CN109214463A - A kind of classification of landform method based on coorinated training - Google Patents

A kind of classification of landform method based on coorinated training Download PDF

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CN109214463A
CN109214463A CN201811119967.6A CN201811119967A CN109214463A CN 109214463 A CN109214463 A CN 109214463A CN 201811119967 A CN201811119967 A CN 201811119967A CN 109214463 A CN109214463 A CN 109214463A
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haptic signal
landform
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刘阳
刘珂
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Anhui fruit Intelligent Technology Co., Ltd.
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Hefei Best Control Technology Co Ltd
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    • G06—COMPUTING; CALCULATING OR COUNTING
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    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G06F18/2411—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on the proximity to a decision surface, e.g. support vector machines
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/10—Terrestrial scenes
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/50—Context or environment of the image
    • G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle

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Abstract

The classification of landform method based on coorinated training that the invention discloses a kind of, strategy based on coorinated training, first with the haptic data and vision data for having mark, two initial supporting vector machine models are respectively trained, then the haptic data not marked and vision data are inputted into two preliminary classification devices respectively, and corresponding vision sample is marked with the high output prediction result of the confidence level of tactile classifier, the high output prediction result of the confidence level of vision sorter device is marked into corresponding tactile sample, and the sample newly marked is added to corresponding training set, it is then based on two classifiers of training set re -training of expansion, such operation is repeated until reaching preset repetitive exercise frequency threshold value.The vision sorter device that final training obtains can be used to the identification for carrying out terrain type to the ground that will be travelling through in robot practical work process.

Description

A kind of classification of landform method based on coorinated training
Technical field
The present invention relates to robot fields, more particularly to a kind of classification of landform method based on coorinated training.
Background technique
Different from doors structure environment when working in robot outer scene out of office, landform-ground in the scene of field is more Sample, complexity can produce bigger effect the travelling performance of robot.Accurate classification of the robot to terrain environment locating for it It is to determine that can it realize the key factor of autonomous.Tactile and vision are the mankind in the judgement for carrying out locating terrain environment When common perceptive mode, therefore we attempt to divide robot also using similar perceptual model to locating terrain environment Class.Wherein tactile can only realize the perception for now locating landform to robot, and larger range of landform perception may be implemented in vision, right The landform that will be travelling through can equally be perceived.In the present invention, we by using the haptic data that has mark on a small quantity and Vision data makes full use of the data not marked largely, the mode of two classifier coorinated training is taken to carry out the instruction of classifier Practice, to reduce dependence of the classifier training process to labeled data, and realizes the ground that will be travelling through ground to robot simultaneously The judgement of shape classification.
Summary of the invention
The technology of the present invention overcome the deficiencies in the prior art solves and marks missing in view-based access control model and the classification of landform of vibration The training problem of situation.
To solve the above problems, the invention discloses a kind of classification of landform method based on coorinated training, specifically include with Lower step:
Step S1: enabling robot, traveling for a period of time, is collected simultaneously machine respectively in every kind of landform in its working environment The ground of the camera record of the haptic signal time series and face forward ground of the touch sensor output of Qi Ren foot installation Face image sequence is split the haptic signal time series acquired in every kind of landform respectively, and segmentation length is α sampling Point obtains the set of the corresponding haptic signal tract of every kind of landform；Feature extraction is carried out to each haptic signal tract, is obtained To the corresponding haptic signal sample set of every kind of landform, the set of the haptic signal sample set of all landform is denoted asFeature extraction is carried out to the image in the corresponding ground image sequence of every kind of landform, obtains every kind of ground The corresponding characteristics of image sample set of shape, the set of the characteristics of image sample set of all landform are denoted asIts Middle aιAnd bιIndicate the haptic signal sample that touch sensor of the robot on same place obtains and the image that video camera obtains Feature samples, Indicate that haptic signal sample set A and characteristics of image sample set B includes the quantity of sample； With landform number 1,2, J is labeled the sample in these sample sets, and wherein J indicates the sum of terrain type, obtains To there is mark sample set { A, B, Ψ },For terrain type set corresponding with A and B, wherein ψι∈ { 1,2, J } is indicated and aιAnd bιCorresponding terrain type；
Step S2: enabling robot random walk in its working environment, walked in the haptic signal time sequence collected Column and ground image sequence, are split haptic signal time series, and segmentation length is α sampled point, obtain in walking The set of the haptic signal tract of collection carries out feature extraction to haptic signal tract, obtains haptic signal sample set, remembers ForFeature extraction is carried out to the image in the ground image sequence collected in walking, obtains image spy Sample set is levied, is denoted asWherein c κ and dκIndicate that touch sensor of the robot on same place obtains The characteristics of image sample that the haptic signal sample and video camera taken obtains, Indicate haptic signal sample Collect the quantity that C and characteristics of image sample set D includes sample；Thus it obtains without mark sample set { C, D }；
Step S3: enabling repetitive exercise serial number e=0, remembers the training set of the classifier based on haptic signal and is based on surface map The training set of the classifier of picture is respectively L(1)、L(2), and enable L(1)={ A, Ψ }, L(2)={ B, Ψ }；
Step S4: it is based on training set L(1)And L(2)Classifier based on haptic signal is respectively trained and based on ground image Classifier, sorter model use support vector machines, remember that trained classifier isWith
Step S5: n is randomly selected from haptic signal sample set CcA sample is input to classifierIn, it respectively obtains ncThe set of the landform prediction result of a sampleAnd the confidence level of landform prediction resultThe wherein highest n of confidence levelc′A landform prediction result is used to mark corresponding characteristics of image Sample forms markd characteristics of image sample set V(2)；N is randomly selected from characteristics of image sample set DdA sample is input to ClassifierIn, respectively obtain ndThe set of the landform prediction result of a sampleAnd landform The confidence level of prediction resultThe wherein highest n of confidence leveld′A landform prediction result is used to mark Corresponding haptic signal sample forms markd haptic signal sample set V(1)；By V(1)And V(2)It is added separately toWithTraining set in, i.e. L(1)←L(1)∪V(1), L(2)←L(2)∪V(2)；It will set V(1)Corresponding nc′A haptic signal sample This is deleted from C, by set V(2)Corresponding nd′A characteristics of image sample is deleted from D, i.e.,；WhereinIndicate set V(1)Corresponding nc′A haptic signal sample composition Set,Indicate set V(2)Corresponding nd′The set of a characteristics of image sample composition；
Step S6: enabling e ← e+1, if e is less than preset repetitive exercise frequency threshold value T, repeatedly step 4 and 5；It is no Then, classifier training terminates, and obtains final classifier C(1)And C(2)；When robot in actual work, by the surface map of acquisition After the operation Jing Guo feature extraction, it is input to classifier C(2)In, obtained output is robot to the ground that will be passed through The prediction result of shape type.
Compared with existing technology, the invention has the following advantages that
Category forecasting is carried out to the landform that will be travelling through 1. can realize, so that robot can change traveling strategy in advance, Reduce the adaptation time to topographic change；
2. classifier is respectively trained using two kinds of perceptive modes, and the mode of coorinated training is carried out, makes full use of and do not mark The accuracy rate of data raising classifier；
3. since training process is smaller to the dependence for having labeled data, the time required to labeled data can be greatlyd save.
Detailed description of the invention
Fig. 1 is flow chart of the present invention.
Specific embodiment
In order to make the objectives, technical solutions, and advantages of the present invention clearer, below in conjunction with attached drawing and specific implementation The present invention is described in detail for example.
A kind of classification of landform method based on coorinated training, as shown in Figure 1, specifically includes the following steps:
Step S1: enabling robot, traveling for a period of time, is collected simultaneously machine respectively in every kind of landform in its working environment The ground of the camera record of the haptic signal time series and face forward ground of the touch sensor output of Qi Ren foot installation Face image sequence is split the haptic signal time series acquired in every kind of landform respectively, and segmentation length is α sampling Point obtains the set of the corresponding haptic signal tract of every kind of landform；Feature extraction is carried out to each haptic signal tract, is obtained To the corresponding haptic signal sample set of every kind of landform, the set of the haptic signal sample set of all landform is denoted asFeature extraction is carried out to the image in the corresponding ground image sequence of every kind of landform, obtains every kind of ground The corresponding characteristics of image sample set of shape, the set of the characteristics of image sample set of all landform are denoted asIts Middle aιAnd bιIndicate the haptic signal sample that touch sensor of the robot on same place obtains and the image that video camera obtains Feature samples, Indicate that haptic signal sample set A and characteristics of image sample set B includes the quantity of sample； With landform number 1,2, J is labeled the sample in these sample sets, and wherein J indicates the sum of terrain type, obtains To there is mark sample set { A, B, Ψ },For terrain type set corresponding with A and B, wherein ψι∈ { 1,2, J } indicate and aιAnd bιCorresponding terrain type；
Step S2: enabling robot random walk in its working environment, walked in the haptic signal time sequence collected Column and ground image sequence, are split haptic signal time series, and segmentation length is α sampled point, obtain in walking The set of the haptic signal tract of collection carries out feature extraction to haptic signal tract, obtains haptic signal sample set, remembers ForFeature extraction is carried out to the image in the ground image sequence collected in walking, obtains image spy Sample set is levied, is denoted asWherein cκAnd dκIndicate that touch sensor of the robot on same place obtains The characteristics of image sample that the haptic signal sample and video camera taken obtains, Indicate haptic signal sample Collect the quantity that C and characteristics of image sample set D includes sample；Thus it obtains without mark sample set { C, D }；
Step S3: enabling repetitive exercise serial number e=0, remembers the training set of the classifier based on haptic signal and is based on surface map The training set of the classifier of picture is respectively L(1)、L(2), and enable L(1)={ A, Ψ }, L(2)={ B, Ψ }；
Step S4: it is based on training set L(1)And L(2)Classifier based on haptic signal is respectively trained and based on ground image Classifier, sorter model use support vector machines, remember that trained classifier isWith
Step S5: n is randomly selected from haptic signal sample set CcA sample is input to classifierIn, it respectively obtains ncThe set of the landform prediction result of a sampleAnd the confidence level of landform prediction resultThe wherein highest n of confidence levelc′A landform prediction result is used to mark corresponding characteristics of image Sample forms markd characteristics of image sample set V(2)；N is randomly selected from characteristics of image sample set DdA sample is input to ClassifierIn, respectively obtain ndThe set of the landform prediction result of a sampleAnd landform The confidence level of prediction resultThe wherein highest n of confidence leveld′A landform prediction result is used to mark Corresponding haptic signal sample forms markd haptic signal sample set V(1)；By V(1)And V(2)It is added separately toWithTraining set in, i.e. L(1)←L(1)∪V(1), L(2)←L(2)∪V(2)；It will set V(1)Corresponding nc′A haptic signal sample This is deleted from C, by set V(2)Corresponding nd′A characteristics of image sample is deleted from D, i.e.,；WhereinIndicate set V(1)Corresponding nc′A haptic signal sample composition Set,Indicate set V(2)Corresponding nd′The set of a characteristics of image sample composition；
Step S6: enabling e ← e+1, if e is less than preset repetitive exercise frequency threshold value T, repeatedly step 4 and 5；It is no Then, classifier training terminates, and obtains final classifier C(1)And C(2)；When robot in actual work, by the surface map of acquisition After the operation Jing Guo feature extraction, it is input to classifier C(2)In, obtained output is robot to the ground that will be passed through The prediction result of shape type.
In implementing process of the present invention, the feature extraction side of a variety of haptic signal tracts and ground image can be used Method.For example, for haptic signal tract Fast Fourier Transform (FFT) can be carried out to it, point of its amplitude in frequency is obtained These amplitudes are arranged in vector according to the sequence of frequency from small to large by cloth, that is, the feature for realizing haptic signal tract mentions It takes.For ground image, its color histogram can be extracted, the frequency of different colours is arranged in vector, that is, realizes ground The feature extraction of image.
Above embodiments are provided just for the sake of the description purpose of the present invention, and are not intended to limit the scope of the invention.This The range of invention is defined by the following claims.It does not depart from spirit and principles of the present invention and the various equivalent replacements made and repairs Change, should all cover within the scope of the present invention.

Claims (1)

1. a kind of classification of landform method based on coorinated training, which is characterized in that specifically includes the following steps: step S1: enabling machine Traveling a period of time, the tactile for being collected simultaneously the installation of foot of robot pass device people respectively in every kind of landform in its working environment The ground image sequence of the camera record of the haptic signal time series and face forward ground of sensor output, to every kind of landform The haptic signal time series of upper acquisition is split respectively, and segmentation length is α sampled point, obtains the corresponding touching of every kind of landform Feel the set of signal sequence section；Feature extraction is carried out to each haptic signal tract, obtains the corresponding tactile letter of every kind of landform Number sample set, the set of the haptic signal sample set of all landform are denoted asIt is corresponding to every kind of landform Image in ground image sequence carries out feature extraction, obtains the corresponding characteristics of image sample set of every kind of landform, all landform The set of characteristics of image sample set is denoted asWherein aιAnd bιIndicate touching of the robot on same place Feel the haptic signal sample that sensor obtains and the characteristics of image sample that video camera obtains, Indicate tactile Sample of signal collection A and characteristics of image sample set B includes the quantity of sample；With landform number 1,2 ..., J is in these sample sets Sample be labeled, wherein J indicate terrain type sum, obtained mark sample set { A, B, Ψ },For terrain type set corresponding with A and B, wherein ψι∈ { 1,2 ..., J } is indicated and aιAnd bιIt is right The terrain type answered；
Step S2: enabling robot random walk in its working environment, walked in the haptic signal time series collected with And ground image sequence, haptic signal time series is split, segmentation length be α sampled point, walked in collect Haptic signal tract set, to haptic signal tract carry out feature extraction, obtain haptic signal sample set, be denoted asFeature extraction is carried out to the image in the ground image sequence collected in walking, obtains characteristics of image Sample set is denoted asWherein cκAnd dκIndicate that touch sensor of the robot on same place obtains Haptic signal sample and video camera obtain characteristics of image sample, Indicate haptic signal sample set C It include the quantity of sample with characteristics of image sample set D；Thus it obtains without mark sample set { C, D }；
Step S3: enabling repetitive exercise serial number e=0, remembers the training set of the classifier based on haptic signal and based on ground image The training set of classifier is respectively L(1)、L(2), and enable L(1)={ A, Ψ }, L(2)={ B, Ψ }；
Step S4: it is based on training set L(1)And L(2)The classifier based on haptic signal and the classification based on ground image is respectively trained Device, sorter model use support vector machines, remember that trained classifier isWith
Step S5: n is randomly selected from haptic signal sample set CcA sample is input to classifierIn, respectively obtain ncA sample The set of this landform prediction resultAnd the confidence level of landform prediction resultThe wherein highest n of confidence levelc′A landform prediction result is used to mark corresponding characteristics of image Sample forms markd characteristics of image sample set V(2)；N is randomly selected from characteristics of image sample set DdA sample is input to ClassifierIn, respectively obtain ndThe set of the landform prediction result of a sampleAnd landform The confidence level of prediction resultThe wherein highest n of confidence leveld′A landform prediction result is used to mark Corresponding haptic signal sample forms markd haptic signal sample set V(1)；By V(1)And V(2)It is added separately toWithTraining set in, i.e. L(1)←L(1)∪V(1), L(2)←L(2)∪V(2)；It will set V(1)Corresponding nc′A haptic signal sample This is deleted from C, by set V(2)Corresponding nd′A characteristics of image sample is deleted from D, i.e.,；WhereinIndicate set V(1)Corresponding nc′A haptic signal sample composition Set,Indicate set V(2)Corresponding nd′The set of a characteristics of image sample composition；
Step S6: enabling e ← e+1, if e is less than preset repetitive exercise frequency threshold value T, repeatedly step 4 and 5；Otherwise, divide The training of class device terminates, and obtains final classifier C(1)And C(2)；When robot in actual work, the ground image of acquisition is passed through After the operation of feature extraction, it is input to classifier C(2)In, obtained output is robot to the terrain type that will be passed through Prediction result.
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