CN105847684A - Unmanned aerial vehicle - Google Patents
Unmanned aerial vehicle Download PDFInfo
- Publication number
- CN105847684A CN105847684A CN201610204540.0A CN201610204540A CN105847684A CN 105847684 A CN105847684 A CN 105847684A CN 201610204540 A CN201610204540 A CN 201610204540A CN 105847684 A CN105847684 A CN 105847684A
- Authority
- CN
- China
- Prior art keywords
- target
- unmanned plane
- rgbd
- processor
- video
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000012545 processing Methods 0.000 claims abstract description 7
- 238000004891 communication Methods 0.000 claims description 8
- 239000000284 extract Substances 0.000 claims description 7
- 230000005540 biological transmission Effects 0.000 claims description 3
- 238000000034 method Methods 0.000 abstract description 24
- 230000008569 process Effects 0.000 abstract description 10
- RZVHIXYEVGDQDX-UHFFFAOYSA-N 9,10-anthraquinone Chemical group C1=CC=C2C(=O)C3=CC=CC=C3C(=O)C2=C1 RZVHIXYEVGDQDX-UHFFFAOYSA-N 0.000 abstract description 4
- 230000000007 visual effect Effects 0.000 description 14
- 230000008859 change Effects 0.000 description 12
- 230000033001 locomotion Effects 0.000 description 12
- 241000282472 Canis lupus familiaris Species 0.000 description 9
- 239000011159 matrix material Substances 0.000 description 7
- 238000013519 translation Methods 0.000 description 6
- 230000009471 action Effects 0.000 description 5
- 230000006399 behavior Effects 0.000 description 5
- 230000001755 vocal effect Effects 0.000 description 4
- 238000001514 detection method Methods 0.000 description 3
- 210000004247 hand Anatomy 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- 238000005352 clarification Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 210000003414 extremity Anatomy 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 230000004044 response Effects 0.000 description 2
- 238000012549 training Methods 0.000 description 2
- 241001465754 Metazoa Species 0.000 description 1
- 208000031481 Pathologic Constriction Diseases 0.000 description 1
- 239000000654 additive Substances 0.000 description 1
- 230000000996 additive effect Effects 0.000 description 1
- 230000003542 behavioural effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000002146 bilateral effect Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 230000001427 coherent effect Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 230000001815 facial effect Effects 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 238000003709 image segmentation Methods 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 230000008447 perception Effects 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 238000005096 rolling process Methods 0.000 description 1
- 230000011218 segmentation Effects 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 238000002604 ultrasonography Methods 0.000 description 1
- 210000001215 vagina Anatomy 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
- H04N23/60—Control of cameras or camera modules
- H04N23/66—Remote control of cameras or camera parts, e.g. by remote control devices
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/10—Simultaneous control of position or course in three dimensions
- G05D1/101—Simultaneous control of position or course in three dimensions specially adapted for aircraft
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T13/00—Animation
- G06T13/20—3D [Three Dimensional] animation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/10—Terrestrial scenes
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
- H04N23/60—Control of cameras or camera modules
- H04N23/667—Camera operation mode switching, e.g. between still and video, sport and normal or high- and low-resolution modes
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Multimedia (AREA)
- Theoretical Computer Science (AREA)
- Signal Processing (AREA)
- Software Systems (AREA)
- Geometry (AREA)
- Computer Graphics (AREA)
- Aviation & Aerospace Engineering (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Automation & Control Theory (AREA)
- Image Analysis (AREA)
Abstract
The invention discloses an unmanned aerial vehicle, comprising a RGBD camera, a flight control unit and a processor, wherein the processor is in connection with the RGBD camera and the flight control unit; the RGBD camera is used for obtaining the RGBD image information of a target in real time in a flight process, the RGBD image information including R (red), G (green) and B (blue) pixel information and corresponding depth information; the processor is used for processing the R,G, B pixel information in real time so as to identify the target, and obtaining the real time distance with the target according to the depth information corresponding to the target; the flight control unit is used for adjusting the flight posture of the unmanned aerial vehicle according to the real time distance, allowing the RGBD camera to perform tracking shooting on the target. According to the mode, the unmanned aerial vehicle can automatically realize target tracking shooting.
Description
Technical field
The present invention relates to unmanned plane field, particularly relate to a kind of unmanned plane.
Background technology
Along with microelectric technique and the development of computer vision technique so that target following is able to
Real-time implementation, is especially installed to target tracker on unmanned plane, it is possible to achieve to target
Dynamic tracking, has higher use value in military and civilian field flexibly.
In the target following technology of tradition unmanned plane, generally use laser, radar and ultrasonic etc. actively
Environment perception method, its shortcoming is to directly obtain the unknown message of target, and multiple nothing
Can interfere during people's machine testing, more drawback is disguised poor, by enemy in battlefield surroundings
The big increase of probability found.
When existing unmanned plane is directed generally to increase boat, improves speed, stealthy body, reduce body
Long-pending, highly intelligence, load weapon, strengthen transmission reliability and versatility, enable unmanned plane by
Predetermined combat duty is completed according to instruction or program prepared in advance.And on existing unmanned plane
Camera be normally applied 2D camera to shoot 2D image, in image, each pixel only includes red
(Red, R), green (Green, G), blue (Blue, B) pixel, do not include depth information D.
Such existing unmanned plane cannot be automatically obtained target following shooting etc. according to shooting 2D image.
Summary of the invention
Embodiments provide a kind of unmanned plane, it is possible to be automatically obtained the track up of target.
The present invention provides a kind of unmanned plane, unmanned plane include RGBD camera, flight controller and
Processor, processor is connected with RGBD camera and flight controller, wherein: RGBD camera,
For obtaining the RGBD image of target, each pixel in RGBD image in flight course in real time
Point includes the depth information of R, G, B Pixel Information and correspondence；Processor in real time to R, G,
B Pixel Information processes, to identify target, and according to Depth Information Acquistion corresponding to target with
The real-time distance of target；Flight controller according in real time distance adjust unmanned plane flight attitude and/
Or screening-mode so that RGBD camera is tracked shooting to target.
Wherein, RGBD camera is additionally operable to shoot the different gestures of user's input, and processor is not according to
Producing corresponding control instruction with gesture, flight controller selects screening-mode according to control instruction.
Wherein, unmanned plane also includes that voice acquisition module, voice acquisition module are connected with processor,
Voice acquisition module is for obtaining the voice of user's input, and processor produces control according to voice and refers to
Order.
Wherein, target is specific human body, and processor is examined according to described R, G, B Pixel Information
Survey the face feature of human body to lock human body.
Wherein, processor utilizes depth information to remove background, extracts target.
Wherein, processor is according to the Depth Information Acquistion target of R, G, B Pixel Information and correspondence
Real-time distance to RGBD camera.
Wherein, processor is according to the depth information identification target of R, G, B Pixel Information and correspondence
For rigid body or non-rigid.
Wherein, processor is further used in real time deep to R, G, B Pixel Information and correspondence
Degree information is identified processing, with lock onto target.
Wherein, target is specific human body, and processor is according to R, G, B Pixel Information and right
The face mask of the depth information detection human body answered is to lock human body.
Wherein, target is one or more, and unmanned plane analyzes the dynamic behaviour of one or more targets
Trend.
Wherein, unmanned plane also includes radio communication unit, and radio communication unit is connected with processor,
Video for being arrived by track up sends to far-end server, and wherein far-end server can be cloud
End server or ground based terminal server.
Wherein, track up to video include 2D video and RGBD image sequence, data are sent out
Module is sent to send 2D video and RGBD image sequence to far-end server, so that far-end clothes
Business device generates 3D video according to 2D video and RGBD image sequence.
Wherein, track up to video include 2D video and RGBD image sequence, processor
Target is carried out characteristic point mark, in target according to 2D video and RGBD image sequence further
Edge, key node arranges characteristic point thus forms the skeleton grid of target, according to skeleton grid
Generate 3D video and send to far-end server.
By such scheme, the invention has the beneficial effects as follows: flown at unmanned plane by RGBD camera
Obtaining RGBD image information during row in real time, RGBD image information includes R, G, B picture
Prime information and corresponding depth information；R, G, B Pixel Information is processed by processor in real time,
To identify target, and device is according to the real-time distance of Depth Information Acquistion corresponding to target with target, flies
Line control unit adjusts flight attitude and/or the screening-mode of unmanned plane according to distance in real time so that
RGBD camera is tracked shooting to target, it is possible to be automatically obtained the track up of target.
Accompanying drawing explanation
For the technical scheme being illustrated more clearly that in the embodiment of the present invention, embodiment will be retouched below
In stating, the required accompanying drawing used is briefly described, it should be apparent that, the accompanying drawing in describing below
It is only some embodiments of the present invention, for those of ordinary skill in the art, is not paying
On the premise of creative work, it is also possible to obtain other accompanying drawing according to these accompanying drawings.Wherein:
Fig. 1 is the structural representation of the unmanned plane of first embodiment of the invention；
Fig. 2 a is the structural representation of the unmanned plane of second embodiment of the invention；
Fig. 2 b is the structural representation of the unmanned plane section in Fig. 2 a；
Fig. 2 c is the structural representation of the RGBD camera rotation of the unmanned plane in Fig. 2 a；
Fig. 3 is the schematic diagram that target is tracked by the unmanned plane of the embodiment of the present invention；
Fig. 4 is the schematic diagram that the unmanned plane of the embodiment of the present invention carries out 3D modeling to target；
Fig. 5 is the structural representation of the unmanned plane of third embodiment of the invention；
Fig. 6 is the structural representation of the unmanned plane of fourth embodiment of the invention.
Detailed description of the invention
Below in conjunction with the accompanying drawing in the embodiment of the present invention, to the technical scheme in the embodiment of the present invention
It is clearly and completely described, it is clear that described embodiment is only that a part of the present invention is real
Execute example rather than whole embodiment.Based on the embodiment in the present invention, ordinary skill people
The every other embodiment that member is obtained under not making performing creative labour premise, broadly falls into this
The scope of invention protection.
Fig. 1 is the structural representation of the unmanned plane of first embodiment of the invention.As it is shown in figure 1, nothing
Man-machine (unmanned air vehicle, UAV) 10 includes: RGBD camera 11, flight control
Device 12 processed and processor 13.Processor 13 and RGBD camera 11 and flight controller 12
Connect.RGBD camera 11 is for obtaining RGBD image in real time in flight course.RGBD
In image, each pixel includes the depth information of R, G, B Pixel Information and correspondence.Wherein as
The depth information of element constitutes the two-dimensional pixel matrix of scene, is called for short depth map.Each pixel with its
Position in scene is corresponding, and has the distance represented from certain reference position to its scene location
Pixel value.In other words, depth map has the form of image, and pixel value points out the object of scene
Topographical information rather than brightness and/or color.Processor 13 is used in real time R, G, B pixel
Information processes, and to identify target and target characteristic, and obtains according to the depth information that target is corresponding
Take the real-time distance with target.Flight controller 12 is for adjusting unmanned plane 10 according to distance in real time
Flight attitude and/or screening-mode so that RGBD camera 11 target is tracked shooting.
Specifically, flight controller 12 can receive by remote controller, the control unit such as voice, gesture
The instruction sent, and according to instruction trace photographic subjects, wherein, the flight attitude of unmanned plane 10
Including taking off, hover, pitching, rolling, go off course, landing etc..
As shown in Figure 2 a, unmanned plane 20 can include at least two RGBD camera 210,211,
And also include flight assembly 24 and The Cloud Terrace 25 (not shown).RGBD camera 210,211 sets
Putting on The Cloud Terrace 25, The Cloud Terrace 25 is made a response for the attitudes vibration measuring carrier with stable The Cloud Terrace
On RGBD camera 210,111, with facilitate 210,211 pairs of targets of RGBD camera carry out with
Track shoots.Being provided with swingle 26 on The Cloud Terrace 25, RGBD camera 210,211 is along swingle
The vertical direction of 26 is arranged.The profile of unmanned plane 20 as shown in Figure 2 b, in unmanned plane 20
Portion arranges circuit board, and processor 23 is arranged on circuit boards.Flight assembly 24 can include rotor
Or fixed-wing, for guaranteeing the steady of flight attitude in the normal flight of unmanned plane and flight course
Fixed.Preferably, as a example by four rotor wing unmanned aerial vehicles, four propellers are decussation structure, phase
To four rotors there is identical direction of rotation, points two groups, the direction of rotation of two groups is different.With biography
The helicopter of system is different, and quadrotor can only realize various by the speed changing propeller
Action.In unmanned plane 20, RGBD camera 210,211 is separate setting, i.e. RGBD
Camera 210,211 is separate to be shot, the most unaffected.Fig. 2 c is unmanned plane 20
Middle RGBD camera 211 rotates the structural representation of 60 degree.In embodiments of the present invention, unmanned
The RGBD camera quantity of machine 20 is not limited to 2, specifically can extend swingle 26, long at it
Degree increases RGBD camera on direction.The most in other embodiments of the invention, it is also possible near
Few two RGBD camera levels are independently positioned on The Cloud Terrace 25, as set on The Cloud Terrace 25
Put multiple swingle to be respectively provided with RGBD camera.
In embodiments of the present invention, processor 13 is for carrying out R, G, B Pixel Information in real time
Identifying processing, with lock onto target, specifically, can apply color image segmentation method, utilize the back of the body
Scape Texture Segmentation goes out background image, then utilizes original image subtracting background image to obtain target figure
Picture.Certainly the most in other embodiments of the present invention, it is also possible to application additive method identification target, and then
Lock onto target.Target is specific human body, and processor 13 can be according to R, G, B Pixel Information
The face feature of detection human body is to lock human body.The mode of track up includes front shooting, side
One in shooting, back side shooting, top shooting or combination in any.
In embodiments of the present invention, deep according to R, G, B Pixel Information and correspondence of processor 13
Degree information identification target is rigid body or non-rigid.Specifically can utilize the depth information profile to target
Being identified, distinguish that profile is rigid body or non-rigid, picking out target is that dynamic biological is (such as people
Body) or the object of non-rigid.If rigid body is then identified as object, and whether target is led
Dynamic motion.Wherein rigid body refers to the object that three dimensional structure will not change along with motion, rather than
Rigid body is then contrary, and its three dimensional structure can change along with motion.
Processor 13 is also with rgb color information and target carries out feature identification, identifies thing
The profile of body, color information, extract more clarification of objective, improves the recognition accuracy of target.
Recognition methods is not limited to the training method commonly used, such as machine learning, degree of depth study scheduling algorithm.Such as
Utilize RGB information, dynamic biological target carried out skin color model, or meets human body complexion feature,
Then identify whether target is human body, is otherwise non-human.Processor 13 be also compatible with process sound,
The information of other sensors such as infrared sensor, is used for identifying and detecting target and feature thereof, improves
Accuracy rate.
If recognizing target is human body, then processor 13 identifies trunk, extremity, hands
The human body such as portion, face, extract height, brachium, shoulder breadth, hand size, face size,
The information such as countenance feature.Owing to human body is non-rigid, in the track up process of long period
In, human body can not keep same posture, is susceptible to non-rigid change, needs to carry out model
Rebuild, it is to avoid the non-rigid change of data.Processor 13 is first to RGBD camera 11 shooting
The depth image of target removes background parts, owing to the depth value of background pixel point is than human body
Depth value is big, and processor 13 can select a suitable threshold value, when the depth value of pixel is big
When this threshold value, this pixel is labeled as background dot, removes from depth image, obtain people
Body cloud data.Cloud data is converted into triangle grid data by processor 13 again, the most permissible
Four fields on depth image are utilized to close according to this topology as the topological relation connected, cloud data
System generates triangle grid data.Point is gone data to carry out denoising by processor 13 further, tool
The multiframe cloud data at each visual angle sum-average arithmetic respectively can be removed big noise by body, then with bilateral
Small noise is removed in filtering.The triangle grid data at multiple visual angles is finally spliced by processor 13
Form an entirety together, for carrying out Model Reconstruction.Processor 13 can use iteration to calculate
Method rebuilds three-dimensional (3 D) manikin.In iterative algorithm, the data first found out master pattern with collect
Between corresponding point, for use as change obligatory point below.Then using obligatory point as energy term,
Minimize object function, thus be deformed to master pattern solve scan data, finally obtain deformation
After master pattern parameter in human space, calculated human parameters is for changing next time
Dai Zhong, completes the reconstruction of three-dimensional (3 D) manikin after so carrying out successive ignition.And then can identify
The human bodies such as trunk, extremity, hand, face, extract height, brachium, shoulder breadth,
The information such as hand size, face size, countenance feature.
RGBD camera 11 follows the tracks of human body target, and people according to the anthropometric dummy that processor 13 is rebuild
The movement locus at each position of body.Processor 13 and then in order to analyze the attitude action of target, and root
Analyze according to the attitude action of target, behavioral pattern etc. and extract identity information, so divide into child,
Old man, adolescence etc..
If recognizing target is animal, then processor 13 can utilize similar human body target
RGBD recognition methods, and RGBD image sequence method for tracking target is identified and target characteristic
Identification extraction, does not repeats them here.
If recognizing target is inanimate, processor 13 utilizes depth information D to identify mesh
Target overall size.Specifically, processor 13 can split depth map to find out the profile of target.
Processor 13 and then utilize the RGB information of target, carries out object detection, identifies its color, or
The information such as Quick Response Code.
In embodiments of the present invention, processor 13 can be further used in real time R, G, B picture
The depth information of prime information and correspondence is identified processing, with lock onto target.Such as, target is
Specific human body, processor 13 is examined according to the depth information of R, G, B Pixel Information and correspondence
Survey the face mask of human body to lock human body.Specifically, processor 13 obtains the 3D of human body head
Posture, and then obtain face mask.The 3D posture of human body head is has 6 degree of freedom
Posture.Processor 13 can include motion sensor, and wherein sensor can be accelerometer, magnetic
One or more in power meter and/or gyroscope.RGBD camera obtains the degree of depth with head
The RGBD image sequence of information, the reference pose of head is by the frame in RGBD image sequence
Middle acquisition, and define coordinate frame of reference.Depth information is used to come really relative to coordinate frame of reference
The fixed spin matrix being associated with the head pose of the human body in multiple images and translation vector.Citing
For, spin matrix and translation can be determined in two dimension by the image zooming-out characteristic point from head
Vector.The depth information being associated with the characteristic point followed the tracks of can be used subsequently to determine head in three dimensions
The spin matrix in portion and translation vector.The characteristic point extracted can be arbitrary.Based on spin matrix
With the dimensional posture that translation vector determines head relative to reference pose.For example, can be by spy
Levy image coordinate a little and corresponding depth information and comprise the reference pose phase with human body head
The state of the spin matrix of association and translation vector and current orientation and position is supplied to expansion card
Thalmann filter.Extended Kalman filter can be used for determining in multiple image relative to reference pose
The spin matrix of each and the estimation of translation vector, can determine that human body head is relative from this estimation
Dimensional posture in reference pose.Processor 13 obtains according to the 3D posture of the human body head obtained
Face mask.
Processor 13 is according to described in the Depth Information Acquistion of described R, G, B Pixel Information and correspondence
The real-time distance of target extremely described RGBD camera, specifically can be according to R, G, B Pixel Information
With face's central point that corresponding depth information calculates human body, and with central point to unmanned plane 10
Distance as real-time distance.Certainly processor 13 can also according to R, G, B Pixel Information and
Corresponding depth information calculates the barycenter of human body, and using the distance of barycenter and unmanned plane 10 as
Distance in real time.Specifically, the depth information according to R, G, B Pixel Information and correspondence can be straight
Obtain a certain body part taking target, such as face, palm etc., with RGBD camera 11
Near distance, takes this minimum distance real-time distance as barycenter Yu unmanned plane 10.
In embodiments of the present invention, target can be one or more, unmanned plane analyze this or
The dynamic behaviour trend of multiple targets.During the unmanned plane 10 multiple target of track up simultaneously, permissible
To multiple goal setting priority, the target that preferential track up priority is the highest, it is also possible to do not set
Put priority, simultaneously the multiple target of track up.Fig. 3 is unmanned plane showing of being tracked target
Being intended to, Fig. 3 a represents and is tracked target 1 and target 2, and Fig. 3 b is to follow the tracks of target 1 to be
Example, the skeleton grid of acquisition target 1, can from Fig. 3 c so that target is carried out dynamic behaviour analysis
Going out, target 1 is by Turning travel.Target 1 can be only tracked, with to mesh by unmanned plane 10
Mark 1 carries out behavior analysis.Two targets 1,2 can also be tracked by unmanned plane 10, such as figure
3a.The priority priority higher than target 2 of target 1 specifically can be set, naturally it is also possible to sets
Putting the priority priority higher than target 1 of target 2, this is not restricted.Multiple targets are set
When putting priority, between RGBD camera 11 and target, preset distance apart is constant, if multiple
Target is positioned in the shooting visual field of RGBD camera 11 simultaneously, then flight controller 12 is simultaneously to this
Multiple targets are tracked shooting, if during track up, a certain moment is due to multiple targets
Between apart from each other, it is impossible to make all of target be positioned at the current bat of RGBD camera 11 the most simultaneously
Taking the photograph in visual field, the most at least a target is not in the shooting visual field of RGBD camera 11, then fly
The target that line control unit 12 selects priority the highest is tracked shooting.With while track up master
As a example by people and pet dog and target that owner is higher priority, if during track up, house pet
Canis familiaris L. runs away, and can not ensure that owner is positioned at the current of RGBD camera 11 with pet dog simultaneously simultaneously
In shooting visual field, the most now flight controller 12 controls RGBD camera 11 track up owner,
No longer track up pet dog.
It is not provided with priority, simultaneously during the multiple target of track up, it is ensured that multiple targets are all located at
The shooting visual field of RGBD camera 11.If during track up, a certain moment is due to multiple
Between target apart from each other, it is impossible to make all of target be positioned at the most simultaneously RGBD camera 11 work as
In front shooting visual field, then flight controller 12 can by adjust RGBD camera 11 focal length,
Or adjust the distance between RGBD camera 11 and target, as increased RGBD camera 11
Focal length, or increase the distance between RGBD camera 11 and target so that multiple targets all positions
In the shooting visual field of the RGBD camera 11 after adjusting.Wherein, RGBD camera 11 and target
Between distance be not more than the trial voyage or flight distance of unmanned plane 10, trial voyage or flight distance refers to guarantee unmanned plane 10
The maximum distance that can fly the most out of touch.Equally with while track up owner and pet dog and
As a example by being not provided with priority target, if during track up, pet dog runs away, and can not protect simultaneously
Card owner and pet dog are positioned in the current shooting visual field of RGBD camera 11 simultaneously, the most now fly
Line control unit 12 controls RGBD camera 11 and increases focal length or control unmanned plane 10 wide,
In current shooting visual field after making owner and pet dog again be positioned at the adjustment of RGBD camera 11,
Continue track up owner and pet dog simultaneously.
In embodiments of the present invention, flight controller 12 controls the mesh that RGBD camera 11 photographs
The video of target continuous print image construction target.And track up to video include 2D video and
RGBD image sequence.Target is entered by processor 13 according to 2D video and RGBD image sequence
Row characteristic point identifies.Specifically, processor 13 can be the most recognizable according to a frame RGBD image
Clarification of objective point, is then modified characteristic point according to the RGBD image of continuous multi-angle.
As a example by target is as human body, i.e. can recognize that the characteristic point of human body according to a frame RGBD image, specifically
Ground, removes background to RGBD image and obtains the profile of human body, then by trunk part
Heart point, the barycenter of head, the turning point at edge are designated as characteristic point, and according to human body proportion and deposit
The articulare of elbow and leg is designated as characteristic point and obtains the whole of human body by the big data of human body of storage
Characteristic point is as shown in the figure a in Fig. 4.Further according to the RGBD image of continuous multi-angle to spy
Levy and be a little modified obtaining the skeleton grid of human body as shown in the figure b in Fig. 4.Processor 13 enters
And according to skeleton mess generation 3D video.Unmanned plane 10 also includes memory element, regards for storage
Frequently, the target 3D model of processor 13 preliminary treatment, 3D video etc..
Wherein, processor 13 includes according to skeleton mess generation 3D video: processor 13 is permissible
To track up to as being tracked shooting to obtain the movement locus of skeleton grid.Meanwhile, RGBD
Camera 11 obtains the RGBD image sequence of multi-angle, can include front, side and reverse side
RGBD image sequence.Each frame in the RGBD image sequence of the multi-angle according to target is deep
Degree image carries out mesh reconstruction, is spliced to form the threedimensional model of target.Processor 13 can be by mesh
The movement locus of target skeleton grid mates with threedimensional model, and according to RGBD camera 11
The RGBD image sequence obtained obtains the 3D video such as the figure c in Fig. 4 of target.
Specifically, need, by RGBD camera 11 acquisition, at least two width carrying out three-dimensional reconstruction
RGBD image, and need to build the deep of three-dimensional scenic according to this at least two width RGBD Image Acquisition
Degree information and rgb pixel information.Flight controller 12 moves relative to RGBD camera 11
Target persistently follow the tracks of, determine the target relative position relative to RGBD camera 11.Root
Determine that according to this relative position the image needing display in three-dimensional scenic is to be tracked shooting.
RGBD camera 11 obtains the RGBD image sequence of multi-angle can pass through multiple RGBD
Scene is shot by camera 11 from different perspectives, it is possible to use single RGBD camera 11 moves
Dynamic different position carries out shooting and obtains.Method based on shooting, user can utilize shooting many
Open image or key frame of video carries out scene rebuilding.Single RGBD camera 11 is in the process of shooting
In, the movement of RGBD camera 11 may be considered the movement at visual angle, if RGBD during shooting
Camera 11 moves horizontally, then can photograph bigger scene, if RGBD camera 11
It is rotated into row shooting around object, then can photograph the visual angle that same object is different.
Position relatively includes but not limited to the information such as direction, angle, distance.And the change of relative position
Change can be RGBD camera 11 self generation movement thus cause the relative position between target
Change, it is also possible to be to make RGBD camera 11 produce with target under user's active operation when target
Give birth to relative change in location, or RGBD camera remains stationary as, and target moves the phase para-position caused
Put change, and both move the change in location between the two caused respectively, seldom repeat at this.
But no matter which kind of reason causes both relative change in location, all can be shot by RGBD camera
RGBD image determines relative position between the two.
Processor 13 needs to build the deep of three-dimensional scenic according to this at least two width RGBD Image Acquisition
Degree information and rgb pixel information include: RGBD camera is by existing local algorithm or complete
Office's algorithm, by the computing between different RGBD images, it is thus achieved that need the three-dimensional scenic built
Depth information and rgb pixel information, for example, it is possible to by bundle adjustment algorithm, calculate three
After the depth information of each pixel in dimension scene, can be by the form of RGBD to three dimensional field
The rgb pixel information of each pixel of scape and depth information is indicated and record.Processor 13
In conjunction with structure and the relative position of three-dimensional scenic, generate the scene visual corresponding with each relative position
Figure, forms RGBD image sequence, and then constitutes 3D video.
In embodiments of the present invention, processor 13 can also be directly by the skeleton grid of first object
Movement locus mate with the threedimensional model of the second target preset, and according to RGBD camera
The RGBD image sequence of 11 the second targets obtained obtains the 3D video of the second target.Wherein
The skeleton grid of one target can be to be pre-stored in processor 13, or processor 13 is first followed the tracks of
Shooting first object is to obtain the skeleton grid of first object.The foundation of the threedimensional model of the second target
The method of the foundation with the threedimensional model of aforesaid target is identical.
In embodiments of the present invention, the memory capacity of the memory element within unmanned plane 10 has
Limit, it is impossible to storing jumbo data, therefore see Fig. 5, unmanned plane 10 also includes channel radio
News unit 14.Radio communication unit 14 is connected with processor 13, for by track up to regard
Take place frequently and deliver to far-end server.Far-end server is transmitted by radio communication unit 14 for processing
RGBD image sequence, process high definition RGBD, generate high definition high-resolution target 3D mould
Type, target 3D video or 3D animation etc..Wherein far-end server includes ground-based server and high in the clouds
Server.Track up to video include 2D video and RGBD image sequence, if 2D
The data volume of video and RGBD image sequence is too big, then radio communication unit 14 can be by 2D
Video and RGBD image sequence send to far-end server, so that far-end server is according to 2D
Video and RGBD image sequence generate 3D video, so can process the RGBD figure of big data
As sequence, facilitate flight controller 12 to continue target and be tracked shooting.Radio communication unit
14 are additionally operable in real time by transmission such as the target 3D model of processor 13 preliminary treatment, 3D videos extremely
Far-end server.
In embodiments of the present invention, RGBD camera 11 is additionally operable to shoot the different handss of user's input
Gesture, processor 13 produces corresponding control instruction, flight controller 12 basis according to different gestures
Control instruction selects screening-mode with target.Wherein, screening-mode include unmanned plane 10 start and stop,
Target type is selected and track up mode is selected, and wherein target type includes human body.Gesture bag
Including the five fingers opening and closing gesture, the five fingers opening and closing gesture includes that the five fingers open gesture and the five fingers Guan Bi gesture.With
The gesture at family can also include but not limited to grasp, naturally raises one's hand, front push away, upper and lower, left and right pendulum
Hands.The corresponding different control instruction of different gestures, as naturally raise one's hand, expression starts unmanned plane 10,
Wave and represent the control instruction etc. adjusting unmanned plane 10 heading, at this not in upper and lower, left and right
Describe in detail again.
Concrete implementation process is as follows:
RGBD camera 11 is for shooting the gesture of user's input, and processor 13 inputs according to user
Gesture obtain the skeleton grid data of staff in real time, and extract relevant to unmanned plane 10 operation
Skeleton grid data, and by the skeleton grid data of acquisition and unmanned plane 10 prestore with behaviour
The skeleton grid data making relevant gesture model compares.If the skeleton grid data obtained
At the grid data of gesture model in default threshold range, then the skeleton that processor 13 obtains
Grid data is bound with the gesture model prestored.Processor 13 is obtained by RGBD camera
The coordinate about the skeleton grid data of frame every in gesture RGBD image sequence realize gesture mould
Type action coherent, reaches the motion effect of the gesture of gesture model simulation real user input in scene
Really.The action that processor 13 mates with gesture model according to the gesture obtained.
In embodiments of the present invention, seeing Fig. 6, unmanned plane 10 also includes voice acquisition module 15,
Voice acquisition module 15 electrically connects with processor 13.Voice acquisition module 15 is used for obtaining user
The voice of input, processor 13 produces control instruction according to voice.Flight controller 12 is according to control
System instruction selects screening-mode with target.
Specifically, remote control unit carries out recognition of face and carries out Application on Voiceprint Recognition.During recognition of face, people
Face data base pre-saves face information (such as detect facial image by infrared signal and stay
Deposit the physiological feature such as people's interorbital space, human eye length), when gathering, collect people by infrared signal
Face data are made comparisons with the data in face database.If by recognition of face, then to receiving
Voice further determine whether the voice for having voice-operated authority, determine that this voice institute is right
The authority answered, and carry out speech recognition.Remote control unit according to the result of recognition of face, is sentenced further
Break and whether receive voice.Every has and sends the personnel of phonetic control command and all upload one section of training language
Sound, and then obtain vocal print storehouse.Carrying out vocal print when comparing, the phonetic order person of sending sends phonetic order,
This phonetic order is carried out vocal print contrast with voice print database.By vocal print and face information lookup sound
Identity information corresponding in stricture of vagina data base and face database, thus confirm its authority.Remote control unit
Further phonetic order is sent to the voice acquisition module 15 of unmanned plane.Voice acquisition module 15
By the security verification of phonetic order, and produced according to phonetic order by checking preprocessor 13
Raw control instruction, is sent to the flight controller 12 of unmanned plane.Flight controller 12 will receive
Instruction corresponding to the symbol lookup of instruction needed for the operation time, then (real at this phonetic order
For code on border) add this operation time afterwards.Flight controller 12 selects to clap according to control instruction
Take the photograph the flight attitude of Schema control unmanned plane 10, such as flight speed, flying height, flight path
And the distance etc. between peripheral obstacle.
Preferably, during track up, flight controller 12 is always according to RGBD image sequence
R, G, B Pixel Information in row and the flight appearance of the depth information adjustment unmanned plane 10 of correspondence
State collides with the target of surrounding preventing unmanned plane 10.Specifically, flight controller 12
According to the depth information of pixel each in RGBD image sequence determine target and surrounding objects with
Distance between RGBD camera 11, and choose target and surrounding objects and RGBD camera 11
Between minimum distance, according to this minimum distance adjust unmanned plane 10 flight attitude.Such as flight
Controller 12 determine this minimum distance less than predeterminable range time, then control unmanned plane 10 to away from
The direction flight of target.And flight controller 12 also judges that this minimum distance is more than trial voyage or flight distance
Time, then control the flight of unmanned plane 10 target-bound direction.
In embodiments of the present invention, in order to obtain target and surrounding objects and RGBD camera 11
Between minimum distance, unmanned plane 10 can arrange a RGBD camera 11, this RGBD phase
Machine 11 rotates and is arranged at unmanned plane 10, and rotational angle is 0-180 degree.Unmanned plane 10 can also set
Putting two RGBD cameras 11, each RGBD camera 11 is rotatably arranged at described unmanned plane
10, corresponding rotational angle is 0-90 degree.RGBD camera 11 rotary scanning shoots, and obtains nothing
The multi-angle RGBD image sequence of man-machine 10 surrounding objects, and then make the flight controller 12 can
Obtain the minimum distance between target and surrounding objects and RGBD camera 11.Certainly at this
In other bright embodiments, unmanned plane 10 can also use other distance-finding method to measure mesh around
Minimum distance between mark and RGBD camera 11.Such as, laser range finder, ultrasound wave are used
The distance between surrounding objects and RGBD camera 11 measured by diastimeter and infrared range-measurement system etc.,
And and target and RGBD camera 11 between distance compare, select minimum distance.And adopt
Surrounding objects and RGBD is measured with laser range finder, ultrasonic range finder and infrared range-measurement system etc.
The method of the distance between camera 11 is same as the prior art, is not described in detail in this.
In sum, the present invention obtains RGBD by RGBD camera in flight course in real time
Image information, RGBD image information includes the depth information of R, G, B Pixel Information and correspondence；
R, G, B Pixel Information is processed by processor in real time, to identify target, and according to target
Corresponding Depth Information Acquistion and the real-time distance of target；Flight controller adjusts according to distance in real time
The flight attitude of unmanned plane so that RGBD camera is tracked shooting, such unmanned plane to target
The distance of RGBD camera and target can be obtained from RGBD image information, it is achieved automatically with
Track shoots.
The foregoing is only embodiments of the invention, not thereby limit the scope of the claims of the present invention,
Every equivalent structure utilizing description of the invention and accompanying drawing content to be made or equivalence flow process conversion, or
Directly or indirectly being used in other relevant technical fields, the patent being the most in like manner included in the present invention is protected
In the range of protecting.
Claims (13)
1. a unmanned plane, it is characterised in that described unmanned plane includes RGBD camera, flight
Controller and processor, described processor and described RGBD camera and described flight controller
Connect, wherein:
Described RGBD camera, for obtaining the RGBD image of target in flight course in real time,
In described RGBD image, each pixel includes the degree of depth letter of R, G, B Pixel Information and correspondence
Breath；
Described processor, for processing described R, G, B Pixel Information in real time, to know
Other target, and according to the real-time distance of Depth Information Acquistion corresponding to described target with described target；
Described flight controller for adjusting the flight appearance of described unmanned plane according to described real-time distance
State and/or screening-mode so that described RGBD camera is tracked shooting to described target.
Unmanned plane the most according to claim 1, it is characterised in that described RGBD camera
Being additionally operable to shoot the different gestures of user's input, it is right that described processor produces according to described different gestures
The control instruction answered, described flight controller selects screening-mode according to described control instruction.
Unmanned plane the most according to claim 1, it is characterised in that described unmanned plane also includes
Voice acquisition module, described voice acquisition module is connected with described processor, and described voice obtains mould
Block is for obtaining the voice of user's input, and described processor produces described control according to described voice and refers to
Order.
Unmanned plane the most according to claim 2, it is characterised in that described target is specific
Human body, the face that described processor detects described human body according to described R, G, B Pixel Information is special
Levy to lock described human body.
Unmanned plane the most according to claim 1, it is characterised in that described processor utilizes institute
State depth information and remove background, extract described target.
Unmanned plane the most according to claim 1, it is characterised in that described processor is according to institute
State target described in the Depth Information Acquistion of R, G, B Pixel Information and correspondence to described RGBD phase
The real-time distance of machine.
Unmanned plane the most according to claim 1, it is characterised in that described processor is according to institute
Stating target described in the depth information identification of R, G, B Pixel Information and correspondence is rigid body or non-rigid.
Unmanned plane the most according to claim 1, it is characterised in that described processor is further
It is identified processing for the real-time depth information to described R, G, B Pixel Information and correspondence,
With lock onto target.
Unmanned plane the most according to claim 8, it is characterised in that described target is specific
Human body, described processor is examined according to the depth information of described R, G, B Pixel Information and correspondence
Survey the face mask of described human body to lock described human body.
Unmanned plane the most according to claim 1, it is characterised in that described target is one
Or multiple, described unmanned plane analyzes the dynamic behaviour trend of the one or more target.
11. unmanned planes according to claim 1, it is characterised in that described unmanned plane also wraps
Including radio communication unit, described radio communication unit is connected with described processor, for tracking being clapped
The video taken the photograph sends to far-end server, and wherein said far-end server can be cloud server
Or ground based terminal server.
12. unmanned planes according to claim 11, it is characterised in that described track up arrives
Video include 2D video and RGBD image sequence, described data transmission blocks is by described 2D
Video and described RGBD image sequence send to described far-end server, so that described far-end clothes
Business device generates 3D video according to described 2D video and described RGBD image sequence.
13. unmanned planes according to claim 11, it is characterised in that described track up arrives
Video include 2D video and RGBD image sequence, described processor is further according to described 2D
Video and described RGBD image sequence carry out characteristic point mark to described target, in described target
Edge, key node arrange characteristic point thus form the skeleton grid of target, according to described skeleton
Lattice generate 3D video and send to described far-end server.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610204540.0A CN105847684A (en) | 2016-03-31 | 2016-03-31 | Unmanned aerial vehicle |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610204540.0A CN105847684A (en) | 2016-03-31 | 2016-03-31 | Unmanned aerial vehicle |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN105847684A true CN105847684A (en) | 2016-08-10 |
Family
ID=56597918
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610204540.0A Pending CN105847684A (en) | 2016-03-31 | 2016-03-31 | Unmanned aerial vehicle |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105847684A (en) |
Cited By (36)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106020227A (en) * | 2016-08-12 | 2016-10-12 | 北京奇虎科技有限公司 | Control method and device for unmanned aerial vehicle |
| CN106347550A (en) * | 2016-09-05 | 2017-01-25 | 北京小米移动软件有限公司 | Method and device for controlling balance car |
| CN106774947A (en) * | 2017-02-08 | 2017-05-31 | 亿航智能设备（广州）有限公司 | A kind of aircraft and its control method |
| CN106778474A (en) * | 2016-11-14 | 2017-05-31 | 深圳奥比中光科技有限公司 | 3D human body recognition methods and equipment |
| CN106843275A (en) * | 2017-04-01 | 2017-06-13 | 成都通甲优博科技有限责任公司 | A kind of unmanned plane pinpoints method, device and the system of being diversion |
| CN106991413A (en) * | 2017-05-04 | 2017-07-28 | 上海耐相智能科技有限公司 | A kind of unmanned plane |
| CN107330371A (en) * | 2017-06-02 | 2017-11-07 | 深圳奥比中光科技有限公司 | Acquisition methods, device and the storage device of the countenance of 3D facial models |
| CN107374638A (en) * | 2017-07-07 | 2017-11-24 | 华南理工大学 | A kind of height measuring system and method based on binocular vision module |
| CN107505951A (en) * | 2017-08-29 | 2017-12-22 | 深圳市道通智能航空技术有限公司 | A kind of method for tracking target, unmanned plane and computer-readable recording medium |
| CN107844744A (en) * | 2017-10-09 | 2018-03-27 | 平安科技（深圳）有限公司 | With reference to the face identification method, device and storage medium of depth information |
| WO2018058264A1 (en) * | 2016-09-27 | 2018-04-05 | 深圳市大疆创新科技有限公司 | Video-based control method, device, and flying apparatus |
| WO2018058309A1 (en) * | 2016-09-27 | 2018-04-05 | 深圳市大疆创新科技有限公司 | Control method, control device, electronic device, and aerial vehicle control system |
| CN107894836A (en) * | 2017-11-22 | 2018-04-10 | 河南大学 | Remote sensing image processing and the man-machine interaction method of displaying based on gesture and speech recognition |
| CN108153325A (en) * | 2017-11-13 | 2018-06-12 | 上海顺砾智能科技有限公司 | The control method and device of Intelligent unattended machine |
| CN108196534A (en) * | 2017-12-26 | 2018-06-22 | 广东工业大学 | A kind of multi-rotor unmanned aerial vehicle control terminal, flight control system and control method |
| CN108375986A (en) * | 2018-03-30 | 2018-08-07 | 深圳市道通智能航空技术有限公司 | Control method, device and the terminal of unmanned plane |
| CN108475072A (en) * | 2017-04-28 | 2018-08-31 | 深圳市大疆创新科技有限公司 | A kind of tracking and controlling method, device and aircraft |
| CN108513643A (en) * | 2017-08-31 | 2018-09-07 | 深圳市大疆创新科技有限公司 | A kind of paths planning method, aircraft, flight system |
| WO2018191840A1 (en) * | 2017-04-17 | 2018-10-25 | 英华达（上海）科技有限公司 | Interactive photographing system and method for unmanned aerial vehicle |
| CN108854031A (en) * | 2018-05-29 | 2018-11-23 | 深圳臻迪信息技术有限公司 | The method and relevant apparatus of exercise data are analyzed by unmanned camera work |
| CN109151435A (en) * | 2018-09-30 | 2019-01-04 | Oppo广东移动通信有限公司 | A kind of data processing method, terminal, server and computer storage medium |
| CN109661631A (en) * | 2018-03-27 | 2019-04-19 | 深圳市大疆创新科技有限公司 | Control method, device and the unmanned plane of unmanned plane |
| CN109709554A (en) * | 2018-12-13 | 2019-05-03 | 广州极飞科技有限公司 | Operating equipment and its control method and device |
| CN109859264A (en) * | 2017-11-30 | 2019-06-07 | 北京机电工程研究所 | A kind of aircraft of view-based access control model guiding catches control tracking system |
| WO2019140699A1 (en) * | 2018-01-22 | 2019-07-25 | SZ DJI Technology Co., Ltd. | Methods and system for multi-target tracking |
| WO2019144291A1 (en) * | 2018-01-23 | 2019-08-01 | 深圳市大疆创新科技有限公司 | Flight control method, apparatus, and machine-readable storage medium |
| CN110325879A (en) * | 2017-02-24 | 2019-10-11 | 亚德诺半导体无限责任公司 | System and method for compress three-dimensional depth sense |
| CN110687902A (en) * | 2016-12-21 | 2020-01-14 | 杭州零零科技有限公司 | System and method for controller-free user drone interaction |
| CN111199576A (en) * | 2019-12-25 | 2020-05-26 | 中国人民解放军军事科学院国防科技创新研究院 | Outdoor large-range human body posture reconstruction method based on mobile platform |
| CN111247792A (en) * | 2019-04-28 | 2020-06-05 | 深圳市大疆创新科技有限公司 | Control method of unmanned aerial vehicle, unmanned aerial vehicle and computer readable storage medium |
| CN111275760A (en) * | 2020-01-16 | 2020-06-12 | 上海工程技术大学 | Unmanned aerial vehicle target tracking system and method based on 5G and depth image information |
| US10719087B2 (en) | 2017-08-29 | 2020-07-21 | Autel Robotics Co., Ltd. | Target tracking method, unmanned aerial vehicle, and computer readable storage medium |
| CN111490491A (en) * | 2020-04-30 | 2020-08-04 | 国网上海市电力公司 | Ultra-high voltage transmission line inspection unmanned aerial vehicle based on deep learning |
| CN112753210A (en) * | 2020-04-26 | 2021-05-04 | 深圳市大疆创新科技有限公司 | Movable platform, control method thereof and storage medium |
| CN113129468A (en) * | 2021-04-06 | 2021-07-16 | 深圳市艾赛克科技有限公司 | Underground pipe gallery inspection method based on unmanned aerial vehicle |
| CN113128447A (en) * | 2021-04-29 | 2021-07-16 | 深圳市道通智能航空技术股份有限公司 | Mask identification method and device, unmanned aerial vehicle and storage medium |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101887587A (en) * | 2010-07-07 | 2010-11-17 | 南京邮电大学 | Multi-target track method based on moving target detection in video monitoring |
| CN102142147A (en) * | 2010-01-29 | 2011-08-03 | 索尼公司 | Device and method for analyzing site content as well as device and method for detecting and tracking target |
| CN102779347A (en) * | 2012-06-14 | 2012-11-14 | 清华大学 | Method and device for tracking and locating target for aircraft |
| US20130136300A1 (en) * | 2011-11-29 | 2013-05-30 | Qualcomm Incorporated | Tracking Three-Dimensional Objects |
| CN103926933A (en) * | 2014-03-29 | 2014-07-16 | 北京航空航天大学 | Indoor simultaneous locating and environment modeling method for unmanned aerial vehicle |
| CN104243901A (en) * | 2013-06-21 | 2014-12-24 | 中兴通讯股份有限公司 | Multi-target tracking method based on intelligent video analysis platform and system of multi-target tracking method |
| CN104236548A (en) * | 2014-09-12 | 2014-12-24 | 清华大学 | Indoor autonomous navigation method for micro unmanned aerial vehicle |
| CN104808799A (en) * | 2015-05-20 | 2015-07-29 | 成都通甲优博科技有限责任公司 | Unmanned aerial vehicle capable of indentifying gesture and identifying method thereof |
| US20150244976A1 (en) * | 2014-02-26 | 2015-08-27 | Microsoft Corporation | Telepresence experience |
| CN205453893U (en) * | 2016-03-31 | 2016-08-10 | 深圳奥比中光科技有限公司 | Unmanned aerial vehicle |
-
2016
- 2016-03-31 CN CN201610204540.0A patent/CN105847684A/en active Pending
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102142147A (en) * | 2010-01-29 | 2011-08-03 | 索尼公司 | Device and method for analyzing site content as well as device and method for detecting and tracking target |
| CN101887587A (en) * | 2010-07-07 | 2010-11-17 | 南京邮电大学 | Multi-target track method based on moving target detection in video monitoring |
| US20130136300A1 (en) * | 2011-11-29 | 2013-05-30 | Qualcomm Incorporated | Tracking Three-Dimensional Objects |
| CN102779347A (en) * | 2012-06-14 | 2012-11-14 | 清华大学 | Method and device for tracking and locating target for aircraft |
| CN104243901A (en) * | 2013-06-21 | 2014-12-24 | 中兴通讯股份有限公司 | Multi-target tracking method based on intelligent video analysis platform and system of multi-target tracking method |
| US20150244976A1 (en) * | 2014-02-26 | 2015-08-27 | Microsoft Corporation | Telepresence experience |
| CN103926933A (en) * | 2014-03-29 | 2014-07-16 | 北京航空航天大学 | Indoor simultaneous locating and environment modeling method for unmanned aerial vehicle |
| CN104236548A (en) * | 2014-09-12 | 2014-12-24 | 清华大学 | Indoor autonomous navigation method for micro unmanned aerial vehicle |
| CN104808799A (en) * | 2015-05-20 | 2015-07-29 | 成都通甲优博科技有限责任公司 | Unmanned aerial vehicle capable of indentifying gesture and identifying method thereof |
| CN205453893U (en) * | 2016-03-31 | 2016-08-10 | 深圳奥比中光科技有限公司 | Unmanned aerial vehicle |
Non-Patent Citations (2)
| Title |
|---|
| 刘焜，蔡江辉，刘小君等: "《变形曲线曲面主动轮廓模型方法》", 30 September 2012 * |
| 朱德海: "《点云库PCL学习教程》", 31 October 2012 * |
Cited By (55)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106020227A (en) * | 2016-08-12 | 2016-10-12 | 北京奇虎科技有限公司 | Control method and device for unmanned aerial vehicle |
| CN106020227B (en) * | 2016-08-12 | 2019-02-26 | 北京奇虎科技有限公司 | The control method of unmanned plane, device |
| CN106347550A (en) * | 2016-09-05 | 2017-01-25 | 北京小米移动软件有限公司 | Method and device for controlling balance car |
| CN106347550B (en) * | 2016-09-05 | 2019-08-06 | 北京小米移动软件有限公司 | Balance car control method and device |
| WO2018058309A1 (en) * | 2016-09-27 | 2018-04-05 | 深圳市大疆创新科技有限公司 | Control method, control device, electronic device, and aerial vehicle control system |
| CN108351651A (en) * | 2016-09-27 | 2018-07-31 | 深圳市大疆创新科技有限公司 | A kind of control method, device and aircraft based on image |
| WO2018058264A1 (en) * | 2016-09-27 | 2018-04-05 | 深圳市大疆创新科技有限公司 | Video-based control method, device, and flying apparatus |
| CN106778474A (en) * | 2016-11-14 | 2017-05-31 | 深圳奥比中光科技有限公司 | 3D human body recognition methods and equipment |
| CN110687902B (en) * | 2016-12-21 | 2020-10-20 | 杭州零零科技有限公司 | System and method for controller-free user drone interaction |
| CN110687902A (en) * | 2016-12-21 | 2020-01-14 | 杭州零零科技有限公司 | System and method for controller-free user drone interaction |
| CN106774947A (en) * | 2017-02-08 | 2017-05-31 | 亿航智能设备（广州）有限公司 | A kind of aircraft and its control method |
| CN110325879B (en) * | 2017-02-24 | 2024-01-02 | 亚德诺半导体国际无限责任公司 | System and method for compressed three-dimensional depth sensing |
| CN110325879A (en) * | 2017-02-24 | 2019-10-11 | 亚德诺半导体无限责任公司 | System and method for compress three-dimensional depth sense |
| CN106843275A (en) * | 2017-04-01 | 2017-06-13 | 成都通甲优博科技有限责任公司 | A kind of unmanned plane pinpoints method, device and the system of being diversion |
| WO2018191840A1 (en) * | 2017-04-17 | 2018-10-25 | 英华达（上海）科技有限公司 | Interactive photographing system and method for unmanned aerial vehicle |
| CN109121434B (en) * | 2017-04-17 | 2021-07-27 | 英华达（上海）科技有限公司 | Unmanned aerial vehicle interactive shooting system and method |
| CN109121434A (en) * | 2017-04-17 | 2019-01-01 | 英华达（上海）科技有限公司 | Unmanned plane interaction camera system and method |
| CN108475072A (en) * | 2017-04-28 | 2018-08-31 | 深圳市大疆创新科技有限公司 | A kind of tracking and controlling method, device and aircraft |
| US11587355B2 (en) * | 2017-04-28 | 2023-02-21 | SZ DJI Technology Co., Ltd. | Tracking control method, device, and aircraft |
| WO2018195979A1 (en) * | 2017-04-28 | 2018-11-01 | 深圳市大疆创新科技有限公司 | Tracking control method and apparatus, and flight vehicle |
| CN106991413A (en) * | 2017-05-04 | 2017-07-28 | 上海耐相智能科技有限公司 | A kind of unmanned plane |
| CN107330371A (en) * | 2017-06-02 | 2017-11-07 | 深圳奥比中光科技有限公司 | Acquisition methods, device and the storage device of the countenance of 3D facial models |
| CN107374638A (en) * | 2017-07-07 | 2017-11-24 | 华南理工大学 | A kind of height measuring system and method based on binocular vision module |
| CN107505951A (en) * | 2017-08-29 | 2017-12-22 | 深圳市道通智能航空技术有限公司 | A kind of method for tracking target, unmanned plane and computer-readable recording medium |
| WO2019041534A1 (en) * | 2017-08-29 | 2019-03-07 | 深圳市道通智能航空技术有限公司 | Target tracking method, unmanned aerial vehicle and computer-readable storage medium |
| CN107505951B (en) * | 2017-08-29 | 2020-08-21 | 深圳市道通智能航空技术有限公司 | Target tracking method, unmanned aerial vehicle and computer readable storage medium |
| US10719087B2 (en) | 2017-08-29 | 2020-07-21 | Autel Robotics Co., Ltd. | Target tracking method, unmanned aerial vehicle, and computer readable storage medium |
| CN108513643A (en) * | 2017-08-31 | 2018-09-07 | 深圳市大疆创新科技有限公司 | A kind of paths planning method, aircraft, flight system |
| CN107844744A (en) * | 2017-10-09 | 2018-03-27 | 平安科技（深圳）有限公司 | With reference to the face identification method, device and storage medium of depth information |
| CN108153325A (en) * | 2017-11-13 | 2018-06-12 | 上海顺砾智能科技有限公司 | The control method and device of Intelligent unattended machine |
| CN107894836B (en) * | 2017-11-22 | 2020-10-09 | 河南大学 | Human-computer interaction method for processing and displaying remote sensing image based on gesture and voice recognition |
| CN107894836A (en) * | 2017-11-22 | 2018-04-10 | 河南大学 | Remote sensing image processing and the man-machine interaction method of displaying based on gesture and speech recognition |
| CN109859264A (en) * | 2017-11-30 | 2019-06-07 | 北京机电工程研究所 | A kind of aircraft of view-based access control model guiding catches control tracking system |
| CN108196534A (en) * | 2017-12-26 | 2018-06-22 | 广东工业大学 | A kind of multi-rotor unmanned aerial vehicle control terminal, flight control system and control method |
| CN111527463A (en) * | 2018-01-22 | 2020-08-11 | 深圳市大疆创新科技有限公司 | Method and system for multi-target tracking |
| CN111527463B (en) * | 2018-01-22 | 2024-02-23 | 深圳市大疆创新科技有限公司 | Method and system for multi-target tracking |
| US11704812B2 (en) | 2018-01-22 | 2023-07-18 | SZ DJI Technology Co., Ltd. | Methods and system for multi-target tracking |
| WO2019140699A1 (en) * | 2018-01-22 | 2019-07-25 | SZ DJI Technology Co., Ltd. | Methods and system for multi-target tracking |
| CN110312978A (en) * | 2018-01-23 | 2019-10-08 | 深圳市大疆创新科技有限公司 | Flight control method, device and machine readable storage medium |
| WO2019144291A1 (en) * | 2018-01-23 | 2019-08-01 | 深圳市大疆创新科技有限公司 | Flight control method, apparatus, and machine-readable storage medium |
| CN110312978B (en) * | 2018-01-23 | 2022-06-24 | 深圳市大疆创新科技有限公司 | Flight control method, flight control device and machine-readable storage medium |
| CN109661631A (en) * | 2018-03-27 | 2019-04-19 | 深圳市大疆创新科技有限公司 | Control method, device and the unmanned plane of unmanned plane |
| CN108375986A (en) * | 2018-03-30 | 2018-08-07 | 深圳市道通智能航空技术有限公司 | Control method, device and the terminal of unmanned plane |
| CN108854031A (en) * | 2018-05-29 | 2018-11-23 | 深圳臻迪信息技术有限公司 | The method and relevant apparatus of exercise data are analyzed by unmanned camera work |
| CN109151435A (en) * | 2018-09-30 | 2019-01-04 | Oppo广东移动通信有限公司 | A kind of data processing method, terminal, server and computer storage medium |
| CN109709554B (en) * | 2018-12-13 | 2021-01-19 | 广州极飞科技有限公司 | Work device, and control method and device thereof |
| CN109709554A (en) * | 2018-12-13 | 2019-05-03 | 广州极飞科技有限公司 | Operating equipment and its control method and device |
| CN111247792A (en) * | 2019-04-28 | 2020-06-05 | 深圳市大疆创新科技有限公司 | Control method of unmanned aerial vehicle, unmanned aerial vehicle and computer readable storage medium |
| CN111199576A (en) * | 2019-12-25 | 2020-05-26 | 中国人民解放军军事科学院国防科技创新研究院 | Outdoor large-range human body posture reconstruction method based on mobile platform |
| CN111199576B (en) * | 2019-12-25 | 2023-08-18 | 中国人民解放军军事科学院国防科技创新研究院 | Outdoor large-range human body posture reconstruction method based on mobile platform |
| CN111275760A (en) * | 2020-01-16 | 2020-06-12 | 上海工程技术大学 | Unmanned aerial vehicle target tracking system and method based on 5G and depth image information |
| CN112753210A (en) * | 2020-04-26 | 2021-05-04 | 深圳市大疆创新科技有限公司 | Movable platform, control method thereof and storage medium |
| CN111490491A (en) * | 2020-04-30 | 2020-08-04 | 国网上海市电力公司 | Ultra-high voltage transmission line inspection unmanned aerial vehicle based on deep learning |
| CN113129468A (en) * | 2021-04-06 | 2021-07-16 | 深圳市艾赛克科技有限公司 | Underground pipe gallery inspection method based on unmanned aerial vehicle |
| CN113128447A (en) * | 2021-04-29 | 2021-07-16 | 深圳市道通智能航空技术股份有限公司 | Mask identification method and device, unmanned aerial vehicle and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105847684A (en) | Unmanned aerial vehicle | |
| CN205453893U (en) | Unmanned aerial vehicle | |
| CN105786016B (en) | The processing method of unmanned plane and RGBD image | |
| CN105892474A (en) | Unmanned plane and control method of unmanned plane | |
| CN105912980B (en) | Unmanned plane and UAV system | |
| US11861892B2 (en) | Object tracking by an unmanned aerial vehicle using visual sensors | |
| US11749124B2 (en) | User interaction with an autonomous unmanned aerial vehicle | |
| US11755041B2 (en) | Objective-based control of an autonomous unmanned aerial vehicle | |
| CN205693767U (en) | Uas | |
| Hu et al. | Bio-inspired embedded vision system for autonomous micro-robots: The LGMD case | |
| CN105930767B (en) | A kind of action identification method based on human skeleton | |
| WO2019006760A1 (en) | Gesture recognition method and device, and movable platform | |
| CN108303994B (en) | Group control interaction method for unmanned aerial vehicle | |
| CN109398688A (en) | A kind of rotor flying double mechanical arms target positioning grasping system and method | |
| CN105717933A (en) | Unmanned aerial vehicle and unmanned aerial vehicle anti-collision method | |
| CN110473232A (en) | Image-recognizing method, device, storage medium and electronic equipment | |
| CN109063532B (en) | Unmanned aerial vehicle-based method for searching field offline personnel | |
| KR102560798B1 (en) | unmanned vehicle simulator | |
| CN113228103A (en) | Target tracking method, device, unmanned aerial vehicle, system and readable storage medium | |
| CN108475442A (en) | Augmented reality method, processor and unmanned plane for unmanned plane | |
| Fischer et al. | Markerless perspective taking for humanoid robots in unconstrained environments | |
| Shen et al. | Person tracking and frontal face capture with UAV | |
| CN111966217A (en) | Unmanned aerial vehicle control method and system based on gestures and eye movements | |
| CN105159452A (en) | Control method and system based on estimation of human face posture | |
| CN105930766A (en) | Unmanned plane |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20160810 |
|
| RJ01 | Rejection of invention patent application after publication |