WO2019061111A1 - Procédé de réglage de trajet et véhicule aérien sans pilote - Google Patents

Procédé de réglage de trajet et véhicule aérien sans pilote Download PDF

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Publication number
WO2019061111A1
WO2019061111A1 PCT/CN2017/103808 CN2017103808W WO2019061111A1 WO 2019061111 A1 WO2019061111 A1 WO 2019061111A1 CN 2017103808 W CN2017103808 W CN 2017103808W WO 2019061111 A1 WO2019061111 A1 WO 2019061111A1
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Prior art keywords
image
return
drone
path
returning
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PCT/CN2017/103808
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English (en)
Chinese (zh)
Inventor
周游
刘洁
武志远
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深圳市大疆创新科技有限公司
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Priority to CN201780009908.0A priority Critical patent/CN108700892A/zh
Priority to PCT/CN2017/103808 priority patent/WO2019061111A1/fr
Publication of WO2019061111A1 publication Critical patent/WO2019061111A1/fr

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    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05DSYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/10Simultaneous control of position or course in three dimensions
    • G05D1/101Simultaneous control of position or course in three dimensions specially adapted for aircraft

Definitions

  • the present invention relates to the field of electronic technologies, and in particular, to a path adjustment method and a drone.
  • GPS Global Positioning System
  • BDS BeiDou Navigation Satellite System
  • the embodiment of the invention discloses a path adjustment method and a drone, which can realize automatic and accurate return of the drone when there is no positioning signal.
  • a first aspect of the embodiments of the present invention discloses a path adjustment method, including: when a positioning signal is lost, determining a return path according to the recorded location information;
  • Determining location information of the first location according to the returning image, the target marker image, and pose information associated with the target marker image;
  • the flight path is adjusted according to the position information of the first position and the return path.
  • the specific when the drone returns in accordance with the return route, the specific includes:
  • the vision module includes a visual odometer (Visual Odometery, VO)
  • a second aspect of an embodiment of the present invention discloses a drone, including: a memory and a processor;
  • the memory is configured to store program instructions
  • the processor is configured to execute the program instructions stored by the memory, when the program instructions are executed, for:
  • the return path is determined according to the recorded position information
  • Determining location information of the first location according to the returning image, the target marker image, and pose information associated with the target marker image;
  • the flight path is adjusted according to the position information of the first position and the return path.
  • the drone may determine the return path when the positioning signal is lost, and collect the returning image at the first position when returning according to the return path, and the target mark image is matched, according to the Determining the returning image, the target marking image, and the pose information associated with the target marking image to determine position information of the first position, and adjusting the flight path according to the position information of the first position and the returning path, when the positioning signal is lost.
  • FIG. 1 is a schematic diagram of a scenario for path adjustment according to an embodiment of the present invention
  • FIG. 2 is a schematic flowchart of a path adjustment method according to an embodiment of the present invention.
  • FIG. 3 is a schematic flowchart of another path adjustment method according to an embodiment of the present invention.
  • FIG. 4 is a schematic diagram of another scenario for path adjustment according to an embodiment of the present invention.
  • FIG. 5 is a schematic structural diagram of a drone according to an embodiment of the present invention.
  • FIG. 6 is a schematic structural diagram of a system according to an embodiment of the present invention.
  • drones use positioning systems (such as GPS systems, BDS systems, etc.) to achieve positioning, but the positioning system sometimes fails, resulting in loss of positioning signals.
  • positioning systems such as GPS systems, BDS systems, etc.
  • the drone can be used in the following two ways:
  • the first way is that when the positioning signal is lost, the drone stops at the position where the signal is lost, waiting for the operator to find it. However, once the drone has traveled a long distance (for example, 3km, 5km, etc.), the operator takes a long time to reach the stop position of the drone, and the convenience is low. In addition, taking the drone as an example, the value of the drone itself is very high, and the equipment carried by the drone is even more valuable. If the drone crashes in the place where the signal is lost, it crashes inadvertently. Will cause huge losses.
  • the second way is when the positioning signal is lost, the operator manually controls the drone to return.
  • manual control has higher professional requirements for operators, and for non-professional operators, manual operation steps are cumbersome and difficult to operate.
  • the operators of agricultural drones are usually ordinary farmers, and their operation level usually does not meet professional requirements, and agricultural drones are often at low altitudes (for example 5 meters, 10 meters, etc., once the operation is not proper, the drone is very easy to crash, resulting in huge losses.
  • FIG. 1 is a schematic diagram of a scenario for path adjustment according to an embodiment of the present invention.
  • the black line is the navigation path of the drone.
  • the drone starts from the takeoff point A and follows the navigation route planned by the black line.
  • the drone can record a series of The location information and the mark image at the location information are collected, and the location information and the corresponding mark image are saved in the image database, wherein the circle shown in FIG. 1 represents the flight point recorded by the drone, and the flight point corresponds to Location information.
  • the location information may be location information recorded by the location system.
  • the drone can record the acquired location as the location information through a GPS positioning system.
  • the drone may select a return point from the recorded flight points when the lost GPS point B loses the GPS signal (shown in Figure 1 by a black circle).
  • the return point may be a flight point located on the return path, that is, the return point may be a subset of the recorded flight points.
  • the drone can determine a plurality of flight points between the takeoff point A and the lost GPS point B.
  • Each flight point can be associated with a corresponding GPS signal, a corresponding marker image, and a corresponding inertial measurement.
  • Unit (IMU) pose information wherein the associated information is merely an example, not an exhaustive one, and a flight point may also be associated with a plurality of other information.
  • the drone may select a plurality of return points from a plurality of flight points and connect the plurality of return points to obtain a return path.
  • the return path is indicated by a broken line ( Wherein, the portion coincident with the navigation path is not shown in FIG. 1).
  • a polygon approximation strategy such as an RDP algorithm (RamerDouglas Peuckeralgorithm), etc.
  • RDP algorithm RamerDouglas Peuckeralgorithm
  • the drone can utilize the VO to capture the return image and estimate the current location of arrival in real time, as well as determine the flight point within the preset range of the location (eg, within 1 meter, within 3 meters, etc.).
  • the drone is estimated to have reached the vicinity of the return point C (the actual arrived position is represented by the first position), and the returning image n can be collected at the first position, and the returning image is acquired.
  • n a mark image c associated with the return point C and a mark image corresponding to each of the flight points within the preset range of the return point C (for example, within 1 meter, within 3 meters, etc.) (here, the mark image c of the return point C will be
  • the mark image in the preset range of the return point C is represented by the collection m) to perform matching processing.
  • the drone can determine that the marker image c in the collection m matches the return image m according to the matching result of the collection m and the returning image n (where the return point C and the flight point C are the same one) Point, the marker image c is a corresponding picture of the flight point C), and the drone can acquire the GPS signal corresponding to the flight point C, the corresponding marker image (ie, the marker image c) and the corresponding IMU posture information.
  • the drone may be based on the GPS signal corresponding to the flight point C, the corresponding mark image (ie, the mark image c) and the corresponding IMU posture information, and the return image n and the captured image at the first position.
  • the corresponding IMU posture information when the returning image n is captured determines the deviation of the first position from the returning point C.
  • the drone can adjust the flight path to the next return point of the return point C based on the deviation.
  • the position information of the first position indicates that 0.5 meters to the left of the return point C, then the drone can travel 0.5 meters to the right side to return to the return point C for returning, or the drone It is also possible to calculate the distance value from the return point D based on the current position information, and then fly to the return point d according to the distance value to continue to return according to the return path.
  • the drone can further obtain a depth map corresponding to the returning image n and a depth map of the marker image c to obtain a camera pose between the marker image c and the returning image n.
  • the pose relationship can include rotation and displacement.
  • the UAV can use the IMU unit to acquire the posture of the IMU corresponding to the marker image c and the IMU posture corresponding to the return image n, and calculate the pose relationship between the marker image c and the return image n. .
  • the drone can determine that the determination process of the marker image c is correct, and the location information corresponding to the marker image c can be used to represent the first Location location information.
  • the flying height of the drone when the drone returns in accordance with the return path, is lower than a preset height.
  • the preset height can be less than 30 meters. Alternatively, the preset height can be less than 20 meters. Alternatively, the preset height can be less than 10 meters.
  • FIG. 2 is a schematic flowchart diagram of a path adjustment method according to an embodiment of the present invention.
  • the path adjustment method described in this embodiment includes:
  • execution body of the embodiment of the present invention may be an unmanned aerial vehicle, and the unmanned aerial vehicle may be an unmanned aerial vehicle.
  • the drone may be an industrial-grade drone, such as an agricultural sprinkler, etc.
  • the agricultural sprinkler may be flying at a low altitude (for example, a flying height of less than 30 m, or When the flying height is less than 20 m, or the flying height is less than 10 m, etc., the method provided by the embodiment of the present invention is performed.
  • the positioning signal may be a positioning module of the drone (eg, a GPS module, etc.)
  • the obtained signal can be used to record the position information of the drone.
  • the location information of the drone may include positioning information, such as GPS information and the like.
  • the drone may lose the positioning signal. When the positioning signal is lost, the drone can determine the return path according to the position information recorded by the positioning module.
  • the method when the location signal is lost, before determining the return path according to the recorded location information, the method further includes: generating the image database, where the image database includes a plurality of marker images and is associated with the marker image Pose information.
  • the mark image may refer to an image taken corresponding to the position information for marking the position where the drone has flown.
  • the drone flies to position a, where the marker image a is taken, and the marker image a can be used to identify the location a.
  • the pose information may include location information and posture information.
  • the posture information may include Rotation and Translation when the marker image is captured.
  • the generating an image database includes: when the UAV is flying according to the positioning signal, recording a mark image during flight and pose information associated with the mark image during flight; The marker image at the time of flight and the pose information associated with the marker image at the time of flight generate an image database.
  • the drone can fly according to the indication of the positioning signal when the positioning signal is not lost, and record the returning point every time (for example, 1 minute, 10 minutes, etc.) or in real time during the flight. And recording the position information of the return point, and calling the imaging device to capture the mark image at the position information, and recording the posture information when the mark image is captured, and finally storing the mark image, the position information, and the posture information in the image database.
  • the return path refers to the path planned by the drone.
  • the drone can preferentially select a straight flight according to the position information recorded immediately before the lost positioning signal and the position information of the drone when starting, and the path through the more returning point is Return route.
  • the drone can also determine the position information recorded by the two positions based on the position information recorded immediately before the lost positioning signal and the position information when the drone starts.
  • the line segment serves as the return path.
  • the drone may adopt a polygon approximation strategy, such as an RDP algorithm, to determine the return path.
  • a polygon approximation strategy such as an RDP algorithm
  • the flying height of the drone is lower than a preset height when returning according to the return path.
  • the preset height may be 30 meters or 20 meters, which is not limited by the embodiment of the present invention.
  • the preset height can be less than 20 meters. For example, 19 meters, 13 meters, 10 meters, 5 meters and so on.
  • the drone can use a visual odometer to capture the returning image.
  • the drone can also use other means to capture the returning image.
  • the VO can realize the return of the drone according to the return path, but the VO usually pays attention to the motion at the local time (for example, the motion between two moments), when the VO samples the time at some interval, It is possible to estimate the motion of a moving object within each time interval. Since this estimation is affected by noise, image brightness, flying height, etc., it may cause the estimation error of the previous time, which will be added to the motion of the latter time, causing drift ( The Drift phenomenon causes the VO not to return completely in accordance with the return path, and there is a certain error with the return path.
  • the return path includes position information of a plurality of return points; the first position is a point at which the distance from the return point is less than a preset distance.
  • the preset distance may be any preset distance of 1 meter, 5 meters, 10 meters, etc., and the present invention does not impose any limitation.
  • the first position may be a position where the aircraft actually arrives, and the first position may have deviated from the return path.
  • the return path includes a plurality of return points, including the return point 1 as an example.
  • the drone flies to the return point 1, it can be estimated that the return point 1 has been reached, but it is actually possible There is a deviation from the position of the return point 1, which may be within the preset distance.
  • the target mark image may be a mark map that most closely matches the return image. image.
  • the drone may select a plurality of marker images within the preset distance range of the return point 1 (for example, centering on the position of the returned flight 1 to preset The distance range is selected for the coverage area, and the plurality of marker images are matched with the return image to determine the target marker image.
  • the matching processing the returning image with the recorded marking image comprises: performing matching processing according to image similarity between the returned image and the recorded marked image.
  • the drone can extract the image features in the returning image and the image features of the respective marker images if the similarity of the image features of one of the marker images a reaches a preset condition (eg, the similarity is 80% or more) ), it can be determined that the mark image a is a target mark image.
  • a preset condition eg, the similarity is 80% or more
  • S204 Determine location information of the first location according to the returning image, the target marker image, and pose information associated with the target marker image.
  • the drone can determine the location information corresponding to the target marker image as the location information of the first location.
  • the drone can determine the difference between the returning image and the target mark image according to the return image and the pose information associated with the target mark image, and calculate the first according to the difference.
  • the deviation of the position from the target mark image can determine the position information of the first position according to the position information corresponding to the target mark image and the deviation between the first position and the target mark image.
  • the flight path refers to the path in which the drone actually flies.
  • the drone can determine the return point 1 and the return point 2, and calculate the distance value D between the return point 1 and the return point 2, and when returning according to the return path, the unmanned person
  • the machine can use VO to determine that it has moved to the vicinity of the return point 1 (but may actually deviate from the position of the return point 1 and the actual position is represented by the first position), and the return image can be collected according to the first position. And matching the return image with the mark image in the image data set to determine the target mark image, and then determining the first position according to the return image, the target mark image, and the pose information associated with the target mark image Location information (for example, at the location of the return point 1) 5 meters to the left)).
  • the UAV can calculate a distance and an orientation between the first location and the returning point 2 according to the location information of the first location, and adjust the flight path based on the distance and the orientation. So that you can fly to the location where the return point 2 is located.
  • the drone may further calculate a distance and an orientation between the first location and the returning point 1 according to the location information of the first location, and may first adjust the flight path to be able to reach The location where the return point 1 is located, and then you can continue to fly so that you can reach the location where the return point 2 is located.
  • the drone may further detect the positioning signal when returning according to the return path; if the positioning signal is detected, return according to the positioning signal and the return path.
  • the drone can preset two flight modes, one can be a mode 1 according to a positioning signal, and the other is a mode 2 according to a visual flight, and the mode of the visual flight is a mode in which a marker image needs to be collected.
  • mode 1 fails, the drone can automatically switch to mode 2.
  • the mode 1 returns to the normal state (that is, the state that the drone can detect the positioning signal), the drone can switch back from mode 2 to mode 1. And can return according to the positioning signal and the determined return path.
  • the drone can determine the return path when the positioning signal is lost, and collect the return image at the first position when the return path is returned according to the return path, and the target mark image is matched. Determining the location information of the first location according to the returning image, the target marker image, and the pose information associated with the target marker image, and adjusting the flight path according to the location information of the first location and the return route, may be lost in positioning When the signal is transmitted, it can automatically return to the air, and during the returning process, the deviation between the actual flight path and the return path can be adjusted, which satisfies the need for automation and intelligence of the drone.
  • FIG. 3 is a schematic flowchart diagram of another path adjustment method according to an embodiment of the present invention. The method described in this embodiment includes:
  • the description information of the returning image may refer to the information generated based on the description of the image.
  • the description information may include an image content profile, a content classification, and the like in the returning image. This is not subject to any restrictions.
  • the drone can determine the description information by a bag of words model (BOW model).
  • BOW model bag of words model
  • the drone may extract an image feature corresponding to the return image, determine a descriptor corresponding to the image feature corresponding to the return image, and then generate description information according to the descriptor.
  • the descriptor corresponding to the image feature may be a description term for the image feature, and the description term may be used to describe the classification, characteristics, and the like of the image feature.
  • the drone may employ clustering in unsupervised ML to classify individual image features to corresponding descriptor descriptors.
  • the generating the description information according to the descriptor comprises: determining a weight value corresponding to the descriptor; and generating description information according to the descriptor and a weight value corresponding to the descriptor.
  • the weight value corresponding to the descriptor may be determined according to the importance degree of the descriptor. Specifically, the degree of importance may be determined according to the proportion of the image content represented by the descriptor in the entire image, the representation in the entire image, and the like.
  • the UAV can use the K-Means++ algorithm (which can guarantee a uniform averaged algorithm), and can represent the description information in a tree structure, and can use the word frequency-inverse document frequency (The term frequency-inverse document frequency, tf-idf) is used to set the weight value of each descriptor.
  • K-Means++ algorithm which can guarantee a uniform averaged algorithm
  • tf-idf word frequency-inverse document frequency
  • the tree structure may be a structure of a K-tree.
  • FIG. 4 is another schematic diagram of a scenario for path adjustment according to an embodiment of the present invention.
  • a solid black line indicates a path when an image feature is searched, and a circle indicates a path.
  • Node also known as a descriptor).
  • the UAV can use the returning image as a root node, and can extract image features in the returned image, determine descriptors corresponding to each image feature, and determine the number of collation layers corresponding to each descriptor, by layer Classification, get the first layer classification, the second layer classification, until the kth layer classification, wherein the kth layer classification can be the last layer classification, the kth layer classification includes the leaf node descriptor That is, the descriptor corresponding to the image content of the image itself, further, the drone can assign a weight value to each node, and the tree structure shown in FIG. 4 can be established, and the content represented by the entire tree structure can be used as Description of the image.
  • the drone can use the returning image a as a root node, and the drone can determine the image feature of the returning image a.
  • Each descriptor in the first layer categorization (indicated by a circle) may each correspond to a weight value, and each descriptor in the second layer categorization may respectively correspond to a weight value, and each description in the third layer categorization
  • the symbols may each correspond to a weight value, wherein the descriptor in the third layer classification may be a leaf node descriptor, and the content represented by the tree structure shown in FIG. 4 may be used as the description information of the return image a.
  • the description information associated with each of the mark images is recorded, including: extracting image features corresponding to the respective mark images, and determining descriptors corresponding to the image features; and generating description information according to the descriptors.
  • the generating the description information according to the descriptor comprises: determining a weight value corresponding to the descriptor; and generating description information according to the descriptor and a weight value corresponding to the descriptor.
  • the drone can determine the corresponding description information by using the same processing manner for each of the return image and each of the marker images, and set a weight value for each descriptor. Specifically, the UAV determines the description information and the manner of setting the weight value according to the image features corresponding to the image, and the parameters of the foregoing step S303 and the corresponding description of FIG. 4 are not described herein.
  • the drone can compare the tree structure determined by the returning image and the tree structure determined by each of the marker images to determine the similarity of the tree structures of the two.
  • the drone may reach a preset similarity condition if the similarity between the tree structure of the return image and the tree structure of the candidate marker image is determined (eg, the similarity is 90% or more) Then, the drone can determine the candidate mark image as the target mark image.
  • a preset similarity condition if the similarity between the tree structure of the return image and the tree structure of the candidate marker image is determined (eg, the similarity is 90% or more) Then, the drone can determine the candidate mark image as the target mark image.
  • the drone can determine that the marker image f is the target marker image.
  • the determining the target mark image according to the comparison result comprises: determining the first mark image according to the comparison result; comparing the depth map corresponding to the first mark image with the depth map corresponding to the return image If the comparison result is that the two depth maps match, the first marker image is used as the target marker image.
  • the drone can utilize a binocular vision system to obtain a depth map of the first marker image and a depth map of the flyback image.
  • comparing the depth map corresponding to the first marker image with the depth map corresponding to the return image includes: a depth map corresponding to the first marker image and a corresponding map of the return image Depth map, obtaining relative pose information between the two depth maps; obtaining a relative rotation relationship between the two images according to the first marker image and the returning image; and comparing the relative rotation relationship with the relative The pose information is compared.
  • the depth information of the screen contents of the two depth images may be different due to the difference in the shooting posture, and the difference of the depth information may be used as the relative of the two images. Rotation relationship.
  • the relative position information between the first mark image and the return image may be a difference between a position and a posture when the first mark image is captured and a position and a posture when the return image is captured.
  • the drone can obtain the two depth images through a binocular vision system, and obtain relative pose information (including position information and posture information) between the two depth images.
  • the location information can be Including rotation
  • the attitude information may include displacement).
  • the drone can also obtain a relative rotation relationship between the returning image and the first marker image through the inertial measurement unit, and match the relative rotation relationship with the relative pose information, and if the matching is performed, the first marker can be The position information corresponding to the image determines the position information of the first position.
  • S306. Determine location information of the first location according to the returning image, the target marker image, and pose information associated with the target marker image.
  • the target return point may be the point closest to the first position, or the target return point may be the next return point that the drone should arrive after reaching the first position according to the return path. .
  • the drone can return to the return path of the return point 1 to the return point 2, and after reaching the return point 1 (the actual position may be offset from the return point 1 and the actual position is represented by the first position), it can be determined
  • the return point 1 is reached, and the exact position of the first position is determined by matching the return image with the marker image, and the distance value between the first position and the return point 1 (ie, the target return point) is determined.
  • the UAV can further determine the first position and the return point 2 (ie, the target return point) after determining the exact position of the first position by matching the return image with the marked image. Distance value.
  • the drone can correct the current flight path according to the distance value, so that the drone can return to the return path and fly according to the return path.
  • the unmanned aerial vehicle can determine the return path when the positioning signal is lost, and collect the returning image when returning according to the return path, determine the description information of the returning image, and describe the returning image.
  • the information is compared with the description information of each mark image, and the target mark image is determined according to the comparison result, and the position information of the first position is determined according to the target mark image, and finally the distance value between the first position and the target return point is determined.
  • the flight path can be adjusted, and the position of the drone can be continuously corrected by the method of matching the returning image and the marked image during the returning process, so that the drone can return according to the returning path, thereby realizing the unmanned Automatic and accurate return of the machine when there is no positioning signal.
  • FIG. 5 is a schematic structural diagram of a drone according to an embodiment of the present invention.
  • the drone described in this embodiment includes: a memory 501 and a processor 502;
  • the memory 501 is configured to store program instructions
  • the processor 502 is configured to execute the program instructions stored in the memory, when the program instructions are executed, to:
  • the return path is determined according to the recorded position information
  • Determining location information of the first location according to the returning image, the target marker image, and pose information associated with the target marker image;
  • the flight path is adjusted according to the position information of the first position and the return path.
  • the processor 502 when the processor 502 is configured to lose the positioning signal, before determining the return path according to the recorded location information, the processor 502 is further configured to:
  • the image database is generated, the image database including a plurality of marker images and pose information associated with the marker images.
  • processor 502 when the processor 502 generates an image database, it is specifically used to:
  • An image database is generated based on the mark image at the time of flight and the pose information associated with the mark image at the time of flight.
  • the return path includes location information of a plurality of return points
  • the first position is a point where the distance from the return point is less than a preset distance.
  • the return path refers to: the path planned by the drone;
  • the flight path refers to the path in which the drone actually flies.
  • the processor 502 when the processor 502 adjusts the flight path according to the location information of the first location and the return path, the processor 502 is specifically configured to:
  • the flight path is adjusted based on the distance value to cause the drone to fly to the target return point.
  • the processor 502 performs matching processing on the return image with the mark image in the image database, and determines the target mark image according to the result of the matching process, specifically for:
  • processor 502 when the processor 502 records the description information associated with each of the tag images, it is specifically used to:
  • Descriptive information is generated based on the descriptor.
  • Descriptive information is generated based on the descriptor and a weight value corresponding to the descriptor.
  • the processor 502 determines the target mark image according to the comparison result, it is specifically used to:
  • the first marker image is used as the target marker image.
  • the processor 502 compares the depth map corresponding to the first marker image with the depth map corresponding to the return image, the processor 502 is specifically configured to:
  • the relative rotational relationship is compared to the relative pose information.
  • the processor 502 when the processor 502 performs matching processing on the return image and the recorded mark image, the processor 502 is specifically configured to:
  • a matching process is performed according to the image similarity between the returning image and the recorded mark image.
  • the processor 502 is further configured to:
  • the processor 502 is further configured to:
  • the flying height of the drone is lower than a preset height.
  • the preset height is less than 20 meters.
  • FIG. 6 is a schematic structural diagram of a system according to an embodiment of the present invention. As shown in FIG. 6, the system includes a camera 601 and a drone 602.
  • the UAV 602 is the UAV 602 disclosed in the foregoing embodiment of the present invention, and the principles and implementations are similar to the foregoing embodiments, and details are not described herein again.
  • the camera device 601 can be disposed on the drone 562 for capturing a marker image and a return image for marking a position during flight.
  • the imaging device 60 can be mounted on the main body of the drone by a pan/tilt or other mounted device.
  • the camera device is used for image or video shooting during flight of the drone, including but not limited to multi-spectral imager, hyperspectral imager, visible light camera and infrared camera, VO, etc., and the camera device can be one or one the above.
  • the drone can control the camera to capture images in real time during flight.
  • the drone 602 can determine the return path according to the recorded position information when the positioning signal is lost, and collect the return image at the first position when returning according to the return path, and mark the return image and the image database. Performing matching processing on the image, and determining a target mark image according to the result of the matching process, and determining position information of the first position according to the return image, the target mark image, and pose information associated with the target mark image, according to the first The location information of the location and the return path adjust the flight path.
  • UAV 602 can be used to perform the path adjustment method shown in the foregoing method embodiment, and the specific implementation process can refer to the method embodiment, and details are not described herein.
  • the program can be stored in a computer readable storage medium, and the storage medium can include: Flash disk, Read-Only Memory (ROM), Random Access Memory (RAM), disk or optical disk.

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  • Automation & Control Theory (AREA)
  • Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
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Abstract

L'invention concerne un procédé de réglage de trajet et un véhicule aérien sans pilote. Le procédé consiste à : lorsqu'un signal de positionnement est perdu, déterminer un trajet de vol de retour en fonction d'informations de position enregistrées ; pendant un vol de retour en fonction du trajet de vol de retour, collecter une image de vol de retour à une première position ; réaliser un traitement de mise en correspondance sur l'image de vol de retour et des images de marqueur dans une base de données d'images, et déterminer une image de marqueur cible en fonction d'un résultat de traitement de mise en correspondance ; déterminer des informations de position concernant la première position en fonction de l'image de vol de retour, de l'image de marqueur cible et d'informations de pose associées à l'image de marqueur cible ; et régler un trajet de vol en fonction des informations de position concernant la première position et du trajet de vol de retour, de telle sorte qu'un vol de retour automatique et précis peut être effectué lorsque le véhicule aérien sans pilote perd un signal de positionnement.
PCT/CN2017/103808 2017-09-27 2017-09-27 Procédé de réglage de trajet et véhicule aérien sans pilote WO2019061111A1 (fr)

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PCT/CN2017/103808 WO2019061111A1 (fr) 2017-09-27 2017-09-27 Procédé de réglage de trajet et véhicule aérien sans pilote

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