CN113554754A - Indoor positioning method based on computer vision - Google Patents

Indoor positioning method based on computer vision Download PDF

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CN113554754A
CN113554754A CN202110873083.5A CN202110873083A CN113554754A CN 113554754 A CN113554754 A CN 113554754A CN 202110873083 A CN202110873083 A CN 202110873083A CN 113554754 A CN113554754 A CN 113554754A
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李爽
蔚保国
李隽�
赵茜
梁晓虎
祝瑞辉
李雅宁
张衡
黄璐
贾浩男
程建强
陈冲
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Abstract

The invention discloses an indoor positioning method based on computer vision, and relates to the technical field of computer vision indoor positioning. The method comprises the steps of firstly establishing an indoor image library, finding the most similar picture by comparing images collected by various indoor terminals with the image library, extracting and matching feature points, establishing a relation between three-dimensional coordinates of the feature points in a world coordinate system and two-dimensional coordinates of the feature points in an image coordinate system, and obtaining the pose which is the current pose of the terminal. The method is easy to realize and can effectively improve the accuracy of indoor navigation positioning.

Description

Indoor positioning method based on computer vision
Technical Field
The invention relates to the field of visual navigation, in particular to the field of visual indoor positioning, and especially relates to an indoor positioning method based on computer vision.
Background
The problem of high-precision positioning in an indoor environment is always a technical problem. Limited by the influence of multipath and indoor complex and variable environment, the traditional indoor positioning method has one or more problems and cannot realize high-precision positioning, such as:
the pseudo satellite is directly adopted for data resolving and positioning, and the positioning precision cannot be guaranteed due to the influence of multipath;
positioning methods based on signal intensity values of Wi-Fi and Bluetooth are greatly influenced by indoor environment changes, and positioning accuracy cannot be guaranteed;
the positioning method using the signal fingerprint has the problems of long time for establishing a fingerprint database and time variation.
The positioning method based on computer vision has the advantages of short positioning time and accurate positioning result, and has better application prospect in indoor buildings. However, there is no prior art attempt.
Disclosure of Invention
In view of the above, the invention provides an indoor positioning method based on computer vision, which is based on image matching of deep learning and solving of n-point perspective projection problem to realize positioning, and can well solve the problem of poor positioning performance in indoor complex environments in the prior art and improve the accuracy of indoor navigation positioning.
In order to achieve the purpose, the invention adopts the technical scheme that:
an indoor positioning method based on computer vision comprises the following steps:
(1) collecting and storing indoor RGB (red, green and blue) images and depth images, recording the pose of a camera when the images are collected, and establishing an indoor map library TMN；
(2) Inputting the RGB images stored in the map library into a deep learning network model as training samples, training the network model, and storing network model parameters when the loss function value is not reduced any more;
(3) when a terminal enters a room, downloading parameters of a deep learning network model, shooting an indoor photo by using a terminal camera, identifying a matching picture most similar to the photo shot by the terminal from a map library by using the deep learning network, and extracting a corresponding depth image and a camera pose;
(4) extracting characteristic points of the terminal shot picture and the matched picture, matching the characteristic points and calculating to obtain coordinates of the matched points in a world coordinate system;
(5) according to the coordinates of the matching points in the world coordinate system and the image coordinates, solving the pose of the terminal in the world coordinate system when the terminal takes a photo by using an n-point perspective projection model solving method, converting the coordinates into a real position according to an indoor map and displaying the real position on the map;
and completing indoor positioning based on computer vision.
Further, the specific mode of the step (1) is as follows: dividing the indoor space into M multiplied by N grids according to the area, acquiring the plane coordinate of the central point of each grid by adopting a laser range finder, and erecting an RGB-D camera at the center of each grid to enable the height of the camera from the ground to be H meters and keep the camera parallel to the ground; RGB images and depth images are collected through the cameras, and the poses of the cameras when the images are collected are recorded, so that an indoor map library with the size of M multiplied by N is established.
Further, the deep learning network model is combined with LBP characteristics to perform scene recognition; when the network model is trained in the step (2), LBP feature extraction is carried out by using the following formula:
Figure BDA0003189397800000021
Figure BDA0003189397800000022
in the formula (x)c,yc) As the coordinates of the central pixel point, N is 8, ic,inThe gray values of the central pixel and the neighborhood pixels respectively, and s (-) is a sign function;
the purpose of the training process is to learn to obtain the parameter values of each layer of the network so as to fit given training data; establishing a log-likelihood function to maximize the value of the log-likelihood function on a training set, so as to obtain network layer parameters; assuming that the training set contains N samples, the log-likelihood function is:
Figure BDA0003189397800000023
in the formula, P (v)(i)| θ) represents the joint distribution of the visible unit and the hidden unit, argmax is a function of an autovariable set corresponding to the maximum value of the function, and θ is a network parameter.
Further, the specific mode of the step (4) is as follows:
respectively extracting SURF characteristic points of the terminal shot picture and the matched picture and matching;
eliminating the error matching points by using a random sampling consistency method;
extracting pixel coordinates of the matching points, searching the depth of the corresponding points in the depth image, and calculating to obtain the coordinates of the matching points in a world coordinate system according to the camera imaging model and the camera pose.
Compared with the prior art, the invention has the following beneficial effects:
1. according to the method, a map library is established by collecting RGB and depth images of different indoor positions and directions, similar picture searching is realized by a depth learning network-based method, and indoor high-precision navigation positioning is realized by solving the camera pose by constructing an n-point perspective projection model equation.
2. The invention adopts image matching based on deep learning and n-point perspective projection problem solving to realize indoor positioning, and compared with the existing indoor positioning method, the positioning precision is greatly improved.
3. The method can well solve the problem that the positioning performance is poor in the indoor complex environment in the prior art, improves the indoor navigation positioning precision, and provides a new idea for solving the indoor high-precision positioning problem.
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FIG. 1 is a flow chart of a method according to an embodiment of the present invention.
Detailed Description
The present invention will be further described with reference to the accompanying drawings.
An indoor positioning method based on computer vision comprises the following steps:
(1) dividing indoor space into planar grids of MxN, acquiring planar coordinates of central point of each grid by using a laser range finder, keeping the height of a camera from the ground by H meters by using an RGB-D camera at the center of each grid and keeping the camera parallel to the ground, collecting RGB images and depth images, storing, recording the pose of the camera during picture collection, and establishing an indoor map library T of MxN sizeMN；
(2) Inputting RGB images stored in a map library into a constructed deep learning network model as training samples, training the network, and storing network model parameters when loss function values are not reduced;
(3) downloading parameters of a deep learning network model after any terminal enters a room, shooting indoor pictures by using a terminal camera, identifying the most similar pictures according to the deep learning network, and extracting a corresponding depth map and a corresponding camera pose;
(4) and respectively extracting SURF characteristic points of the terminal shot picture and the picture output by the network model, matching and removing the error matching points, extracting pixel coordinates of the matching points, searching the depth of the corresponding points in the depth map, and calculating to obtain the coordinates of the matching points in a world coordinate system according to the camera imaging model and the camera pose.
(5) After the coordinates and the image coordinates of the matching points in the world coordinate system are obtained, the pose of the terminal in the world coordinate system when the terminal takes a picture can be solved by using an n-point perspective projection problem solving method, and then the coordinates corresponding to the maximum value are converted into the real positions according to an indoor map and displayed on the map;
and completing indoor positioning based on computer vision.
Wherein, the network model training in the step (2) specifically comprises: LBP feature extraction, DBN training and the like. The LBP operator is formulated as:
Figure BDA0003189397800000041
wherein (x)c,yc) Coordinates of a central pixel point; the size of N is 8; i.e. ic,inGray values of the central pixel and the neighborhood pixels respectively; s (-) is a sign function.
The purpose of the training process is to learn the parameter values for each layer of the RBM network to fit given training data. Specifically, a log-likelihood function is established to maximize the value of the log-likelihood function on a training set, and then the network layer parameters are obtained. Assuming that the training set contains N samples, the log-likelihood function is:
Figure BDA0003189397800000042
wherein, P (v)(i)| θ) represents a visible unit andthe joint distribution of the hidden units, θ is a network parameter, and argmax is a known function in the fields of computers and mathematics, which are not described herein again.
The pose of the terminal under a world coordinate system when the terminal takes a picture is solved by using the perspective projection problem of n points in the step (5), and the specific flow is as follows:
assuming a calibrated camera (i.e. the focal length, optical center position and distortion parameters of the camera are known), a spatial reference point P in the world coordinate system is giveni1, n and the point v of the corresponding reference point in the camera coordinate systemi1.. n, the conversion equation between the two coordinate systems is obtained by the correspondence between the 3D/2D reference points as:
λivi＝RPi+t (3)
wherein λ isiIs v isiDepth of the point, viSatisfy | | viAnd 1, R is a rotation matrix, and t is a translation vector.
Spatial reference point PiThe projection point in the image coordinate system is pi'＝[x'y']TThere is a certain uncertainty for the observation of the projection points, which uncertainty is described by a two-dimensional covariance matrix. The calculation formula is shown as (4):
Figure BDA0003189397800000051
converting points in the image coordinate system into points in the camera coordinate system through a coordinate system transformation matrix A:
Figure BDA0003189397800000052
wherein, JAThe Jacobian matrix for coordinate system transformation A, so the uncertainty for point p in the camera coordinate system is represented as:
Figure BDA0003189397800000053
the rank of the covariance matrix is 2, and the covariance matrix is a singular matrix and is irreversible. Normalizing the coordinates of the points into a vector:
Figure BDA0003189397800000054
wherein the covariance matrix is derived from the following equation:
Figure BDA0003189397800000055
similarly, the covariance matrix is a singular matrix, and the correlation between components does not satisfy the requirement of independence of elements of the maximum likelihood solution, so a null space is introduced to represent the vector v:
Figure BDA0003189397800000056
wherein the function f (-) is a singular value decomposition function,
Figure BDA0003189397800000061
is transformed into a vector vrThe jacobian matrix of (a) is,
Figure BDA0003189397800000062
determinism from vector v to vector vrThe transformation of (d) is expressed as:
Figure BDA0003189397800000063
the pose of the camera can be obtained by the following formula.
Figure BDA0003189397800000064
Will be the above formulaIs developed wherein Pi＝[px py pz]TThe following can be obtained:
Figure BDA0003189397800000065
the above equation is expressed as a homogeneous system of linear equations:
Bu＝0 (14)
wherein B is the coefficient of the homogeneous equation set,
Figure BDA0003189397800000066
solving the camera rotation matrix and translation vector by the above equation requires at least 6 pairs of 2D/3D corresponding points.
Adding the uncertainty description matrix of the reference point into a homogeneous linear equation to obtain:
Figure BDA0003189397800000071
the final expression is:
BTCBu＝Nu＝0 (16)
wherein u satisfies the constraint condition | | | u | | | ═ 1. Performing singular value decomposition on the coefficient matrix:
N＝UDVT (17)
wherein the rotation matrix
Figure BDA0003189397800000072
And translation vector
Figure BDA0003189397800000073
The eigenvectors solved by the above equation are obtained:
Figure BDA0003189397800000074
wherein the translation vector
Figure BDA0003189397800000075
Representing only the direction, the scale factor is calculated by the following equation, and the translation vector is:
Figure BDA0003189397800000076
the rotation matrix obtained by singular value decomposition is:
Figure BDA0003189397800000077
R＝URVR T (21)
and obtaining the rotation matrix and the translation vector of the camera through the calculation process.
The following is a more specific example:
as shown in fig. 1, an indoor positioning method based on computer vision includes the following steps:
step 1: establishing an indoor map library, dividing an indoor space into M multiplied by N grids, acquiring a plane coordinate of a central point of each grid by adopting a laser range finder, utilizing an RGB-D camera at the center of each grid to enable the height of the camera to be H meters away from the ground and keep the camera parallel to the ground, acquiring RGB images and depth images for storage, recording the pose of the camera during image acquisition, and establishing an M multiplied by N indoor map library TMN；
Step 2: inputting RGB images stored in a map library into a constructed deep learning network model as training samples, training the network, and storing network model parameters when loss function values are not reduced;
the network training process is described as: the file name and the corresponding label of each image in the map library are obtained, a training model (comprising initialization parameters, parameters such as convolution, pooling layer and the like and a network) is defined, then training is started, when the numerical value of the loss function is not reduced obviously any more, the classification accuracy of the network model parameters is optimal, and the network parameters are stored.
And step 3: after any terminal enters the room, downloading parameters of a deep learning network model through WiFi, shooting indoor pictures by using a terminal camera, identifying the most similar pictures according to the deep learning network, and extracting a corresponding depth map and a camera pose;
(4) and respectively extracting SURF characteristic points of the terminal shot picture and the picture output by the network model, matching and removing the error matching points, extracting pixel coordinates of the matching points, searching the depth of the corresponding points in the depth map, and calculating to obtain the coordinates of the matching points in a world coordinate system according to the camera imaging model and the camera pose.
(5) After the coordinates and the image coordinates of the matching points in the world coordinate system are obtained, the pose of the terminal in the world coordinate system when the terminal takes a photo can be solved by using an n-point perspective projection problem solving method, the true longitude and latitude coordinates are converted according to the longitude and latitude coordinates corresponding to the reference true value, and the true longitude and latitude coordinates are displayed on an indoor map.
And completing indoor positioning based on computer vision.

Claims (4)

1. An indoor positioning method based on computer vision is characterized by comprising the following steps:
(1) collecting and storing indoor RGB (red, green and blue) images and depth images, recording the pose of a camera when the images are collected, and establishing an indoor map library TMN；
(2) Inputting the RGB images stored in the map library into a deep learning network model as training samples, training the network model, and storing network model parameters when the loss function value is not reduced any more;
(3) when a terminal enters a room, downloading parameters of a deep learning network model, shooting an indoor photo by using a terminal camera, identifying a matching picture most similar to the photo shot by the terminal from a map library by using the deep learning network, and extracting a corresponding depth image and a camera pose;
(4) extracting characteristic points of the terminal shot picture and the matched picture, matching the characteristic points and calculating to obtain coordinates of the matched points in a world coordinate system;
(5) according to the coordinates of the matching points in the world coordinate system and the image coordinates, solving the pose of the terminal in the world coordinate system when the terminal takes a photo by using an n-point perspective projection model solving method, converting the coordinates into a real position according to an indoor map and displaying the real position on the map;
and completing indoor positioning based on computer vision.
2. The indoor positioning method based on computer vision according to claim 1, characterized in that, the specific manner of step (1) is: dividing the indoor space into M multiplied by N grids according to the area, acquiring the plane coordinate of the central point of each grid by adopting a laser range finder, and erecting an RGB-D camera at the center of each grid to enable the height of the camera from the ground to be H meters and keep the camera parallel to the ground; RGB images and depth images are collected through the cameras, and the poses of the cameras when the images are collected are recorded, so that an indoor map library with the size of M multiplied by N is established.
3. The computer vision-based indoor positioning method of claim 1, wherein the deep learning network model combines LBP features for scene recognition; when the network model is trained in the step (2), LBP feature extraction is carried out by using the following formula:
Figure FDA0003189397790000011
Figure FDA0003189397790000012
in the formula (x)c,yc) As the coordinates of the central pixel point, N is 8, ic,inThe gray values of the central pixel and the neighborhood pixels respectively, and s (-) is a sign function;
the purpose of the training process is to learn to obtain the parameter values of each layer of the network so as to fit given training data; establishing a log-likelihood function to maximize the value of the log-likelihood function on a training set, so as to obtain network layer parameters; assuming that the training set contains N samples, the log-likelihood function is:
Figure FDA0003189397790000021
in the formula, P (v)(i)| θ) represents the joint distribution of the visible unit and the hidden unit, argmax is a function of an autovariable set corresponding to the maximum value of the function, and θ is a network parameter.
4. The indoor positioning method based on computer vision as claimed in claim 1, wherein the specific manner of step (4) is as follows:
respectively extracting SURF characteristic points of the terminal shot picture and the matched picture and matching;
eliminating the error matching points by using a random sampling consistency method;
extracting pixel coordinates of the matching points, searching the depth of the corresponding points in the depth image, and calculating to obtain the coordinates of the matching points in a world coordinate system according to the camera imaging model and the camera pose.
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