithm, clustering the observation points according to the density degree of the observation points in the observation data after the coordinate conversion so as to eliminate the miscellaneous points.
8. The terrain detection method of any of claims 1-7 wherein the constructing a covariance matrix corresponding to the set of ground points from the observation data and the set of ground points comprises:
based on Gaussian process regression, constructing a covariance matrix comprising position information of the ground points according to the position information of the observation points in the observation data and the position information of one 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 matrices of each ground point belong to joint normal distribution.
9. The terrain detection method of claim 8, wherein the gaussian process regression comprises: determining a kernel function and determining a hyper-parameter of the kernel function.
10. The terrain detection method of claim 9 wherein determining the hyperparameters of the kernel function comprises determining the hyperparameters of the kernel function using a maximum likelihood method.
11. The terrain detection method of claim 9 wherein constructing a covariance matrix including location information for the ground points based on the location information for observation points in the observation data and the location information for one of the set of ground points comprises:
constructing a covariance matrix of observation points in the observation data according to the position information of the observation points in the observation data by using a kernel function for determining the hyper-parameters;
and constructing a covariance matrix comprising the position information of the ground points according to the covariance matrix of the observation points and the position information of one ground point in the ground point set.
12. The terrain detection method of claim 9, wherein the kernel function comprises one of a spline kernel function, a polynomial kernel function, a perceptron kernel function, and a gaussian kernel function.
13. The terrain detection method of claim 11 wherein said determining elevation location information for each ground point in the set of ground points from the covariance matrix comprises:
and carrying out regression analysis on the covariance matrix of each ground point in the ground point set according to the covariance matrix of the observation points to obtain the height position information of each ground point.
14. The terrain detection method of claim 8, wherein the position information includes first position information and second position information, the first position information and the second position information being different.
15. The terrain detection method of claim 1 wherein the sampling of ground points for the target area to obtain a set of ground points corresponding to the target area comprises:
and uniformly sampling the target area for multiple times at preset space step intervals to obtain a plurality of ground points, wherein the ground points form a ground point set.
16. The terrain detection method of claim 1 wherein the sampling of ground points for the target area to obtain a set of ground points corresponding to the target area comprises:
and carrying out multiple random sampling on the target area to obtain a plurality of ground points, wherein the ground points form a ground point set.
17. The terrain detection method of claim 1, further comprising:
and determining the topographic information of the scanning area according to the observation data of the scanning area.
18. The terrain detection method of claim 1 wherein said determining terrain information for the target area from the elevation location information for each ground point comprises:
fitting according to the position information and the height position information of each ground point in the ground point set to obtain a fitting plane of the target area;
and determining the terrain information of the target area according to the fitting plane.
19. The terrain detection method of claim 1, wherein the terrain information comprises one or more of a ground height, a ground flatness, and a ground slope.
20. The terrain detection method of claim 1, further comprising:
splicing the observation data of the scanning area with the prediction data of the target area to obtain spliced data;
and fitting the spliced data to obtain a fitting plane of the scanning area and the target area, and determining the terrain information of the scanning area and the target area according to the fitting plane.
21. A movable platform, comprising a detection device, a memory, and a processor;
the detection device is used for terrain detection and acquisition of observation data of a scanning area;
the memory is used for storing a computer program;
the processor is configured to execute the computer program and, when executing the computer program, implement the following steps:
acquiring an unscanned target area, and performing ground point sampling on the target area to obtain a ground point set corresponding to the target area;
acquiring observation data corresponding to a scanning area;
constructing a covariance matrix corresponding to the ground point set according to the observation data and the ground point set;
determining the height position information of each ground point in the ground point set according to the covariance matrix; and
and determining the terrain information of the target area according to the height position information of each ground point.
22. The movable platform of claim 21, wherein the target area is a scanning shadow area adjacent to the scanning area.
23. The movable platform of claim 21 wherein the processor performs the step of constructing covariance matrices corresponding to the set of ground points from the observation data and the set of ground points prior to the step of:
and carrying out 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 remove the miscellaneous points.
24. The movable platform of claim 23, wherein the processor performs the step of coordinate transforming coordinates of the observation points in the observation data, comprising:
acquiring an attitude quaternion of a detection device, wherein the detection device is used for detecting a scanning area to obtain observation data;
and converting the coordinates of the observation point in the observation data from a first coordinate system to a second coordinate system according to the attitude quaternion, wherein the first coordinate system and the second coordinate system are different.
25. The movable platform of claim 24 wherein the first coordinate system comprises a radar coordinate system and the second coordinate system comprises a geodetic coordinate system.
26. The movable platform of claim 23, wherein the clustering algorithm comprises one of a K-MEANS clustering algorithm, a mean shift clustering algorithm, a DBSCAN algorithm clustering, a max-expected clustering, and a hierarchical clustering algorithm.
27. The movable platform of claim 26, wherein the processor implements the clustering of observation points in coordinate-transformed observation data according to a clustering algorithm to remove outliers, comprising:
based on clustering of a DBSCAN algorithm, clustering the observation points according to the density degree of the observation points in the observation data after the coordinate conversion so as to eliminate the miscellaneous points.
28. The movable platform of any one of claims 21-27 wherein the processor implements the step of constructing a covariance matrix corresponding to the set of ground points from the observation data and the set of ground points, comprising:
based on Gaussian process regression, constructing a covariance matrix comprising position information of the ground points according to the position information of the observation points in the observation data and the position information of one 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 matrices of each ground point belong to joint normal distribution.
29. The movable platform of claim 28, wherein the gaussian process regression comprises: determining a kernel function and determining a hyper-parameter of the kernel function.
30. The movable platform of claim 29, wherein determining the hyperparameters of the kernel function comprises determining the hyperparameters of the kernel function using maximum likelihood.
31. The movable platform of claim 29 wherein the processor implements the step of constructing a covariance matrix including location information for the ground points based on the location information for observation points in the observation data and location information for one of the set of ground points, comprising:
constructing a covariance matrix of observation points in the observation data according to the position information of the observation points in the observation data by using a kernel function for determining the hyper-parameters;
and constructing a covariance matrix containing the position information of the ground points according to the covariance matrix of the observation points and the position information of one ground point in the ground point set.
32. The movable platform of claim 29, wherein the kernel function comprises one of a spline kernel function, a polynomial kernel function, a perceptron kernel function, and a gaussian kernel function.
33. The movable platform of claim 31 wherein the processor implements the step of determining elevation location information for each ground point in the set of ground points from the covariance matrix comprising:
and carrying out regression analysis on the covariance matrix of each ground point in the ground point set according to the covariance matrix of the observation points to obtain the height position information of each ground point.
34. The movable platform of claim 28, wherein the position information comprises first position information and second position information, the first position information and the second position information being different.
35. The movable platform of claim 21 wherein the processor implements the step of ground point sampling the target region to obtain a set of ground points corresponding to the target region, comprising:
and uniformly sampling the target area for multiple times at preset space step intervals to obtain a plurality of ground points, wherein the ground points form a ground point set.
36. The movable platform of claim 21 wherein the processor implements the step of ground point sampling the target region to obtain a set of ground points corresponding to the target region, comprising:
and carrying out multiple random sampling on the target area to obtain a plurality of ground points, wherein the ground points form a ground point set.
37. The movable platform of claim 21, wherein the processor further implements:
and determining the topographic information of the scanning area according to the observation data of the scanning area.
38. The movable platform of claim 21 wherein the processor implements the step of determining terrain information for the target area based on the elevation position information for each ground point comprising:
fitting according to the position information and the height position information of each ground point in the ground point set to obtain a fitting plane of the target area;
and determining the terrain information of the target area according to the fitting plane.
39. The movable platform of claim 21, wherein the terrain information comprises one or more of ground height, ground flatness, and ground slope.
40. The movable platform of claim 21, wherein the processor further implements:
splicing the observation data of the scanning area with the prediction data of the target area to obtain spliced data;
and fitting the spliced data to obtain a fitting plane of the scanning area and the target area, and determining the terrain information of the scanning area and the target area according to the fitting plane.
41. A control device, characterized in that the control device comprises a memory and a processor;
the memory is used for storing a computer program;
the processor for executing the computer program and when executing the computer program implementing the steps of the terrain detection method according to any of claims 1-20 and sending the determined terrain information to the movable platform.
42. A control system comprising a movable platform and the control apparatus of claim 41; the movable platform is used for sampling a target area to obtain a ground point set corresponding to the target area, acquiring observation data of a scanning area, and sending the ground point set and the observation data to the control equipment.
43. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program which, when executed by a processor, causes the processor to carry out the terrain detection method according to any one of claims 1 to 20.
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