CN105261060A - Point cloud compression and inertial navigation based mobile context real-time three-dimensional reconstruction method - Google Patents
Point cloud compression and inertial navigation based mobile context real-time three-dimensional reconstruction method Download PDFInfo
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Abstract
The invention relates to a PFH and inertial navigation based mobile context real-time three-dimensional reconstruction method. The PFH and inertial navigation based mobile context real-time three-dimensional reconstruction method is divided into three stages below: a first stage of designing a PFH based point cloud compression algorithm; a second stage of designing an inertial navigation based point cloud registration algorithm; and a third stage of finishing point cloud fusion. The PFH and inertial navigation based mobile context real-time three-dimensional reconstruction method can realize a relatively high compression ratio and can effectively reduce number of input point sets of an ICP algorithm, thereby saving computation time for point cloud registration and point cloud fusion. The number of iterations of the ICP algorithm is reduced, the rate of convergence is increased, and the real-time performance of the whole reconstruction algorithm is consequently improved.
Description
Technical field
The present invention relates to a kind of method based on real-time three-dimensional reconstruct under the mobile context of point cloud compression and inertial navigation, belong to computer machine vision, augmented reality field.
Background technology
Three Dimensional Reconfiguration is one of hot research direction in computer vision field, and how main research obtains object three-dimensional information in space, realizes true reappearance objective environment in a computer, for machine and the mankind provide 3D scene information accurately.
The main direction of studying of three-dimensionalreconstruction comprises scene reconstruction, map structuring and Model Reconstruction etc.The 3D scene information of surrounding environment can help environment residing for robot understanding clearly self and realize with the close friend of environment mutual, plays a significant role in the fields such as robot navigation, archaeology exploration, disaster assistance; In addition, 3D scene information also can be used on the fields such as virtual reality, military simulation, city planning, historical relic's protection.In the CAD processing of industrial products part, Mold Making, medical science plastic operation, face modeling and identification, computer three-dimensional animation etc. are applied, often need the three-dimensional model of object, this has also made requirement to Three Dimensional Reconfiguration.At present, three-dimensional scenic reconfiguration technique relies on its important application in these fields, is more and more subject to the favor of all circles researcher, becomes multi-field study hotspot gradually.
The three-dimensional reconstruction method of current 3D scene point cloud model is mainly divided into two large classes: based on the three-dimensional reconstruction method of multiple image with based on three-dimensional reconstruction method that is infrared or laser scanner technique.The former recovers the three-dimensional information of building surface by the two dimensional image that Digital Video etc. is taken, as publication number be CN101398937, name is called the patent of " three-dimensional reconstruction method based on Same Scene photograph collection at random ".Although these class methods are with low cost, automaticity is higher, and the three-dimensional model rebuild comprises the information such as abundant texture, but need to carry out a large amount of process to pictorial information, travelling speed is slower, be applicable to small-sized single Building Modeling, but for the large scale scene modeling of landforms complexity, owing to being subject to the restriction of computer processing time and inapplicable.
Therefore, become study hotspot based on Three Dimensional Reconfiguration that is infrared or laser scanning in recent years, infrared or laser scanner directly can obtain the depth information of scenery, and have speed fast, precision is high, not by advantages such as surperficial complexity effect.In addition, utilize laser scanner technique to carry out three-dimensionalreconstruction and can effectively recover the three-dimensional model with accurate geometry information and photorealistic.These accurately three-dimensional model the function of environment Visualization and virtual roaming aspect can not only be provided, more can meet the file of data, measure and demand that analysis etc. is higher level.
Summary of the invention
The object of this invention is to provide one utilizes sensor to move at indoor scene, constantly obtain the some cloud under different visual angles, and Real-time Reconstruction goes out the method for whole scene three-dimensional model.
In order to achieve the above object, technical scheme of the present invention there is provided a kind of mobile context real-time three-dimensional reconstructing method based on point cloud compression and inertial navigation, it is characterized in that, comprises the steps:
The first step, for scene S, utilize the camera of Kinect somatosensory controller respectively from visual angle V
1..., V
k... V
nacquisition point is converged and is closed M successively
1..., M
k... M
n, M
kfor the k moment is from vision V
kthe point cloud obtained;
Second step, point is converged conjunction { M
1..., M
k... M
nboil down to { P
1..., P
k... P
n, wherein, P
kfor M
ka subset, and meet:
in formula, ρ is for presetting ratio, F
c() be unique point quality metric function, δ is the unique point retention rate of setting;
3rd step, inertial navigation technology is utilized to obtain in scene S carrier at visual angle V
1..., V
k... V
nunder position, offset, be converted into the initial transformation matrix needed for ICP algorithm
with
4th step, utilize initial transformation matrix
with
to the some cloud { P after compression
1..., P
k... P
ncarry out rough registration, then based on ICP algorithm determination transformation matrix R
kwith T
k, complete accuracy registration;
5th step, the roaming of sensor in scene S along with Kinect somatosensory controller, constantly splice the every a slice point cloud newly obtained and partial model, merge, until reconstruct whole three-dimensional scene models.
Preferably, described 3rd step comprises:
Step 3.1, the depth camera of carrier being placed Kinect somatosensory controller in scene S, accelerometer and gyroscope;
Step 3.2, set a fixed coordinate system, if motion reference position is true origin, depth camera inceptive direction overlaps with X-axis positive dirction, utilize accelerometer and the acceleration of gyroscope survey carrier in X, Y, Z axis, degree of will speed up integration obtains the speed of carrier, and degree of will speed up quadratic integral obtains the position of carrier, thus reaches the object to carrier location, meanwhile, the anglec of rotation of carrier is obtained by gyroscope;
Step 3.3, by the relative displacement of carrier between different positions and pose, rotation angle information, be converted into the initial transformation matrix between two panels point cloud
with
Preferably, described 4th step comprises:
Step 4.1, by the compression point cloud P under two different visual angles
kwith P
k-1as the input set of ICP algorithm, by initial transformation matrix
with
as the initial transformation matrix of ICP algorithm;
Step 4.2, utilize initial transformation matrix
with
to the reference point clouds P after compression
kcarry out rotating, translation process, obtain P
k';
Step 4.3, screening matching double points: adopt most proximal point algorithm impact point cloud P upon compression
k-1middle searching and P
k' in the minimum matching characteristic point of unique point Euclidean distance, composition characteristic point is to set;
Step 4.4, the error of calculation: to calculate after conversion the right square distance of 2 stack features points and, be designated as d
k;
Step 4.5, the rotation utilizing the coordinate transform of matching double points solution room based on Quaternion Method, translation matrix
with
be used further to reference point clouds, obtain P
k", again screen matching double points, upgrade d simultaneously
k;
Computing in step 4.6, iterative step 4.5, until convergence, d
kbe less than threshold value or reach set iterations
The innovative point of this method is:
(1) a kind of point cloud compression algorithm based on PFH feature is used: higher compression ratios can be realized, the quantity of effective minimizing ICP algorithm input point set, thus save point cloud registering, some cloud fusion calculation time, remain abundant unique point, for point cloud registering is provided convenience simultaneously.
(2) a kind of point cloud registering based on inertial navigation technology is proposed: the essence of point cloud registering calculates to obtain rotation under different visual angles between two panels point cloud, translation matrix R and T, use ICP algorithm to solve this kind of problem in a lot of experiment, but the computational accuracy of traditional IC P algorithm depend on given initial value.Herein in test, Kinect camera moves in indoor scene with carrier, obtain the some cloud under different visual angles, if the Kinect camera position on carrier is fixed, so R and T equally also can be represented by the rotation of carrier between different visual angles, translation matrix, there is provided position and offset by inertial sensor, an approximate initial value accurately can be provided to ICP
with
reduce the iterations of ICP algorithm with this, improve speed of convergence, thus improve the real-time of whole restructing algorithm.
Accompanying drawing explanation
Fig. 1 is three-dimensionalreconstruction module introduction figure;
Fig. 2 is three-dimensionalreconstruction algorithm flow chart;
Fig. 3 is point cloud compression algorithm flow chart;
Fig. 4 is point cloud registration algorithm process flow diagram.
Embodiment
For making the present invention become apparent, hereby with preferred embodiment, and accompanying drawing is coordinated to be described in detail below.
The main research and utilization sensor of the present invention moves at indoor scene, constantly obtain the some cloud under different visual angles, and Real-time Reconstruction goes out the technique study of whole scene three-dimensional model.
The description of problem:
For scene S, Kinect camera respectively from visual angle { V
1..., V
k... V
nsuccessively acquisition point converge conjunction { M
1..., M
k... M
n, the object of moving three dimension reconstruct is the cloud data by obtaining under different visual angles, under the effect of restructing algorithm R (), asks for S '=R (M
1..., M
k... M
n), make S and S ' meet the constraint of similarity measurements flow function E ():
|E(S)-E(S′)|＜ε
The analysis of problem:
Due to the some cloud substantial amounts in scene, being the real-time of implementation algorithm, first needing the some cloud to obtaining to carry out the pre-service such as denoising, compression; Meanwhile, in restructing algorithm R (), a most important step asks for M
kwith M
k-1transformation matrix R needed for registration
kwith T
k, and registration process depends on M
kwith M
k-1in feature point pairs.Therefore, compression algorithm not only will have higher ratio of compression, also needs to retain abundant unique point.
ICP algorithm is the study on classics method of carrying out point cloud registering, but traditional ICP algorithm is owing to lacking registration initial value or initial value precision is poor, is easy to be absorbed in locally optimal solution, affects iterative convergence speed.Therefore, be that there is a good registration initial value based on the precondition of ICP algorithm realization point cloud registering.
For the analysis of above problem, the present invention proposes a kind of mobile context real-time three-dimensional reconstructing method based on PFH and inertial navigation, comprises the steps:
Step 1, start up system, reset sensor information, set up a fixed coordinate system;
Step 2, the some cloud obtained under initial pose (namely carrier is positioned at true origin), carry out after compression process for setting up scene partial points cloud model;
Step 3, by inertance element (accelerometer, gyroscope) Real-time Obtaining posture information (pose refers to position and the angle information of carrier), when posture information changes greatly, when exceeding threshold value, obtain the some cloud under New Century Planned Textbook, carry out compression process simultaneously;
Step 4, to be calculated under current point cloud and fixed coordinate system between scene partial points cloud initial transformation matrix by change in location;
Step 5, based on improve ICP algorithm (initial transformation matrix is as ICP initial value) carry out point cloud registering;
Step 6, by after registration two panels point cloud merge, repeat step 3 to 6, until judge that reconstruct completes, stop test.
As shown in Figure 3, be the step 2 in three-dimensionalreconstruction algorithm flow, based on the mobile context point cloud compression algorithm flow chart of PFH.Concrete operation step is as follows:
(1) acceptance point cloud M from a some cloud acquisition device
k.
(2) the PFH feature operator of each point is calculated.
(3) calculate the standard deviation of PFH feature histogram, known by observing, analyzing, what standard deviation was little is angle point, and what standard deviation was large is planar point, thus utilizes standard deviation to simplify criterion as characteristic of division design.
(4) determine the standard deviation threshold method needed for compression, any one point in selected point cloud, judges the magnitude relationship of its PFH standard deviation and threshold value; If be less than or equal to threshold value, retain this point, otherwise reject this point; Repeat above-mentioned steps, until some Yun Zhongsuo a little calculates complete, the point after compression is kept at set P
kin, be designated as the some cloud after compression.
Based on above-mentioned compression algorithm, point cloud compression rate can reach more than 95%, is follow-up point cloud registering, fusion process saving a large amount of computing time, improves the feasibility of Real-time Reconstruction.Meanwhile, utilize PFH feature operator in the significant difference of diverse location, remain the unique point that can complete scene reconstruction, for follow-up point cloud registering is provided convenience.The implementation procedure of above-mentioned compression algorithm and theoretical foundation can with reference to " based on the point patterns histogrammic mobile context point cloud compressing algorithm " patents of invention proposed before author.
Be the step 4 in three-dimensionalreconstruction algorithm flow below, utilize inertial sensing information to obtain step and the principle explanation of initial transformation matrix:
(1) inertance element (accelerometer, gyroscope) is utilized to obtain relative displacement between different positions and pose, rotation angle information:
This experiment, by having laid a dolly in scene, dolly is installed the sensor of obtaining information, and used herein is kinect depth camera, accelerometer and gyroscope.
First, heat transfer agent is reset, set a fixed coordinate system, if motion reference position is true origin, camera inceptive direction overlaps with X-axis positive dirction, utilize inertance element (accelerometer, gyroscope) measure carrier originally in X, Y, acceleration on Z axis, as formula (1), (2) shown in, one time integration obtains speed, quadratic integral obtains position, speed and the position of carrier itself is obtained through twice integral operation, thus the object reached carrier location, simultaneously, the rotation angle information of carrier can be obtained by gyroscope.
In formula,
be respectively acceleration, speed and position.
Although the information obtained after long-time integration can accumulate certain error, but under the pose that any two intervals are less, obtain dolly relative displacement, rotation angle information can realize very little error, and the equipment of composition positioning system is all arranged in carrier, external information is not relied on during work, also not outwardly emittance, is thus not easily interfered.
(2) by the relative displacement of dolly between different positions and pose, rotation angle information, the initial transformation matrix between two panels point cloud is converted into
with
As shown in Figure 4, be the step 5 in three-dimensionalreconstruction algorithm flow, the point cloud registration algorithm process flow diagram based on inertial navigation:
(1) by the compression point cloud P under two different visual angles
kwith P
k-1as the input set of ICP algorithm, by initial transformation matrix
with
as the initial transformation matrix of ICP algorithm;
(2) initial transformation matrix is utilized
with
to the reference point clouds P after compression
kcarry out rotating, translation process, obtain P
k';
(3) matching double points is screened: adopt most proximal point algorithm impact point cloud P upon compression
k-1middle searching and P
k' in the minimum matching characteristic point of unique point Euclidean distance, composition characteristic point is to set;
(4) error of calculation: to calculate after conversion the right square distance of 2 stack features points and, be designated as d
k;
(5) rotation, the translation matrix of the coordinate transform of matching double points solution room is utilized based on Quaternion Method
with
be used further to reference point clouds, obtain P
k", again screen matching double points, upgrade d simultaneously
k.
(6) computing in iterative step (5), until convergence, d
kbe less than threshold value or reach set iterations.
Claims (3)
1., based on a mobile context real-time three-dimensional reconstructing method for point cloud compression and inertial navigation, it is characterized in that, comprise the steps:
The first step, for scene S, utilize the camera of Kinect somatosensory controller respectively from visual angle V
1..., V
k... V
nacquisition point is converged and is closed M successively
1..., M
k... M
n, M
kfor the k moment is from vision V
kthe point cloud obtained;
Second step, point is converged conjunction { M
1..., M
k... M
nboil down to { P
1..., P
k... P
n, wherein, P
kfor M
ka subset, and meet:
in formula, ρ is for presetting ratio, F
c() be unique point quality metric function, δ is the unique point retention rate of setting;
3rd step, inertial navigation technology is utilized to obtain in scene S carrier at visual angle V
1..., V
k... V
nunder position, offset, be converted into the initial transformation matrix needed for ICP algorithm
with
4th step, utilize initial transformation matrix
with
to the some cloud { P after compression
1..., P
k... P
ncarry out rough registration, then based on ICP algorithm determination transformation matrix R
kwith T
k, complete accuracy registration;
5th step, the roaming of sensor in scene S along with Kinect somatosensory controller, constantly splice the every a slice point cloud newly obtained and partial model, merge, until reconstruct whole three-dimensional scene models.
2. a kind of mobile context real-time three-dimensional reconstructing method based on point cloud compression and inertial navigation as claimed in claim 1, it is characterized in that, described 3rd step comprises:
Step 3.1, the depth camera of carrier being placed Kinect somatosensory controller in scene S, accelerometer and gyroscope;
Step 3.2, set a fixed coordinate system, if motion reference position is true origin, depth camera inceptive direction overlaps with X-axis positive dirction, utilize accelerometer and the acceleration of gyroscope survey carrier in X, Y, Z axis, degree of will speed up integration obtains the speed of carrier, and degree of will speed up quadratic integral obtains the position of carrier, thus reaches the object to carrier location, meanwhile, the anglec of rotation of carrier is obtained by gyroscope;
Step 3.3, by the relative displacement of carrier between different positions and pose, rotation angle information, be converted into the initial transformation matrix between two panels point cloud
with
3. a kind of mobile context real-time three-dimensional reconstructing method based on point cloud compression and inertial navigation as claimed in claim 1, it is characterized in that, described 4th step comprises:
Step 4.1, by the compression point cloud P under two different visual angles
kwith P
k-1as the input set of ICP algorithm, by initial transformation matrix
with
as the initial transformation matrix of ICP algorithm;
Step 4.2, utilize initial transformation matrix
with
to the reference point clouds P after compression
kcarry out rotating, translation process, obtain P
k';
Step 4.3, screening matching double points: adopt most proximal point algorithm impact point cloud P upon compression
k-1middle searching and P
k' in the minimum matching characteristic point of unique point Euclidean distance, composition characteristic point is to set;
Step 4.4, the error of calculation: to calculate after conversion the right square distance of 2 stack features points and, be designated as d
k;
Step 4.5, the rotation utilizing the coordinate transform of matching double points solution room based on Quaternion Method, translation matrix
with
be used further to reference point clouds, obtain P
k", again screen matching double points, upgrade d simultaneously
k;
Computing in step 4.6, iterative step 4.5, until convergence, d
kbe less than threshold value or reach set iterations.
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