CN113689502A - Multi-information fusion obstacle measuring method - Google Patents
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Abstract
The invention relates to the technical field of measurement, and discloses a multi-information fusion obstacle measurement method, which comprises the following steps: s1, transforming the laser radar coordinate to the coordinate system of the camera through coordinate transformation, and then obtaining surrounding obstacle information through the depth camera and the laser radar sensor; s2, training the data of the measured obstacle information by using an RBF neural network; and S3, making a fusion rule, and estimating and updating the motion state of the unmanned vehicle by combining Bayes estimation updating. The invention utilizes the laser radar and the depth camera sensor, measures the distance of the obstacle through corresponding coordinate transformation, trains the data obtained by the sensor through selecting a proper transfer function and a learning algorithm and utilizing the RBF neural network, estimates and updates the motion state of the unmanned vehicle by formulating a corresponding fusion rule and combining Bayesian estimation and update, realizes the accurate measurement of the obstacle, and has the characteristics of high accuracy and high speed.
Description
Technical Field
The invention relates to the technical field of measurement, in particular to a multi-information fusion obstacle measurement method.
Background
In recent years, intelligent technologies such as unmanned vehicles and robots have been the focus of research. Road obstacle measurement is an important branch of the method, because it determines the decision and precision of tasks such as feasible region extraction, path planning, target identification and the like which are completed by intelligent bodies such as unmanned vehicles, robots and the like, and for small and medium-sized unmanned vehicles and robots, the tasks which greatly influence the small and medium-sized unmanned vehicles and robots are not pedestrians, road signs, lane lines and the like but small obstacles on a traveling road, such as stones, road surface garbage and the like. In order to ensure that an intelligent body can sense the environment quantitatively, measurement of parameters such as the maximum width of an obstacle, the distance between the intelligent body and the obstacle, the azimuth angle and the like is a basic and important task, and the application range and depth of small and medium-sized unmanned vehicles and robots are determined.
The lidar is a high-precision sensor commonly used in the related field, but the lidar is relatively expensive, has sparse time cloud and low vertical density in remote measurement, particularly has poor sensitivity to obstacles below 16 lines, is more prominent in measuring small objects, and is a difficulty in the application field of the existing lidar. The influence of external factors such as illumination, shadow and the like on the measurement precision is considered in the road obstacle measurement based on the visual information, an auxiliary algorithm is added for reducing the influence, the complexity of the algorithm is generally improved, and in addition, the precision of the measurement method based on the visual information is relatively low.
Disclosure of Invention
In order to solve the above mentioned drawbacks in the background art, the present invention provides a method for measuring an obstacle with multi-information fusion, which utilizes a laser radar and a depth camera sensor, and performs corresponding coordinate transformation to train data obtained by the sensor by using an RBF neural network, and combines the training result with bayesian estimation by using a fusion rule to obtain data of the obstacle.
The purpose of the invention can be realized by the following technical scheme:
a multi-information fusion obstacle measuring method comprises the following steps:
s1, transforming the laser radar coordinate to the coordinate system of the camera through coordinate transformation, and then obtaining surrounding obstacle information through the depth camera and the laser radar sensor;
s2, training the data of the measured obstacle information by using an RBF neural network;
and S3, making a fusion rule, and estimating and updating the motion state of the unmanned vehicle by combining Bayes estimation updating.
Further, in step S1, a spatial coordinate system is first established on the robot itself, and the data of the laser radar is projected under the pixel coordinate system through the transformation of the coordinate system;
p point coordinate (X) under camera coordinate systemc,Yc,Zc) And P point coordinates (X) in radar coordinate systemr,Yr) The relationship of (a) to (b) is as follows:
in the formulae (1) and (2), HcHeight of the center of the camera from the ground, HrThe height from the center of the laser radar sensor to the ground is taken as L, and the distance difference between the center of the camera and the center of the laser radar sensor in the transverse direction is taken as L;
obtaining the coordinates of the point P in the image coordinate system through the formulas (1) and (2):
the conversion between the image and the pixel coordinates can be realized by f being the focal length of the camera in the formula (3), and the position (u ', v') of the point P on the photo is obtained:
in the formula (4), u0,v0Respectively representing the coordinates of the origin of the coordinate system of the image. dx and dy represent the displacement of the pixel point in both the u and v planes.
Further, in step S2, the RBF neural network includes an input layer, a hidden layer and an output layer, the transformation from the output layer to the hidden layer is non-linear, the output layer trains and learns the data through the radial basis function, the transformation from the hidden layer to the output layer is linear transformation, and the output of the network is a linear weighted sum of the hidden unit outputs.
Further, the input layer comprises data of a laser radar and a depth camera, the radial base layer comprises the center of a basic function, the obtained result is sent to the linear layer through the radial basic function, and a predicted value of the data is obtained through linear transformation;
the RBF neural network uses a radial basis function method, and the activation function of the radial basis neural network can be expressed as:
in the formula (5), xpRepresenting an input vector, ciIs the center of the Gaussian function, and sigma is the variance of the Gaussian function;
xpthe structure of the radial basis function neural network can be derived as the network output:
in the formula (6), wijRepresenting hidden layers toA connection weight of the output layer and j ═ 1, 2.., n;
loss function representation using least squares:
in the formula (7), d is an expected value, and σ is a variance of a Gaussian function; j is 1, 2,. said, m; m is the number of input vectors;
further, the output layer is completed by two steps by using a learning method of self-organizing selecting centers, the first step is an unsupervised learning process, the variance of the basis function is calculated, the second step is a supervised learning process, the weight from the radial basic layer to the linear layer is calculated, and the specific algorithm is as follows:
the first step, h centers are selected to perform k-means clustering, and for the radial basis of the Gaussian kernel function, the variance is solved by a formula:
in the formula (8), cmaxThe maximum distance between the selected central points is taken;
secondly, calculating the weight from the radial base layer to the linear layer, and simplifying to obtain a formula as follows:
in formula (9), P is 1, 2., P; 1, 2,. h; p is the number of input vectors;
further, in step S3, the fusion model of the lidar detection area and the depth camera detection area is:
in the formula (10), a ZonelasterZone for lidar detected areascameraFor regions detected by the depth camera, glaster(i, j) and gcamera(i, j) represent the matrices of the two corresponding regions, q (g), respectivelylaster(i,j),gcamera(i, j)) represents a fusion rule;
and (3) updating the Bayesian estimation to obtain the posterior probability density of the system according to the following steps:
first using the prior probability density p (c) at time t-1t-1|z1:t-1) Calculating p (x)t|z1:t-1) Let us assume xtOnly sum of xt-1Related and p (x)t-1|z1:t-1) Knowing, we have:
p(xt|z1:t-1)=∫p(xct|xt-1)p(xt-1|z1:t-1)dxt-1 (11),
then the observed value z at time ttP (x)t|z1:t-1) Correcting to obtain the posterior probability density p (x) of the systemt|z1:t):
ztCan be obtained as independent values:
transforming the above formula by a joint distribution probability formula and a conditional probability formula:
and (3) setting each observation value to be independent to obtain the final posterior probability:
in formulae (11) to (15), p (z)t|xt) Likelihood probability, p (x), for system observation equationt|x1:t-1) As a prior probability, p (z)t|z1:t-1) Constant for final normalization of equation;
and fusing the Bayesian estimation and the previous region fusion rule to obtain fused environmental obstacle data.
The invention has the beneficial effects that:
the invention utilizes a laser radar and a depth camera sensor, measures the distance of the obstacle through corresponding coordinate transformation, trains the data obtained by the sensor by selecting a proper transfer function and a learning algorithm and utilizing a RBF neural network, and has the result of simulation verification that the distance measurement error of the result obtained by the method is lower than 0.1 percent and the speed is improved by 24 percent compared with the traditional bp algorithm. The motion state of the unmanned vehicle is estimated and updated by formulating a corresponding fusion rule and combining Bayesian estimation and updating, so that the obstacle can be accurately measured, the method has the characteristics of high accuracy and high speed, and the unmanned vehicle has great advantages in the aspect of detecting small obstacles.
Drawings
The invention will be further described with reference to the accompanying drawings.
FIG. 1 is a flow chart of obstacle information detection according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a coordinate system according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of an RBF neural network according to an embodiment of the present invention;
FIG. 4 is a diagram of data fusion rules according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The flow chart of the measuring method is shown in figure 1, firstly, the laser radar coordinate is transformed to the coordinate system of the camera through the transformation of the coordinate system, then, the measured data is trained by using an RBF neural network and matching with a certain learning algorithm, finally, the trained predicted value is obtained, then, the predicted value is put into a fusion rule, and the surrounding obstacle information can be obtained through the combination with Bayesian estimation.
1. And transforming the laser radar coordinate to the coordinate system of the camera through coordinate transformation, and acquiring the information of surrounding obstacles through the depth camera and the laser radar sensor.
O in FIG. 1rRepresented by the center of the lidar sensor, OCThe center of the camera is represented, and the two sensors are installed at fixed positions, have certain heights relative to the ground, and have a height difference HC-HrThe difference in distance in the transverse direction is L, since the lidar scanning plane is parallel to the cross section of the camera, i.e. XrOrYrAnd XcOcYCParallel. We can get the coordinates of the P point (X) in the camera coordinate systemC,YC,ZC) And P point coordinates (X) in a radar coordinate systemr,Yr) The relationship of (1):
by the above formula, we can obtain the coordinates of the point P in the image coordinate system:
and then, converting the image and the pixel coordinates by the following formula to obtain the position (u ', v') of the point P on the photo:
according to the steps, the laser radar coordinate system point P (X) is realizedr,Yr) The transformation to the pixel coordinate system P (u ', v') contains only some internal parameters of the camera, which can be determined by the Zhang friend method, thereby completing the transformation of coordinates.
2. And training the measured data by using the RBF neural network.
In fig. 2, the input layer contains data of a laser radar and a depth camera, the radial base layer contains the center of a basis function, the obtained result is sent to the linear layer through the radial basis function, and a predicted value of the data is obtained through linear transformation.
The RBF neural network uses a Radial Basis Function (RBF) method, and the activation function of the RBF neural network can be expressed as:
in the above formula, xpRepresenting an input vector, ciσ is the variance of the gaussian function, centered on the gaussian function. Wherein xpThe structure of the radial basis function neural network can be derived as the network output:
in the above formula, wijDenotes the connection weight of the hidden layer to the output layer and j is 1, 2. Finally, the loss function of least squares is adopted to represent:
in the above formula, d is an expected value, and σ is a variance of a gaussian function, which controls a radial acting range of the function, and adjusts the sensitivity of neurons, so that the computing capability of the RBF neural network is greatly improved.
Therefore, the selection of the center of the radial basis greatly influences the final predicted value, the center of the first radial basis is assumed to be data if 40 groups of data exist, the analogy is repeated, the calculation result of the radial basis network can be obtained by utilizing the activation function of the radial basis neural network, and then the variance and the weight from the radial basis layer to the linear layer are obtained.
The method is mainly completed in two steps, namely, in the first step, the unsupervised learning process is carried out, and the variance of a basis function is calculated; and the second step is a process of supervised learning, and the weight from the radial basic layer to the linear layer is calculated.
The specific algorithm is that h centers are selected for k-means clustering in the first step, and for the radial basis of the Gaussian kernel function, the variance is solved by a formula:
in the above formula, cmaxThe maximum distance between the selected center points.
And secondly, calculating the weight from the radial base layer to the linear layer, and simplifying to obtain a formula as follows:
in the above formula, P is 1, 2.., P; i 1, 2, h, P is the number of input vectors.
3. And making a corresponding fusion rule, and estimating and updating the motion state of the unmanned vehicle by combining Bayesian estimation updating.
Let us assume that the Zone detected by the lidar is ZonelasterThe Zone detected by the depth camera is Zonecamera,glaster(i, j) and gcamera(i, j) represent the matrix of these two corresponding areas separately, store the information of the obstacle of two areas, the fusion model between them is:
wherein q (g)laster(i,j),gcamera(i, j)) represents a fusion rule, the specific rule is shown in FIG. 3, d in FIG. 3lExpressed is the distance measured by the lidar, dcThe measured distance of the depth camera is shown, and the probability density of the initial state of the robot is assumed to be p (x)0|z0)=p(x0),x0Is the state of the system at the initial time, z0For the observed value at the initial time, the system posterior probability density can be obtained by using the prior probability density p (x) at the t-1 timet-1|z1:t-1) Calculating p (x)t|z1:t-1) Let us assume xtOnly sum of xt-1Related and p (x)t-1|z1:t-1) Knowing, we have:
p(xt|z1:t-1)=∫p(xt|xt-1)p(xt-1|z1:t-1)dxt-1
then the observed value z at time ttP (x)t|z1:t-1) Correcting to obtain the posterior probability density p (x) of the systemt|z1:t)
ztCan be obtained as independent values:
transforming the above formula by a joint distribution probability formula and a conditional probability formula
And (3) setting each observation value to be independent to obtain the final posterior probability:
in the formula, p (z)t|xt) Likelihood probability, p (x), for system observation equationt|z1:t-1) As a prior probability, p (z)t|z1:t-1) Is a constant for the final normalization of the equation. And then fusing the Bayesian estimation and the previous region fusion rule to obtain fused environmental obstacle data.
In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed.
Claims (6)
1. A multi-information fusion obstacle measuring method is characterized by comprising the following steps:
s1, transforming the laser radar coordinate to the coordinate system of the camera through coordinate transformation, and then obtaining surrounding obstacle information through the depth camera and the laser radar sensor;
s2, training the data of the measured obstacle information by using an RBF neural network;
and S3, making a fusion rule, and estimating and updating the motion state of the unmanned vehicle by combining Bayes estimation updating.
2. The method for measuring the obstacle with multi-information fusion according to claim 1, wherein in step S1, a spatial coordinate system is first established on the robot, and the data of the lidar is projected under a pixel coordinate system through the transformation of the coordinate system;
p point coordinate (X) under camera coordinate systemc,Yc,Zc) And P point coordinates (X) in a radar coordinate systemr,Yr) The relationship of (a) to (b) is as follows:
in the formulae (1) and (2), HcHeight of the center of the camera from the ground, HrThe height from the center of the laser radar sensor to the ground is taken as L, and the distance difference between the center of the camera and the center of the laser radar sensor in the transverse direction is taken as L;
obtaining the coordinates of the point P in the image coordinate system through the formulas (1) and (2):
the conversion between the image and the pixel coordinate can be realized by f in the formula (3) as the focal length of the camera, and the position (u ', v') of the point P on the photo is obtained:
in the formula (4), u0,v0Respectively representing the coordinates of the origin of the coordinate system of the image. dxAnd dyRepresenting the displacement of the pixel point on two planes of the u and v axes.
3. The method for measuring the obstacle with multi-information fusion of claim 1, wherein the RBF neural network in step S2 includes an input layer, a hidden layer and an output layer, the transformation from the output layer to the hidden layer is non-linear, the output layer trains and learns the data through the radial basis function, the transformation from the hidden layer to the output layer is a linear transformation, and the output of the network is a linear weighted sum of the hidden unit outputs.
4. The method according to claim 3, wherein the input layer comprises data of a laser radar and a depth camera, the radial base layer comprises a center of a basis function, the obtained result is sent to the linear layer through the radial basis function, and a predicted value of the data is obtained through linear transformation;
the RBF neural network uses a radial basis function method, and the activation function of the radial basis neural network can be expressed as:
in the formula (5), xpRepresenting an input vector, ciIs the center of the Gaussian function, and delta is the variance of the Gaussian function;
xpthe structure of the radial basis function neural network can be derived as the network output:
in the formula (6), the reaction mixture is,wijrepresents the connection weight of the hidden layer to the output layer, and j is 1, 2.
Loss function representation using least squares:
in formula (7), d is an expected value, σ is a variance of a gaussian function, and j is 1, 2. m is the number of input vectors.
5. The method for measuring the obstacle with multi-information fusion according to claim 3, wherein the output layer is completed in two steps by using a learning method of self-organizing and selecting centers, the unsupervised learning process of the first step is used for calculating the variance of the basis function, the supervised learning process of the second step is used for calculating the weight from the radial basic layer to the linear layer, and the specific algorithm is as follows:
the first step, h centers are selected to perform k-means clustering, and for the radial basis of the Gaussian kernel function, the variance is solved by a formula:
in the formula (8), cmaxThe maximum distance between the selected central points is taken;
secondly, calculating the weight from the radial base layer to the linear layer, and simplifying to obtain a formula as follows:
in formula (9), p is 1, 2.
6. The multi-information-fused obstacle measuring method according to claim 1, wherein the laser radar detection area and depth camera detection area fusion model in step S3 is:
in the formula (10), a ZonelasterZone for lidar detected areascameraFor regions detected by the depth camera, glaster(i, j) and gcamera(i, j) respectively represent matrices of the two corresponding regions,
q(glaster(i,j),gcamera(i, j)) represents a fusion rule;
and (3) updating the Bayesian estimation to obtain the posterior probability density of the system according to the following steps:
firstly, the prior probability density p (x) at the t-1 moment is utilizedt-1|z1:t-1) Calculating p (x)t|z1:t-1),xtOnly sum of xt-1Related and p (x)t-1|z1:t-1) Knowing, we have:
p(xt|z1:t-1)=∫p(xt|xt-1)p(xt-1|z1:t-1)dxt-1 (11),
then the observed value z at time ttP (x)t|z1:t-1) Correcting to obtain the posterior probability density p (x) of the systemt|z1:t):
ztCan be obtained as independent values:
transforming the above formula by a joint distribution probability formula and a conditional probability formula:
each observation is independent, and the final posterior probability is obtained:
in formulae (11) to (15), p (z)t|xt) Likelihood probability, p (x), for system observation equationt|x1:t-1) As a prior probability, p (z)t|z1:t-1) Constant for final normalization of equation;
and fusing the Bayesian estimation and the previous region fusion rule to obtain fused environmental obstacle data.
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CN116358561A (en) * | 2023-05-31 | 2023-06-30 | 自然资源部第一海洋研究所 | Unmanned ship obstacle scene reconstruction method based on Bayesian multi-source data fusion |
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