CN112585553A

CN112585553A - Control method for movable platform, device and storage medium

Info

Publication number: CN112585553A
Application number: CN202080004213.5A
Authority: CN
Inventors: 王石荣; 陈文平; 王俊喜
Original assignee: SZ DJI Technology Co Ltd
Current assignee: SZ DJI Technology Co Ltd
Priority date: 2020-02-26
Filing date: 2020-02-26
Publication date: 2021-03-30
Also published as: WO2021168708A1

Abstract

The embodiment of the application provides a control method for a movable platform, the movable platform, equipment and a storage medium, wherein a radar configured on the movable platform detects first point cloud data, and the first point cloud data is screened according to feature information of different dimensions in the first point cloud data so as to obtain second point cloud data corresponding to an obstacle through screening. And then, identifying the barrier according to the second point cloud so as to further control the motion state of the movable platform according to the identified barrier. By using the multi-dimensional characteristic information in the point cloud data, the point cloud data corresponding to the obstacle can be accurately screened out, and the obstacle can be further accurately identified so as to ensure the normal movement of the movable platform.

Description

Control method for movable platform, device and storage medium

Technical Field

The present invention relates to the field of device control, and in particular, to a control method for a movable platform, a device, and a storage medium.

Background

Movable platforms such as unmanned aerial vehicles, automobiles, ships and the like are widely applied to numerous fields at present. The movable platform has the requirement of obstacle avoidance in different fields. One commonly used obstacle avoidance method is: the movable platform can observe the surrounding environment by utilizing the radar configured by the movable platform, and then identify the obstacles contained in the surrounding environment according to the point cloud data obtained by observation, so that obstacle avoidance is realized.

However, in practical application, various reasons such as the environment of the movable platform, the installation position of the radar, and the error of the radar may cause noise in the collected point cloud data. These noises, in turn, can affect the identification of obstacles and may further cause damage to the movable platform during movement.

Disclosure of Invention

The invention provides a control method for a movable platform, the movable platform, equipment and a storage medium, which are used for accurately identifying obstacles and ensuring the normal motion of the movable platform.

A first aspect of the present invention is directed to a control method for a movable platform, the method comprising:

acquiring first point cloud data detected by a radar, wherein the radar is arranged on the movable platform;

filtering noise point cloud data corresponding to a non-obstacle in the first point cloud data according to at least one type of characteristic information of the first point cloud data to obtain second point cloud data;

performing obstacle identification according to the second point cloud data;

and controlling the motion state of the movable platform according to the obstacle identification result.

A second aspect of the present invention is to provide a movable platform, comprising at least: the radar comprises a machine body, a radar, a power system and a control device;

the radar is arranged on the machine body and used for detecting point cloud data;

the power system is arranged on the machine body and used for providing power for the movable platform;

the control device includes a memory and a processor;

the memory for storing a computer program;

the processor is configured to execute the computer program stored in the memory to implement:

performing obstacle identification according to the second point cloud data;

A third aspect of the present invention is directed to a control apparatus for a movable platform, the apparatus comprising:

a memory for storing a computer program;

a processor for executing the computer program stored in the memory to implement:

performing obstacle identification according to the second point cloud data;

A fourth aspect of the present invention is to provide a computer-readable storage medium, which is a computer-readable storage medium having stored therein program instructions for the control method for a movable platform according to the first aspect.

According to the control method for the movable platform, the equipment and the storage medium, provided by the invention, the movable platform can accurately identify the obstacles contained in the self motion environment by filtering the noise point cloud data, so that the normal motion of the movable platform is ensured.

Drawings

The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:

fig. 1 is a schematic flowchart of a control method for a movable platform according to an embodiment of the present invention;

fig. 2 is a schematic flow chart illustrating a filtering method for noise point cloud data according to an embodiment of the present invention;

fig. 3 is a schematic flow chart illustrating another noise point cloud data filtering method according to an embodiment of the present invention;

fig. 4 is a schematic flow chart illustrating an obstacle identification method according to an embodiment of the present invention;

FIG. 5 is a graphical representation of a first reference distance provided by an embodiment of the present invention;

fig. 6 is a schematic flow chart of another obstacle identification method according to an embodiment of the present invention;

FIG. 7 is a graphical representation of a second reference distance provided by an embodiment of the present invention;

FIG. 8 is a schematic structural diagram of a control device for a movable platform according to an embodiment of the present invention;

fig. 9 is a schematic structural diagram of a movable platform according to an embodiment of the present invention;

fig. 10 is a schematic structural diagram of a control apparatus for a movable platform according to an embodiment of the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. 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 invention.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.

According to the control method for the movable platform, the equipment and the storage medium, the first point cloud data detected by the radar arranged on the movable platform are firstly obtained, and then the first point cloud data are filtered according to at least one type of characteristic information included in the first point cloud data, so that noise point cloud data corresponding to non-obstacles are filtered. And then, identifying the barrier according to the second point cloud data obtained after filtering, and controlling the motion state of the movable platform according to the position of the barrier. Therefore, in the control method provided by the invention, the noise point cloud data is filtered by using the multi-dimensional characteristic information in the point cloud data, so that the second point cloud data corresponding to the obstacle can be accurately obtained, the obstacle can be accurately identified according to the second point cloud data, and the normal motion of the movable platform is further ensured.

Based on the above description, an embodiment of the present invention provides a control method for a movable platform, including:

performing obstacle identification according to the second point cloud data;

An embodiment of the present invention further provides a movable platform, where the platform at least includes: the device comprises a machine body, a power system and a control device;

the control device comprises a memory and a processor;

the memory for storing a computer program;

performing obstacle identification according to the second point cloud data;

An embodiment of the present invention further provides a control device for a movable platform, where the control device includes:

a memory for storing a computer program;

performing obstacle identification according to the second point cloud data;

An embodiment of the present invention further provides a computer-readable storage medium, where the storage medium is a computer-readable storage medium, and program instructions are stored in the computer-readable storage medium, where the program instructions are used in the control method for a movable platform described above.

Some embodiments of the present invention can be explained in detail based on this scenario with reference to the attached drawings. However, it should be noted that the agricultural scenario is only an example, and the present invention is not limited to the use scenario, as long as there is a scenario that requires the radar disposed on the movable platform to be detached. And features in the embodiments described below and in the embodiments may be combined with each other without conflict between the embodiments.

Fig. 1 is a flowchart illustrating a control method for a movable platform according to an embodiment of the present invention. The execution subject of the control method for the movable platform is a control apparatus. It will be appreciated that the control device may be implemented as software, or a combination of software and hardware. The detection device executes the control method for the movable platform to realize the control of the motion state of the movable platform.

The control device in this embodiment and the following embodiments may be specifically any movable platform such as an unmanned aerial vehicle, an automobile, a ship, and the like, wherein the automobile may be an unmanned vehicle, a general automobile, and the like, and the ship may be an unmanned ship, a general ship, and the like. The following embodiments will be described by taking as an example a case where the movable platform is an unmanned aerial vehicle. Specifically, the method may include:

s101, first point cloud data detected by the radar are obtained.

During the flying process of the unmanned aerial vehicle, the radar configured by the unmanned aerial vehicle can continuously detect point cloud data, and the point cloud data detected at the moment can be called as first point cloud data for subsequent clear description.

The first point cloud data can reflect the distribution situation of obstacles in the flight environment where the unmanned aerial vehicle is located. In order to more accurately control the motion of the movable platform, an optional mode may be to set up a plurality of radars around the fuselage of the drone, so that it detects the omnidirectional point cloud data of the drone. Of course, in view of the volume and self weight of the drone, in another alternative, the radar configured on the drone may be a rotating radar, which is rotated to detect point cloud data in all directions.

S102, according to at least one type of characteristic information of the first point cloud data, noise point cloud data corresponding to non-obstacles in the first point cloud data are filtered out, and second point cloud data are obtained.

At least one characteristic information can be contained in each point cloud data detected by the radar. In practical applications, the first point cloud data can be screened by using feature information of different dimensions alone or in combination. At least one type of characteristic information is explained below:

the at least one characteristic information may include: bearing information relative to the radar, speed of motion relative to the drone, received echo signal energy, and so forth. Wherein, the orientation information may specifically include: distance from the radar, angle from the radar, and position from the radar, etc. Because the electromagnetic wave signal emitted by the radar irradiates an object and is reflected, namely, a point cloud data is formed, the distance in the azimuth information is actually the distance between the object and the radar, the angle in the azimuth information is also the angle between the object and the radar, and the position in the azimuth information can be expressed as a three-dimensional coordinate, namely, the position of the object relative to the radar is indicated in the form of the three-dimensional coordinate.

Optionally, the first point cloud data may be filtered according to a distance from the radar in the orientation information. Taking the point cloud data a in the first point cloud data as an example, first, it is determined whether the distance from the radar in the point cloud data a is smaller than a preset distance. If the distance is smaller than the preset distance, it is indicated that the object corresponding to the point cloud data A is close to the radar, and the object is usually an unmanned aerial vehicle body. And the organism obviously does not belong to the barrier that influences unmanned aerial vehicle flight. That is, the point cloud data a corresponding to the body is also noise point cloud data, and should be filtered out. The point cloud data a may be any one of the first point cloud data. And filtering the first point cloud data according to the method to obtain second point cloud data.

Because unmanned aerial vehicle and radar are rigid connection usually, consequently, unmanned aerial vehicle will shelter from to the radar production, just also can make the radar detect the noise point cloud data A that corresponds to the organism. In addition, the preset distance can be understood as a lower distance limit, and the point cloud data smaller than the lower distance limit are noise point cloud data. And for the setting of the above-mentioned preset distance, optionally, the size of the drone may be referenced. For example, the larger the unmanned aerial vehicle volume, the larger the preset distance is set.

The above manner of filtering the point cloud data according to the preset distance may be actually understood as follows: use the radar as the center, use preset distance as a sphere region of radius planning, the spatial dimension at unmanned aerial vehicle organism place can roughly be represented in this sphere region, also can think that the point cloud data that falls into in this region all corresponds to unmanned aerial vehicle's organism, and the point cloud data that falls into wherein all need be filtered out.

In practical applications, the sphere region may be replaced by a cube region, a cone region, a triangle region, or the like, and different preset distances may be set according to different shapes of the regions.

The region of planning is comparatively rough in the above-mentioned mode, and the space range that this region and unmanned aerial vehicle organism really are located has certain gap in addition, like this, very easily will fall into the non-noise point cloud data filtering of this within range to lead to the accuracy of barrier discernment to reduce.

To avoid the above problem, the orientation information may optionally include a position relative to the radar, which is embodied as a three-dimensional coordinate. At this time, the first point cloud data can be filtered according to the position of the relative radar.

Specifically, the drone may be scanned in advance to obtain measured data, which may be referred to as reference point cloud data, and a preset spatial range may be formed from the reference point cloud data. And then, receiving the example of the point cloud data A, and judging whether the position of the point cloud data A relative to the radar is within a preset space range. If the object corresponding to the point cloud data A is the unmanned aerial vehicle body, the point cloud data A is filtered.

Because the outline that obtains preset space range and can accurately describe the unmanned aerial vehicle organism after the scanning, consequently filtering according to above-mentioned mode just also can avoid appearing the condition of the cloud data filtering of the point cloud that corresponds to non-organism in the cloud data of first point.

The filtering method is actually used for filtering point cloud data used for describing the unmanned aerial vehicle body in the first point cloud data. Moreover, the above only lists two ways of filtering noise point cloud data according to the feature information, and the filtering of the noise point cloud data may also include other ways, and the specific process may refer to the detailed description in the embodiments shown in fig. 2 to fig. 3 below.

And S103, identifying the obstacle according to the second point cloud data.

And S104, controlling the motion state of the movable platform according to the obstacle identification result.

And clustering the second point cloud data to obtain at least one cluster of point cloud data. Each cluster can be regarded as an obstacle, namely, the distribution of the obstacles in the flight environment where the unmanned aerial vehicle is currently located is identified. At this moment, can be according to the position at barrier place for unmanned aerial vehicle planning flight path, control unmanned aerial vehicle's flight.

In the control method for the movable platform provided in this embodiment, first point cloud data detected by a radar disposed on the movable platform is obtained, and then the first point cloud data is filtered according to at least one type of feature information included in the first point cloud data to filter noise point cloud data corresponding to a non-obstacle. And then, identifying the barrier according to the second point cloud data obtained after filtering, and controlling the motion state of the movable platform according to the position of the barrier. Therefore, in the control method provided by the invention, the noise point cloud data is filtered by using the multi-dimensional characteristic information in the point cloud data, so that the second point cloud data corresponding to the obstacle can be accurately obtained, the obstacle can be accurately identified according to the second point cloud data, and the normal motion of the movable platform is further ensured.

In practical application, because the flight environment of the unmanned aerial vehicle is relatively complex, the first point cloud data collected by the radar usually comprises noise point cloud data corresponding to water clutter, ground clutter and the like. And whether the point cloud data corresponds to clutter or not can be determined by azimuth information and echo signal energy of the relative radar in characteristic information included in the point cloud data. That is, in the first point cloud data, the point cloud data in which the azimuth information and the echo signal energy relative to the radar do not satisfy the respective corresponding preset threshold values is determined as noise point cloud data corresponding to the clutter, and the noise point cloud data needs to be filtered.

Specifically, in an optional case, the orientation information may specifically include a distance and an angle relative to the radar at the same time, and at this time, the point cloud data a in the above example is continuously received, and if the distance of the point cloud data a relative to the radar is smaller than a preset distance, and the angle of the point cloud data a relative to the radar is within a preset angle range, and the energy of the echo signal of the point cloud data a is smaller than a preset energy value, it is indicated that the point cloud data a is noise point cloud data, and the noise point cloud data is filtered. The preset distance, the preset angle range and the preset energy value can be set according to historical experience.

This historical experience can be considered as: after multiple times of actual flight, noise point cloud data in the point cloud data collected by the radar is separated, and the separated noise point cloud data can be regarded as being composed ofClutter is generated. The noise point cloud data can be counted while echo signal energy of the noise point cloud data is obtained, so that the probability of the noise point cloud data appearing at different distances and different angles relative to the radar is obtained. And determining the distance and the angle range with higher occurrence probability of the noise point cloud data as a preset distance and a preset angle range. In practical application, the preset distance can be 5 meters, the preset angle range can be 10-15 degrees, and the preset energy value can be 5000W/m²。

Alternatively, the orientation information may also include a distance to the radar. At this time, the filtering of the first point cloud data may be: after a task execution signal of the unmanned aerial vehicle is started, if the distance of the point cloud data A radar is smaller than a preset distance and the energy of an echo signal of the target point cloud data is smaller than a preset energy value, filtering the target point cloud data. In practical applications, the preset distance may be 5 meters, and the preset energy value may be 20000W/m²。

Because the function that the unmanned aerial vehicle of different grade type can realize is different, for example agricultural unmanned aerial vehicle can realize spraying of pesticide or the broadcast of seed, consequently, the task execution signal that unmanned aerial vehicle corresponds is exactly spraying the signal or broadcast the signal.

In addition to the various filtering methods provided above, optionally, the noise point cloud data may be filtered according to the movement speed of the relative radar, as shown in fig. 2, that is, an optional realizable method regarding step S102 may be:

s201, acquiring the movement speed of the movable platform.

S202, filtering point cloud data, of which the relative radar motion speed is not matched with the motion speed of the movable platform, in the first point cloud data.

An Inertial measurement unit (IMU for short) configured on the unmanned aerial vehicle can measure the movement speed V of the unmanned aerial vehicle_a. Then, the movement speed V of the relative radar in the first point cloud data is judged_rmWith this unmanned aerial vehicle's velocity of motion V_aWhether there is a match between them. Wherein, the motion speed V acquired by the IMU_aIs shown as V_a＝[V_ax,V_ay,V_az]^T(ii) a At the same time, the radar can only measure the speed in the direction of the transmitted beam, so the moving speed V relative to the radar_rmIs shown as V_rm＝[V_rmx，0，0]^T。

Easily understood, the point cloud data corresponds to an object in the flying environment of the unmanned aerial vehicle, and the moving speed V of the relative radar in the point cloud data_rmIn fact the speed of movement of the object relative to the drone, i.e. a relative speed. And since the object is usually at rest in a flying environment, this relative velocity V_rmShould be in contact with the velocity of motion V of the drone_aEqual in size and opposite in direction.

Continuing to take over the example of the point cloud data a, judging whether the speed is matched, in an optional manner, if the movement speed V in the point cloud data a_rmxWith unmanned aerial vehicle's velocity of motion V_axIf the vector sum of (1) is not 0, the two velocities are not matched, and the point cloud data A can be filtered out.

In one embodiment, the velocity of motion V_rmAnd a speed of movement V_aIs acquired by different devices on the unmanned aerial vehicle, so that the movement speed V_rmCorresponding to the radar coordinate system, velocity of motion V_aCorresponding to the body coordinate system of the drone. In order to further ensure the filtering effect of the noise point cloud data, optionally, the motion speed V in the body coordinate system can also be obtained by using the transformation matrix R_aConversion into a velocity of motion V in a radar coordinate system_r。

The transformation of the coordinate system can be performed in particular by the following formula: v_r＝R*V_aThe converted motion velocity may be expressed as: v_r＝[V_rx,V_ry,V_rz]^T. Then, the velocity of motion V relative to the radar can be calculated_rmxAnd the converted speed V_rxThe sum of the velocity vectors. If the velocity vector sum is not 0, filtering the point cloud data A.

In practical application, considering the influence of IMU measurement precision, unmanned aerial vehicle and radar equal attitude error and the like, the method is used for measuring the attitude of the unmanned aerial vehicleThe condition for determining whether the moving speed matches can be relaxed appropriately. Then, alternatively, the velocity V of motion of the relative radar is calculated_rmxAnd the converted speed V_rxAfter the velocity vector sum, the relationship between the velocity vector sum and the preset velocity error Δ v is judged. And if the vector sum is larger than the preset speed error delta v, filtering the point cloud data A. The preset speed error Δ v may be set according to historical experience, and the preset speed error may be a speed range.

Therefore, the screening method actually determines the point cloud data with unqualified movement speed as the noise point cloud data and filters out the noise point cloud data. The process of judging whether the movement speed is qualified can be understood as follows: according to the movement speed V of the unmanned aerial vehicle_aAnd relative movement velocity V_rmA criterion is set. If the vector sum of the two speeds meets the judgment standard, the speed is qualified, otherwise, the speed is unqualified. In addition, according to the two situations, the set judgment standard may be a speed value or a speed range according to the actual requirement.

In addition to the various filtering manners provided above, optionally, the noise point cloud data may be filtered according to the echo signal energy and the position relative to the radar in the azimuth information, as shown in fig. 3, that is, another optional implementation manner of step S102 may be:

s301, fitting a target plane corresponding to the ground according to the position of the first point cloud data relative to the radar.

In the above embodiment, it is mentioned that the position of the first point cloud data relative to the radar may be specifically represented as a three-dimensional coordinate, and then a target plane may be fitted according to the three-dimensional coordinate, where the target plane is used to indicate the ground in the flight environment of the unmanned aerial vehicle, where the three-dimensional coordinate may be represented as [ x, y, z ], and an equation of the target plane may be represented as z ═ Ax + By + C.

Since the point cloud data is collected by the radar, the three-dimensional coordinate [ x, y, z ] is in the radar coordinate system, and optionally, the three-dimensional coordinate in the radar coordinate system may be converted into the horizontal coordinate system to obtain a new three-dimensional coordinate [ x ', y ', z '). And fitting the target plane according to the three-dimensional coordinates in the horizontal coordinate system to ensure that the target plane is a horizontal plane and is closer to the real ground.

In addition, it is easy to understand that the ground is located under the drone in the flight environment, which makes the point cloud data describing the ground to have a certain angle with respect to the radar, such as within a certain angle range. Therefore, for point cloud data with obviously unsatisfactory angles, if the point cloud data is applied to the process of fitting the target plane, the accuracy of plane fitting is affected.

Based on the above description, optionally, the first point cloud data may also be filtered according to an angle of the first point cloud data relative to the radar. For example, third point cloud data, the angle of which relative to the radar meets a preset angle range, is screened from the first point cloud data, and then a target plane is fitted according to the position of the relative radar in the third point cloud data.

It should be noted that the coordinate transformation of the point cloud data and the screening of the point cloud data are both for ensuring the fitting accuracy of the target plane. In practical applications, two of them may be selected to be executed simultaneously or one may be selected to be executed alternatively.

S302, calculating the distance between the first point cloud data and the target plane according to the position, relative to the radar, in the first point cloud data.

And S303, filtering point cloud data, of which the distance relative to a target plane and the energy of an echo signal do not meet respective corresponding preset thresholds, in the first point cloud data.

And then, calculating the distance between the three-dimensional coordinates in the first point cloud data and the target plane, and simultaneously obtaining the echo signal energy in the first point cloud data. Suppose the three-dimensional coordinate of the point cloud data A is [ x ]₁,y₁,z₁]The equation for the target plane is z ═ Ax + By + C, and the distance can be expressed as

This distance can be understood as the distance between the point cloud data and the ground.

And if the distance h is within the preset distance range and the echo signal energy of the point cloud data A also meets the preset energy range, determining that the point cloud data A is valid, and then retaining the point cloud data A. And if the point cloud data A does not meet the condition, filtering the point cloud data A.

The preset distance range and the preset energy range can be set according to historical experience. The historical experience can be that after multiple actual flights, the three-dimensional coordinates of the point cloud data acquired by the radar can be counted to obtain the probability of the point cloud data appearing at different distances relative to the radar, so as to obtain the probability of the point cloud data appearing under different echo signal energies. And setting the distance range with the larger probability of the point cloud data as a preset distance range, and setting the energy range with the larger probability of the point cloud data as a preset energy range.

It can be seen that the embodiment shown in fig. 3 actually calculates the height between the point cloud data and the ground according to the three-dimensional coordinates of the point cloud data, and determines the effective point cloud data according to the height and the echo signal energy included in the point cloud data.

In summary, the embodiments shown in fig. 1 to 3 provide various ways of filtering noise point cloud data, and one or more of the ways may be selected according to actual requirements. Of course, the more types of implementation, the better the filtering effect on the noise point cloud data.

After the first point cloud data is screened by the different methods, the remaining point cloud data is the second point cloud data. At this time, the second point cloud data may be clustered to obtain at least one cluster of point cloud data. As to how to use the at least one cluster of point cloud data to realize the identification of the obstacle, as shown in fig. 4, an alternative way may be:

s401, for target cluster point cloud data in at least one cluster of point cloud data, acquiring the center of the target cluster point cloud data, wherein the azimuth information of the center comprises the position relative to the radar.

S402, calculating a first distance between the center and the ground.

And after the second point cloud data is clustered, the center of each cluster of point cloud data can be obtained. For the target cluster point cloud data K in the at least one cluster of point cloud data, based on the target plane which is already fitted in the embodiment shown in fig. 3 and used for representing the ground, the distance between the center of the target cluster point cloud data K and the target plane may be further calculated. The target cluster point cloud data K may be any one of at least one cluster of point cloud data.

Similar to the embodiment shown in fig. 3, assume that the three-dimensional coordinate of the center of the target cluster point cloud data K is x₂,y₂,z₂]The first distance between the center and the target plane, i.e. the distance between the center and the ground, can be expressed as:

optionally, before performing distance clustering, the point cloud data in the radar coordinate system may also be converted into the horizontal coordinate system.

And S403, calculating a first reference distance according to the first distance.

S404, if the position of the relative radar is matched with the first reference distance, determining that the target cluster point cloud data corresponds to the obstacle.

When the first distance h is obtained, optionally, the first reference distance corresponding to the first distance h may also be calculated according to the following manner

Wherein z is_max,z_min,h_max,h_minAre all preset values.

The first reference distance described above may be regarded as a distance lower limit value. If the coordinate value z of the center of the point cloud data K of the target cluster₂Is at a first reference distance

Matching, then determining the target cluster point cloudThe data K corresponds to an obstacle. The matching relationship may be, in particular, a coordinate value z₂Greater than or equal to the first reference distance

For example, assume that the first distance h is 3.0m, z_max＝0.0m,z_min＝-1.0m，h_max＝4.0m,h_min2.0m, the first reference distance may be represented as a curve as shown in fig. 5. And z is₂1.0m, calculated by the above formula

I.e. z of the center of the target cluster point cloud data K₂Located above the curve, it is indicated that the target cluster point cloud data K corresponds to an obstacle.

As can be seen, the method shown in fig. 4 is to first determine the distance h between the center of the target cluster point cloud data and the ground and the z of the center of the target cluster point cloud data₂The magnitude relationship between the values determines whether the cluster of point cloud data corresponds to an obstacle.

As for the way of identifying the obstacle according to at least one cluster of point cloud data, as shown in fig. 6, another alternative way is:

s501, for target cluster point cloud data in at least one cluster of point cloud data, acquiring the center of the target cluster point cloud data, wherein the azimuth information of the center comprises the position relative to a radar.

The execution process of step S401 is similar to the corresponding steps in the foregoing embodiment, and reference may be made to the relevant description in the embodiment shown in fig. 4, which is not repeated herein.

And S502, calculating a second distance between the center and the center of the movable platform.

And S503, calculating a second reference distance according to the second distance.

S504, if the position of the relative radar is matched with the second reference distance, the target cluster point cloud data is determined to correspond to the obstacle.

The target cluster point cloud data K may be any one of at least one cluster of point cloud data. Suppose thatThe three-dimensional coordinate of the center of the target cluster point cloud data K is [ x ]₂,y₂,z₂]The horizontal distance between the center and the movable platform, i.e. the center of the drone radar, i.e. the second distance, is expressed as:

when the second distance d is obtained, optionally, the second reference distance corresponding to the second distance d may be calculated according to the following manner

Wherein z is_max,z_min,d_max,d_minAre all preset values.

The second reference distance described above may be regarded as a distance lower limit value. If the coordinate value z of the center of the point cloud data K of the target cluster₂Is at a second reference distance

And matching, and determining that the target cluster point cloud data K corresponds to the obstacle. The matching relationship may be, in particular, a coordinate value z₂Greater than or equal to the second reference distance

For example, assume that the second distance d is 3.0m, z_max＝-0.5m,z_min＝-1.0m,d_max＝10.0m,d_minThe second reference distance may be represented as a curve as shown in fig. 7, when it is 5.0 m. And when z is₂When 1.0m, the value is calculated by the above formula

I.e. z of the center of the target cluster point cloud data K₂The value is above the curve, indicating that the target cluster point cloud data K corresponds to an obstacle.

FIG. 6 shows the method according to the horizontal distance d between the center of the point cloud data of the target cluster and the radar and the distance z between the center and the ground₂The magnitude relationship between the values determines whether the cluster of point cloud data corresponds to an obstacle.

After the embodiments shown in fig. 4 to 7, noise point cloud data corresponding to non-obstacles in at least one cluster of point cloud data can be filtered out. For the remaining multi-cluster point cloud data, optionally, the following screening may also be performed:

and counting the number of the point cloud data contained in the cluster of point cloud data for one cluster of point cloud data P in the rest multiple clusters of point cloud data. And if the number is larger than a preset value, determining that the cluster of point cloud data P corresponds to the obstacle. Wherein, the cluster of point cloud data P can be any cluster of the remaining clusters of point cloud data.

It should be noted that, the above process of screening according to the number of point cloud data in one cluster of point cloud data may be performed after the embodiment shown in fig. 4 or fig. 6 is executed, or may be performed after the second point cloud data is clustered to obtain at least one cluster of point cloud data, or certainly may be performed after both of the above two cases, so as to more accurately filter noise point cloud data in the point cloud data, ensure the accuracy of obstacle identification, and further ensure the normal flight of the unmanned aerial vehicle. Simultaneously, the obstacle in the unmanned aerial vehicle flight environment of discernment that can be accurate alright in order to realize accurate control to unmanned aerial vehicle, for example can be that unmanned aerial vehicle realizes target tracking etc..

Fig. 8 is a schematic structural diagram of a control device for a movable platform according to an embodiment of the present invention. As shown in fig. 8, the present embodiment provides a control apparatus for a movable platform, which can execute the above-described control method for a movable platform; specifically, the control device includes:

and the acquisition module 11 is used for acquiring first point cloud data detected by a radar, and the radar is arranged on the movable platform.

And the filtering module 12 is configured to filter noise point cloud data corresponding to a non-obstacle in the first point cloud data according to at least one type of feature information of the first point cloud data to obtain second point cloud data.

And the identification module 13 is used for identifying the obstacle according to the second point cloud data.

And the control module 14 is used for controlling the motion state of the movable platform according to the obstacle identification result.

The apparatus shown in fig. 8 can also perform the method of the embodiment shown in fig. 1 to 7, and the related description of the embodiment shown in fig. 1 to 7 can be referred to for the part not described in detail in this embodiment. The implementation process and technical effect of the technical solution refer to the descriptions in the embodiments shown in fig. 1 to 7, and are not described herein again.

Fig. 9 is a schematic structural diagram of a movable platform according to an embodiment of the present invention; referring to fig. 9, an embodiment of the present invention provides a movable platform, which is at least one of the following: the system comprises an unmanned aircraft, an automobile and a ship, wherein the automobile can be an unmanned automobile, a common automobile and the like, and the ship can be an unmanned ship, a common ship and the like. Specifically, the movable platform includes: a body 21, a radar 22, a power system 23, and a control device 24.

The radar 22 is disposed on the body 21 and configured to detect point cloud data.

And the power system 23 is arranged on the machine 21 body and used for providing power for the movable platform.

The control device 24 includes a memory 241 and a processor 242.

The memory for storing a computer program;

performing obstacle identification according to the second point cloud data;

Further, the at least one feature information includes: azimuth information relative to the radar, echo signal energy, and speed of motion relative to the movable platform; the azimuth information includes a distance to the radar;

processor 242 is further configured to: and filtering point cloud data, of which the distance relative to the radar is smaller than a preset distance, in the first point cloud data, wherein the preset distance corresponds to the size information of the movable platform.

Further, the azimuth information includes a position relative to the radar;

processor 242 is further configured to: and filtering point cloud data which are positioned in a preset space range relative to the position of the radar, wherein the preset space range comprises the movable platform.

Further, the processor 242 is further configured to: and filtering point cloud data, of which the azimuth information and the echo signal energy relative to the radar in the first point cloud data do not meet respective corresponding preset thresholds, from the first point cloud data.

Further, the azimuth information includes a distance and an angle with respect to the radar;

processor 242 is further configured to: and for target point cloud data in the first point cloud data, if the distance between the target point cloud data and the radar is smaller than a preset distance, the angle between the target point cloud data and the radar is within a preset angle range, and the energy of an echo signal of the target point cloud data is smaller than a preset energy value, filtering the target point cloud data.

Further, the azimuth information includes a distance to the radar;

processor 242 is further configured to: and for target point cloud data in the first point cloud data, if the distance between the target point cloud data and the radar is smaller than a preset distance and the energy of an echo signal of the target point cloud data is smaller than a preset energy value, filtering the target point cloud data, wherein a task execution signal of the movable platform is started.

Further, the processor 242 is further configured to: acquiring the movement speed of the movable platform;

and filtering point cloud data which are unmatched with the movement speed of the movable platform relative to the movement speed of the radar in the first point cloud data.

Further, the processor 242 is further configured to: and for target point cloud data in the first point cloud data, if the vector sum of the motion speed of the target point cloud data relative to the radar and the motion speed of the movable platform is greater than a set speed error, filtering the target point cloud data.

Further, the azimuth information includes a position relative to the radar;

processor 242 is further configured to: according to the position, relative to the radar, of the first point cloud data, a target plane corresponding to the ground is fitted;

calculating the distance between the first point cloud data and the target plane according to the position, relative to the radar, in the first point cloud data;

and filtering point cloud data, of which the distance relative to the target plane and the energy of the echo signal do not meet respective corresponding preset thresholds, in the first point cloud data.

Further, the azimuth information further includes an angle with respect to the radar;

processor 242 is further configured to: screening out third point cloud data of which the angle relative to the radar meets a preset angle range from the first point cloud data, wherein the preset angle range corresponds to the angle of the radar relative to the ground;

and fitting the target plane according to the position of the third point cloud data relative to the radar.

Further, the processor 242 is further configured to: clustering the second point cloud data to obtain at least one cluster of point cloud data;

and identifying an obstacle according to the at least one cluster of point cloud data.

Further, the processor 242 is further configured to: for target cluster point cloud data in the at least one cluster of point cloud data, acquiring a center of the target cluster point cloud data, wherein azimuth information of the center comprises a position relative to the radar;

calculating a first distance of the center relative to the ground;

calculating a first reference distance according to the first distance;

and if the position relative to the radar is matched with the first reference distance, determining that the target cluster point cloud data corresponds to an obstacle.

calculating a second distance of the center relative to the center of the movable platform;

calculating a second reference distance according to the second distance;

and if the position relative to the radar is matched with the second reference distance, determining that the target cluster point cloud data corresponds to an obstacle.

Further, the processor 242 is further configured to: counting the number of point cloud data contained in the target cluster point cloud data;

and if the number is larger than a preset value, determining that the target cluster point cloud data corresponds to the obstacle.

The movable platform shown in fig. 9 can perform the method of the embodiment shown in fig. 1 to 7, and the details of this embodiment, which are not described in detail, can refer to the related description of the embodiment shown in fig. 1 to 7. The implementation process and technical effect of the technical solution refer to the descriptions in the embodiments shown in fig. 1 to 7, and are not described herein again.

In one possible design, the structure of the control device for a movable platform shown in fig. 10 may be implemented as an electronic device, which may be an unmanned vehicle, an automobile, a ship, or the like, wherein the automobile may be an unmanned vehicle, a general automobile, or the like, and the ship may be an unmanned ship, a general ship, or the like. As shown in fig. 10, the electronic device may include: one or more processors 31 and one or more memories 32. The memory 32 is used for storing a program for supporting the electronic device to execute the control method for the movable platform provided in the embodiments shown in fig. 1 to 7. The processor 31 is configured to execute programs stored in the memory 32.

In particular, the program comprises one or more computer instructions, wherein the one or more computer instructions, when executed by the processor 31, enable the following steps to be performed:

performing obstacle identification according to the second point cloud data;

The structure of the pan/tilt/zoom control device may further include a communication interface 33, which is used for the electronic device to communicate with other devices or a communication network.

the processor 31 is further configured to: and filtering point cloud data, of which the distance relative to the radar is smaller than a preset distance, in the first point cloud data, wherein the preset distance corresponds to the size information of the movable platform.

Further, the azimuth information includes a position relative to the radar;

the processor 31 is further configured to: and filtering point cloud data which are positioned in a preset space range relative to the position of the radar, wherein the preset space range comprises the movable platform.

Further, the processor 31 is further configured to: and filtering point cloud data, of which the azimuth information and the echo signal energy relative to the radar in the first point cloud data do not meet respective corresponding preset thresholds, from the first point cloud data.

the processor 31 is further configured to: and for target point cloud data in the first point cloud data, if the distance between the target point cloud data and the radar is smaller than a preset distance, the angle between the target point cloud data and the radar is within a preset angle range, and the energy of an echo signal of the target point cloud data is smaller than a preset energy value, filtering the target point cloud data.

Further, the azimuth information includes a distance to the radar;

the processor 31 is further configured to: and for target point cloud data in the first point cloud data, if the distance between the target point cloud data and the radar is smaller than a preset distance and the energy of an echo signal of the target point cloud data is smaller than a preset energy value, filtering the target point cloud data, wherein a task execution signal of the movable platform is started.

Further, the processor 31 is further configured to: acquiring the movement speed of the movable platform;

Further, the processor 31 is further configured to: and for target point cloud data in the first point cloud data, if the vector sum of the motion speed of the target point cloud data relative to the radar and the motion speed of the movable platform is greater than a set speed error, filtering the target point cloud data.

Further, the azimuth information includes a position relative to the radar;

the processor 31 is further configured to: according to the position, relative to the radar, of the first point cloud data, a target plane corresponding to the ground is fitted;

the processor 31 is further configured to: screening out third point cloud data of which the angle relative to the radar meets a preset angle range from the first point cloud data, wherein the preset angle range corresponds to the angle of the radar relative to the ground;

Further, the processor 31 is further configured to: clustering the second point cloud data to obtain at least one cluster of point cloud data;

Further, the processor 31 is further configured to: for target cluster point cloud data in the at least one cluster of point cloud data, acquiring a center of the target cluster point cloud data, wherein azimuth information of the center comprises a position relative to the radar;

calculating a first distance of the center relative to the ground;

calculating a first reference distance according to the first distance;

calculating a second reference distance according to the second distance;

Further, the processor 31 is further configured to: counting the number of point cloud data contained in the target cluster point cloud data;

The apparatus shown in fig. 10 can perform the method of the embodiment shown in fig. 1 to 7, and the detailed description of this embodiment can refer to the related description of the embodiment shown in fig. 1 to 7. The implementation process and technical effect of the technical solution refer to the descriptions in the embodiments shown in fig. 1 to 7, and are not described herein again.

In addition, an embodiment of the present invention provides a computer-readable storage medium, where the storage medium is a computer-readable storage medium, and program instructions are stored in the computer-readable storage medium, where the program instructions are used to implement the control method for a movable platform in fig. 1 to 7.

The technical solutions and the technical features in the above embodiments may be used alone or in combination in case of conflict with the present disclosure, and all embodiments that fall within the scope of protection of the present disclosure are intended to be equivalent embodiments as long as they do not exceed the scope of recognition of those skilled in the art.

In the embodiments provided in the present invention, it should be understood that the disclosed correlation detection apparatus (e.g., imu) and method may be implemented in other ways. For example, the above-described remote control device embodiments are merely illustrative, and for example, the division of the modules or units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, remote control devices or units, and may be in an electrical, mechanical or other form.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.

The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer processor (processor) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.

The above description is only an embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes performed by the present specification and drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.

Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A control method for a movable platform, the method comprising:

performing obstacle identification according to the second point cloud data;

2. The method of claim 1, wherein the at least one feature information comprises: azimuth information relative to the radar, echo signal energy, and speed of motion relative to the movable platform.

3. The method of claim 2, wherein the bearing information includes a distance to the radar;

the filtering out noise point cloud data corresponding to a non-obstacle in the first point cloud data according to at least one feature information of the first point cloud data comprises:

and filtering point cloud data, of which the distance relative to the radar is smaller than a preset distance, in the first point cloud data, wherein the preset distance corresponds to the size information of the movable platform.

4. The method of claim 2, wherein the orientation information includes a position relative to the radar;

and filtering point cloud data which are positioned in a preset space range relative to the position of the radar, wherein the preset space range comprises the movable platform.

5. The method of claim 2, wherein the filtering out noise point cloud data corresponding to non-obstacles in the first point cloud data according to at least one feature information of the first point cloud data comprises:

and filtering point cloud data, of which the azimuth information and the echo signal energy relative to the radar in the first point cloud data do not meet respective corresponding preset thresholds, from the first point cloud data.

6. The method of claim 5, wherein the bearing information includes a distance and an angle relative to the radar;

the filtering of the point cloud data, in the first point cloud data, of which the azimuth information and the echo signal energy relative to the radar do not meet the set threshold includes:

and for target point cloud data in the first point cloud data, if the distance between the target point cloud data and the radar is smaller than a preset distance, the angle between the target point cloud data and the radar is within a preset angle range, and the energy of an echo signal of the target point cloud data is smaller than a preset energy value, filtering the target point cloud data.

7. The method of claim 5, wherein the bearing information includes a distance to the radar;

and for target point cloud data in the first point cloud data, if the distance between the target point cloud data and the radar is smaller than a preset distance and the energy of an echo signal of the target point cloud data is smaller than a preset energy value, filtering the target point cloud data, wherein a task execution signal of the movable platform is started.

8. The method of claim 2, wherein the filtering out noise point cloud data corresponding to non-obstacles in the first point cloud data according to at least one feature information of the first point cloud data comprises:

acquiring the movement speed of the movable platform;

9. The method of claim 8, wherein filtering out point cloud data in the first point cloud data that does not match the speed of motion of the movable platform relative to the speed of motion of the radar comprises:

and for target point cloud data in the first point cloud data, if the vector sum of the motion speed of the target point cloud data relative to the radar and the motion speed of the movable platform is greater than a set speed error, filtering the target point cloud data.

10. The method of claim 2, wherein the orientation information includes a position relative to the radar;

according to the position, relative to the radar, of the first point cloud data, a target plane corresponding to the ground is fitted;

11. The method of claim 10, wherein the orientation information further comprises an angle relative to the radar;

the fitting of a target plane corresponding to the ground according to the position of the first point cloud data relative to the radar includes:

screening out third point cloud data of which the angle relative to the radar meets a preset angle range from the first point cloud data, wherein the preset angle range corresponds to the angle of the radar relative to the ground;

12. The method of claim 2, wherein the performing obstacle identification from the second point cloud data comprises:

clustering the second point cloud data to obtain at least one cluster of point cloud data;

13. The method of claim 12, wherein identifying an obstacle from the at least one cluster of point cloud data comprises:

for target cluster point cloud data in the at least one cluster of point cloud data, acquiring a center of the target cluster point cloud data, wherein azimuth information of the center comprises a position relative to the radar;

calculating a first distance of the center relative to the ground;

calculating a first distance reference value according to the first distance;

and if the position relative to the radar meets the first distance reference value, determining that the target cluster point cloud data corresponds to an obstacle.

14. The method of claim 12, wherein identifying an obstacle from the at least one cluster of point cloud data comprises:

calculating a second distance reference value according to the second distance;

and if the position relative to the radar meets a second distance reference value, determining that the target cluster point cloud data corresponds to an obstacle.

15. The method according to claim 13 or 14, characterized in that the method further comprises:

counting the number of point cloud data contained in the target cluster point cloud data;

16. A movable platform, comprising at least: the radar comprises a machine body, a radar, a power system and a control device;

the control device comprises a memory and a processor;

the memory for storing a computer program;

performing obstacle identification according to the second point cloud data;

17. The movable platform of claim 16, wherein the at least one characterization information comprises: azimuth information relative to the radar, echo signal energy, and speed of motion relative to the movable platform; the azimuth information includes a distance to the radar;

the processor is further configured to: and filtering point cloud data, of which the distance relative to the radar is smaller than a preset distance, in the first point cloud data, wherein the preset distance corresponds to the size information of the movable platform.

18. The movable platform of claim 17, wherein the orientation information includes a position relative to the radar;

the processor is further configured to: and filtering point cloud data which are positioned in a preset space range relative to the position of the radar, wherein the preset space range comprises the movable platform.

19. The movable platform of claim 17, wherein the processor is further configured to:

20. The movable platform of claim 19, wherein the orientation information includes a distance and an angle relative to the radar;

the processor is further configured to: and for target point cloud data in the first point cloud data, if the distance between the target point cloud data and the radar is smaller than a preset distance, the angle between the target point cloud data and the radar is within a preset angle range, and the energy of an echo signal of the target point cloud data is smaller than a preset energy value, filtering the target point cloud data.

21. The movable platform of claim 19, wherein the orientation information comprises a distance from the radar;

the processor is further configured to: and for target point cloud data in the first point cloud data, if the distance between the target point cloud data and the radar is smaller than a preset distance and the energy of an echo signal of the target point cloud data is smaller than a preset energy value, filtering the target point cloud data, wherein a task execution signal of the movable platform is started.

22. The movable platform of claim 17, wherein the processor is further configured to:

acquiring the movement speed of the movable platform;

23. The movable platform of claim 22, wherein the processor is further configured to:

24. The movable platform of claim 17, wherein the orientation information includes a position relative to the radar;

the processor is further configured to:

25. The movable platform of claim 24, wherein the orientation information further comprises an angle relative to the radar;

the processor is further configured to:

26. The movable platform of claim 17, wherein the processor is further configured to:

27. The movable platform of claim 26, wherein the processor is further configured to:

calculating a first distance of the center relative to the ground;

calculating a first reference distance according to the first distance;

28. The movable platform of claim 26, wherein the processor is further configured to:

calculating a second reference distance according to the second distance;

29. The movable platform of claim 26 or 27, wherein the processor is further configured to: counting the number of point cloud data contained in the target cluster point cloud data;

30. A control device for a movable platform, characterized in that the detection device comprises:

a memory for storing a computer program;

performing obstacle identification according to the second point cloud data;

31. The apparatus of claim 30, wherein the at least one feature information comprises: azimuth information relative to the radar, echo signal energy, and speed of motion relative to the movable platform; the azimuth information includes a distance to the radar;

32. The device of claim 31, wherein the orientation information comprises a position relative to the radar;

33. The device of claim 31, wherein the processor is further configured to:

34. The apparatus of claim 33, wherein the orientation information comprises a distance and an angle relative to the radar;

35. The apparatus of claim 33, wherein the orientation information comprises a distance from the radar;

36. The device of claim 31, wherein the processor is further configured to:

acquiring the movement speed of the movable platform;

37. The device of claim 36, wherein the processor is further configured to:

38. The device of claim 31, wherein the orientation information comprises a position relative to the radar;

the processor is further configured to:

39. The apparatus of claim 38, wherein the orientation information further comprises an angle relative to the radar;

the processor is further configured to:

40. The device of claim 31, wherein the processor is further configured to:

41. The device of claim 40, wherein the processor is further configured to:

calculating a first distance of the center relative to the ground;

calculating a first reference distance according to the first distance;

42. The device of claim 40, wherein the processor is further configured to:

calculating a second reference distance according to the second distance;

43. The device of claim 41 or 42, wherein the processor is further configured to: counting the number of point cloud data contained in the target cluster point cloud data;

44. A computer-readable storage medium, characterized in that the storage medium is a computer-readable storage medium in which program instructions for implementing the control method for a movable platform according to any one of claims 1 to 15 are stored.