CN113160143A - Method and system for measuring material liquid level in material stirring tank - Google Patents
Method and system for measuring material liquid level in material stirring tank Download PDFInfo
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Abstract
The invention discloses a method and a system for measuring the material liquid level in a material stirring tank, which are used for acquiring original three-dimensional point cloud data of working conditions of unloading to the material stirring tank and constructing a three-dimensional point cloud picture; calibrating key points of a material stirring tank model in a laser radar coordinate system, and performing primary filtering on original three-dimensional point cloud data according to the key point position range of the material stirring tank model, wherein only laser points in the material stirring tank are reserved; screening seed points by dividing an ideal laser plane and according to the distance of laser points; constructing a plurality of irregular triangular nets to extract new feature points; correcting and screening the characteristic points according to the two frames of filtered point cloud data before and after the current data frame; and performing curve fitting on the characteristic points, and acquiring the distances between different liquid level positions and the screen surface in a mode of sampling and calculating the distances from the sampling points to the screen surface and averaging. The invention can improve the efficiency and the safety of the stirring work in the material stirring tank.
Description
Technical Field
The invention relates to the field of industrial detection, in particular to a method and a system for measuring the material liquid level in a material stirring tank.
Background
The filtration and agitation processes play an important role in our production life, particularly in industrial processes. Many materials all need to pass through the filtration before further processing, the stirring process, for example, the building worker ground is to the stirring of concrete, the process of filling, some chemical enterprises refine the in-process at the product and just often need filter the raw materials, the stirring process, input, stirring, the output process of raw materials are real-time continuous in the course of working usually, the mixed raw materials of solid-liquid passes through transfer passage and constantly carries the raw materials to material mixing tank, the raw materials gets into material mixing tank after sieving the net and stirs, then export to next working link through the discharge gate. Due to the complexity and the non-uniformity of the properties of the solid-liquid mixed raw materials before stirring, the stirring time of the materials in the stirring tank is uncertain and has great randomness, and in the processing process, a worker needs to manually control the input speed of the materials by visually observing the distance between the liquid level of the materials in the material stirring tank and a screen of the stirring tank. However, such a situation actually has some problems:
(1) the spraying phenomenon exists in the process of stirring the materials in the material stirring tank, and particularly when the amount of the materials in the stirring tank is insufficient (the maximum amount can reach tens of meters), the spraying phenomenon threatens workers to a certain extent;
(2) due to the fact that workers do not concentrate attention or misjudge visually, and factors such as improper control and the like are added, materials in the material stirring tank overflow;
(3) the speed of the special hiring personnel for manually controlling the input of the materials improves the engineering cost.
During research, the sensor for providing real-time information of the material liquid level in the material stirring tank for the design of the control system is greatly influenced by the actual working condition, and the contact type tuning fork level meter and the pressure sensor have short service life and large consumption in the stirring tank in which solid-liquid mixed raw materials continuously roll, so that the maintenance is difficult and the cost is increased. Common non-contact sensors such as infrared sensors, ultrasonic sensors, depth cameras and the like cannot achieve good measuring effects due to the reasons of splashing of materials in the stirring process in the material stirring tank, high-frequency noise of the field environment, no characteristic points on the liquid level of the materials below the screen surface and the like. Therefore, a non-contact sensor laser radar is selected to model a three-dimensional environment, the distance from the liquid level of the material to the screen surface is calculated, but due to splashing of liquid drops in the stirring process, outliers which do not represent the liquid level of the material appear in point cloud generated by the laser radar, and corresponding filtering processing is required.
The commonly used point cloud filtering methods at present include a straight-through filtering algorithm, a bilateral filtering algorithm, a gaussian filtering algorithm, a conditional filtering algorithm, a random sampling consistency-based filtering algorithm and the like. However, due to the complexity and the changeability of the working condition environment, the randomness of the discrete points is very large, and the situation that the number of the discrete points is far from the number of the ultra-real points may occur in some positions, a single traditional filtering method cannot achieve an ideal filtering effect, and a straight-through filtering algorithm can only filter the known discrete points and cannot act on point cloud data of position positions; the bilateral filtering algorithm corrects the position of the current sampling point by taking the weighted average of the adjacent sampling points, so that the filtering effect is achieved, but the adjacent sampling points which are too different from the current sampling point are removed, and the loss of effective data is caused; when a Gaussian filtering algorithm is adopted, although the point cloud data has a good smoothing effect and can enable the curved surface data to have better continuity, the method cannot be well adapted to sudden fluctuation of the material liquid level height under severe working conditions; while the random sampling consistency algorithm can estimate the parameters of the mathematical model in an iterative manner from a group of observation data sets containing 'local outliers', the number of iterations must be increased in order to increase the probability, and the real-time requirement in the actual engineering is difficult to meet. Therefore, a comprehensive filtering method is required, and how to integrate the advantages of the traditional filtering method and design a corresponding adaptive transformation mechanism according to actual conditions becomes a key for solving the engineering problem.
Disclosure of Invention
The invention aims to solve the technical problem that the prior art is not enough, and provides a method and a system for measuring the liquid level of material in a material stirring tank, which can continuously and stably measure the liquid level of concrete in a hopper.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows: a method for measuring the material liquid level in a material stirring tank comprises the following steps:
s1, collecting spatial position information of the material stirring tank, the material liquid level and peripheral engineering equipment under a laser radar coordinate system in the working process of conveying materials into the material stirring tank for mixing and stirring in real time, and acquiring original laser radar three-dimensional point cloud data;
s2, calibrating key points of the material stirring tank model in a laser radar coordinate system, performing primary filtering on original laser radar three-dimensional point cloud data according to the key point position coordinates of the material stirring tank model, filtering all point cloud data outside the material stirring tank, and only keeping laser points in the material stirring tank;
s3, dividing the ideal laser surface, screening seed points according to the distance of the laser points reserved after the processing of the step S2, judging the reliability of the seed points, and determining the seed points on the material liquid surface;
s4, constructing a plurality of irregular triangular nets according to the material liquid level seed points, and extracting new feature points;
s5, screening and correcting the characteristic points;
and S6, performing curve fitting on the characteristic points obtained after the processing in the step S5, and obtaining the distances between different liquid level positions and the screen surface by using the fitted curve.
According to the method, the acquired laser radar original data are preprocessed, so that the data calculation amount in the later filtering method implementation process is effectively reduced, and the real-time performance of the filtering algorithm is improved; by dividing the ideal laser surface and screening the seed points according to the distance of the laser points, the complexity of a filtering algorithm is reduced, and the seed points with high reliability are obtained; a plurality of irregular triangular nets are constructed, and new feature points are extracted, so that the completeness and richness of data are guaranteed, and the possibility of occurrence of overfitting is reduced. And correcting the characteristic points for representing the liquid level of the material according to the laser radar data of the previous and next frames, further ensuring the effectiveness of the characteristic points and reducing the possibility of random errors caused by random discrete points.
The specific implementation process of step S2 includes:
1) calibrating eight key points of the hopper model in a laser radar coordinate system, wherein the eight key points are marked as follows: fliter _ point1 (x)1,y1,z1),fliter_point2(x2,y2,z2),..., fliter_point8(x8,y8,z8) (ii) a Wherein, the fliter _ point1, the fliter _ point2, the fliter _ point3 and the fliter _ point4 are four vertices of a rectangular screen, the fliter _ point5, the fliter _ point6, the fliter _ point7 and the fliter _ point8 are projections of the four vertices of the rectangular screen on a horizontal ground;
2) and judging whether the laser points in the laser radar three-dimensional point cloud data are inside a space polyhedron formed by the eight key points one by one, if a certain laser point is not inside the space polyhedron, removing the laser point from the current three-dimensional point cloud data, and if not, keeping the laser point.
According to the invention, the eight key points of the hopper model are calibrated in advance, so that the original data of the laser radar can be conveniently acquired for preprocessing, the data calculation amount of the later filtering method in the implementation process is effectively reduced, and the real-time performance of the filtering algorithm is improved; meanwhile, part of invalid data is removed in advance, so that new interference introduced in a later projection process can be avoided, and the robustness of the algorithm is improved.
The specific implementation process of step S3 includes:
a) through hopper and laser radar's relative position to and the declination of each line laser on the vertical direction, acquire under current laser radar mounted position, can characterize the laser radar ID number of muddy earth liquid level height, obtain an effectual ID number set: valid _ id ═ { valid _ id _1, valid _ id _2, · valid _ id _ n }; valid _ ID _1, valid _ ID _ 2.,. valid _ ID _ n is the 1 st, 2 nd,.. the nth valid ID number;
b) grouping the three-dimensional point cloud data obtained after the processing of the step S2 according to different effective ID numbers to obtain n point cloud sub data sets sub _ points _1, sub _ points _2, sub _ points _ i, sub _ points _ n; according to the laser vertical emission angle gamma corresponding to each effective ID numberiN theoretical laser plane courses are generated,
c) from the position coordinates fliter _ point1 (x) of the apex of the screen face1,y1,z1), fliter_point2(x2,y2,z2)，fliter_point3(x3,y3,z3) Extracting a screen plane equation;
d) respectively projecting the laser points in the n point cloud subdata sets to corresponding theoretical laser planes, and segmenting the theoretical laser planes into a plurality of rectangles with equal size along the x-axis direction of a coordinate system;
e) calculating the distance between the projection point in each partition area and the intersection line segment, and adjusting the number of the selected seed points in the partition area according to the distance value and the adaptive control rate adj to finally obtain an initial seed point set;
f) and (4) performing reliability judgment on each initial seed point set, and filtering abnormal outliers to obtain a final seed point set, namely a material liquid level seed point set.
According to the invention, the point cloud data acquired by the laser radar are grouped according to different vertical laser emission angles, compared with a method for directly processing the whole point cloud data, the grouping processing can better utilize the characteristic that the point cloud data with the same number belong to the same plane, the complexity of a filtering algorithm is reduced, an integral filtering algorithm is not required to be designed for the complicated whole point cloud data, but the point cloud data corresponding to each number share one filtering algorithm, and the robustness of the filtering algorithm is improved.
The concrete implementation process of the step d) comprises the following steps:
i) spatial screen plane equation and n theoretical laser squaresForming n intersection line segments, wherein the intersection line segments are marked as line _1, line _2, and line _ n; line _1, line _2, and line _ n are equally divided into m, each segment having a length of m
Projecting the laser points in the data set to the corresponding ideal laser plane to obtain the same number of laser projection points;
ii) on each theoretical laser plane, taking each divided line segment as the short side of the rectangle, and selecting the long side of the rectangle to be more than or equal to VmaxThereby dividing the theoretical laser plane into m rectangles, wherein VmaxIs the maximum of the distance from all laser spots projected onto the laser plane to the intersection line.
Before selecting the seed points, the ideal laser plane is segmented, so that the seed points can be selected in all horizontal directions of the material stirring tank, the situation that effective data is lost under the condition that the distance value from the laser projection point to the intersection line is used as a single scale for initially screening the seed points is avoided, and the integrity and the richness of the data are ensured.
The concrete implementation process of the step e) comprises the following steps:
I) adjusting the quantity Q of the initial seed points selected in different rectangular areas according to an adaptive control law adj:
wherein G is the number of laser points in the rectangular region, v1，v2，...，vfIs the distance from the f laser projection points on the laser plane to the intersection line, u1，u2，...，ugThe distances from g laser projection points in the divided rectangular area to the intersection line are calculated; w is satisfied with | u in the rectangular region after divisioni-VmaxThe number of laser projection points with the value of less than or equal to T, i is 1, 2, g and s satisfy the value of | v on the whole planej-VmaxThe number of laser projection points with the value of | < T, j ═ 1, 2]；
II) in each rectangular area, adjusting the number of seed points in the segmentation area according to the distance value and the adaptive control rate adj, wherein the selected seed points meet the following conditions: the distance value between the ideal laser plane and the intersection line of the screen surface is less than T.
Due to the fact that the distribution of point cloud data under complex working conditions has high randomness, effective data are lost and interference of outliers is introduced when a fixed number of seed points are selected in each partitioned rectangular area, the method designs a self-adaptive control law, dynamically adjusts the number of initial seed points selected in different rectangular areas according to the number meeting the distance requirement from laser projection points to intersecting lines in different partitions, and improves the robustness of a filtering algorithm. The specific implementation process of the step f) comprises the following steps:
A) calculating the average value h of the distances from each seed point to the intersection line in 8 rectangular areas adjacent to the rectangular area jj-4，hj-3，hj-2，hj-1，hj+1，hj+2，hj+3，hj+4；
B) Introducing a discriminant:
setting a threshold HmaxFor the initial seed point PjIf Σ H is less than or equal to the threshold HmaxThen the initial seed point P of the rectangular area j is selectedjDetermining as a material liquid level seed point, if sigma H is larger than a threshold value HmaxThen, the average value of the distances from the seed points to the line _1 of the intersection line in the adjacent 8 rectangular areas is given to the point PjAnd determining the material level seed point of the rectangular area j.
According to the method, the characteristic that the liquid level of the material has continuity is utilized, the distance threshold condition which needs to be met by the characteristic points of the adjacent rectangular subareas is calculated by the characteristic points of the adjacent rectangular subareas, and the seed points which do not meet the condition are corrected, so that the data is more continuous, and the curve and the curved surface which are fitted later are smoother.
In step S4, constructing a plurality of irregular triangulation networks according to the material liquid level seed points, and a specific implementation process of extracting new feature points includes:
A. constructing a plurality of irregular triangles according to the material liquid level seed points obtained in the step B), sequentially selecting key points of the irregular triangles on an ideal laser plane along the negative direction of the x axis of a laser radar coordinate system, taking the key point with the minimum x coordinate of the currently selected three key points as the first key point of the next irregular triangle, and constructing the last irregular triangular net along the positive direction of the x axis if the last remaining material liquid level seed points cannot form an irregular triangle;
B. constructing an irregular triangular net by original space laser points corresponding to key points of the irregular triangle and other point cloud data except the material liquid level seed points, and judging the original space laser points corresponding to laser projection points in a rectangular area where the irregular triangular net is located, wherein the specific judgment process comprises the following steps: for a certain point W to be judged, the distance from the point W to an irregular triangular plane ABC in the original space is fliter _ h, the included angles between straight lines WA, WB and WC and the plane are respectively alpha, beta and omega, and the distance threshold is set to be fliter _ hmaxAngle threshold value of thetamaxIf the distance from the point to be detected W to the plane ABC is smaller than the set distance threshold, namely fliterh＜fliter_hmaxAnd the maximum included angle between the straight line WA, WB, WC and the plane ABC is smaller than the set angle threshold value, namely max (alpha, beta, omega) < thetamaxThen the point W to be determined becomes a new feature point.
According to the method, the irregular triangulation network is constructed based on the screened seed points, and the new characteristic points capable of representing the liquid level of the material are obtained by judging and screening other non-seed points in the original space, so that the quantity of effective data is increased, and the over-fitting problem caused by distance calculation and surface fitting which are carried out by only depending on the initial seed points is avoided.
In step S4, the specific implementation process of screening and correcting the feature points includes:
s51, processing the previous frame of original lidar point cloud data and the next frame of original lidar point cloud data of the current frame of original lidar point cloud data according to the method of the steps S1-S4 to obtain two feature point data sets, namely match _ points _ before and match _ points _ after;
s52, setting a search Radius as Radius for searching each feature point obtained currently by taking the feature point as a circle center according to a new feature point set, reserving the feature point when the feature point has to have at least 2 points in feature point data sets, namely, feature _ points _ before and feature _ points _ after, in the Radius, and otherwise deleting the feature point;
s53, correcting the current feature point which is reserved after the processing of the step S52 by adopting a Radius filtering algorithm, namely setting the search Radius as Radius by taking each feature point which is obtained currently as the center of a circle, correcting the feature point when the feature point has 5 data points which exceed the feature point data sets, namely, the feature point coordinates are the average value of the six point coordinates.
According to the method, the characteristic points representing the liquid level of the material are corrected according to the laser radar data of the previous and next frames by utilizing the characteristics that the sampling rate of the laser radar is high and the instantaneous change of the liquid level of the material is often slow, so that the effectiveness of the characteristic points is further ensured, the distance calculation and the curve and curved surface fitting after the distance calculation are closer to the real situation, and the possibility of random errors caused by the occurrence of random discrete points is reduced.
The specific implementation process of step S6 includes: respectively adopting a least square polynomial to perform curve fitting on the x coordinate of the corrected characteristic point by using the y coordinate and the z coordinate of the corrected characteristic point to obtain a current height liquid level line cur; and (3) equally dividing a current height liquid level line cur into k key points, for each key point, calculating the distance from the key point to the screen surface by using a point-surface distance formula, and calculating the average value of the k distances to obtain the distance between the slurry liquid level and the screen surface.
The invention also provides a material liquid level height measuring system in the material stirring tank, which comprises computer equipment; the computer device is configured or programmed for performing the steps of the above-described method.
Compared with the prior art, the invention has the beneficial effects that:
1) the invention overcomes the problem that the common contact measurement and non-contact measurement can not continuously and stably measure the height of the concrete liquid level in the hopper, and provides a technical basis for designing a control system for the cooperative work of the mixer truck and the pump truck in the later period;
2) extracting initial seed points by dividing a laser plane and according to the quantity and distance distribution of projection points in a divided area, and performing reliability analysis by comparing the initial seed points with adjacent seed points to ensure high-quality screening of seed points;
3) the method for obtaining the characteristic points representing the concrete liquid level by utilizing the plurality of irregular triangular meshes and the radius filtering algorithm based on the front and back frame data ensures the quantity of the data points of the fitting liquid level curve and avoids the random deviation caused by low reliability of single frame data.
Drawings
FIG. 1 is a schematic model diagram of a material stirring tank to be tested according to the present invention;
FIG. 2 is a schematic diagram of a coordinate system of a velodyne16 lidar employed in the present invention; (a) a laser radar side view; (b) a laser radar top view;
FIG. 3 is a schematic diagram of the emitting directions of laser beams with different ID numbers of the velodyne16 laser radar adopted in the present invention;
FIG. 4 is a three-dimensional cloud point of an environment in which the present invention is applied;
FIG. 5 is a schematic diagram of key points of a model of a material stirring tank selected during first filtering;
FIG. 6 is a schematic diagram of only retaining the point cloud inside the material stirring tank;
FIG. 7 is a schematic view of the division of an ideal laser plane;
FIG. 8 is a schematic diagram of selecting key points for constructing an irregular triangle from the seed points;
FIG. 9 is a schematic diagram of an irregular triangular network constructed by irregular triangles and feature points to be judged;
FIG. 10 is a schematic view of feature points obtained after filtering to characterize the levels of materials at different heights;
Detailed Description
The method for measuring the material liquid level in the material stirring tank comprises the following steps:
Step 2, calibrating key points of the material stirring tank in a laser radar coordinate system, performing primary filtering on original three-dimensional point cloud data according to the position coordinate range of the key points of the material stirring tank model, filtering all point cloud data except the stirring tank model, and only keeping the point cloud data in the stirring tank model;
step 3, screening seed points by dividing the ideal laser surface and according to the distance value between the laser surface and the screening surface, and judging the reliability of the seed points;
step 4, constructing a plurality of irregular triangular nets to extract new feature points;
step 5, taking the original data of the laser radar of the previous frame and the next frame, and carrying out the same filtering processing of the steps 1, 2 and 3, thereby correcting and screening the characteristic points of the current frame;
7, establishing an experiment platform to verify the measurement precision of the velodyne16 line laser radar;
the details of each step are as follows:
Step 1.1, constructing a working model of the material stirring tank shown in the figure 1, wherein the working model comprises the material stirring tank, a screen surface, a mechanical device (used for simulating a stirrer to roll the liquid level of a material) in the stirring tank and appropriate raw materials for simulating the liquid level of the material, such as solid-liquid mixed raw materials similar to concrete;
step 1.2, rigidly connecting the laser radar with a material stirring tank model, selecting a proper installation angle and distance by constructing an environment three-dimensional point cloud picture in real time to enable the number of laser lines entering the material stirring tank to be the largest, wherein the coordinate axis direction of the velodyne16 line laser radar is shown in figure 2, the laser radar coordinate system takes a laser emission center as an origin of the coordinate system, the upper surface perpendicular to the laser radar is in the positive direction of a Z axis, the direction which passes through the origin of the coordinates on a horizontal symmetrical plane of the laser radar, deviates from a rear end interface of the radar and is parallel to the lower surface of the radar is in the positive direction of a Y axis, and the X axis and the Y, Z axis form a right-handed system. The corresponding relationship of the laser beam number and the vertical emission angle is shown in table 1 and fig. 3;
table 1 corresponding relation between laser radar ID number and vertical emission angle of velodyne16
And 1.3, operating the model, acquiring original point cloud data of the laser radar in the dynamic working process through data capture software, and constructing a similar three-dimensional point cloud model in a simulation environment as shown in a figure 4, wherein a screen surface of the material stirring tank, a material pouring tank above the screen surface and a point to be screened below the screen surface are identified.
And 2, as shown in fig. 4, except point cloud data to be processed below the screen and point cloud data representing the screen surface, points outside the material stirring tank are all outliers which need to be filtered in the first step. Calibrating key points of the material stirring tank model in a laser radar coordinate system, and filtering original three-dimensional point cloud data for the first time according to the key point coordinate range of the material stirring tank model to filter all point cloud data outside a hopper.
And 2.1, calibrating eight key points of the hopper model in a laser radar coordinate system as shown in figure 5. Eight key points are noted as:
fliter_point1(x1，y1，z1)，fliter_point2(x2，y2，z2)，...， fliter_point8(x8，y8，z8)；
wherein, the fliter _ point1, the fliter _ point2, the fliter _ point3 and the fliter _ point4 are four vertexes of a rectangular screen, the fliter _ point5, the fliter _ point6, the fliter _ point7 and the fliter _ point8 are projections of the four vertexes of the rectangular screen on a horizontal ground;
and 2.2, judging the position coordinates of each point cloud data obtained in the step 1.3 one by one, and judging whether the laser point is in the interior of a space polyhedron formed by the eight key points.
The screening method comprises the following specific steps:
judging maximum values and minimum values of the selected eight feature points in the x, y and z directions:
max_x＝max(x1，x2，…，x8)，min_x＝min(x1，x2，…，x8)；
max_y＝max(y1，y2，…，y8)，min_y＝min(y1，y2，…，y8)；
max_z＝max(z1，z2，…，z8)，min_z＝min(z1，z2，…，z8)；
selecting point P to be judged in original point cloud dataw(xw，yw，zw)；
(iii) judgment of Pw(xw，yw，zw) Whether it is within the model space: if min _ x < x is satisfied at the same timew＜ max_x，min_y＜yw＜max_y，min_z＜zwIf < max _ z, the point is reserved; if the point is not in the model spaceWithin the range, the point is removed and the step II is carried out
As shown in fig. 6, only the laser spot in the material-agitating tank remains.
Step 3, screening seed points by dividing an ideal laser surface and according to the distance of laser points;
and 3.1, extracting the laser radar ID number representing the height of the concrete liquid level under the current laser radar installation position by analyzing the laser radar point cloud data and reducing the environmental three-dimensional point cloud picture, namely acquiring the laser ID number of a laser beam emitted by a multi-line laser radar and hitting the laser ID number on the material liquid level in the material stirring tank.
As shown in table 1, each ID number corresponds to a specific vertical transmission angle, and a valid ID number set is obtained:
valid_id＝{valid_id_1，valid_id_2，…，valid_id_n}
step 3.2, separating the point cloud data obtained after the primary filtering is finished according to different effective ID numbers to obtain n point cloud sub data sets sub _ points _1, sub _ points _2, sub _ points _ i, sub _ points _ n;
and 3.3, generating n theoretical laser plane equations according to the laser vertical emission angle gamma i corresponding to each effective ID number: selecting a valid ID number valid _ ID _ i from a valid ID number set;
② numbering according to ID and laser vertical emission angle gammaiThe corresponding relation lookup table obtains the laser vertical emission angle gamma corresponding to the ID numberi；
③ according to gammaiExtracting a plane equation of the plane where the ID numbered laser beam is positioned:
πi：aix+biy+ciz+di＝0
since the plane of the laser beam passes through the origin and x-axis of the laser radar coordinate system, d isi＝0，ai0. By vertical emission angle gamma of laseriThe plane equation under the emission angle can be obtained as follows:
z-y*tan γi＝0
if the plane equations corresponding to all the valid ID numbers in the valid ID number set are extracted, the procedure is ended, otherwise, the procedure goes to the first step.
Finally, generating n theoretical laser plane equations:
step 3.4, the position coordinate fliter _ point1 (x) of the top point of the screen surface1，y1，z1)， fliter_point2(x2，y2，z2)，fliter_point3(x3，y3，z3) Extracting a screen plane equation:
πs：asx+bsy+csz+ds＝0
and 3.5, respectively projecting the laser points in the n point cloud subdata data sets to corresponding laser planes, and performing de-segmentation processing on the laser planes.
Step 3.5.1, forming n intersection line sections by a screen plane equation and n theoretical laser equations in space, and recording the intersection line sections as line _1, line _2, … and line _ n;
step 3.5.2, equally dividing the intersecting line segments line _1, line _2, line _ n into m parts, wherein the length of each segment is equal to that of each line segment
Step 3.5.3, as shown in fig. 7, performing rectangular cutting on the n ideal laser planes, which includes the following specific steps:
firstly, selecting a data set from point cloud data sets 1-n, and projecting laser points in the data set to a corresponding ideal laser plane;
calculating the distance between the projected laser point and the intersection line, and recording the maximum value of the distances between all the laser points projected on the laser plane and the intersection line as Vmax；
Thirdly, on each ideal laser plane, each segmented line segment is taken as the short side of the rectangle, the long side of the rectangle can be any value, but the minimum value isMust be greater than the maximum distance from the point of intersection projected onto the laser plane, i.e., the long side of the cut rectangle may be selected to be greater than or equal to VmaxThereby dividing the laser plane into m rectangles; if the n planes are cut, the process is finished, otherwise, the step I is carried out.
Step 3.6, adjusting the number of the selected seed points in the partitioned area according to the distance value and the adaptive control rate adj to finally obtain an initial seed point set, wherein the specific steps are as follows:
extracting a certain plane pi from n ideal laser planesiA data set of upper proxels;
counting the number of projection points in each rectangular partition;
adjusting the number of the initial seed points selected in different rectangular areas according to an adaptive control law adj:
wherein G is the number of laser points in the rectangular region, v1，v2，...，vfIs the distance from the f laser projection points on the laser plane to the intersection line, u1，u2，...，ugThe distances from g laser projection points in the divided rectangular area to the intersection line are calculated; w is satisfied with | u in the rectangular region after divisioni-VmaxThe number of laser projection points with the value of less than or equal to T, i is 1, 2, g and s satisfy the value of | v on the whole planej-VmaxThe number of laser projection points with the value of | < T, j ═ 1, 2]；
Adjusting the number of the selected seed points in the segmentation area according to the distance value and the adaptive control rate adj in each rectangular area to form an initial seed point set, wherein the selected seed points meet the following conditions: the distance value of the intersection line of the ideal laser plane and the screen surface is less than T; if all the point cloud sub-data sets finish the selection of the initial seed points, the process is finished, otherwise, the step I is carried out.
Step 3.7, reliability judgment is carried out on each initial seed point set, and some abnormal outliers are filtered out to obtain a final seed point set;
step 3.7.1, calculating the average value h of the distances from each seed point to the intersection line in 8 adjacent rectangular areas of the rectangular partition jj-4，hj-3，hj-2，hj-1，hj+1，hj+2，hj+3，hj+4；
Step 3.7.2, the reliability of the seed points is judged, and a discriminant is introduced:
for the primary seed point PjMaking a judgment and setting a threshold value HmaxIf the seed point is less than or equal to the threshold value, the primary selection seed point P of the rectangular areajThe seed points can be determined as concrete surface seed points, if sigma H is larger than a threshold value, the average value of the distances from the seed points in the adjacent 8 rectangular areas to the intersecting line 1 is given to the point PjAnd determining the seed point of the rectangular area. Finally, n seed point sets representing different material liquid level heights can be obtained.
Step 4, constructing a plurality of irregular triangular nets to extract new feature points;
step 4.1, selecting one seed point set from the n seed point sets representing different material liquid level heights obtained in the step 3.7.2 to construct an irregular triangular net for screening more characteristic points of the liquid level heights;
step 4.2, selecting key points forming the irregular triangulation network on the ideal laser plane, wherein the specific selection method comprises the following steps:
and sequentially selecting key points of the irregular triangulation network on the ideal laser plane along the negative direction of the x axis of the laser radar coordinate system, wherein the key point with the minimum x coordinate in the currently selected three key points is used as the first key point of the next irregular triangulation network. As shown in FIG. 8, E, F, G is selected as the key point of the first irregular triangulation network in rectangular partition 1, and the G point with the smallest x coordinate is selected as the first key point of the second irregular triangulation network, and if the last remaining seed points cannot form an irregular triangulation network, the last irregular triangulation network is constructed along the positive direction of the x axis from the last partition.
Note that: the key points used for constructing the irregular triangulation are not projection points on an ideal laser plane, but laser points in an original space corresponding to the projection points.
Step 4.3, as shown in fig. 9, respectively performing feature point discrimination on non-seed points in the rectangular region where the irregular triangle is located, wherein the specific method is as follows:
selecting one of the plurality of irregular triangles constructed in the step 4.2, and extracting all non-seed points in the rectangular area where the irregular triangle is located:
selecting one point from the non-seed points to construct an irregular triangulation network;
calculating the distance fliter _ h from the point to the irregular triangular plane in the original space;
fourthly, calculating included angles alpha, beta and omega between the connecting line of the point and the vertex of the plane of the irregular triangle in the original space and the plane of the irregular triangle;
fifthly, remember fliter _ hmaxIs a distance threshold, θmaxIs an angle threshold value, if the distance from the point W to be detected to the plane ABC is less than a set threshold value, namely fliterh＜fliter_hmaxAnd the maximum included angle between the straight line WA, WB, WC and the plane ABC is smaller than a set threshold value, namely max (alpha, beta, omega) < thetamaxThen the point becomes the new feature point. If all the non-seed points in the rectangular area where the irregular triangle is located are judged to be finished, the step I is carried out, and if the non-seed points are not judged to be finished, the step II is carried out.
This results in a feature point set that marks n liquid level height positions.
Step 5, correcting and screening the feature points according to the two frames of filtered point cloud data before and after the current data frame, and finally obtaining the feature points representing the liquid level lines with different heights, as shown in fig. 10;
step 5.1, processing the previous frame of original lidar point cloud data and the next frame of original lidar point cloud data according to the method in the steps 1 to 4 to obtain two feature point data sets, namely, compare _ points _ before and compare _ points _ after;
and 5.2, screening the current characteristic points by adopting a radius filtering algorithm.
Selecting a feature point set g from n feature point sets for marking n liquid level height positions obtained in the step 4.3;
second, the same liquid level is selected in compare _ points _ before and compare _ points _ after
Feature point set g of height position1，g2；
Thirdly, selecting a feature point s from the feature point set g for judgment:
setting the search Radius as Radius by using the currently obtained feature point s as the center of circle, if there is at least feature point data set g in the Radius1And g2If the feature point is not deleted, the feature point is deleted. If all the feature points in the feature point set are judged, the process is ended, otherwise, the step I is carried out.
And 5.3, correcting the current characteristic points by adopting a radius filtering algorithm:
selecting a feature point set g from n feature point sets for marking n liquid level height positions obtained in the step 4.3;
second, the same liquid level is selected in compare _ points _ before and compare _ points _ after
Feature point set g of height position1，g2；
Thirdly, selecting a feature point s from the feature point set g for judgment:
setting the search Radius as Radius by using the currently acquired feature point s as the center of circle, if the Radius must exceed the feature point data set g1And g2And (5) correcting the characteristic point, wherein the coordinate of the corrected characteristic point is the average value of the six points. If all the feature points in the feature point set are judged, the process is ended, otherwise, the step I is carried out.
From this, a set of feature point data can be obtained that is finally used to fit n curves characterizing different material level heights, as shown in fig. 10.
firstly, selecting a characteristic point set u from n characteristic point sets which finally mark n liquid level height positions and are obtained by a radius filtering algorithm in step 5.3;
secondly, performing curve fitting on x by respectively adopting a least square polynomial to the y coordinate and the z coordinate of the characteristic point (for example, the order is 5), and then the current height liquid level line Cur can be expressed as:
and thirdly, k key points are obtained at the curve Cur if the key points are equidistant, the distance from each key point to the screen surface is obtained by using a point-surface distance formula, and the average value of the obtained k distances is obtained to be used as the distance between the slurry liquid level and the screen surface. If all feature point sets are fitted, the process is ended, otherwise, the step I is carried out.
From this, height measurements at n different locations above the concrete level (this height measurement refers to the distance from the screen level somewhere on the concrete level, not the height from the level ground) can be obtained. And performing surface fitting on the liquid levels representing the heights of the n different positions to further fit to obtain a whole material liquid level model.
Claims (10)
1. A method for measuring the material liquid level in a material stirring tank is characterized by comprising the following steps:
s1, collecting spatial position information of the material stirring tank, the material liquid level and peripheral engineering equipment under a laser radar coordinate system in the working process of conveying materials into the material stirring tank for mixing and stirring in real time, and acquiring original laser radar three-dimensional point cloud data;
s2, calibrating key points of the material stirring tank model in a laser radar coordinate system, performing primary filtering on original laser radar three-dimensional point cloud data according to the key point position coordinates of the material stirring tank model, filtering all point cloud data outside the material stirring tank, and only keeping laser points in the material stirring tank;
s3, dividing the ideal laser surface, screening seed points according to the distance of the laser points reserved after the processing of the step S2, judging the reliability of the seed points, and determining the seed points on the material liquid surface;
s4, constructing a plurality of irregular triangular nets according to the material liquid level seed points, and extracting new feature points;
s5, screening and correcting the characteristic points;
and S6, performing curve fitting on the characteristic points obtained after the processing of the step S5, and obtaining the distances between different liquid level positions and the screen surface by using the fitted curve.
2. The method for measuring the material liquid level in the material stirring tank as claimed in claim 1, wherein the step S2 is implemented by the following steps:
1) calibrating eight key points of the hopper model in a laser radar coordinate system, wherein the eight key points are marked as: fliter _ point1 (x)1，y1，z1)，fliter_point2(x2，y2，z2)，…，fliter_point8(x8，y8，z8) (ii) a Wherein, the fliter _ point1, the fliter _ point2, the fliter _ point3 and the fliter _ point4 are four vertexes of a rectangular screen, the fliter _ point5, the fliter _ point6, the fliter _ point7 and the fliter _ point8 are projections of the four vertexes of the rectangular screen on a horizontal ground;
2) and judging whether the laser points in the laser radar three-dimensional point cloud data are inside a space polyhedron formed by the eight key points one by one, if a certain laser point is not inside the space polyhedron, removing the laser point from the current three-dimensional point cloud data, and if not, keeping the laser point.
3. The method for measuring the material liquid level in the material stirring tank as claimed in claim 1, wherein the step S3 is implemented by the following steps:
a) through hopper and laser radar's relative position to and the declination of each line laser on the vertical direction, acquire under current laser radar mounted position, can characterize the laser radar ID number of material liquid level height, obtain an effectual ID number set: valid _ id { valid _ id _1, valid _ id _2, …, valid _ id _ n }; valid _ ID _1, valid _ ID _2, and valid _ ID _ n are the 1 st, 2 nd, and … … nth valid ID numbers;
b) grouping the three-dimensional point cloud data obtained after the processing of the step S2 according to different effective ID numbers to obtain n point cloud sub data sets sub _ points _1, sub _ points _2, sub _ points _ i, sub _ points _ n; according to the laser vertical emission angle gamma corresponding to each effective ID numberiGenerating n theoretical laser plane equations,
c) from the position coordinates fliter _ point1 (x) of the apex of the screen face1，y1，z1)，fliter_point2(x2，y2，z2)，fliter_point3(x3，y3，z3) Extracting a screen plane equation;
d) respectively projecting the laser points in the n point cloud subdata sets to corresponding theoretical laser planes, and segmenting the theoretical laser planes into a plurality of rectangles with equal size along the x-axis direction of a coordinate system;
e) calculating the distance between the projection point in each segmentation region and the intersection line segment, and adjusting the number of seed points in the segmentation region according to the distance value and the adaptive control rate adj to finally obtain an initial seed point set;
f) and (4) performing reliability judgment on each initial seed point set, and filtering abnormal outliers to obtain a final seed point set, namely a material liquid level seed point set.
4. The method for measuring the material liquid level in the material stirring tank as claimed in claim 3, wherein the step d) is realized by the following specific steps:
i) the screen plane equation and n theoretical laser equations in the space form n intersecting line sections, and the intersecting line sections are marked as line _1, line _2, … and line _ n; dividing line _1, line _2, … and line _ n into m parts, wherein each part is of the length
Projecting the laser points in the data set to the corresponding ideal laser plane;
ii) on each theoretical laser plane, taking each divided line segment as the short side of the rectangle, and selecting the long side of the rectangle to be more than or equal to VmaxThereby dividing the theoretical laser plane into m rectangles, wherein VmaxIs the maximum of the distance from all laser spots projected onto the laser plane to the intersection line.
5. The method for measuring the material liquid level in the material stirring tank as claimed in claim 4, wherein the concrete implementation process of the step e) comprises the following steps:
I) adjusting the quantity Q of the initial seed points selected in different rectangular areas according to an adaptive control law adj:
wherein G is the number of laser points in the rectangular region, v1，v2，...，vfIs the distance from the f laser projection points on the laser plane to the intersection line, u1，u2，...，ugThe distances from g laser projection points in the divided rectangular area to the intersection line are calculated; w is satisfied with | u in the rectangular region after divisioni-VmaxThe number of laser projection points with the value of less than or equal to T, i is 1, 2, g and s satisfy the value of | v on the whole planej-VmaxThe number of laser projection points with the value of | < T, j ═ 1, 2]；
II) in each rectangular region, selecting the number of seed points in the segmentation region according to an adaptive control law adj, wherein the selected seed points meet the following conditions: the distance value of the intersection line of the ideal laser plane and the screen surface is less than T.
6. The method for measuring the material liquid level in the material stirring tank as claimed in claim 4, wherein the step f) is realized by the following specific steps:
A) calculating the average value h of the distances from various selected sub-points in 8 rectangular areas adjacent to the rectangular area j to the intersection linej-4，hj-3，hj-2，hj-1，hj+1，hj+2，hj+3，hj+4；
B) Introducing a discriminant:
setting a threshold HmaxFor the initial seed point PjIf Σ H is less than or equal to the threshold HmaxThen the initial seed point P of the rectangular area j is selectedjDetermining as a material liquid level seed point, if sigma H is larger than a threshold value HmaxThen, the average value of the distances from the seed points to the line _1 of the intersection line in the adjacent 8 rectangular areas is assigned to the point PjAnd determining the material level seed point of the rectangular area j.
7. The method for measuring the material liquid level in the material stirring tank according to claim 6, wherein in step S4, a plurality of irregular triangular meshes are constructed according to the material liquid level seed points, and the specific implementation process for extracting new feature points includes:
A. constructing a plurality of irregular triangles according to the material liquid level seed points obtained in the step B), sequentially selecting key points of the irregular triangles on an ideal laser plane along the negative direction of the x axis of a laser radar coordinate system, taking the key point with the minimum x coordinate of the currently selected three key points as the first key point of the next irregular triangle, and constructing the last irregular triangular net along the positive direction of the x axis if the last remaining material liquid level seed points cannot form an irregular triangle;
B. original space laser point corresponding to key point of irregular triangle and removed objectConstructing an irregular triangular net by other point cloud data outside the seed points of the material liquid surface, and judging the original space laser point corresponding to the laser projection point in the rectangular area where the irregular triangular net is located, wherein the specific judgment process comprises the following steps: for a certain point W to be judged, the distance from the point W to an irregular triangular plane ABC in the original space is fliter _ h, the included angles between straight lines WA, WB and WC and the plane are respectively alpha, beta and omega, and the distance threshold is set to be fliter _ hmaxAngle threshold value of thetamaxIf the distance from the point to be detected W to the plane ABC is smaller than the set distance threshold, namely fliterh＜fliter_hmaxAnd the maximum included angle between the straight line WA, WB, WC and the plane ABC is smaller than the set angle threshold value, namely max (alpha, beta, omega) < thetamaxThen the point W to be determined becomes a new feature point.
8. The method for measuring the material liquid level in the material stirring tank as claimed in claim 6, wherein in the step S4, the specific implementation process for screening and correcting the characteristic points comprises:
s51, processing the previous frame of original lidar point cloud data and the next frame of original lidar point cloud data of the current frame of original lidar point cloud data according to the method of the steps S1-S4 to obtain two feature point data sets, namely, match _ points _ before and match _ points _ after;
s52, setting a search Radius as Radius to search each feature point obtained currently by taking the feature point as a circle center, reserving the feature point when the feature point must have at least 2 points in feature point data sets match _ points _ before and match _ points _ after in the Radius, and otherwise deleting the feature point according to a new feature point set;
s53, correcting the current feature point which is reserved after the processing of the step S52 by adopting a Radius filtering algorithm, namely setting the search Radius as Radius by taking each feature point which is obtained currently as the center of a circle, correcting the feature point when the feature point has 5 data points which exceed the feature point data sets, namely, the feature point coordinates are the average value of the six point coordinates.
9. The method for measuring the material liquid level in the material stirring tank as claimed in claim 1, wherein the step S6 is implemented by the following steps:
respectively adopting a least square polynomial to perform curve fitting on the x coordinate of the corrected characteristic point by using the y coordinate and the z coordinate of the corrected characteristic point to obtain a current height liquid level line cur; k key points are obtained at the current height liquid level line cur at equal intervals, for each key point, the distance from the key point to the screen surface is obtained by using a point-surface distance formula, and the obtained k distances are averaged to be used as the distance between the material liquid level and the screen surface.
10. A material liquid level height measuring system in a material stirring tank is characterized by comprising computer equipment; the computer device is configured or programmed for carrying out the steps of the method according to one of claims 1 to 9.
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| CN114359224A (en) * | 2022-01-05 | 2022-04-15 | 三一汽车制造有限公司 | Material level height detection method, device and system and operation machine |
| CN117268498A (en) * | 2023-11-20 | 2023-12-22 | 中国航空工业集团公司金城南京机电液压工程研究中心 | Oil mass measurement method and system |
| CN117268498B (en) * | 2023-11-20 | 2024-01-23 | 中国航空工业集团公司金城南京机电液压工程研究中心 | Oil mass measurement method and system |
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