CN106780605A - A kind of detection method of the object crawl position based on deep learning robot - Google Patents
A kind of detection method of the object crawl position based on deep learning robot Download PDFInfo
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- CN106780605A CN106780605A CN201611181461.9A CN201611181461A CN106780605A CN 106780605 A CN106780605 A CN 106780605A CN 201611181461 A CN201611181461 A CN 201611181461A CN 106780605 A CN106780605 A CN 106780605A
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
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Abstract
The present invention is applied to robot and captures field, there is provided a kind of detection method of the object crawl position based on deep learning robot, the method comprises the following steps：RGB D images comprising object are gathered by sensor；Candidate capture area is divided in the target area of RGB D images；Keep the length-width ratio of candidate capture area constant, the size of candidate capture area is amplified to the size of neutral net input requirements；Input vector is built to the candidate capture area after amplification；Whitening processing is carried out to input vector, the input vector after the whitening processing is input to the neutral net for training；The score of each candidate capture area is obtained, the candidate capture area of highest scoring is defined as crawl position.The RGB D images of the object by obtaining, you can determine the crawl position of the object, robot by the crawl position can realize it is any realize the crawl of object, and do not need artificial participation.
Description
Technical field
The invention belongs to robot crawl field, more particularly to a kind of object crawl position based on deep learning robot
The detection method put.
Background technology
In order to nurse the handicapped personage such as the elderly, disabled person, to familiar object in home environment, such as teacup beverage
The crawl of bottle, books etc., as the indispensable critical function demand of home-services robot.Different from industrial robot in knot
To the crawl of workpiece under structure environment, intelligent grabbing of the service robot under home environment is faced with lot of challenges, for example, move
State environment, illumination variation, tens or even hundreds of target object, mutually blocking between complex background, object.
At present, robot crawl detection technique includes following several：The crawl feature of engineer's object, by target
Crawl feature sets up crawl model, detects crawl position, and the method for the crawl feature of existing engineer's object both took
Substantial amounts of artificial participation is needed again, and cannot accurately detect crawl position for the unseen object of robot, it is impossible to
Perform grasping movement.
The content of the invention
The embodiment of the present invention provides a kind of detection method of the object crawl position based on deep learning robot, it is intended to
The method for solving the crawl feature of existing engineer's object, not only took but also needed substantial amounts of artificial participation, and for
The unseen object of robot cannot accurately detect crawl position, it is impossible to perform grasping movement problem.
The present invention is achieved in that a kind of detection method of the object crawl position based on deep learning robot,
Methods described comprises the following steps：
S1. the RGB-D images comprising object are gathered by sensor；
S2. candidate capture area is divided in the target area of RGB-D images；
S3. keep the length-width ratio of the candidate capture area constant, the size of the candidate capture area is amplified to god
Through the size of network inputs requirement；
S4. input vector is built to the candidate capture area after the amplification；
S5. whitening processing is carried out to the input vector, the input vector after the whitening processing is input to and is trained
Neutral net；
S6. the score of each candidate capture area is obtained, the candidate capture area of the highest scoring is defined as crawl
Position.
The embodiment of the present invention divides candidate and grabs by obtaining the RGB-D images of object, to the target area of RGB-D images
Take region, and be amplified to the size of neutral net input requirements, input vector is built to the candidate capture area after amplification, by structure
The input vector input neutral net built up, obtains the score of a candidate capture area, and the candidate capture area of highest scoring is true
It is set to the crawl position of object, the RGB-D images of the object by obtaining, you can determine the crawl position of the object,
Robot by the crawl position can realize it is any realize the crawl of object, and do not need artificial participation.
Brief description of the drawings
Fig. 1 is the stream of the object crawl position detection method based on deep learning robot provided in an embodiment of the present invention
Cheng Tu.
Specific embodiment
In order to make the purpose , technical scheme and advantage of the present invention be clearer, it is right below in conjunction with drawings and Examples
The present invention is further elaborated.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, and
It is not used in the restriction present invention.
Fig. 1 is the stream of the object crawl position detection method based on deep learning robot provided in an embodiment of the present invention
Cheng Tu, the method comprises the following steps：
S1. the RGB-D images comprising object are gathered by sensor；
The embodiment of the present invention obtains the high-resolution RGB image and depth of crawl object using Microsoft's Kinect sensor
Degree image, RGB image contains crawl target surface colouring information and texture information, and depth image contains crawl object
Spatial form information, each pixel value in depth image illustrate sensor to crawl object distance, RGB image and
Pixel between depth image is one-to-one, constitutes RGB-D images.
S2. candidate capture area is divided in the target area of RGB-D images；
In embodiments of the present invention, the target area of RGB-D images is extracted using background subtraction, is set in target area
Determine sliding window, candidate capture area is extracted by the movement of sliding window, the size of the active window is candidate's crawl
The size in region, the embodiment of the present invention uses baxter tow-armed robots, and the end effector of robot is clamping jaw, sliding window
It is rectangular slide window, the size of sliding window is the size according to gripper to be determined, active window is set to 30 pixel × 10
The rectangular active window of pixel, therefore the size of candidate capture area is candidate's crawl rectangle of 30 pixel × 10 pixels.
S3. keep the length-width ratio of candidate capture area constant, the size of candidate capture area is amplified to neutral net defeated
Enter the size of requirement；
In embodiments of the present invention, the requirement according to neutral net to input sample size, is keeping candidate capture area
Length-width ratio it is constant in the case of, filled by 0 value, or the size of candidate capture area is amplified to neutral net by border extended
The size of input requirements, in embodiments of the present invention, requirement of the neutral net to input sample size is 32 pixel × 32 pixels.
S4. input vector is built to the candidate capture area after amplification；
In embodiments of the present invention, to candidate capture area build 7 input vectors of passage, 7 inputs of passage to
Amount includes：Surface normal of the depth data on three directions of x, y, z axle, yuv data is obtained from depth image to be converted into
On tri- passages of Y, U, V vector and depth image is converted into vector.
In embodiments of the present invention, if step S3 is to fill for the size of candidate capture area to be amplified to nerve by 0 value
The size of network inputs requirement, due to having substantial amounts of 0 value, and different candidate capture areas in the input vector after filling
Input vector in fill 0 be worth quantity different, can finally influence the output score of neutral net, therefore, filled out to eliminate
The influence of 0 value filled to waiting the element in input vector, it is necessary to be multiplied by a zoom factor, the value to element in input vector is entered
Row scaling, the computing formula of zoom factor is as follows：
Wherein,It is i-th zoom factor of element in t-th input vector of sample,For passage r amplification because
Son, when i-th element x in input vectoriWhen belonging to passage r, Sr,iValue be 1, otherwise Sr,iValue be 0, work as xthiIt is not 0
During Filling power,Value be 1, otherwiseValue be 0.
Additionally, as the preferred embodiments of the present invention, it is contemplated that zoom factor crosses conference causes the distortion of input data, contracting
Put the factor and be up to certain value C, i.e.,The value of C is 4
S5. whitening processing is carried out to the input vector for building, the input vector after whitening processing is input to what is trained
Neutral net；
In embodiments of the present invention, in order to reduce the redundancy of input, the input vector to building carries out whitening processing,
The process of whitening processing includes：The input vector for being input into each passage is subtracted into respective average value, then divided by by 7 passages
The standard deviation of the mix vector of input vector composition.
S6. the score of each candidate capture area is obtained, the candidate region of highest scoring is defined as crawl position.
The embodiment of the present invention divides candidate and grabs by obtaining the RGB-D images of object, to the target area of RGB-D images
Take region, and be amplified to the size of neutral net input requirements, input vector is built to the candidate capture area after amplification, by structure
The input vector input neutral net built up, obtains the score of a candidate capture area, and the candidate capture area of highest scoring is true
It is set to the crawl position of object, the RGB-D images of the object by obtaining, you can determine the crawl position of the object,
Robot by the crawl position can realize it is any realize the crawl of object, and do not need artificial participation.
In embodiments of the present invention, also included before the step S1：
S7. neutral net is built；
In embodiments of the present invention, the neutral net of structure is sparse certainly by 7168 neuron input layers, 200 neurons
Encoder and sigmoid output layers are constituted.
S8. off-line training is carried out to the neutral net for building.
In embodiments of the present invention, by giving the input and output of sample, W when obtaining optimal by training, then use W
Calculate the prediction output of given input.
In embodiments of the present invention, off-line training is carried out to the neutral net for building and specifically includes following steps：
S81. the sample for giving is pre-processed using step S1-S5；
S82. the given sample input neutral net that will have been pre-processed, and the output result of sample is given, using unsupervised
Training iteration 200 times, trains 2 sparse self-encoding encoders of hidden layer to initialize hidden layer weights；
Initialization hidden layer weights W of the sparse self-encoding encoder when cost function is optimal*Formula is as follows：
Wherein,It is input vector x(t)Reconstruction, g (h) be openness penalty, λ is openness penalty
Coefficient, f (W) is Regularization function, and β is the coefficient of Regularization function,For t-th i-th of input sample input vector yuan
Element, Wi,jIt is weights of i-th element on j-th hidden neuron,It is that t-th input sample is vectorial in j-th hidden layer
Output on neuron, σ is sigmoid activation primitives, W*Sparse self-encoding encoder initializes hidden layer when being optimal cost function
Weights.
Sparse self-encoding encoder in the embodiment of the present invention includes the Sparse self-encoding encoder and the second layer of ground floor hidden layer
The standardized sparse self-encoding encoder of hidden layer；
When the sparse self-encoding encoder is the first hidden layer Sparse self-encoding encoder, the regularization combined using L2 and L1
Method, regular function isWherein | | W | |1It is the corresponding Regularization functions of L1,It is L2 pairs
The Regularization function answered, the regularization coefficient ε of L12=0.0003, L2 regularization coefficient ε1=0.001, wherein to f1(W) add
Slight bias amount 0.00001；0 worth interference in input vector is wherein avoided by adding the method for slight bias, this layer dilute
It is 3 to dredge property penalty coefficient, and the output of Sparse self-encoding encoder is the real number between 0 to 1；
When the sparse self-encoding encoder is the second hidden layer standardized sparse self-encoding encoder, using L1 regularization methods, canonical
Function is f2(W)=ε2||W||1, L1 regularization coefficients, ε2=0.0003, this layer of openness penalty coefficient is 3, and standardized sparse is certainly
The output of encoder is 0 or 1.
S83. the sample that will have been pre-processed passes through back-propagation algorithm Training iteration 10 times, to whole network parameter
Carry out global optimization.
Presently preferred embodiments of the present invention is the foregoing is only, is not intended to limit the invention, it is all in essence of the invention
Any modification, equivalent and improvement made within god and principle etc., should be included within the scope of the present invention.
Claims (9)
1. a kind of detection method of the object crawl position based on deep learning robot, it is characterised in that methods described bag
Include following steps：
S1. the RGB-D images comprising object are gathered by sensor；
S2. candidate capture area is divided in the target area of the RGB-D images；
S3. keep the length-width ratio of the candidate capture area constant, the size of the candidate capture area is amplified to nerve net
The size of network input requirements；
S4. input vector is built to the candidate capture area after the amplification；
S5. whitening processing is carried out to the input vector, the input vector after the whitening processing is input to the god for training
Through network；
S6. the score of each candidate capture area is obtained, the candidate capture area of the highest scoring is defined as crawl position.
2. the method for inspection of the object crawl position of deep learning robot is based on as claimed in claim 1, and its feature exists
In the candidate capture area is that the sliding window by setting moves to extract in the target area of the RGB-D images
's；
The size of the active window is the size of the candidate capture area.
3. the method for inspection of the object crawl position of deep learning robot is based on as claimed in claim 1, and its feature exists
In being filled by 0 value or the size of the candidate capture area be amplified to border extended the size of neutral net input requirements.
4. the detection method of the object crawl position of deep learning robot is based on as claimed in claim 3, and its feature exists
In when the size of the candidate capture area being amplified into the size of neutral net input requirements by 0 value filling, in step
Also include after rapid S4：
Element in the input vector is multiplied by a zoom factor, the value to element in the input vector is zoomed in and out；
The computing formula of the zoom factor is as follows：
Wherein,It is i-th zoom factor of element in t-th input vector of sample,It is the amplification factor of passage r,
When i-th element x in input vectoriWhen belonging to passage r, Sr,iValue be 1, otherwise Sr,iValue be 0, work as xthiIt is not 0 filling
During value,Value be 1, otherwiseValue be 0.
5. the detection method of the object crawl position of deep learning robot is based on as claimed in claim 4, and its feature exists
In the zoom factorThe value of C is 4.
6. the detection method of the object crawl position of deep learning robot is based on as claimed in claim 1, and its feature exists
In also including before step S1：
S7. neutral net is built；
S8. off-line training is carried out to the neutral net for building.
The neutral net is by 7168 neuron input layers, 200 sparse self-encoding encoders of neuron and sigmoid output layer groups
Into.
7. the detection method of the object crawl position of deep learning robot is based on as claimed in claim 6, and its feature exists
In the step S8 specifically includes following steps：
S81. the sample for giving is pre-processed using step S1-S5；
S82. the given sample that has pre-processed is input into neutral net, and given sample output result, using unsupervised
Training iteration 200 times, trains 2 sparse self-encoding encoders of hidden layer to initialize hidden layer weights；
S83. the sample for having pre-processed is passed through into back-propagation algorithm Training iteration 10 times, to whole network parameter
Carry out global optimization.
8. the detection method of the object crawl position of deep learning robot is based on as claimed in claim 7, and its feature exists
In initialization hidden layer weights W of the sparse self-encoding encoder when cost function is optimal*Computing formula is as follows：
Wherein,It is input vector x(t)Reconstruction, g (h) be openness penalty, λ is the coefficient of openness penalty, f
(W) it is Regularization function, β is the coefficient of Regularization function,It is t-th i-th element of input sample input vector, Wi,j
It is weights of i-th element on j-th hidden neuron,It is that t-th input sample is vectorial in j-th hidden neuron
On output, σ be sigmoid activation primitives.
9. the detection method of the object crawl position of deep learning robot is based on as claimed in claim 8, and its feature exists
In when the sparse self-encoding encoder is the first hidden layer Sparse self-encoding encoder, the Sparse self-encoding encoder uses L2
The regularization method combined with L1, regular function isWherein | | W | |1It is the corresponding canonicals of L1
Change function,It is the corresponding Regularization functions of L2, the regularization coefficient ε of L12=0.0003, L2 regularization coefficient ε1=
0.001, wherein to f1(W) addition slight bias amount 0.00001, openness the punishing of the first hidden layer Sparse self-encoding encoder
The coefficient lambda of penalty function1=3；
When the sparse self-encoding encoder is the second hidden layer standardized sparse self-encoding encoder, the standardized sparse self-encoding encoder uses L1
Regularization method, regular function is f2(W)=ε2||W||1, L1 regularization coefficients ε2=0.0003, the second hidden layer standard is dilute
Dredge the coefficient lambda of the openness penalty of self-encoding encoder2=3.
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Application publication date: 20170531 |