UAV positioning method and device and UAV parking posture adjustment method and device

Abstract

The present application discloses a method and device for positioning a drone and a method and device for adjusting the parking posture of a drone. The positioning method obtains point cloud data of a characteristic section of a drone based on a time-of-flight TOF sensor; determines a number of first position coordinates based on the point cloud data, each of which corresponds to a characteristic point on the characteristic section where the drone is located; determines the positioning information of the drone based on the first position coordinates, and the positioning information of the drone includes the yaw angle of the drone and a second position coordinate that characterizes the position of the drone. The beneficial effect of the present application is that, based on the time-of-flight ranging method, by obtaining point cloud data of the characteristic section of the drone and combining it with the corresponding characteristic points of the drone, the posture of the drone can be quickly and accurately located. The algorithm is simple, the accuracy is high, and the required hardware cost is low, and reliable data information can be provided for the needs of adjusting the parking posture of the drone.

Description

Technical Field

[0001] The present application relates to the technical field of unmanned aerial vehicles (UAVs), and in particular to a method and device for positioning an UAV and a method and device for adjusting the parking posture of an UAV. Background Art

[0002] Currently in the drone field, only when the drone lands at a designated parking space in a preset posture, subsequent tasks such as automatic delivery of meal boxes and automatic battery replacement can be fully automated.

[0003] However, in actual situations, the drone cannot land at the designated parking position with a high-precision preset posture. Generally speaking, the drone may be some distance or angle away from the designated parking position. At this time, it is necessary to use external equipment to adjust the drone's posture. During the adjustment process, the external equipment first needs to accurately locate the position of the drone when it lands.

[0004] However, the existing positioning technology for drones after landing still has many shortcomings. Some methods use visual positioning, but the camera device is expensive, slow, and has poor accuracy, and has very high requirements for ambient light; some methods directly install sensors on the manipulator of external equipment and collect data from a bird's-eye view, resulting in limited collection range, distortion of the edge of the picture, and reduced judgment accuracy. Summary of the invention

[0005] In view of the above problems, the present application is proposed to provide a method and device for positioning a UAV and a method and device for adjusting the parking posture of a UAV, which overcome the above problems or at least partially solve the above problems.

[0006] According to one aspect of the present application, a method for positioning a drone is provided, the method comprising:

[0007] Obtain point cloud data of the characteristic cross section of the drone based on the time-of-flight TOF sensor;

[0008] Determine a plurality of first position coordinates according to the point cloud data, each of the first position coordinates corresponding to a characteristic point on the characteristic cross section where the drone is located;

[0009] The drone positioning information is determined according to the first position coordinates, where the drone positioning information includes the drone yaw angle and a second position coordinate representing the position of the drone.

[0010] Preferably, in the above-mentioned UAV positioning method, the TOF sensor is arranged on a movable component of the UAV landing pad, and point cloud data can be collected in the horizontal direction;

[0011] The characteristic cross section is the horizontal cross section of the landing gear of the UAV when it is parked, and each characteristic point corresponds to a column of the landing gear.

[0012] Preferably, in the above-mentioned UAV positioning method, obtaining point cloud data of the characteristic cross section of the UAV based on the time-of-flight TOF sensor includes:

[0013] Driving the movable component to drive the TOF sensor to realize scanning motion from the initial acquisition position to the end acquisition position, and the TOF sensor performs point cloud sampling at a preset time period or a preset distance tolerance during the scanning motion;

[0014] Get the point cloud data collected by the TOF sensor during the scanning process.

[0015] Preferably, in the above-mentioned drone positioning method, determining a plurality of first position coordinates according to the point cloud data includes:

[0016] Classify point cloud data into homogeneous categories based on clustering algorithms;

[0017] Determine the coordinates of the most congruent points corresponding to each type of homogeneous point cloud data.

[0018] Preferably, in the above-mentioned drone positioning method, determining the drone positioning information according to the first position coordinates includes:

[0019] According to the coordinates of each most probable point, determine the distance between every two most probable points;

[0020] According to the above distances, the corresponding relationship between each most reliable point and the landing gear column is established;

[0021] The drone positioning information is determined based on the coordinates of each of the most reliable points and their corresponding relationships.

[0022] According to a second aspect of the present application, a method for adjusting the parking posture of a drone is provided, the method comprising:

[0023] Determine the drone positioning information according to the above method;

[0024] Determine the adjustment amount according to the attitude information of the preset attitude and the positioning information of the UAV;

[0025] The drone is driven to adjust the device according to the adjustment amount to adjust the parking posture of the drone to be consistent with the preset posture.

[0026] Preferably, in the above-mentioned method for adjusting the parking posture of the drone, the posture information of the preset posture includes a preset yaw angle and preset coordinates, and determining the adjustment amount according to the posture information of the preset posture and the positioning information of the drone includes:

[0027] Determine a rotation adjustment amount according to a preset yaw angle and the yaw angle of the drone, and determine a translation adjustment amount according to the preset center point coordinates and the second position coordinates;

[0028] The equipment that drives the drone to adjust according to the adjustment amount includes:

[0029] Drive the drone to adjust the device to grab the drone so that the drone is separated from the drone parking surface;

[0030] Driving the rotation axis of the drone adjustment device to rotate according to the rotation adjustment amount, and driving the translation component of the drone adjustment device according to the translation adjustment amount;

[0031] After the drone's posture is consistent with the preset posture, the drone adjustment device is driven to place the drone on the parking plane.

[0032] According to a third aspect of the present application, a drone positioning device is provided, the device comprising:

[0033] An acquisition unit, used for acquiring point cloud data of a characteristic cross section of the UAV based on a time-of-flight TOF sensor;

[0034] A data processing unit is used to determine a plurality of first position coordinates based on the point cloud data, each first position coordinate corresponds to a feature point on the feature section where the drone is located; and to determine the drone positioning information based on the first position coordinates, the drone positioning information including the drone yaw angle and a second position coordinate characterizing the drone position.

[0035] Preferably, in the above-mentioned UAV positioning device, the TOF sensor is arranged on a movable component of the UAV apron, and point cloud data can be collected in the horizontal direction; the characteristic section is the horizontal section of the landing gear of the UAV when the UAV is parked, and each characteristic point corresponds to a column of the landing gear.

[0036] Preferably, in the above-mentioned drone positioning device, the acquisition unit is used to drive the movable component to drive the TOF sensor to realize a scanning movement from an initial collection position to a terminal collection position, and the TOF sensor performs point cloud sampling at a preset time period or a preset distance tolerance during the scanning movement; and is used to obtain point cloud data collected by the TOF sensor during the scanning movement.

[0037] Preferably, in the above-mentioned drone positioning device, the data processing unit is used to classify the point cloud data into homogeneity based on a clustering algorithm; and to determine the coordinates of the most likely points corresponding to each type of homogeneous point cloud data.

[0038] Preferably, in the above-mentioned UAV positioning device, the data processing unit is used to determine the distance between every two most probable points based on the coordinates of each most probable point; and to establish a correspondence between each most probable point and the landing gear column based on the above-mentioned distance; and to determine the UAV positioning information based on the coordinates of each most probable point and the corresponding relationship.

[0039] According to a fourth aspect of the present application, a device for adjusting the parking posture of a drone is provided, the device comprising:

[0040] A positioning unit, used to determine the positioning information of the drone according to any of the above mentioned drone positioning devices;

[0041] An adjustment amount determination unit, used to determine the adjustment amount according to the posture information of the preset posture and the positioning information of the UAV;

[0042] The driving unit is used to drive the drone adjustment device according to the adjustment amount to adjust the parking posture of the drone to be consistent with the preset posture.

[0043] Preferably, in the above-mentioned drone parking posture adjustment device, the posture information of the preset posture includes a preset yaw angle and preset coordinates, and the adjustment amount determination unit is used to determine the rotation adjustment amount according to the preset yaw angle and the drone yaw angle, and determine the translation adjustment amount according to the preset center point coordinates and the second position coordinates;

[0044] The driving unit is used to drive the drone adjustment device to grab the drone so that the drone is detached from the drone parking plane; and is used to drive the rotation axis of the drone adjustment device to rotate according to the rotation adjustment amount, and drive the translation component of the drone adjustment device according to the translation adjustment amount; and is used to drive the drone adjustment device to place the drone on the parking plane after the drone posture is consistent with the preset posture.

[0045] According to a fifth aspect of the present application, an electronic device is provided, wherein the electronic device comprises: a processor; and a memory arranged to store computer executable instructions, wherein the executable instructions, when executed, cause the processor to execute any of the above drone positioning methods.

[0046] According to the sixth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores one or more programs, and when the one or more programs are executed by a processor, any of the above drone positioning methods is implemented.

[0047] According to the seventh aspect of the present application, an electronic device is provided, wherein the electronic device includes: a processor; and a memory arranged to store computer executable instructions, and when the executable instructions are executed, the processor executes any of the above drone parking posture adjustment methods.

[0048] According to the eighth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores one or more programs, and when the one or more programs are executed by a processor, any of the above-mentioned methods for adjusting the parking posture of a drone is implemented.

[0049] From the above, it can be seen that the technical solution of the present application obtains point cloud data of the characteristic cross section of the drone based on the time-of-flight TOF sensor; determines a number of first position coordinates based on the point cloud data, each first position coordinate corresponds to a characteristic point on the characteristic cross section where the drone is located; determines the positioning information of the drone based on the first position coordinates, and the positioning information of the drone includes the yaw angle of the drone and the second position coordinates that characterize the position of the drone. The beneficial effect of the present application is that, based on the time-of-flight ranging method, by obtaining the point cloud data of the characteristic cross section of the drone and combining it with the corresponding characteristic points of the drone, the drone posture can be quickly and accurately located. The algorithm is simple, the accuracy is high, and the required hardware cost is low, which can provide reliable data information for the needs of adjusting the parking posture of the drone.

[0050] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present application. Also, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0052] Figure 1 A schematic diagram of a flow chart of a method for positioning a drone according to an embodiment of the present application is shown;

[0053] Figure 2 A schematic diagram of a flow chart of a method for positioning a drone according to another embodiment of the present application is shown;

[0054] Figure 3 A point cloud data diagram of a horizontal cross section of a drone landing gear acquired by a drone TOF sensor according to another embodiment of the present application is shown;

[0055] Figure 4 A schematic diagram of a process for adjusting the parking posture of a drone according to an embodiment of the present application is shown;

[0056] Figure 5A schematic diagram of the structure of a drone parking posture adjustment device according to an embodiment of the present application is shown;

[0057] Figure 6 A schematic structural diagram of a drone positioning device according to an embodiment of the present application is shown;

[0058] Figure 7 A schematic diagram of the structure of a drone parking posture adjustment device according to an embodiment of the present application is shown;

[0059] Figure 8 A schematic diagram of an electronic structure according to an embodiment of the present application is shown;

[0060] Fig. 9 A schematic diagram of the structure of a computer-readable storage medium according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0061] The exemplary embodiments of the present application will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present application are shown in the accompanying drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present application and to fully convey the scope of the present application to those skilled in the art.

[0062] The idea of ​​this application is to use a time-of-flight TOF sensor to obtain precise positioning information of the drone based on point cloud data of the drone's characteristic cross-section, in view of the current situation that the positioning method of the drone after landing is complex, low in precision and high in cost. The algorithm is simple, the precision is high and the required hardware cost is low.

[0063] Figure 1 A schematic diagram of a method for positioning a drone according to an embodiment of the present application is shown, the method comprising:

[0064] Step S110, acquiring point cloud data of characteristic cross sections of the drone based on a time-of-flight TOF sensor.

[0065] Time of flight (TOF) is a method where the sensor emits modulated near-infrared light, which is reflected by an object. The sensor calculates the time difference or phase difference between the emission and reflection of the light to convert the distance of the photographed scene to generate depth information. In addition, combined with traditional camera shooting, the three-dimensional outline of the object can be presented in the form of a topographic map with different colors representing different distances. The acquisition of point cloud data in this application is based on the time of flight method.

[0066] The time-of-flight sensor can be any one or several existing laser radars, and this application does not impose any limitation.

[0067] The characteristic cross section of the drone in this application is a cross section that can realize the positioning of the drone in combination with the structural characteristics of the drone, such as the cross section of the drone in the horizontal direction, that is, the horizontal cross section of the drone. Different horizontal cross sections of the drone correspond to different structural characteristics of the drone. For example, in addition to the edge of the drone body, there are corresponding points of the drone arm on a certain horizontal cross section.

[0068] Point cloud data refers to the scanning data recorded in the form of points. Each point contains three-dimensional coordinates, and some may contain color information (RGB) or reflection intensity information (Intensity). Among them, the reflection intensity information can characterize the geometric position of the scanned object. The acquisition of intensity information is the echo intensity collected by the laser scanner receiving device. This intensity information is related to the surface material, roughness, incident angle direction of the target, and the emission energy of the instrument, and the laser wavelength. In addition to the geometric position, some point cloud data also have color information. The color information is usually obtained by obtaining a color image through a camera, and then the color information (RGB) of the pixel at the corresponding position is assigned to the corresponding point in the point cloud. In this application, a TOF sensor is used to obtain point cloud data, mainly using the three-dimensional coordinates and reflection intensity information of the point cloud data.

[0069] By using a time-of-flight sensor to scan any horizontal surface of the drone, the point cloud data of the horizontal surface can be obtained, and the horizontal surface is the characteristic cross-section of the drone. Due to the existence of specific structures on some horizontal surfaces of the drone, it is more conducive to simplifying the calculation and improving the accuracy, and can be used as a preferred method.

[0070] Step S120, determining a plurality of first position coordinates according to the point cloud data, each of the first position coordinates corresponding to a feature point on the feature cross section where the drone is located.

[0071] The processing of point cloud data includes but is not limited to point cloud filtering (data preprocessing), point cloud focus point analysis, etc.

[0072] Among them, point cloud filtering is to filter out noise. The original collected point cloud data often contains a large number of scattered points and isolated points. The main methods of point cloud filtering are: bilateral filtering, Gaussian filtering, conditional filtering, straight-through filtering, random sampling consistent filtering, VoxelGrid filtering, etc.

[0073] Determining the first position coordinates according to the point cloud data is actually to first determine the point cloud points corresponding to the feature points from the point cloud data, and then determine their position coordinates, so as to obtain a number of first position coordinates. Specifically, it is achieved by clustering or focus point extraction.

[0074] For example, by using focus point extraction algorithms such as Harris, SIFT, SURF, and KAZE, several focus points can be extracted from the original point cloud data. The number of focus points is greatly reduced compared to the amount of original point cloud data. They are combined with local feature descriptors to form focus point descriptors, which are often used to form representations of the original data without losing representativeness and descriptiveness.

[0075] The extracted focus points are the first positions described in this application. In practice, there are often more than one focus point, because multiple focus points are usually required to characterize a geometric structure. These focus points correspond to the feature points on the feature section where the drone is located.

[0076] Furthermore, the coordinates of the point of interest can be obtained through the point cloud data of the point, and the coordinates can be represented by any method, such as the Cartesian coordinate system, which is two-dimensional and can be represented as (x, y).

[0077] Step S130: determining the drone positioning information according to the first position coordinates, wherein the drone positioning information includes the drone yaw angle and a second position coordinate representing the position of the drone.

[0078] Continuing from the above, after obtaining the coordinates corresponding to the characteristic points of the drone, these coordinates can be converted into the coordinates of the drone through calculation and processing, that is, the positioning information of the drone is obtained. The coordinates of the drone can be represented in a variety of ways, which are not limited in this application, such as the WGS-84 coordinate system, the NED coordinate system, and the body coordinate system. Here, this application recommends a method for representing the horizontal coordinates of a drone (x, y, YAW), where the x-coordinate and the y-coordinate are scalars, that is, they correspond to the offset on the X-axis and the offset on the Y-axis in the Cartesian two-dimensional coordinate system, and YAW is the heading angle, which represents the direction of the drone.

[0079] By calculating and processing the first position coordinates corresponding to the feature points of the above-mentioned several drones, the coordinate values ​​of the drones in the above-mentioned coordinate system can be obtained, and thus the positioning information of the drones can be obtained.

[0080] Depend on Figure 1 It can be seen from the described method that the present application is based on the time-of-flight ranging method, which obtains the point cloud data of the characteristic cross-section of the UAV and combines it with the corresponding characteristic points of the UAV to quickly and accurately locate the UAV's posture. The algorithm is simple, the accuracy is high, and the required hardware cost is low. It can provide reliable data information for the needs of adjusting the parking posture of the UAV, etc.

[0081] In one embodiment of the present application, the TOF sensor is disposed on a movable component of the drone landing pad, and point cloud data can be collected in the horizontal direction; the characteristic section is a horizontal section of the landing gear of the drone in the parked state, and each characteristic point corresponds to a column of the landing gear.

[0082] In the prior art, sensors are often mounted directly on the manipulator of external equipment to collect data from a bird's-eye view, which results in problems such as limited collection range, distortion of image edges, and reduced judgment accuracy.

[0083] In this embodiment, the TOF sensor is set on the movable component of the drone landing pad, and point cloud data can be collected in the horizontal direction, thus overcoming the above-mentioned defects in the prior art.

[0084] The landing gear is columnar and is lowered during the landing of the UAV to support the UAV. Due to its significant structural characteristics, this embodiment recommends using the horizontal cross-section of the landing gear when the UAV is parked as the feature cross-section as a preferred method, and in the process of point cloud data processing, each feature point corresponds to a column of the landing gear, so that the positioning information of the UAV can be obtained by processing the coordinates corresponding to the four columns.

[0085] In one embodiment of the present application, obtaining point cloud data of a characteristic cross-section of a drone based on a time-of-flight TOF sensor includes: driving the movable component to drive the TOF sensor to perform a scanning movement from an initial collection position to a terminal collection position, wherein the TOF sensor performs point cloud sampling at a preset time period or a preset distance tolerance during the scanning movement; and obtaining the point cloud data collected by the TOF sensor during the scanning movement.

[0086] In this embodiment, the mobile component can drive the TOF sensor to perform scanning movement from the initial collection position to the final collection position. During this scanning process, only one TOF sensor is needed to complete data collection. The sensor is the most expensive part of the entire component, which saves hardware costs to a great extent.

[0087] In one embodiment of the present application, determining a plurality of first position coordinates based on point cloud data includes: classifying the point cloud data into homogeneous types based on a clustering algorithm; and determining the coordinates of the most probable points corresponding to each type of homogeneous point cloud data.

[0088] In this embodiment, when processing point cloud data, it is recommended to use a clustering algorithm, such as a density-based clustering algorithm. The goal of clustering is to make the similarity of objects of the same class as large as possible, and the similarity between objects of different classes as small as possible. In this embodiment, point cloud data for the same feature point is considered to be a class of data with the same properties.

[0089] In each type of data, there is a most probable point, and the coordinates of the most probable point are used to represent the coordinates of the response feature point. The most probable point can be understood as the point with the highest probability in each type of homogeneous point cloud data. For example, in the density-based clustering algorithm, the point with the highest point cloud density can be considered as the most probable point.

[0090] In one embodiment of the present application, determining the positioning information of the drone based on the first position coordinates includes: determining the distance between every two most probable points based on the coordinates of each most probable point; establishing a correspondence between each most probable point and a landing gear column based on the distance; and determining the positioning information of the drone based on the coordinates of each most probable point and the correspondence.

[0091] Starting from the sensor, looking at a horizontal cross-section of the drone when it is parked, according to the geometric structure of the drone and the geometric perspective relationship, the distance between each feature point is different and follows a certain pattern. Therefore, according to the distance between the feature points, each most probable point can be matched with the structural features that form the most probable point. Based on this correspondence, the coordinates of each most probable point can be combined to solve the coordinates of the drone.

[0092] Figure 2 A flow chart of a method for positioning a drone according to another embodiment of the present application is shown. After the drone lands, the movable component drives the TOF sensor to perform a scanning motion on the horizontal cross section of the drone landing gear from the initial acquisition position to the final acquisition position, performs point cloud sampling at a preset time period, and obtains point cloud data collected during the scanning motion.

[0093] Based on the clustering method, the above point cloud data can be classified into four categories based on homogeneity, and the most probable points and the coordinates of the most probable points in each category of point cloud data are determined. The four most probable points are represented as P1, P2, P3, and P4; the coordinates of the four most probable points are (x1, y1), (x2, y2), (x3, y3), and (x4, y4), respectively. Figure 3 shown.

[0094] Calculate the distance between every two most probable points and generate a distance list. The distances between any two points in the distance list are expressed as: P1P2, P1P3, P1P4, P2P3, P2P4, P3P4.

[0095] According to the above distance list, determine which of the most probable points P1 to P4 corresponds to the left front pillar, left rear pillar, right front pillar, and right rear pillar of the aircraft. Here, it is assumed that P3 corresponds to the right front pillar, P4 corresponds to the left front pillar, P1 corresponds to the right rear pillar, and P2 corresponds to the left and right pillars.

[0096] According to the coordinates of the feature points, the coordinates Q of the drone are calculated, which can be specifically expressed as ΣP / n. In this example, ΣP=P1+P2+P3+P4, n=4; the direction vector of the drone's direction information YAW=(P1+P2) / 2-(P3+P4) / 2.

[0097] It should be noted that the above Figure 2 In the embodiment corresponding to the method, capital letters represent vectors and lowercase letters represent scalars.

[0098] Figure 4 A schematic diagram of a method for adjusting the parking posture of a drone according to an embodiment of the present application is shown. The method for adjusting the parking posture of a drone can be performed by Figure 5 The drone adjustment device shown in an embodiment of the present application is completed, but is not limited to the drone adjustment device. Any device that can complete the drone parking posture adjustment method can be used. It is only explained here as an example. The drone parking posture adjustment device can be set on the drone apron and can move on the apron. The drone parking posture adjustment device specifically includes: a support shaft 510, a sliding shaft 520, a lifting shaft 530, a rotating shaft 540, a clamp 550 and a TOF sensor 560.

[0099] The supporting shaft comprises two sub-shafts arranged in parallel with each other and supported on the UAV landing pad; the two ends of the sliding shaft are respectively slidably connected to the two sub-shafts of the supporting shaft and can slide along the supporting shaft; the lifting shaft is vertically arranged on the sliding shaft and is slidably connected to the sliding shaft and can slide in a vertical direction; the rotating shaft is fixedly connected to the lower end of the lifting shaft and can drive the clamping claw arranged under the rotating shaft to rotate; the device as a whole can adjust the UAV to a predetermined parking posture according to a driving instruction, wherein the driving instruction is obtained according to the UAV positioning information obtained by the above method and the posture information of the preset posture.

[0100] Among them, the moving parts, including the support shaft 510, the sliding shaft 520, the lifting shaft 530, and the rotating shaft 540, can all adopt electric cylinder modules or other linear motion devices. Among them, the support shaft 510, the sliding shaft 520, and the lifting shaft 530 form an orthogonal XYZ three-coordinate form, and the rotating shaft 540 is a rotating joint. The end effector adopts a clamping mechanism. According to the structural design of the drone, multiple clamps can be used to clamp the machine arm or the body.

[0101] When designing the drone adjustment device, two details can be paid attention to in order to improve the ease of use. First, Figure 5 The size of the middle B is selected according to the cross-sectional principle, and is determined by the height of the selected horizontal cross-section on the drone; second, Figure 5 The A dimension must ensure that the TOF sensor does not interfere with the aircraft when the end effector grabs the aircraft.

[0102] In this way, a device with a four-axis motion system composed of three coordinates and a rotating joint is constructed, which can grasp the drone parked at any position and angle on the apron through the gripper; that is to say, no matter how large the landing position error of the drone is due to insufficient landing accuracy, as long as it lands on the apron, it can be grasped by the above-mentioned device; further, the above-mentioned device can place the grasped drone at any workstation with high precision, which can be an automatic potential changer or a lunch box loading and unloading station.

[0103] Figure 4 The method for adjusting the parking posture of a drone according to one embodiment of the present application includes:

[0104] S410: Determine the drone positioning information according to any of the aforementioned drone positioning methods.

[0105] In actual situations, when landing, the drone cannot land on the designated parking position with complete accuracy in the preset posture. There is often a certain angle deviation or distance deviation from the preset posture and position. At this time, external equipment is required to correct its posture. In the prior art, two sets of horizontal push rods are usually used to push the drone straight. In this process, there is sliding friction between the drone landing gear and the landing plane. Pushing the drone multiple times will wear the drone landing gear and the landing plane, resulting in a shortened life of the landing gear and the landing plane, and increased maintenance costs; the rigidity requirements for the drone landing gear are relatively high, which also brings difficulties to the weight reduction of the drone; due to the existence of sliding friction, the pushing speed of the drone cannot be greatly improved, which reduces the delivery efficiency of the drone; due to the existence of sliding friction, a lot of noise will be generated during the pushing process of the drone, affecting the use effect of the equipment. This embodiment provides a method for adjusting the parking posture of a drone to overcome the above problems.

[0106] First, according to the UAV positioning method described above, the position where the UAV lands on the apron is accurately located, and the coordinate point Q1 of the UAV can be obtained, and its coordinates can be expressed as (x5, y5, YAW1).

[0107] S420, determining an adjustment amount according to the posture information of the preset posture and the positioning information of the UAV.

[0108] The adjustment amount can be determined according to the degree of deviation between the positioning information of the drone and the posture information of the preset posture.

[0109] S430, driving the drone adjustment device according to the adjustment amount to adjust the parking posture of the drone to be consistent with the preset posture.

[0110] According to the above determined adjustment amount, the parking posture of the drone can be adjusted to be consistent with the preset posture through the above drone adjustment device.

[0111] Depend on Figure 4-Figure 5 The method and device for adjusting the parking posture of a drone shown in the figure can catch the drone to a designated location and adjust it to a designated posture. In this process, the movement of the drone has no sliding friction, and no wear is caused to the drone and the landing surface, which reduces maintenance costs; reduces equipment noise and improves the use effect of the equipment; reduces the rigidity requirements of the drone landing gear, which can reduce the weight of the drone; the drone adjustment equipment is not affected by friction, can move quickly, and improves operating efficiency.

[0112] In one embodiment of the present application, the posture information of the preset posture includes a preset yaw angle and preset coordinates, and the determination of the adjustment amount according to the posture information of the preset posture and the positioning information of the drone includes: determining the rotation adjustment amount according to the preset yaw angle and the yaw angle of the drone, and determining the translation adjustment amount according to the preset center point coordinates and the second position coordinates. If the posture information of the preset posture is the preset posture coordinate point Q2 of the drone, its coordinates can be expressed as (x6, y6, YAW2), then the horizontal translation adjustment amount is (x6-x5, y6-y5), and the yaw angle adjustment amount is YAW2-YAW1. It should be noted that in this embodiment, uppercase letters represent vectors and lowercase letters represent scalars.

[0113] The specific adjustment process can refer to the following steps: drive the drone adjustment device to grab the drone so that the drone is separated from the drone parking plane; drive the rotating shaft of the drone adjustment device to rotate according to the rotation adjustment amount, and drive the translation component of the drone adjustment device according to the translation adjustment amount; after the drone posture is consistent with the preset posture, drive the drone adjustment device to place the drone on the parking plane. It should be noted here that in the adjustment process, the posture of the drone refers to the posture of the plane, that is, the information in the vertical direction is ignored, so that the posture of the drone after adjustment when it is grabbed and suspended in the air is consistent with the preset parking posture.

[0114] Figure 6 The structure diagram of a UAV positioning device according to an embodiment of the present application is shown. The UAV positioning device 600 includes:

[0115] The acquisition unit 610 is used to acquire point cloud data of a characteristic cross section of the drone based on a time-of-flight TOF sensor.

[0116] Time of flight (TOF) is a method where the sensor emits modulated near-infrared light, which is reflected by an object. The sensor calculates the time difference or phase difference between the emission and reflection of the light to convert the distance of the photographed scene to generate depth information. In addition, combined with traditional camera shooting, the three-dimensional outline of the object can be presented in the form of a topographic map with different colors representing different distances. The acquisition of point cloud data in this application is based on the time of flight method.

[0117] The time-of-flight sensor can be any one or several existing laser radars, and this application does not impose any limitation.

[0118] The characteristic cross section of the drone in this application is a cross section that can realize the positioning of the drone in combination with the structural characteristics of the drone, such as the cross section of the drone in the horizontal direction, that is, the horizontal cross section of the drone. Different horizontal cross sections of the drone correspond to different structural characteristics of the drone. For example, in addition to the edge of the drone body, there are corresponding points of the drone arm on a certain horizontal cross section.

[0119] Point cloud data refers to the scanning data recorded in the form of points. Each point contains three-dimensional coordinates, and some may contain color information (RGB) or reflection intensity information (Intensity). Among them, the reflection intensity information can characterize the geometric position of the scanned object. The acquisition of intensity information is the echo intensity collected by the laser scanner receiving device. This intensity information is related to the surface material, roughness, incident angle direction of the target, and the emission energy of the instrument, and the laser wavelength. In addition to the geometric position, some point cloud data also have color information. The color information is usually obtained by obtaining a color image through a camera, and then the color information (RGB) of the pixel at the corresponding position is assigned to the corresponding point in the point cloud. In this application, a TOF sensor is used to obtain point cloud data, mainly using the three-dimensional coordinates and reflection intensity information of the point cloud data.

[0120] By using a time-of-flight sensor to scan any horizontal surface of the drone, the point cloud data of the horizontal surface can be obtained, and the horizontal surface is the characteristic cross-section of the drone. Due to the existence of specific structures on some horizontal surfaces of the drone, it is more conducive to simplifying the calculation and improving the accuracy, and can be used as a preferred method.

[0121] The data processing unit 620 is used to determine a plurality of first position coordinates based on the point cloud data, each first position coordinate corresponding to a feature point on the feature section where the drone is located; and to determine the drone positioning information based on the first position coordinates, the drone positioning information including the drone yaw angle and a second position coordinate representing the drone position.

[0122] The processing of point cloud data includes but is not limited to point cloud filtering (data preprocessing), point cloud focus point analysis, etc.

[0123] Among them, point cloud filtering is to filter out noise. The original collected point cloud data often contains a large number of scattered points and isolated points. The main methods of point cloud filtering are: bilateral filtering, Gaussian filtering, conditional filtering, straight-through filtering, random sampling consistent filtering, VoxelGrid filtering, etc.

[0124] Determining the first position coordinates according to the point cloud data is actually to first determine the point cloud points corresponding to the feature points from the point cloud data, and then determine their position coordinates, so as to obtain a number of first position coordinates. Specifically, it is achieved by clustering or focus point extraction.

[0125] For example, by using focus point extraction algorithms such as Harris, SIFT, SURF, and KAZE, several focus points can be extracted from the original point cloud data. The number of focus points is greatly reduced compared to the amount of original point cloud data. They are combined with local feature descriptors to form focus point descriptors, which are often used to form representations of the original data without losing representativeness and descriptiveness.

[0126] The extracted focus points are the first positions described in this application. In practice, there are often more than one focus point, because multiple focus points are usually required to characterize a geometric structure. These focus points correspond to the feature points on the feature section where the drone is located.

[0127] Furthermore, the coordinates of the point of interest can be obtained through the point cloud data of the point, and the coordinates can be represented by any method, such as the Cartesian coordinate system, which is two-dimensional and can be represented as (x, y).

[0128] Continuing from the above, after obtaining the coordinates corresponding to the characteristic points of the drone, these coordinates can be converted into the coordinates of the drone through calculation and processing, that is, the positioning information of the drone is obtained. The coordinates of the drone can be represented in a variety of ways, which are not limited in this application, such as the WGS-84 coordinate system, the NED coordinate system, and the body coordinate system. Here, this application recommends a method for representing the horizontal coordinates of a drone (x, y, YAW), where the x-coordinate and the y-coordinate are scalars, that is, they correspond to the offset on the X-axis and the offset on the Y-axis in the Cartesian two-dimensional coordinate system, and YAW is the heading angle, which represents the direction of the drone.

[0129] By calculating and processing the first position coordinates corresponding to the feature points of the above-mentioned several drones, the coordinate values ​​of the drones in the above-mentioned coordinate system can be obtained, and thus the positioning information of the drones can be obtained.

[0130] Depend on Figure 6It can be seen from the described UAV positioning device that this application is based on the time-of-flight ranging method. By acquiring point cloud data of the characteristic cross-section of the UAV and combining it with the corresponding characteristic points of the UAV, the UAV posture can be quickly and accurately located. The algorithm is simple, the accuracy is high, and the required hardware cost is low. It can provide reliable data information for the needs of adjusting the parking posture of the UAV, etc.

[0131] In one embodiment of the present application, in the above-mentioned drone positioning device, the TOF sensor is arranged on a movable component of the drone apron, and point cloud data can be collected in the horizontal direction; the characteristic section is the horizontal section of the landing gear of the drone when the drone is parked, and each characteristic point corresponds to a column of the landing gear.

[0132] In one embodiment of the present application, in the above-mentioned drone positioning device, the acquisition unit 610 is used to drive the movable component to drive the TOF sensor to realize a scanning movement from an initial collection position to a terminal collection position, and the TOF sensor performs point cloud sampling at a preset time period or a preset distance tolerance during the scanning movement; and is used to obtain point cloud data collected by the TOF sensor during the scanning movement.

[0133] In one embodiment of the present application, in the above-mentioned drone positioning device, the data processing unit 620 is used to classify the point cloud data into homogeneous types based on a clustering algorithm; and to determine the coordinates of the most likely points corresponding to each type of homogeneous point cloud data.

[0134] In one embodiment of the present application, in the above-mentioned drone positioning device, the data processing unit 620 is used to determine the distance between every two most probable points based on the coordinates of each most probable point; and to establish a correspondence between each most probable point and the landing gear column based on the above-mentioned distance; and to determine the drone positioning information based on the coordinates of each most probable point and the correspondence.

[0135] It should be noted that the specific implementation methods of the above-mentioned drone positioning device embodiments can refer to the specific implementation methods of the above-mentioned corresponding drone positioning method embodiments, which will not be repeated here.

[0136] Figure 7 The structure diagram of a UAV parking posture adjustment device according to an embodiment of the present application is shown. The UAV parking posture adjustment device 700 includes:

[0137] The positioning unit 710 is used to determine the drone positioning information according to any of the above-mentioned drone positioning devices.

[0138] In actual situations, when landing, the drone cannot land on the designated parking position with complete accuracy in the preset posture. There is often a certain angle deviation or distance deviation from the preset posture and position. At this time, external equipment is required to correct its posture. In the prior art, two sets of horizontal push rods are usually used to push the drone straight. In this process, there is sliding friction between the drone landing gear and the landing plane. Pushing the drone multiple times will wear the drone landing gear and the landing plane, resulting in a shortened life of the landing gear and the landing plane, and increased maintenance costs; the rigidity requirements for the drone landing gear are relatively high, which also brings difficulties to the weight reduction of the drone; due to the existence of sliding friction, the pushing speed of the drone cannot be greatly improved, which reduces the delivery efficiency of the drone; due to the existence of sliding friction, a lot of noise will be generated during the pushing process of the drone, affecting the use effect of the equipment. This embodiment provides a method for adjusting the parking posture of a drone to overcome the above problems.

[0139] First, according to the UAV positioning method described above, the position where the UAV lands on the apron is accurately located, and the coordinate point Q1 of the UAV can be obtained, and its coordinates can be expressed as (x5, y5, YAW1).

[0140] The adjustment amount determination unit 720 is used to determine the adjustment amount according to the posture information of the preset posture and the positioning information of the drone.

[0141] The adjustment amount can be determined according to the degree of deviation between the positioning information of the drone and the posture information of the preset posture.

[0142] The driving unit 730 is used to drive the drone adjustment device according to the adjustment amount to adjust the parking posture of the drone to be consistent with the preset posture.

[0143] According to the above determined adjustment amount, the parking posture of the drone can be adjusted to be consistent with the preset posture through the above drone adjustment device.

[0144] Depend on Figure 7 The drone parking posture adjustment device shown can grab the drone to a designated location and adjust it to a designated posture. In this process, the drone moves without sliding friction, and does not cause wear to the drone and the landing surface, which reduces maintenance costs; reduces equipment noise and improves equipment use effects; reduces the rigidity requirements for the drone landing gear, which can reduce the drone's own weight; the drone adjustment equipment is not affected by friction, can move quickly, and improves operating efficiency.

[0145] In one embodiment of the present application, in the above-mentioned drone parking posture adjustment device, the posture information of the preset posture includes a preset yaw angle and preset coordinates, an adjustment amount determination unit, which is used to determine the rotation adjustment amount according to the preset yaw angle and the drone yaw angle, and to determine the translation adjustment amount according to the preset center point coordinates and the second position coordinates; a driving unit 730, which is used to drive the drone adjustment device to grab the drone so that the drone is separated from the drone parking plane; and is used to drive the rotating axis of the drone adjustment device to rotate according to the rotation adjustment amount, and to drive the translation component of the drone adjustment device according to the translation adjustment amount; and is used to drive the drone adjustment device to place the drone on the parking plane after the drone posture is consistent with the preset posture.

[0146] If the posture information of the preset posture is the preset posture coordinate point Q2 of the drone, its coordinates can be expressed as (x6, y6, YAW2), then the horizontal translation adjustment amount is (x6-x5, y6-y5), and the yaw angle adjustment amount is YAW2-YAW1. It should be noted that in this embodiment, uppercase letters represent vectors and lowercase letters represent scalars.

[0147] It should be noted here that during the adjustment process, the posture of the drone refers to the plane posture, that is, the information in the vertical direction is ignored, so that the adjusted posture of the drone when it is picked up and suspended in the air is consistent with the preset parking posture.

[0148] As can be seen from the above, the technical solution of the present application obtains point cloud data of the characteristic cross section of the drone based on the time-of-flight TOF sensor; determines a number of first position coordinates based on the point cloud data, each of which corresponds to a characteristic point on the characteristic cross section where the drone is located; determines the positioning information of the drone based on the first position coordinates, and the positioning information of the drone includes the yaw angle of the drone and the second position coordinates that characterize the position of the drone. The beneficial effect of the present application is that, based on the time-of-flight ranging method, by obtaining the point cloud data of the characteristic cross section of the drone and combining it with the corresponding characteristic points of the drone, the landing posture of the drone can be quickly and accurately located. The algorithm is simple, the accuracy is high, and the required hardware cost is low, which provides reliable data information for the subsequent adjustment of the parking posture of the drone.

[0149] It should be noted that:

[0150] The algorithm and display provided herein are not inherently related to any particular computer, virtual device or other equipment. Various general purpose devices can also be used together with the teachings based on this. According to the above description, it is obvious to construct the structure required for this type of device. In addition, the application is not directed to any specific programming language either. It should be understood that various programming languages ​​can be utilized to realize the content of the application described herein, and the description of the specific language above is to disclose the best mode of implementation of the application.

[0151] In the description provided herein, a large number of specific details are described. However, it is understood that the embodiments of the present application can be practiced without these specific details. In some instances, well-known methods, structures and techniques are not shown in detail so as not to obscure the understanding of this description.

[0152] Similarly, it should be understood that in order to streamline the present application and help understand one or more of the various application aspects, in the above description of the exemplary embodiments of the present application, the various features of the present application are sometimes grouped together into a single embodiment, figure, or description thereof. However, the disclosed method should not be interpreted as reflecting the following intention: the claimed application requires more features than the features clearly stated in each claim. More specifically, as reflected in the claims below, the application aspects are less than all the features of the single embodiment disclosed above. Therefore, the claims following the specific embodiment are hereby expressly incorporated into the specific embodiment, wherein each claim itself serves as a separate embodiment of the present application.

[0153] Those skilled in the art will appreciate that the modules in the devices in the embodiments may be adaptively changed and arranged in one or more devices different from the embodiments. The modules or units or components in the embodiments may be combined into one module or unit or component, and in addition they may be divided into a plurality of submodules or subunits or subcomponents. Except that at least some of such features and / or processes or units are mutually exclusive, all features disclosed in this specification (including the accompanying claims, abstracts and drawings) and all processes or units of any method or device disclosed in this manner may be combined in any combination. Unless otherwise expressly stated, each feature disclosed in this specification (including the accompanying claims, abstracts and drawings) may be replaced by an alternative feature providing the same, equivalent or similar purpose.

[0154] In addition, those skilled in the art will appreciate that, although some embodiments described herein include certain features included in other embodiments but not other features, the combination of features of different embodiments is meant to be within the scope of the present application and form different embodiments. For example, in the claims below, any one of the claimed embodiments may be used in any combination.

[0155] The various component embodiments of the present application may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. It should be understood by those skilled in the art that a microprocessor or a digital signal processor (DSP) may be used in practice to implement some or all of the functions of some or all of the components of the drone positioning device or the drone parking attitude adjustment device according to the embodiment of the present application. The present application may also be implemented as a device or apparatus program (e.g., a computer program and a computer program product) for executing part or all of the methods described herein. Such a program implementing the present application may be stored on a computer-readable medium, or may be in the form of one or more signals. Such a signal may be downloaded from an Internet website, or provided on a carrier signal, or provided in any other form.

[0156] For example, Figure 8 A schematic diagram of the structure of an electronic device according to an embodiment of the present application is shown. The electronic device 800 includes a processor 810 and a memory 820 arranged to store computer executable instructions (computer readable program code). The memory 820 can be an electronic memory such as a flash memory, an EEPROM (electrically erasable programmable read-only memory), an EPROM, a hard disk or a ROM. The memory 820 has a storage space 830 for storing a computer readable program code 831 for executing any method step in the above method. For example, the storage space 830 for storing computer readable program code may include individual computer readable program codes 831 for implementing various steps in the above method respectively. The computer readable program code 831 can be read from or written to one or more computer program products. These computer program products include program code carriers such as a hard disk, a compact disk (CD), a memory card or a floppy disk. Such a computer program product is usually, for example Fig. 9 The computer-readable storage medium. Fig. 9 A schematic diagram of the structure of a computer-readable storage medium according to an embodiment of the present application is shown. The computer-readable storage medium 900 stores a computer-readable program code 831 for executing the method steps according to the present application, which can be read by the processor 810 of the electronic device 800. When the computer-readable program code 831 is executed by the electronic device 800, the electronic device 800 executes each step of the method described above. Specifically, the computer-readable program code 831 stored in the computer-readable storage medium can execute the drone positioning method or the drone parking attitude adjustment method shown in any of the above embodiments. The computer-readable program code 831 can be compressed in an appropriate form.

[0157] It should be noted that the above embodiments illustrate the present application rather than limit the present application, and that those skilled in the art may design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference symbol between brackets should not be constructed as a limitation to the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "one" or "an" preceding an element does not exclude the presence of multiple such elements. The present application may be implemented by means of hardware including several different elements and by means of a suitably programmed computer. In a unit claim that lists several devices, several of these devices may be embodied by the same hardware item. The use of the words first, second, and third, etc. does not indicate any order. These words may be interpreted as names.

Claims

1. A method for positioning a drone, characterized in that: The method comprises: Obtain point cloud data of the characteristic cross section of the drone based on the time-of-flight TOF sensor; Determine a plurality of first position coordinates according to the point cloud data, each of the first position coordinates corresponding to a characteristic point on the characteristic cross section where the drone is located; Determine the drone positioning information according to the first position coordinates, where the drone positioning information includes the drone yaw angle and a second position coordinate representing the position of the drone; Among them, the TOF sensor is set on the movable component of the drone apron, and can collect point cloud data in the horizontal direction; the characteristic section is the horizontal section of the landing gear of the drone in the parked state, and each characteristic point corresponds to a column of the landing gear.

2. The method for positioning a UAV according to claim 1, characterized in that: The point cloud data of the characteristic cross section of the UAV obtained based on the TOF sensor includes: Driving the movable component to drive the TOF sensor to realize scanning motion from an initial acquisition position to a termination acquisition position, wherein the TOF sensor performs point cloud sampling at a preset time period or a preset distance tolerance during the scanning motion; The point cloud data collected by the TOF sensor during the scanning motion is obtained.

3. The method for positioning a UAV according to claim 1, characterized in that: Determining a plurality of first position coordinates according to the point cloud data includes: Classifying the point cloud data into homogeneity categories based on a clustering algorithm; Determine the coordinates of the most congruent points corresponding to each type of homogeneous point cloud data.

4. The method for positioning a UAV according to claim 3, characterized in that: Determining the drone positioning information according to the first position coordinates includes: According to the coordinates of each most probable point, determine the distance between every two most probable points; According to the distance, establishing a corresponding relationship between each of the most probable points and the landing gear column; The positioning information of the drone is determined according to the coordinates of the most probable points and the corresponding relationship.

5. A method for adjusting the parking posture of an unmanned aerial vehicle, characterized in that: The method comprises: Determine the positioning information of the drone according to the method according to any one of claims 1 to 4; Determining an adjustment amount according to the posture information of the preset posture and the positioning information of the drone; The drone adjustment device is driven according to the adjustment amount to adjust the parking posture of the drone to be consistent with the preset posture.

6. The method for adjusting the parking posture of a drone according to claim 5, characterized in that: The posture information of the preset posture includes a preset yaw angle and preset coordinates, and the determining of the adjustment amount according to the posture information of the preset posture and the positioning information of the drone includes: Determine a rotation adjustment amount according to a preset yaw angle and the yaw angle of the drone, and determine a translation adjustment amount according to a preset center point coordinate and the second position coordinate; Driving the drone adjustment device according to the adjustment amount includes: Driving the drone adjustment device to grab the drone so that the drone is separated from the drone parking plane; driving a rotating shaft of the drone adjustment device to rotate according to the rotation adjustment amount, and driving a translation component of the drone adjustment device according to the translation adjustment amount; After the posture of the drone is consistent with the preset posture, the drone adjustment device is driven to place the drone on the parking plane.

7. An electronic device, wherein: The electronic device includes: a processor; and a memory arranged to store computer executable instructions, wherein when the executable instructions are executed, the processor executes the drone positioning method as described in any one of claims 1 to 4, or the processor executes the drone parking posture adjustment method as described in any one of claims 5 to 6.

8. A computer-readable storage medium, wherein: The computer-readable storage medium stores one or more programs, which, when executed by a processor, implement the drone positioning method as described in any one of claims 1-4, or implement the drone parking posture adjustment method as described in any one of claims 5-6.

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