CN115308763B - Ice hockey elbow protection angle measurement method based on laser radar three-dimensional point cloud - Google Patents
Ice hockey elbow protection angle measurement method based on laser radar three-dimensional point cloud Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/88—Lidar systems specially adapted for specific applications
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- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/02—Systems using the reflection of electromagnetic waves other than radio waves
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Abstract
The invention discloses an ice hockey elbow guard angle measurement method based on laser radar three-dimensional point cloud, which comprises the following steps: building a three-dimensional point cloud measuring system; acquiring point cloud data of a mechanical artificial limb and an ice hockey elbow guard; preprocessing the obtained point cloud data to obtain preprocessed point cloud data; based on the preprocessed point cloud data, intercepting a fine rod point cloud of the extending end of the mechanical artificial limb forearm by using a semicircular ring conditional filtering algorithm; clustering the thin rod point cloud according to columns, dividing the thin rod point cloud into a plurality of sections of point clouds, obtaining key points of each section, projecting the key points onto an XOY plane, performing linear regression on the key points by using a least square method, fitting out a center line of a forearm, obtaining a direction vector according to the center line, and obtaining the angle of the ice hockey elbow protector. The measuring method of the elbow guard angle of the ice hockey can replace manual measurement, can measure the elbow guard movable angle of the ice hockey in a non-contact manner, and provides data support for the dexterity performance of the elbow guard.
Description
Technical Field
The invention relates to the technical field of engineering measurement, in particular to an ice hockey elbow protection angle measurement method based on laser radar three-dimensional point cloud.
Background
In order to detect whether the puck protector is ergonomic and of acceptable quality, advanced testing methods are required. In order to check the dexterity of the ice hockey elbow pad, the angle thereof needs to be measured. In conventional engineering measurements, angle measurements are often measured using a ruler with a dial. With the development of technology in recent years, the angle measurement method also has a great breakthrough. Three-dimensional laser scanning technology has gained acceptance by the industry as a means of obtaining high-precision spatial information. Since the first introduction of this technology into the field of industrial production, three-dimensional laser scanning techniques have been widely studied and applied (El-Hakim Sabry F, brenner Claus, roth Gerhard. A Multi-sensor approach to creating accurate virtual environments [ J ]. ISPRS Journal of Photogrammetry and Remote Sensing,1998,53 (6): 379-391.). LiDAR (Light Detection And Ranging, liDAR) is a non-contact active detection device (Liu, zhang Jun, lu Min, etc.. Laser radar application technology research advances [ J ]. Laser and infrared, 2015 (02): 117-122.) three-dimensional information on the exterior surface of objects and in spatial scenes can be collected. The laser radar can provide accurate three-dimensional information, and is less influenced by natural environments such as illumination, cloud and the like in the process of collecting data (Liu Bo, in the ocean, jiang Shuo. Laser radar detection and three-dimensional imaging research progress [ J ]. Photoelectric engineering, 2019,46 (07): 21-33.). With the development of computer vision, three-dimensional point clouds are increasingly being applied to various engineering mapping and target detection.
The experiments were performed with the hockey elbow pads worn on the mechanical prostheses. For the problem of measuring the angle of the joints of the mechanical arm, some researchers use rotary encoders for measurement, as in (Oguntosin Victoria, akndele ayoola. Design of a joint angle measurement system for the rotary joint of a robotic arm using an Incremental Rotary Encoder [ C ]. Journal of Physics: conference Series,2019,1299 (1): 012108.) designed a robotic exoskeleton rotary joint angle measurement system based on an incremental rotary encoder, using 3D printing and ABS plastic to make a prototype rotary joint with 270 ° range of motion, and then connect to the incremental rotary encoder to measure its speed and angle of motion; the measuring device is low in cost and high in precision, but a mechanical arm with a rotary encoder is not easy to imitate an arm to wear an ice hockey elbow pad. Also, there are elbow joint angle measurements using wearable devices, and a high resolution and low cost electromechanical angle measurement system is proposed in (Botero Valencia J S, restrepo Zapata J P, de Ossa Jimenez M t. Design and implementation of a high-resolution angle measurement system for the upper limbs using a low-cost server [ J ]. International Journal on Interactive Design and Manufacturing (ijdem), 2018,12 (1): 173-177.) for elbow biomechanical analysis based on embedded servo motors, which are not used as actuators, but as angular positioning sensors; the measuring method is to fix the measuring device at two ends of the angle to be measured, but the device needs to be in contact binding with the artificial limb, which may limit the motion of the artificial limb to a certain extent and is inconvenient to wear the elbow guard for measurement. Furthermore, the use of non-contact devices for joint angle measurement (Du Xiaoguo, chen qijun. Dual-Laser Goniometer: A Flexible Optical Angular Sensor for Joint Angle Measurement [ J ]. IEEE Transactions on Industrial Electronics,2021,68 (7): 6328-6338.) describes a flexible optical Goniometer for measuring angular displacement of a single axis joint, the design concept behind this device being that the angular displacement of the proxy is converted into a distance measurement of the Laser by geometric conversion; the sensor shows advantages over conventional rotary encoders in terms of ease of installation and flexibility of deployment, but the prototype sensor measures joint angles in the range of [ -30 °,30 ° ] with a limited range of angle measurements.
At present, three-dimensional point cloud has become one of the common data sources in various fields such as photogrammetry and remote sensing, computer vision, machine learning and the like, and has various types (Zhang Jixian, lin Xiangguo, liang Xinlian. Point cloud information extraction research progress and hope [ J ]. Mapping school newspaper, 2017,46 (10): 1460-1469.). The three-dimensional size data of the object is measured, the quantitative description of the three-dimensional object becomes more and more important, the traditional size measurement method has low measurement speed, low precision and low automation degree, the requirement of batch rapid measurement of the same object is difficult to meet, and the defects (Tian Qingguo, ge Baozhen, du Piao, and the like) of the traditional measurement method can be well solved by using the three-dimensional point cloud data to measure the object. For example, text (Yan Wu, xu Chen, wu Hongmin, et al real Time Volume Measurement of Logistics Cartons Through 3D Point Cloud Segmentation[C ]. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2021,13015 (2021):324-335.) real-time measurement of the volume of a logistics carton by 3D point cloud segmentation, a non-contact volume measurement system based on the geometric characteristics of the logistics package point cloud is realized, and the system can identify and measure different types of objects and even deformed cartons in various scenes; the article measures the volume of objects having a shape corresponding to a cube, but there is room for improvement in the quality of the predicted size information in clustered scenes, as well as in other deformed parts. At present, most of the measurement by utilizing the laser radar three-dimensional point cloud technology is volume size and the like, and the research on the measurement of the joint angle is less.
Disclosure of Invention
The invention provides a laser radar three-dimensional point cloud-based ice hockey elbow protection angle measurement method, which aims to solve the problems that the traditional angle measurement method is low in measurement speed, low in automation degree and incapable of non-contact measurement; the mechanical arm for measuring the angle by using the rotary encoder is not easy to imitate the arm to wear the elbow pad; the technical problems that the wearable device and the elbow pad are worn and are not easy to measure are solved.
In order to solve the technical problems, the invention provides the following technical scheme:
in one aspect, the invention provides an ice hockey elbow protection angle measurement method based on laser radar three-dimensional point cloud, which comprises the following steps:
building a three-dimensional point cloud measuring system; the measuring system comprises a mechanical artificial limb, a laser radar camera, a servo motor and a bracket; the mechanical artificial limb, the laser radar camera and the servo motor are all arranged on the bracket, the mechanical artificial limb is used for wearing an ice hockey ball elbow pad with an angle to be measured, the servo motor is used for adjusting the angle of a forearm of the mechanical artificial limb, and the mechanical artificial limb is positioned in a scanning area of the laser radar camera;
acquiring point cloud data of a mechanical artificial limb and an ice hockey elbow pad by using the three-dimensional point cloud measuring system; when the laser radar in the three-dimensional point cloud measuring system is used for measuring point cloud data of a mechanical artificial limb and an ice hockey elbow pad, a large arm of the mechanical artificial limb is fixed and calibrated to be in a Y-axis direction through the laser radar;
preprocessing the obtained point cloud data to obtain preprocessed point cloud data;
based on the preprocessed point cloud data, intercepting a thin rod point cloud of the extending end of the mechanical artificial limb forearm;
clustering the thin rod point clouds according to columns to divide the thin rod point clouds into a plurality of sections of point clouds, obtaining key points of each section, projecting the key points onto an XOY plane, performing linear regression on the key points by using a least square method, fitting out a center line of a forearm, obtaining a direction vector according to the center line, and obtaining the angle of the ice hockey elbow protector.
Further, the preprocessing the obtained point cloud data includes:
filtering out scene point clouds and redundant point clouds in the point cloud data by adopting a conditional filtering algorithm in the PCL library;
and removing sparse outliers in the point cloud data by adopting a statistical filtering algorithm in the PCL library.
Further, when a condition filtering algorithm in a PCL library is adopted to filter out scene point clouds and redundant point clouds in the point cloud data, the ranges of threshold parameters X, Y and Z corresponding to the X axis, the Y axis and the Z axis of the coordinate system are respectively as follows: -0.073 < x < 0.426, -0.166 < y < 0.389, -0.8 < z < -0.7.
Further, when sparse outliers in the point cloud data are removed by adopting a statistical filtering algorithm in the PCL library, searching the neighbor point number K of each point to obtain 50, and obtaining a proportionality coefficient alpha to obtain 1.0.
Further, the intercepting the thin rod point cloud of the extension end of the mechanical artificial limb forearm comprises the following steps:
step 1, setting a circle center O, a minimum radius R and a maximum radius R of the rotation of the forearm in an XOY plane, and setting a straight line which passes through the circle center O and has a slope of K;
step 2, calculating the distance H from each point A to the circle center O in the preprocessed point cloud data;
step 3, if R < H < R and the point A is above the straight line, saving the screened point A;
and 4, repeatedly executing the steps 2 to 3 until all points in the preprocessed point cloud data are screened, so as to intercept the thin rod point cloud of the forearm extension end between the minimum radius R and the maximum radius R.
Further, the coordinates of the center O of rotation of the forearm are taken (0.028,0.024), the minimum radius R is taken to be 0.2m, the maximum radius R is taken to be 0.3m, and the slope K of the straight line passing through the center of the circle is taken to be tan120 °.
Further, clustering the thin-rod point cloud according to columns to divide the thin-rod point cloud into a plurality of segments of point clouds and obtain key points of each segment, including:
sorting the points in the thin rod point cloud; when the angle of the ice hockey elbow protector is not larger than a preset angle, ordering the points in the thin rod point cloud according to the size of an X-axis coordinate; when the angle of the ice hockey elbow protector is larger than a preset angle, ordering the points in the thin rod point cloud according to the size of the Y-axis coordinate;
calculating coordinate difference values of two adjacent points in the thin rod point cloud; when the angle of the ice hockey elbow protector is not larger than a preset angle, calculating the X-axis coordinate difference value of two adjacent points; when the angle of the ice hockey elbow protector is larger than a preset angle, calculating the Y-axis coordinate difference value of two adjacent points;
when the calculated coordinate difference value of two adjacent points is smaller than the set parameter D, putting the two points into the same set, otherwise, putting the two points into different sets; dividing the thin rod point cloud into a plurality of sections of point clouds;
and calculating a mean value according to the space coordinate values of the divided point cloud data to obtain the key points of each section.
Further, the setting parameter D is 0.001, and the preset angle has a value of 135 °.
In yet another aspect, the present invention also provides an electronic device including a processor and a memory; wherein the memory stores at least one instruction that is loaded and executed by the processor to implement the above-described method.
In yet another aspect, the present invention also provides a computer readable storage medium having at least one instruction stored therein, the instruction being loaded and executed by a processor to implement the above method.
The technical scheme provided by the invention has the beneficial effects that at least:
the measuring method of the angle of the ice hockey elbow protector is expected to replace manual measurement, can rapidly calculate the movable angle of the ice hockey elbow protector, provides data support for the dexterity of the elbow protector, and has the advantages of high efficiency, non-contact and the like. Experimental results show the feasibility of applying the laser radar three-dimensional point cloud technology to the angle measurement problem.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic diagram of an execution flow of an ice hockey elbow protection angle measurement method based on laser radar three-dimensional point cloud according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of point cloud data after conditional filtering according to an embodiment of the present invention;
FIG. 3 is a diagram of statistically filtered point cloud data according to an embodiment of the present invention;
FIG. 4 is a schematic flow chart of a semicircular ring conditional filtering algorithm according to an embodiment of the present invention;
FIG. 5 is a schematic flow chart of a per-column clustering algorithm provided by an embodiment of the present invention;
fig. 6 is a schematic diagram of a process for fitting from a prosthetic point cloud to a centerline, according to an embodiment of the invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the embodiments of the present invention will be described in further detail with reference to the accompanying drawings.
First embodiment
In order to rapidly measure the movable angle of the ice hockey elbow pad by using non-contact equipment, the embodiment provides an ice hockey elbow pad angle measurement method based on laser radar three-dimensional point cloud. The method can be implemented by an electronic device
Specifically, the execution flow of the method is shown in fig. 1, and the method comprises the following steps:
s1, building a three-dimensional point cloud measurement system;
the three-dimensional point cloud measuring system built in the embodiment mainly comprises a mechanical artificial limb, a laser radar camera L515, a servo motor and a bracket; the mechanical artificial limb, the laser radar camera and the servo motor are all arranged on the bracket, the mechanical artificial limb is used for wearing an ice hockey elbow pad of an angle to be measured, and the servo motor is used for adjusting the angle of a forearm in the mechanical artificial limb; the depth resolution of lidar is as high as 1024×768, the frame rate is 30 frames/second, the depth field of view (FOV) is 70×55 ° (±3°), the minimum depth distance (Min-Z) is 0.25m, and the furthest distance of laser scan is 9m. Before the point cloud is collected, the laser radar is adjusted to be opposite to the scanning area. And the mechanical artificial limb after the ice hockey elbow protector is worn is positioned in the scanning area of the laser radar camera.
S2, acquiring point cloud data of a mechanical artificial limb and an ice hockey elbow protector by using the three-dimensional point cloud measuring system;
when the laser radar in the three-dimensional point cloud measuring system is used for measuring point cloud data of the mechanical artificial limb and the ice hockey elbow protector, the big arm of the mechanical artificial limb is fixed and calibrated to be in the Y-axis direction through the laser radar.
S3, preprocessing the obtained point cloud data to obtain preprocessed point cloud data;
the method includes that mechanical artificial limb and ice hockey elbow protection point cloud data acquired by using a laser radar are stored in scene point cloud and redundant point cloud, and in order to improve efficiency and accuracy of a follow-up algorithm, the scene point cloud and the redundant point cloud are filtered by adopting a conditional filtering algorithm in a PCL library. Among them, point Cloud Library (PCL) is an open source tool for processing point cloud data of three-dimensional images (Guo Hao. Point cloud base PCL from entry to proficiency [ M ]. Beijing: mechanical industry Press, 2019.). The cloud coordinates of the points are looked up by using CloudCompare software, and the ranges of threshold parameters X, Y and Z corresponding to the X axis, the Y axis and the Z axis of the coordinate system are respectively-0.073 < X < 0.426, -0.166 < Y < 0.389, -0.8 < Z < -0.7. Taking one measurement of the ice hockey elbow guard at 90 degrees as an example, the original point cloud data has 250375 points, and 16228 points are filtered, and the result is shown in fig. 2.
In addition, when the laser radar is used for measuring point cloud data of a mechanical artificial limb and an ice hockey elbow protector, sparse outliers can appear in the three-dimensional point cloud data, and the numerical value affecting the estimation of the local characteristics of the point cloud needs to be removed. In this regard, the present embodiment uses a statistical filtering algorithm in the PCL library to perform culling. After multiple experiments, searching the number K of the neighbor points of each point to obtain 50, and obtaining the proportionality coefficient alpha to obtain 1.0. The result of the statistical filtering algorithm after outliers are removed is shown in fig. 3.
S4, based on the preprocessed point cloud data, intercepting a thin rod point cloud of the extending end of the mechanical artificial limb forearm;
the big arm of the artificial limb is fixed and is calibrated to be in the Y-axis direction through a laser radar; therefore, only the forearm direction needs to be measured. The shape of the ice hockey elbow protector is irregular, cannot be directly measured, and the slender rod part at the extending end of the forearm is cut off to obtain the direction of the forearm. The forearm is rotated and the threshold value of X, Y, Z axis needs to be reset each time a different angle is measured, provided that a conventional conditional filtering algorithm is used. In order to improve efficiency, the embodiment provides a semicircular ring conditional filtering algorithm, and set conditions are used for restraining X, Y axis coordinates of point cloud data. The initial parameters need to be set once, and subsequent resetting is not needed. The execution flow of the semicircular ring conditional filtering algorithm is shown in fig. 4, and mainly comprises the following steps:
step 1, setting a circle center O, a minimum radius R and a maximum radius R of the rotation of the forearm in an XOY plane, and setting a straight line which passes through the circle center O and has a slope of K;
step 2, calculating the distance H from each point A to the circle center O in the preprocessed point cloud data;
step 3, if R < H < R and the point A is above the straight line, saving the screened point A;
and 4, repeatedly executing the steps 2 to 3 until all points in the preprocessed point cloud data are screened, so as to intercept the thin rod point cloud of the forearm extension end between the minimum radius R and the maximum radius R.
In the XOY plane, the coordinate of the center O of rotation of the forearm is (0.028,0.024), the minimum radius R is 0.2m, the maximum radius R is 0.3m, and the slope K of a straight line passing through the center of the circle is tan120 degrees.
By using the semicircular ring conditional filtering algorithm, the problem that parameters need to be reset every time the traditional conditional filtering measures different angles can be solved, and the efficiency of point cloud processing is improved.
S5, clustering the thin rod point clouds according to columns to divide the thin rod point clouds into a plurality of sections of point clouds, obtaining key points of each section, projecting the key points onto an XOY plane, performing linear regression on the key points by using a least square method, fitting out a center line of a forearm, obtaining a direction vector according to the center line, and obtaining the angle of the ice hockey elbow.
In this embodiment, in order to simplify the calculation of the point cloud data and improve the accuracy of the linear regression, a method for extracting the key points of the thin rod point cloud at the extending end of the forearm by a column clustering method is provided. The algorithm clusters the thin rod point cloud according to columns, divides the thin rod point cloud into a plurality of small segment point clouds, calculates the mean value according to the space coordinate value of the point cloud data, and obtains the key point of each small segment, and the flow of the algorithm is shown in figure 5 and mainly comprises the following steps:
step 1, ordering the points in the thin rod point cloud according to the size of an X-axis coordinate;
step 2, calculating the X-axis coordinate difference value of two adjacent points in the thin rod point cloud;
step 3, when the calculated coordinate difference value of two adjacent points is smaller than the set parameter D, putting the two points into the same set, otherwise putting the two points into different sets; to divide the thin rod point cloud into segments of point cloud.
When the angle of the ice hockey elbow protector is larger than 135 degrees, the X-axis coordinate in the algorithm flow is changed into the Y-axis coordinate.
Further, after the calibration of the laser radar, the mechanical artificial limb is parallel to the XOY plane, after the key points are obtained, the key points are projected onto the XOY plane, the linear regression is carried out on the key points by using a least square method, the fitted straight line is the center line of the forearm, and the fitting process from the artificial limb point cloud to the center line is shown in fig. 6. Finally, the direction vector is obtained according to the central line, the included angle of the direction vectors of the big arm and the small arm is the angle of the ice hockey elbow protector, the result can be obtained according to the formula (1), c 1 And c 2 Are all 0.
Wherein beta is the angle of the ice hockey elbow pad (a) 1 ，b 1 ，c 1 ) And (a) 2 ，b 2 ，c 2 ) The direction vectors of the large arm of the mechanical artificial limb and the small arm of the mechanical artificial limb are respectively.
By using the column-wise clustering method, the embodiment solves the problems of large operand and large error amplitude of linear regression by directly using a least square method. In order to verify the performance of the method according to the present embodiment, the result of the goniometer measurement is taken as the true value β t Two measurement methods were used for comparison:
1) Projecting the small arm fine rod point cloud to an XOY plane, directly using a least square method to carry out linear regression on the fine rod point cloud data, fitting out a small arm central line, and obtaining an angle which is recorded as beta 1 Error is recorded as theta 1 。
2) The key points of the small arm thin rod point cloud are extracted by using the column clustering method provided by the invention, and then the linear regression is performed by using a least square method to obtain the angle marked as beta 2 Error is recorded as theta 2 。
The measurement results are shown in table 1, and it can be found that: the method 1) has a large error magnitude of the measurement result, and an abnormal value exists when the measurement result is close to 180 degrees. The error amplitude of the measurement result of the method 2) is relatively small, the maximum value of the measurement error is-1.7742 degrees, and no abnormal value exists.
Table 1 measurement results
In summary, the embodiment provides an ice hockey elbow protection angle measurement method based on laser radar three-dimensional point cloud. Firstly, when the forearm rotates, aiming at the problem that only the forearm fine rod point cloud with a fixed angle can be extracted after the parameters are set by the traditional conditional filtering, a semicircular conditional filtering algorithm is provided for intercepting the forearm fine rod point cloud with different angles. Secondly, aiming at the problems of large operand and large error amplitude of linear regression by directly using a least square method, a column clustering method is provided for extracting key points, so that the operation of point cloud data is simplified and the accuracy of the linear regression is improved. Finally, fitting the central line of the artificial limb, and calculating to obtain the elbow guard angle. Has the advantages of high efficiency, non-contact and the like. Experimental results show the feasibility of applying the laser radar three-dimensional point cloud technology to the angle measurement problem.
Second embodiment
The embodiment provides an electronic device, which comprises a processor and a memory; wherein the memory stores at least one instruction that is loaded and executed by the processor to implement the method of the first embodiment.
The electronic device may vary considerably in configuration or performance and may include one or more processors (central processing units, CPU) and one or more memories having at least one instruction stored therein that is loaded by the processors and performs the methods described above.
Third embodiment
The present embodiment provides a computer-readable storage medium having stored therein at least one instruction that is loaded and executed by a processor to implement the method of the first embodiment described above. The computer readable storage medium may be, among other things, ROM, random access memory, CD-ROM, magnetic tape, floppy disk, optical data storage device, etc. The instructions stored therein may be loaded by a processor in the terminal and perform the methods described above.
Furthermore, it should be noted that the present invention can be provided as a method, an apparatus, or a computer program product. Accordingly, embodiments of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the invention may take the form of a computer program product on one or more computer-usable storage media having computer-usable program code embodied therein.
Embodiments of the present invention are described with reference to flowchart illustrations and/or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It should also be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or terminal device comprising the element.
It is finally pointed out that the above description of the preferred embodiments of the invention, it being understood that although preferred embodiments of the invention have been described, it will be obvious to those skilled in the art that, once the basic inventive concepts of the invention are known, several modifications and adaptations can be made without departing from the principles of the invention, and these modifications and adaptations are intended to be within the scope of the invention. It is therefore intended that the following claims be interpreted as including the preferred embodiment and all such alterations and modifications as fall within the scope of the embodiments of the invention.
Claims (7)
1. An ice hockey elbow guard angle measurement method based on laser radar three-dimensional point cloud is characterized by comprising the following steps:
building a three-dimensional point cloud measuring system; the measuring system comprises a mechanical artificial limb, a laser radar camera, a servo motor and a bracket; the mechanical artificial limb, the laser radar camera and the servo motor are all arranged on the bracket, the mechanical artificial limb is used for wearing an ice hockey ball elbow pad with an angle to be measured, the servo motor is used for adjusting the angle of a forearm of the mechanical artificial limb, and the mechanical artificial limb is positioned in a scanning area of the laser radar camera;
acquiring point cloud data of a mechanical artificial limb and an ice hockey elbow pad by using the three-dimensional point cloud measuring system; when the laser radar in the three-dimensional point cloud measuring system is used for measuring point cloud data of a mechanical artificial limb and an ice hockey elbow pad, a large arm of the mechanical artificial limb is fixed and calibrated to be in a Y-axis direction through the laser radar;
preprocessing the obtained point cloud data to obtain preprocessed point cloud data;
based on the preprocessed point cloud data, intercepting a thin rod point cloud of the extending end of the mechanical artificial limb forearm;
clustering the thin rod point clouds according to columns to divide the thin rod point clouds into a plurality of sections of point clouds, obtaining key points of each section, projecting the key points onto an XOY plane, performing linear regression on the key points by using a least square method, fitting out a center line of a small arm, and obtaining a direction vector according to the center line to obtain an angle of an ice hockey elbow protector;
the intercepting of the thin rod point cloud of the extension end of the mechanical artificial limb forearm comprises the following steps:
step 1, setting a circle center O, a minimum radius R and a maximum radius R of the rotation of the forearm in an XOY plane, and setting a straight line which passes through the circle center O and has a slope of K;
step 2, calculating the distance H from each point A to the circle center O in the preprocessed point cloud data;
step 3, if R < H < R and the point A is above the straight line, saving the screened point A;
and 4, repeatedly executing the steps 2 to 3 until all points in the preprocessed point cloud data are screened, so as to intercept the thin rod point cloud of the forearm extension end between the minimum radius R and the maximum radius R.
2. The method for measuring the angle of the ice hockey puck elbow-protection based on the laser radar three-dimensional point cloud according to claim 1, wherein the preprocessing of the acquired point cloud data comprises the following steps:
filtering out scene point clouds and redundant point clouds in the point cloud data by adopting a conditional filtering algorithm in the PCL library;
and removing sparse outliers in the point cloud data by adopting a statistical filtering algorithm in the PCL library.
3. The method for measuring the angle of the ice hockey ball elbow guard based on the three-dimensional point cloud of the laser radar according to claim 2, wherein when a condition filtering algorithm in a PCL library is adopted to filter out scene point clouds and redundant point clouds in point cloud data, the ranges of threshold parameters X, Y and Z corresponding to an X axis, a Y axis and a Z axis of a coordinate system are respectively as follows: -0.073 < x < 0.426, -0.166 < y < 0.389, -0.8 < z < -0.7.
4. The method for measuring the angle of the ice hockey elbow guard based on the three-dimensional point cloud of the laser radar according to claim 2, wherein when sparse outliers in point cloud data are removed by adopting a statistical filtering algorithm in a PCL library, the number K of neighbor points of each point is searched to obtain 50, and the proportionality coefficient alpha is 1.0.
5. The method for measuring the angle of the ice hockey elbow pad based on the three-dimensional point cloud of the laser radar according to claim 1, wherein the coordinate of the center O of rotation of the forearm is taken (0.028,0.024), the minimum radius R is taken to be 0.2m, the maximum radius R is taken to be 0.3m, and the slope K of a straight line passing through the center of the circle is taken to be tan120 degrees.
6. The method for measuring the angle of the ice hockey ball elbow guard based on the laser radar three-dimensional point cloud according to claim 1, wherein the thin rod point cloud is clustered in columns to divide the thin rod point cloud into a plurality of sections of point clouds and obtain key points of each section, and the method comprises the following steps:
sorting the points in the thin rod point cloud; when the angle of the ice hockey elbow protector is not larger than a preset angle, ordering the points in the thin rod point cloud according to the size of an X-axis coordinate; when the angle of the ice hockey elbow protector is larger than a preset angle, ordering the points in the thin rod point cloud according to the size of the Y-axis coordinate;
calculating coordinate difference values of two adjacent points in the thin rod point cloud; when the angle of the ice hockey elbow protector is not larger than a preset angle, calculating the X-axis coordinate difference value of two adjacent points; when the angle of the ice hockey elbow protector is larger than a preset angle, calculating the Y-axis coordinate difference value of two adjacent points;
when the calculated coordinate difference value of two adjacent points is smaller than the set parameter D, putting the two points into the same set, otherwise, putting the two points into different sets; dividing the thin rod point cloud into a plurality of sections of point clouds;
and calculating a mean value according to the space coordinate values of the divided point cloud data to obtain the key points of each section.
7. The method for measuring the angle of the ice hockey puck elbow rest based on the three-dimensional point cloud of the laser radar according to claim 6, wherein the set parameter D is 0.001, and the preset angle has a value of 135 °.
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