CN106813570B - 3D recognition and localization of long cylindrical objects based on line structured light scanning - Google Patents

Abstract

Object manipulator automatic charging of the present invention, propose it is a kind of based on line-structured light scanning elongated cylindrical object dimensional identification and localization method: the elongated cylindrical body surface divided in m axial scan hopper using structural light measurement sensor with fixed step size obtains the structural light measurement data of m section；Data segmentation is carried out respectively to the structural light measurement data of each section, so that the data for belonging to same object are segmented in one section of circular arc；Circular fitting is carried out to every section of segmentation data, obtains central coordinate of circle；Each circular arc center of circle of m section is matched, so that belonging to the circular arc matching of same object together；The three-dimensional coordinate of object is calculated by linear interpolation algorithm；Determine the label of crawl object.Online, real-time, the automatic, non-cpntact measurement of elongated cylindrical object dimensional coordinate may be implemented in the present invention, and measuring speed is fast, precision is high；Small to object itself constraint, radius and length can arbitrarily change；Object placement position is random in hopper, can tilt, intersect；There is robustness to noise.

Description

3D recognition and localization of long cylindrical objects based on line structured light scanning

technical field

The invention belongs to the field of computer vision, in particular to a three-dimensional identification and positioning method of long cylindrical objects based on line structured light scanning.

Background technique

With the rapid development of the national economy, automation has become the future development direction. The use of robots to replace manual labor to realize automatic loading and unloading not only saves production costs, improves production efficiency, but also improves safety factor and reduces labor intensity of workers, which has become an ideal choice for more and more companies.

In order to realize the automatic loading and unloading of the robot, the position of the material needs to be measured, and then passed to the robot to guide the manipulator to grasp it. The structured light measurement method has been highly valued because of its simple equipment and strong real-time performance. Especially in the application occasions where the size, weight and power consumption of the measurement equipment are strictly required, the structured light measurement shows its advantages.

The structured light measurement method is an active optical measurement technology. Its basic principle is to project a controllable light spot, light strip or light surface structure to the surface of the measured object through a structured light projector, and obtain an image by an image sensor (such as a camera). , and then through the geometric relationship of the system, the three-dimensional coordinates of the object are calculated by using the principle of trigonometry. Structured light can be divided into point structured light, line structured light and surface structured light according to the controllable light spot, light bar or light surface structure projected by the structured light projector to the surface of the object to be measured. Since the point structured light measurement method needs to scan the object point by point for measurement, with the increase of the measured object, the image capture and image processing time will increase sharply, and it is difficult to realize real-time measurement; while the surface structured light obtains a large amount of data of 3D coordinate points , the amount of calculation will increase accordingly, so the application of linear structured light in engineering is more common.

Due to the harsh production environment and serious noise pollution, the radius and length of the long cylindrical objects in the material box change randomly, and the placement is disordered, so the accuracy of the general measurement method is difficult to meet the actual requirements.

SUMMARY OF THE INVENTION

In order to solve the influence of the serious noise pollution in the production environment, the random change of the radius and length of the long cylindrical objects in the material box, and the random placement on the measurement accuracy, the invention provides a measurement speed, high precision, and robustness. A strong, real-time and automatic method for realizing three-dimensional recognition and localization of long cylindrical objects.

The technical scheme adopted by the present invention to achieve the above object is: a three-dimensional identification and positioning method for long cylindrical objects based on line structured light scanning, which is used to realize the measurement of the position of the long cylindrical objects in the material box, comprising the following steps:

The linear structured light measurement sensor is used to axially scan the long cylindrical object in the bin in m times with a fixed step size, and the structured light measurement data of m sections are obtained;

Perform data segmentation on the structured light measurement data of each section, so that the data belonging to the same object is divided into a segment of arc;

Perform arc fitting on each segment of segmented data to obtain the coordinates of the center of the circle;

According to the constraints that the arcs on the same object should satisfy, match the arc centers of m sections;

Use linear interpolation algorithm to calculate the three-dimensional coordinates of long cylindrical objects;

Determine the number of the object being grabbed.

The set section of m can basically cover the axial direction of the entire long cylindrical object, and needs to be determined by itself according to the actual situation.

The number of rays projected by the line structured light measurement sensor is one.

During the data segmentation process, the data points of the same arc should meet the following conditions at the same time:

|x _i -x _i-1 |+|z _i -z _i-1 |＜k ₁ (1)

|z _i -z _i-5 |＜k ₂ (2)

(z _i-10 -z _i ≤k ₃ )||(z _i+10 -z _i ≤k ₃ ) (3)

Among them, (x _i , z _i ) is the coordinate value of the structured light measurement data at the i-th point in the x and z directions, and (x _i-1 , z _i-1 ) is the structured light measurement data at the i-1th point. The coordinate values in the x and z directions, z _i-5 , z _i-10 , and z _i+10 are divided into the coordinates of the i-5, i-10, and i+10th points in the z direction of the structured light measurement data. value, k ₁ , k ₂ , k ₃ are preset constants.

After the data is divided, the interference of the structured light measurement data satisfying the following conditions is excluded:

Among them, (x _i , z _i ) is the coordinate value of the structured light measurement data in the x and z directions at the i-th point, is the structured light measurement data of the last point on the arc, k ₄ and k ₅ are preset constants, and k ₄ <k ₅ exists.

The arc fitting requires that the number of data points on the same arc segment simultaneously meet the following conditions:

n>r/T _sample (6)

n＜2*r/T _sample (7)

Among them, n is the number of data points on the same arc, r is the radius of the arc, and T _sample is the resolution of the structured light.

The arc fitting is performed by the Gauss-Newton iteration method, and the objective function is:

f(x ₀ ,z ₀ )=(xx ₀ ) ² +(zz ₀ ) ² -r ² (8)

Among them, x and z are the coordinates of the data points on the arc, and x ₀ and z ₀ are the coordinates of the center of the arc, which are the parameters to be determined. The solution process is as follows:

Step 1: Set the initial values of x ₀ and z ₀

Among them, x _k is the kth data on the arc in the x direction, n is the amount of data on the arc, z _k is the maximum value of the data on the arc in the z direction, and r is the radius of the arc;

Step 2: Find the second-order partial derivative for the function f(x ₀ , z ₀ ), that is

At the same time order

b ₁₁ Δ ₁ +b ₁₂ Δ ₂ =B ₁ (16)

b ₂₁ Δ ₁ +b ₂₂ Δ ₂ =B ₂ (17)

Among them, f _k is the objective function value of the kth data on the arc, Δ ₁ and Δ ₂ are the increments of the coordinates of the center of the circle, respectively. According to equations (16) and (17), we can get:

The third step: update x ₀ , z ₀ , namely:

in, are the coordinates of the arc center at the i-1th iteration, respectively, are the coordinates of the arc center at the ith iteration;

Step 4: Calculate the mean squared error:

If MS<T, T is the maximum mean square error value, stop the iteration, and obtain the arc center coordinates x ₀ , z ₀ ; otherwise, go to the second step.

The constraints that arcs on the same object should satisfy are:

Among them, (x _q , z _q ) is the last matched arc center coordinate, are the coordinates of the arc center of the unmatched section, and k ₆ and k ₇ are preset constants.

After the arc centers of the m sections are matched, the object corresponding to the arc is a candidate grasping object.

Described using the linear interpolation algorithm to calculate the three-dimensional coordinates of the long cylindrical object, specifically:

Knowing the y value at the grasping position of the object, calculate the two sections closest to the value, and obtain the center coordinates (x ₁ , z ₁ ) and (x ₂ , z ₂ ) of the arc corresponding to the corresponding section object, and use linear interpolation The x and z values corresponding to the y value calculated by the method are:

Among them, y ₁ and y ₂ are the y values corresponding to the two profiles, respectively.

The determining the label of the object to be grasped is specifically:

Randomly select objects that can be grasped by the top layer, if the following inequality holds:

Then the object labeled s is the object to be grasped; among them, rand() is a random number, are the z values corresponding to the two grasping positions of the object labeled s, respectively, are the z values corresponding to the two grasping positions of the object labeled k, and m is the number of candidate grasping objects.

The present invention has the following advantages and beneficial effects:

1. Using structured light measurement sensor and PC to realize three-dimensional identification and positioning of long cylindrical objects, which has the characteristics of simple equipment, high measurement accuracy and strong real-time performance.

2. Although the structured light measurement data is seriously polluted by noise and has straight line interference, and the data itself has arcs, it can still accurately segment arcs and has good anti-interference performance.

3. The constraint on the long cylindrical object itself is small, and its radius and length can be changed at will.

4. In the case that the long cylindrical objects in the material box are placed in various situations such as multi-layer, cross-layer and cross, it can accurately locate the object to be grasped, and has good robustness.

Description of drawings

Fig. 1 is the overall flow chart of the present invention;

2 is a schematic diagram of line structured light data obtained by scanning a certain profile;

Figure 3 is a schematic diagram of the arc segment segmentation result;

Figure 4 is a schematic diagram of the arc fitting results.

Detailed ways

The present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

The present invention provides a three-dimensional identification and positioning method for long cylindrical objects based on line structured light scanning. The structured light measurement sensor is used to axially scan the surface of the object to obtain structured light measurement data, and the data is divided and circle fitted to obtain the coordinates of the center of the circle, And match, calculate the three-dimensional coordinates of the object. As shown in Figure 1, it specifically includes the following steps:

1. Structured light measurement data acquisition

Using the structured light measurement sensor, the long cylindrical object in the bin is scanned axially in m times with a fixed step size, and the structured light measurement data of m sections are obtained. The data of one section is shown in Figure 2. The abscissa and The ordinate is the length data in the X direction and the Z direction respectively; the number of rays projected by the structured light measurement sensor is one.

2. Data segmentation

By analyzing the data characteristics of the same arc in the structured light measurement data, it is found that they should meet the following conditions at the same time:

|x _i -x _i-1 |+|z _i -z _i-1 |＜k ₁ (1)

|z _i -z _i-5 |＜k ₂ (2)

(z _i-10 -z _i ≤k ₃ )||(z _i+10 -z _i ≤k ₃ ) (3)

Among them, (x _i , z _i ) are the coordinate values of the structured light measurement data in the x and z directions at the i-th point, and k ₁ , k ₂ , and k ₃ are constants, which can be analyzed according to the sample data, and through a large number of Experiments in the actual situation are verified to ensure that the data segmentation on the same arc is correct. Figure 3 is a schematic diagram of the result of arc segment segmentation on a section, and the + sign in the figure indicates the starting point and end point of each segmented arc segment.

However, due to the large amount of noise in the measurement data, an arc is divided into two or more segments, which affects the subsequent arc fitting process. The noise interference in the data segmentation process includes large noise and small noise. The large noise refers to the structured light measurement data (x _i , z _i ) at the ith point, if it is the same as the structure at the last point on the current arc. Light measurement data If the absolute value of the difference in the z direction is greater than a certain number, it is considered to be large noise; small noise refers to the structured light measurement data (x _i , z _i ) at the ith point, if it is the same as the last one on the current arc Structured light measurement data at one point If the absolute value of the direction difference is less than a certain number, the point is considered to be a small noise point. Therefore, the interference of noise data that meets the following conditions should be excluded, and the arc segmentation process should continue, namely:

in, are the coordinate values in the z direction of the structured light measurement data at the last point on the current arc, k ₄ and k ₅ are constants, which can be analyzed according to the sample data and verified through a large number of experiments under actual conditions to eliminate noise interference, so as to ensure the correct data segmentation. In this paper, the data satisfying the formula (4) is defined as large noise, and the data satisfying the formula (5) is small noise.

3. Arc fitting

If the number of data points on the same arc segment satisfies the following conditions, the arc fitting is performed:

n>r/T _sample (6)

n＜2*r/T _sample (7)

Among them, n is the number of data points on the same arc, r is the radius of the arc, and T _sample is the axial scan step. In this paper, the Gauss-Newton iteration method is used to fit the arc center. The expression for a standard circle is:

(xx ₀ ) ² +(zz ₀ ) ² =r ² (30)

In the formula, x and z are the coordinates of the point on the circle, and x ₀ and z ₀ are the coordinates of the center of the circle. make

f(x ₀ ,z ₀ )=(xx ₀ ) ² +(zz ₀ ) ² (31)

Then the Gauss-Newton iteration method obtains the coordinates of the center of the circle (x ₀ , z ₀ ) by taking the minimum value of the following expression, namely

The specific steps are as follows:

Step 1: Set the initial values of x ₀ and z ₀ , where x ₀ is the average value of the data on the arc in the x-direction, and z ₀ is the maximum value of the data on the arc in the z-direction minus the arc radius, that is

Step 2: Find the second-order partial derivative of the function f(x ₀ , z ₀ ) with respect to x ₀ , z ₀ ,

At the same time order

b ₁₁ Δ ₁ +b ₁₂ Δ ₂ =B ₁ (16)

b ₂₁ Δ ₁ +b ₂₂ Δ ₂ =B ₂ (17)

Among them, Δ ₁ and Δ ₂ are the increments of the coordinates of the center of the circle, respectively. According to the formulas (16) and (17), we can get:

The third step: update x ₀ , z ₀ , namely:

Step 4: Calculate the mean squared error:

If MS<T, stop the iteration and obtain the arc center coordinates x ₀ , z ₀ ; otherwise, go to the second step. Among them, T is the maximum mean square error value. Figure 4 shows a schematic diagram of the arc fitting result. The circle in the figure represents the circle obtained by dividing the arc segment, and the + sign indicates the center of the circle obtained by the fitting.

4. Arc matching

Let the coordinates of the centers of all arcs of the first unmatched section be The center coordinates of all arcs used for matching are (x ₁ , z ₁ ),(x ₂ ,z ₂ ),...,(x _q ,z _q ),...,(x _m ,z _m ). Therefore, two arcs belonging to the same object should satisfy the following conditions at the same time:

Among them, k ₆ and k ₇ are constants, which can be obtained by analyzing the sample data and verifying through a large number of experiments under actual conditions. After satisfying the above two formulas, update the arc center coordinates for matching, namely:

5. Calculate the three-dimensional coordinates of the object

If the matching times of a certain arc is equal to the scanning times m, then the long cylindrical object corresponding to this arc is the candidate grasping object. Knowing the y value at the grasping position of the object, calculate the two sections closest to this value, and obtain the center coordinates (x ₁ , z ₁ ) and (x ₂ , z ₂ ) of the arc corresponding to the corresponding section object, then there are The following linear relationship:

Finally, the x and z values corresponding to the y value can be obtained as:

Among them, y ₁ and y ₂ are the y values corresponding to the two profiles, respectively.

6. Determine the label of the grasped object

If the method of determining the object to be grasped from the candidate long cylindrical objects is fixed, and this object cannot be grasped for some reason, the system will repeat the above process and become paralyzed. Therefore, this paper chooses a random method to determine the object to be grasped. If the following inequality holds:

Then the long cylindrical object marked with s is the object to be grasped. Among them, rand() is a random number, z ₁ and z ₂ are the z values corresponding to the two grasping positions, respectively, and m is the number of candidate grasping objects.

Claims (10)

1. A three-dimensional identification and positioning method for a long cylindrical object based on line structured light scanning is used for realizing measurement of the position of the long cylindrical object in a material box, and is characterized by comprising the following steps:

axially scanning a long cylindrical object in the material box m times at a fixed step length by using a linear structured light measuring sensor to obtain structured light measuring data of m sections;

respectively carrying out data segmentation on the structured light measurement data of each section so that the data belonging to the same object are segmented in a section of circular arc;

performing arc fitting on each segment of the segmentation data to obtain a circle center coordinate;

matching the circle centers of the arcs of the m sections according to constraint conditions to be met by the arcs on the same object;

calculating the three-dimensional coordinates of the long cylindrical object by using a linear interpolation algorithm;

determining the mark number of the grabbed object;

the arc fitting is carried out by adopting a Gaussian-Newton iteration method, and the objective function is as follows:

f(x₀,z₀)＝(x-x₀)²+(z-z₀)²-r² (8)

wherein x and z are data point coordinates on the circular arc, and x₀、z₀The circular arc center coordinates are parameters to be solved, and the solving process is as follows:

the first step is as follows: setting x₀、z₀Initial value of (2)

Wherein x is_kIs the k-th data in the x-direction on the arc, n is the data amount on the arc, z_kIs the maximum value of the data on the arc in the z direction, and r is the radius of the arc;

the second step is that: for function f (x)₀,z₀) Taking the second partial derivative, i.e.

At the same time order

b₁₁Δ₁+b₁₂Δ₂＝B₁ (16)

b₂₁Δ₁+b₂₂Δ₂＝B₂ (17)

Wherein f is_kThe value of the objective function, Δ, for the k-th data on the arc₁、Δ₂The increments of the center coordinates are obtained from equations (16) and (17):

the third step: updating x₀、z₀Namely:

wherein,respectively are the circular arc center coordinates in the i-1 st iteration,respectively are the circular arc center coordinates in the ith iteration;

the fourth step: calculating the mean square error:

if MS is less than T and T is the maximum mean square error value, stopping iteration to obtain the circular arc center coordinate x₀、z₀(ii) a Otherwise, go to the second step.

2. The linear structured light scanning-based three-dimensional identification and positioning method for the long cylindrical object according to claim 1, wherein the setting of the m sections can substantially cover the axial direction of the whole long cylindrical object and needs to be determined according to actual conditions.

3. The method for three-dimensionally identifying and positioning the long cylindrical object based on the line-structured light scanning as claimed in claim 1, wherein the number of rays projected by the line-structured light measuring sensor is 1.

4. The three-dimensional identification and positioning method for the long cylindrical object based on the line structured light scanning as claimed in claim 1, wherein during the data segmentation process, the data points of the same arc should simultaneously satisfy the following conditions:

|x_i-x_i-1|+|z_i-z_i-1|＜k₁ (1)

|z_i-z_i-5|＜k₂ (2)

(z_i-10-z_i≤k₃)||(z_i+10-z_i≤k₃) (3)

wherein (x)_i,z_i) The coordinate values of the structured light measurement data in the x and z directions at the ith point (x)_i-1,z_i-1) The coordinate values of the x and z directions of the i-1 point of the structured light measured data, z_i-5、z_i10、z_i+10Dividing the coordinate value k of the structured light measurement data in the z direction at the (i-5) th, i-10 th and i +10 th points₁、k₂、k₃Is a preset constant.

5. The three-dimensional identification and positioning method for the long cylindrical object based on the line structured light scanning as claimed in claim 4, wherein the data segmentation excludes the interference of structured light measurement data satisfying the following conditions:

wherein (x)_i,z_i) The coordinate values of the structured light measurement data in the x and z directions at the ith point,the structured light measurement data, k, for the last point on the arc₄、k₅Is a predetermined constant and k is present₄＜k₅。

6. The linear structured light scanning-based three-dimensional identification and positioning method for the long cylindrical object according to claim 1, wherein the arc fitting requires that the number of points of data on the same arc satisfy the following conditions:

n＞r/T_sample (6)

n＜2*r/T_sample (7)

wherein n is the number of points of data on the same arc, r is the radius of the arc, and T_sampleIs structured light resolution.

7. The linear structured light scanning-based three-dimensional identification and positioning method for the long cylindrical object according to claim 1, wherein the constraint conditions that the arcs on the same object should satisfy are as follows:

wherein (x)_q,z_q) Is the coordinate of the center of the last matched arc,is the circular arc center coordinate of the unmatched section, k₆、k₇Is a preset constant.

8. The linear structured light scanning-based three-dimensional identification and positioning method for the long cylindrical object according to claim 1, wherein after the centers of the arcs of the m cross sections are matched, the object corresponding to the arc is the candidate grasped object.

9. The linear interpolation algorithm based three-dimensional identification and positioning method for the long cylindrical object according to claim 1, wherein the linear interpolation algorithm is used to calculate the three-dimensional coordinates of the long cylindrical object, specifically:

knowing the value of y at the location where the object is grasped, calculating the two sections closest to this value,obtaining the center coordinates (x) of the corresponding arc of the object with the corresponding section₁,z₁)、(x₂,z₂) And calculating by using a linear interpolation method to obtain x and z values corresponding to the y value as follows:

wherein, y₁、y₂The y values corresponding to the two profiles are respectively.

10. The linear structured light scanning-based three-dimensional identification and positioning method for the long cylindrical object according to claim 1, wherein the determination of the captured object label is specifically as follows:

randomly selecting the top layer of objects that can be grabbed if the following inequality holds:

then the object with the label s is the object to be grabbed; wherein, rand () is a random number,the z values corresponding to the two gripping positions of the object respectively marked s,the z values corresponding to two grabbing positions of the object with the mark number k respectively, and m is the number of candidate grabbing objects.