Method, system, terminal and medium for measuring distance of dynamic object movement based on laser point cloud

Abstract

The application discloses a dynamic object moving distance measurement method and system based on a laser point cloud, a terminal and a medium, and relates to the technical field of point cloud analysis. The method comprises the following steps: acquiring current frame point cloud data of a moving target, registering the current frame point cloud data with a target point cloud template sequence to obtain a best matching template; moving the best matching template to a target position corresponding to the current frame point cloud data to obtain a reference point of the current frame point cloud data; judging whether the current frame point cloud data is end frame point cloud data, and if the current frame point cloud data is end frame point cloud data, determining the moving distance of the moving target. The application realizes coarse registration by finding the best matching template, and then realizes fine registration by moving the best matching template to the target position corresponding to the current frame point cloud data, so that the moving target can be accurately positioned, the moving distance of the dynamic object can be accurately measured, and the application has high robustness.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of point cloud analysis, and in particular to a dynamic object moving distance measurement method and system based on laser point cloud, a terminal and a medium. BACKGROUND

[0002] Measuring the actual pushing distance of a hydraulic support in a coal mine has been a technical pain point in the coal industry. The manual measurement method is dangerous, as personnel need to be exposed to the coal mining environment in the mine, and the movement of the hydraulic machine can easily cause harm to personnel. The moving distance of the hydraulic support is related to the production safety of coal mining, and an automatic measurement method is urgently needed.

[0003] However, using a laser radar to measure requires processing and analyzing the laser radar data, comparing the distance of the markers in the laser radar data before and after movement, and thus measuring the length. However, the markers of the laser radar are unstable in the mine, which is due to the fact that the markers may be covered by objects or obscured by ash layers, making it difficult to be completely or accurately collected by the laser radar. Therefore, in a high-noise environment, using traditional SLAM (Simultaneous localization and mapping), point cloud registration, and target recognition techniques for object movement analysis based on laser point cloud is affected by the high noise of the laser radar, and these algorithms cannot accurately identify and distinguish the markers through single-frame point cloud data, resulting in inaccurate measurement of the moving distance.

[0004] Therefore, the prior art still has defects. SUMMARY

[0005] The technical problem to be solved by the present application is to provide a dynamic object moving distance measurement method and system based on laser point cloud, a terminal and a medium to solve the above-mentioned defects of the prior art. The technical solution adopted by the present application is as follows:

[0006] In a first aspect, the present application provides a dynamic object moving distance measurement method based on laser point cloud, wherein the method comprises:

[0007] obtaining current frame point cloud data of a moving target, registering the current frame point cloud data with a preset target point cloud template sequence to obtain a best matching template;

[0008] moving the best matching template to a target position corresponding to the current frame point cloud data to obtain a reference point of the current frame point cloud data;

[0009] judging whether the current frame point cloud data is end frame point cloud data based on the reference point of the current frame point cloud data, and if the current frame point cloud data is end frame point cloud data, obtaining the moving distance of the moving target based on the reference point of the initial frame point cloud data and the reference point of the current frame point cloud data.

[0010] In an implementation manner, the moving target is a landmark component or a landmark position on a scanned device, and when the method is applied to measurement of moving distance of a downhole hydraulic support, the moving target is a front head position of a worktable of the downhole hydraulic support.

[0011] In an implementation manner, the target point cloud template sequence is obtained in the following manner:

[0012] scanning a moving process of the moving target in an entire moving cycle to obtain a point cloud flow of the moving target;

[0013] converting the point cloud flow of the moving target into single frame point cloud data for storage, and segmenting the single frame point cloud data to obtain point cloud data of the moving target of each frame;

[0014] obtaining the target point cloud template sequence according to time and space distribution of the point cloud data of the moving target of different frames.

[0015] In an implementation manner, the current frame point cloud data is registered with a preset target point cloud template sequence to obtain a best matching template, including:

[0016] obtaining a point cloud data set of each template in the target point cloud template sequence;

[0017] calculating a one-way Hausdorff distance between the current frame point cloud data and the point cloud data set of each template;

[0018] taking a template corresponding to a minimum value of the one-way Hausdorff distance as the best matching template.

[0019] In an implementation manner, the best matching template is moved to a target position corresponding to the current frame point cloud data to obtain a reference point of the current frame point cloud data, including:

[0020] determining a highest point of the best matching template, determining a nearest point in the current frame point cloud data to the highest point, and moving the best matching template and the corresponding highest point to the nearest point to obtain a point cloud data set of the best matching template after first movement;

[0021] Taking the highest point of the best matching template after the first movement as a starting point, highest points within a certain range in the current frame point cloud data are determined, and the best matching template is moved to the highest points within the certain range after the first movement to obtain the point cloud data set of the best matching template after the second movement;

[0022] The Hausdorff distances between the point cloud data set of the current frame point cloud data and the best matching template, the point cloud data set of the best matching template after the first movement, and the point cloud data set of the best matching template after the second movement are respectively calculated;

[0023] Based on the minimum value of the Hausdorff distances, a reference point of the current frame point cloud data is obtained.

[0024] In an implementation manner, based on the reference point of the current frame point cloud data, it is judged whether the current frame point cloud data is end frame point cloud data, comprising:

[0025] The Euclidean distance between the reference point of the current frame point cloud data and the reference point of the initial frame point cloud data is calculated.

[0026] Based on the Euclidean distance and the distance mean calculated according to the time window in advance, it is judged whether the Euclidean distance meets the motion end condition.

[0027] If the motion end condition is met, the current frame point cloud data is determined as end frame point cloud data.

[0028] In an implementation manner, the method further comprises:

[0029] Point-by-point analysis is performed on all points in the current frame point cloud data to obtain the best reference point of the current frame point cloud data, and the movement distance of the moving target is obtained according to the best reference point.

[0030] In a second aspect, the embodiments of the present application further provide a dynamic object movement distance measurement system based on laser point cloud, wherein the system comprises:

[0031] A coarse registration module is configured to acquire current frame point cloud data of a moving target, register the current frame point cloud data with a preset target point cloud template sequence to obtain a best matching template.

[0032] A fine registration module is configured to move the best matching template to a target position corresponding to the current frame point cloud data to obtain a reference point of the current frame point cloud data.

[0033] The distance determination module is configured to determine whether the current frame point cloud data is end frame point cloud data based on a reference point of the current frame point cloud data, and if the current frame point cloud data is end frame point cloud data, obtain the moving distance of the moving target based on a reference point of initial frame point cloud data and the reference point of the current frame point cloud data.

[0034] In a third aspect, an embodiment of the present application further provides a terminal, wherein the terminal comprises a memory, a processor, and a laser point cloud based dynamic object moving distance measurement program stored in the memory and executable on the processor, and the processor implements the steps of the laser point cloud based dynamic object moving distance measurement method of any of the above solutions when executing the laser point cloud based dynamic object moving distance measurement program.

[0035] In a fourth aspect, an embodiment of the present application further provides a computer readable storage medium, wherein the computer readable storage medium stores a laser point cloud based dynamic object moving distance measurement program, and the laser point cloud based dynamic object moving distance measurement program implements the steps of the laser point cloud based dynamic object moving distance measurement method of any of the above solutions on the computer readable storage medium.

[0036] Advantages: Compared with the prior art, the present application provides a laser point cloud based dynamic object moving distance measurement method, which firstly acquires current frame point cloud data of a moving target, performs registration on the current frame point cloud data and a preset target point cloud template sequence to obtain a best matching template. Then, the best matching template is moved to a target position corresponding to the current frame point cloud data to obtain a reference point of the current frame point cloud data. Next, it is determined whether the current frame point cloud data is end frame point cloud data based on the reference point of the current frame point cloud data, and if the current frame point cloud data is end frame point cloud data, the moving distance of the moving target is obtained based on a reference point of initial frame point cloud data and the reference point of the current frame point cloud data. The present application realizes coarse registration by finding the best matching template, and then realizes fine registration by moving the best matching template to the target position corresponding to the current frame point cloud data, so as to realize accurate positioning of the moving target, and accurate measurement of the moving distance of the dynamic object, and has high robustness. BRIEF DESCRIPTION OF DRAWINGS

[0037] Figure 1 The flow chart of the preferred embodiment of the laser point cloud based dynamic object moving distance measurement method provided by the embodiment of the present application.

[0038] Figure 2 The technical roadmap of the laser point cloud based dynamic object moving distance measurement method provided by the embodiment of the present application.

[0039] Figure 3The schematic diagram of the target point cloud template sequence and a single template in the laser point cloud based dynamic object moving distance measurement method provided by the embodiment of the present application.

[0040] Figure 4 The process diagram of moving the best matching template in the laser point cloud based dynamic object moving distance measurement method provided by the embodiment of the present application.

[0041] Figure 5 The schematic diagram of the fine registration case in the laser point cloud based dynamic object moving distance measurement method provided by the embodiment of the present application.

[0042] Figure 6 The principle block diagram of the laser point cloud based dynamic object moving distance measurement system provided by the embodiment of the present application.

[0043] Figure 7 The principle block diagram of the terminal provided by the embodiment of the present application. DETAILED DESCRIPTION

[0044] In order to make the objectives, technical solutions and effects of the present application clearer and more explicit, the present application is further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0045] The flowchart shown in the drawings is only an example and does not necessarily include all the contents and operations or steps, nor does it necessarily execute in the order described. For example, some operations or steps can be further divided, combined or partially combined, so the actual execution order may be changed according to the actual situation.

[0046] It should be understood that the terms used in this present application specification are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the present application specification and the appended claims, the singular forms "a", "an" and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0047] It should be understood that in order to facilitate the clear description of the technical solutions of the embodiments of the present application, in the embodiments of the present application, the same or similar items with basically the same function and role are distinguished by using "first", "second" and the like. For example, the first control information and the second control information are only used to distinguish different control information and do not limit the order.

[0048] Those skilled in the art can understand that the "first", "second" and the like do not limit the quantity and execution order, and the "first", "second" and the like do not necessarily mean different.

[0049] It should also be appreciated that the term "and / or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items, and that the term "at least one of' denotes one, or a plurality of, of the enumerated terms that it precedes, and that the term "per" denotes one, or a plurality of, of the enumerated terms that it precedes.

[0050] The embodiment is based on laser radar to measure the moving distance of dynamic objects, such as the advancing degree of hydraulic support. However, the current measurement of the advancing degree of the underground hydraulic support mainly adopts the traditional manual measurement method, including visual measurement and ruler measurement, and lacks an automatic measurement method. Laser radar actively collects the environment and can work in a dark environment, and the measured data is three-dimensional point set data, which accurately records the three-dimensional coordinates of the scanned object to describe the surrounding environment in points instead of surfaces. However, the current use of laser radar for real-time advancing degree measurement of hydraulic support faces great technical challenges. The reasons are as follows: (1) Large dust noise in the underground. The working environment of coal mining underground has a large amount of dust, and laser radar itself is sensitive to the dust layer, which is easy to produce a large number of discrete flying point noise, especially when the object moves and dusts, which causes the scanned structure data to be distorted; (2) Difficulty in point cloud motion detection. Point cloud data can only record the local coordinates of the hydraulic support at a certain moment, but when the entire device moves, changes need to be found between multiple frames of data, which makes it difficult to measure the actual moving distance of the hydraulic support; (3) Difficulty in target detection. Using traditional point cloud registration or object detection methods, it is difficult to cope with the changes of the support itself, because the environment of the underground device is different, and the accessories on the device are also different, and the traditional pattern recognition method cannot confirm the specific pattern to be recognized, and the shape of the hydraulic support will change during the movement.

[0051] Currently, for real-time laser point cloud motion estimation, methods such as SLAM (Simultaneous localization and mapping), point cloud registration, and target recognition can be used. SLAM technology is not suitable for the narrow environment underground, as it requires a large number of environmental feature points, which can easily fail in a high-noise environment. The hydraulic support only changes locally, so the point cloud registration technology is not suitable for this global registration method, and the registration error correction will be restricted by a large number of fixed points. The target recognition method is mainly a deep learning method, but this method is not suitable for simple devices without GPU (Graphics Processing Unit) in the underground, and cannot be processed in real time. Traditional machine learning methods require the creation of artificial features, but such methods are sensitive to noise and are not suitable for target detection in a high-noise environment.

[0052] To this end, the embodiment provides a dynamic object moving distance measurement method based on laser point cloud. The method based on the embodiment can achieve accurate positioning of a moving target, thereby achieving accurate measurement of the moving distance of a dynamic object, and has high robustness. In a specific application, the embodiment first acquires current frame point cloud data of a moving target, performs registration on the current frame point cloud data and a preset target point cloud template sequence to obtain a best matching template. Then, the best matching template is moved to a target position corresponding to the current frame point cloud data to obtain a reference point of the current frame point cloud data. Next, based on the reference point of the current frame point cloud data, it is judged whether the current frame point cloud data is end frame point cloud data. If the current frame point cloud data is end frame point cloud data, the moving distance of the moving target is obtained based on a reference point of initial frame point cloud data and the reference point of the current frame point cloud data.

[0053] The embodiment takes measuring the moving distance of a downhole hydraulic support as an example. The downhole hydraulic support mainly plays a role of moving a digging device when digging underground. When the digging device needs to move, a moving signal is sent to the hydraulic support. After receiving the moving signal, the hydraulic support raises the height of the support and moves forward by a certain distance. After executing the moving instruction, the hydraulic support lowers the height to realize the fixation of the hydraulic support. Therefore, the purpose of the embodiment is to calculate the actual moving distance of the hydraulic support during each moving process.

[0054] In actual application, the downhole hydraulic support is divided into upper and lower parts. The lower part of the base moves first, and after the movement stops, the upper part of the support moves. The overall moving mode is to first raise, then move forward, and then lower to a reasonable height. In the embodiment, the laser radar device is installed at the top of the upper part of the support and scans the lower part of the support from above. Therefore, the field of view of the laser radar is the space range from the top support to the bottom base. The laser radar is installed at the top because the top position moves after the movement of the bottom base is completed. Therefore, the laser radar at the top can completely record the entire movement process of the bottom base during the stationary process, and the length of the measured distance depends only on the maximum movement distance of the bottom base. When the bottom base moves forward, the laser radar point cloud flow shows that the bottom base moves away from the coordinate origin of the laser radar. When the top moves forward, the laser radar point cloud flow shows that the bottom base moves close to the coordinate origin of the laser radar. Therefore, theoretically, the distance measured in real time is a gradual and progressive process of first becoming larger and then becoming smaller. The actual moving distance of the digging device where the hydraulic support is located is the maximum value of the entire measurement process.

[0055] The dynamic object moving distance measurement method based on laser point cloud of the embodiment can be applied to a terminal. The terminal can be a computer, a smart television, or other intelligent devices. As shown in FIG. 8, the method of the embodiment specifically includes the following steps: Figure 1 ​

[0056] Step S100, acquiring current frame point cloud data of a moving target, registering the current frame point cloud data with a preset target point cloud template sequence to obtain a best matching template.

[0057] The essence of the embodiment is to calculate the moving distance of a moving target before and after a period of time. Essentially, only the moving distance of the same point before and after movement of two frames needs to be found. In combination with Figure 2 As shown in the figure, the embodiment analyzes single-frame point cloud data, which is the frame-by-frame data of real-time laser point cloud flow. The receiving frequency of single-frame data is 10 Hz, i.e., new single-frame data is acquired every 0.1 seconds, so the processing time also only has a 0.1-second gap. The embodiment also needs to be assisted by a target point cloud template sequence, which is a set of artificial point cloud templates. Usually, the point cloud templates of each device are fixed. The moving target of the embodiment is a landmark component or landmark position on the scanned device, without specific size and range requirements, and weak semantic association. When the method of the embodiment is applied to the measurement of the moving distance of an underground hydraulic support, the moving target can only be the bottom of a specific support or the operating table part, etc. In actual application, the front position of the operating table of the underground hydraulic support is taken as the moving target, there is no other object above the moving target, and the moving target can move in real time with the device and is not easily affected by noise. The point cloud data obtained by scanning the front position of the operating table using a laser scanner is the point cloud data of the moving target, which can be recorded as a target point cloud template.

[0058] The target point cloud template sequence refers to the point cloud data of the moving target recorded multiple times in the entire movement cycle. When making the target point cloud template sequence, the embodiment scans the movement process of the moving target in the entire movement cycle to obtain the point cloud flow of the moving target. Then, the point cloud flow of the moving target is converted into single-frame point cloud data for saving, and the single-frame point cloud data is segmented using third-party software to obtain the point cloud data of the moving target of each frame. Since the moving target moves in the movement process, the point cloud data of the moving target of different frames appears at different positions, forming different point cloud distributions in time and space. Therefore, the embodiment can record the distribution of the point cloud data of the moving target of different frames in time and space to obtain the target point cloud template sequence. Since the movement mode of the device is fixed, matching the current frame point cloud data with the target point cloud template sequence can find the corresponding movement action and movement state of the current frame point cloud data. Specifically, Figure 3 The (a) figure in FIG. 1 is a target point cloud template sequence, which includes multiple target point cloud templates, and different target point cloud templates are distinguished by different colors. Figure 3The (b) figure in the figure is a single template. In step S100, the present embodiment registers the current frame point cloud data with each template in the target point cloud template sequence, thereby obtaining the best matching template, which is a coarse registration process.

[0059] The present embodiment obtains the point cloud data set of each template in the target point cloud template sequence. Then, the one-way Hausdorff distance between the current frame point cloud data and the point cloud data set of each template is calculated, and finally, the template corresponding to the minimum value of the one-way Hausdorff distance is taken as the best matching template. Specifically, let the point set of the single frame point cloud data at the current time be , that is, the point set of the current frame point cloud data is , the point cloud data set of the th template is , then the similarity between the two is calculated by using the one-way Hausdorff distance:

[0060] .

[0061] Specifically, for each point in each template in the target point cloud template sequence , and the constituent point , is the position of a point in the template, there is . is a three-dimensional coordinate, , and . is the number of points in the template, which has no prior requirement and is consistent with the conventional number of points in single frame point cloud data. For single frame point cloud data , and the constituent point , is the position of a point in the single frame point cloud data, there is . is also a three-dimensional coordinate, , and . is the number of points in the single frame point cloud data, and when , the subsequent operation is performed, otherwise it is treated as a noise frame and directly skipped. The value is determined according to the stability of the laser radar, for example, the value is selected as . When calculating the one-way Hausdorff distance, the one-way Hausdorff distance refers to the farthest distance from the template to the point cloud data in the template. The purpose of one-way calculation is to correctly find the proximity of the point cloud data of the moving target and the current frame point cloud data from the local, otherwise from the perspective of the current frame point cloud data, there will always be a current frame point cloud data far away from the point cloud data of the moving target. At this time, the one-way Hausdorff distance is calculated as follows:

[0062] .

[0063] For each template, a distance value corresponding to one distance value can be obtained. Therefore, according to the Hausdorff distance calculated by each template , the best matching template can be determined , and the formula is as follows:

[0064] .

[0065] That is, the embodiment is to take the template corresponding to the minimum value of the one-way Hausdorff distance as the best matching template. Therefore, after the coarse registration process in the above step S100, the nearest distance can be obtained in the first template among all templates, which indicates that the template is closest to the current frame, that is, the current device is approximately moved to this action state.

[0066] It should be noted that the one-way Hausdorff distance in the embodiment is a point set distance measurement method, and other distances such as chamfer distance can also be used as a distance measurement method, and the embodiment is not limited thereto. In addition, the size and number of the target point cloud template in the embodiment are not limited, and the target point cloud template can be obtained by manual production, algorithm generation and the like in specific application, and the properties of various templates do not affect the final effect.

[0067] Step S200, moving the best matching template to the target position corresponding to the current frame point cloud data to obtain the reference point of the current frame point cloud data.

[0068] After obtaining the optimal matching template, this embodiment performs a fine registration process. The principle of fine registration is registration guided by the highest point, with the aim of further moving the optimal matching template to the potential target location. This movement is essentially just a point movement, moving the highest point of the optimal matching template to the highest point of a nearby potential target. This embodiment first determines the highest point of the optimal matching template, identifies the point in the current frame point cloud data closest to the highest point, and moves the optimal matching template and its corresponding highest point to the closest point, obtaining the point cloud dataset of the optimal matching template after the first movement. Then, using the highest point of the optimal matching template after the first movement as the starting point, it determines the highest points within a certain range in the current frame point cloud data, and continues to move the optimal matching template to the highest points within that range, obtaining the point cloud dataset of the optimal matching template after the second movement. Next, it calculates the Hausdorff distance between the current frame point cloud data and the point cloud dataset of the optimal matching template, the point cloud dataset of the optimal matching template after the first movement, and the point cloud dataset of the optimal matching template after the second movement, respectively. Finally, based on the minimum value of the Hausdorff distance, the reference point of the current frame point cloud data is obtained.

[0069] In practical applications, combined with Figure 4 As shown in the figure, the fine registration in this embodiment mainly includes three steps: (1) Calculate the highest point of the best matching template. ( Figure 4 (The dot in the upper left corner of the dashed box) Next, based on the highest point of the best matching template, calculate the point in the current frame's point cloud data that is closest to the highest point mentioned above. , ( Figure 4 (From the position indicated by the second sequence arrow from the left). Then, move the highest point and the best-matching template to that nearest point. Let the point cloud dataset after the first move be the optimal matching template at this point. (2) The highest point of the best matching template after the first move. Starting from this point, continue searching within a certain range of the current frame's point cloud data. The highest point inside, obtained The best matching template after the first move is from Move to Let the point cloud dataset after the second move be the optimal matching template at this point. .in, and Represent and exist The values ​​on the axis ( (3) Calculate the number of point clouds in the current frame and the best matching template obtained by coarse registration respectively. The set of points after the first step of the best matching template movement The set of points after the second step of the best matching template movement The Hausdorff distances between them are respectively , , And retain the minimum value in the Hausdorff distance:

[0070] .

[0071] in, For the current frame The Hausdorff distance values ​​after the above fine registration process. The reference point corresponding to the minimum Hausdorff distance is the reference point for the current frame's point cloud data. Specifically:

[0072] .

[0073] like Figure 4 As shown, the red dashed box represents the point cloud shape of the best matching template, the green solid box represents the point cloud shape near the current frame's point cloud data, and the orange dots represent reference points for motion calculation. The fine registration order is from left to right. (Calculation of reference points) This is the highest point, representing the moving position of the target in the current frame. The fine registration described above can usually achieve local optima, maintaining the accuracy of coarse registration even when the local high points contain significant noise, ensuring the result does not degrade. This approach aims to ensure real-time performance; calculating the reference point only requires two movements, and in noise-free conditions, the optimal match can be found.

[0074] like Figure 5 As shown, Figure 5 A case study of precise registration is provided. Figure 5 The gray dots represent local parts of the current frame's point cloud data; the blue dots represent the point cloud of the best matching template obtained after coarse registration; the blue sphere represents the highest point of the best matching template; the red dots represent the point cloud of the best matching template after the first move; the red sphere represents the reference point at this time; and the green dots represent the point cloud of the best matching template after the second move; the green sphere represents the reference point at this time.

[0075] Step S300: Based on the reference point of the current frame point cloud data, determine whether the current frame point cloud data is the end frame point cloud data. If the current frame point cloud data is the end frame point cloud data, then based on the reference point of the initial frame point cloud data and the reference point of the current frame point cloud data, obtain the moving distance of the moving target.

[0076] The embodiment determines the reference point of the current frame point cloud data, calculates the Euclidean distance between the reference point of the current frame point cloud data and the reference point of the initial frame point cloud data, and then determines whether the Euclidean distance meets the motion end condition based on the Euclidean distance and the distance mean calculated according to the time window in advance. If the motion end condition is met, the current frame point cloud data is determined as the end frame point cloud data. The determination of the end frame point cloud data can be determined by an algorithm or by the signal input of the hydraulic support itself.

[0077] Specifically, in the embodiment, it is determined whether the current frame point cloud data is the end frame point cloud data, that is, whether the Euclidean distance between the reference point of the current frame point cloud data and the reference point of the initial frame point cloud data is the maximum value in the device movement process. The reference point of the initial frame point cloud data before movement is , and the reference point of the current frame point cloud data during movement is Therefore, the Euclidean distance between the reference points of the two frames is

[0078] .

[0079] The values of the device motion initial frame and the end frame can be obtained by signals, that is, the device receives the instruction of device movement or stop at a certain time. However, the embodiment mainly adopts the way of autonomous estimation of motion cycle, and the motion initial time is recorded as , and the calculated reference point after coarse registration and fine registration is Therefore, there is a corresponding value for each time, and the best value is:

[0080] .

[0081] The embodiment can convert the problem into how to calculate the value of the motion end time , that is, how to determine that the device has completed the movement. Although the symbol of ending the movement is to obtain the maximum value, it is obvious that even if is obtained, the movement cannot be immediately determined to be ended, because there may be noise and registration errors, etc., which cause local jumps. Therefore, the embodiment introduces a time window mean function as a judgment standard, that is, for a time window , The distance mean in the motion cycle is calculated as:

[0082] .

[0083] The distance mean represents the mean value of the measured movement distance in a certain time, and usually ​Obviously, when the device is stationary, the following will occur: Therefore, the condition for determining the end of motion is:

[0084] .

[0085] in, This is the distance threshold, which is typically set to 0.05. Based on this, when the device's movement distance is calculated over a time period... Within the range, the difference between the currently measured distance value and the average distance is less than... When the device is stationary, it is considered to be in a static state. This method can autonomously determine the start and stop times of device movement without external signal input as a reference. Based on this, the [missing information] is determined. and middle and The size was determined, and the optimal Euclidean distance was also achieved. Value. At this point, you can retain... Corresponding best matching template And determine the point cloud data of the current frame. This is to end the frame point cloud data. That is, the distance the moving target has traveled.

[0086] Furthermore, if the fine registration process is accurate enough, it is possible to directly... This is considered the optimal result. However, to ensure real-time performance, the fine registration only employs a three-step registration process, which may introduce some errors. To obtain an accurate solution, this implementation performs point-by-point analysis on all points in the current frame's point cloud data, such as... Figure 2 As shown, the optimal reference point for the current frame point cloud data is obtained, and thus, the most accurate movement distance of the moving target is obtained based on the optimal reference point. Furthermore, this embodiment performs point-by-point registration in both the initial and final frames, thereby obtaining accurate calculation reference points. The Euclidean distance between the two calculation reference points is then calculated to obtain the final movement distance of the device.

[0087] In summary, the embodiment can form a target point cloud template sequence by recording all possible moving positions, then compare each frame of point cloud data with the target point cloud template sequence to find the best matching template, and then move the best matching template for fine registration to achieve accurate positioning of the moving position. In addition, the embodiment calculates the distance between the target point cloud template and the entire single-frame point cloud data by one-way Hausdorff distance, and simultaneously adopts the highest point traction to realize the movement of the best matching template to the corresponding target position in the single-frame point cloud data, and finally calculates the accurate movement value of the best matching template by traversing all points to solve the best matching template. Moreover, the embodiment calculates the rough movement result of each frame in real time, then intelligently selects the motion initial frame and the motion end frame through the distance mean, and finally calculates the distance between the initial frame and the end frame.

[0088] The method of the present application has been fully tested and verified, and has been run on a large number of physical machines in the downhole, and has obtained accurate results, which are consistent with the results of manual measurement, with an error of centimeter level. The experiment and verification of the present application are carried out in the hydraulic support equipment working in the real mine, and the laser radar is used and installed on the wall of the hydraulic support of the equipment, i.e. the top of the equipment. When the equipment moves, the equipment below the hydraulic support moves first, so that the laser radar can record the whole movement process, which is represented as the point cloud data of the moving target moving away from the origin of the local coordinates of the laser radar. When the equipment below stops moving, the equipment above the support starts to move forward and is retracted to match the position of the equipment below, which is represented as the point cloud data of the moving target moving close to the origin of the local coordinates of the laser radar. Through the method of the present application, the moving distance of the downhole equipment is accurately measured, and the measurement response time is fast, the accuracy is high, and the error is reached to centimeter level, effectively replacing the traditional manual measurement method.

[0089] The method of the present application is based on real-time laser point cloud, and the obtained moving distance measurement result is also real-time calculation, so the result can be obtained immediately after the hydraulic support moves, and the application value is high. The present application can maximize the matching accuracy to the data point scale through the processes of coarse matching, fine matching and point-by-point analysis. The method of the present application utilizes the finiteness of the motion space, so that the final result is within a reasonable range. At the same time, the maximum moving distance is obtained by distance mean, which has high robustness.

[0090] Based on the above embodiment, the present application further provides a dynamic object moving distance measurement system based on laser point cloud, which can be used to realize the steps in the above method embodiment. As Figure 6As shown, specifically, the system includes: a coarse registration module 10, a fine registration module 20, and a distance determination module 30. The coarse registration module 10 is used to acquire the current frame point cloud data of the moving target, and register the current frame point cloud data with a preset target point cloud template sequence to obtain the best matching template. The fine registration module 20 is used to move the best matching template to the target position corresponding to the current frame point cloud data to obtain the reference point of the current frame point cloud data. The distance determination module 30 is used to determine whether the current frame point cloud data is the end frame point cloud data based on the reference point of the current frame point cloud data. If the current frame point cloud data is the end frame point cloud data, then based on the reference point of the initial frame point cloud data and the reference point of the current frame point cloud data, the moving distance of the moving target is obtained.

[0091] The principles of each device or module in the dynamic object movement distance measurement system based on laser point cloud in this embodiment are the same as the working principles of each step in the above method embodiment, and will not be repeated here.

[0092] The modules in the aforementioned dynamic object movement distance measurement system based on laser point clouds can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of the terminal in hardware form or independent of it, or stored in the terminal's memory in software form, so that the processor can call and execute the corresponding operations of each module.

[0093] Based on the above embodiments, the present invention also provides a terminal, the principle block diagram of which can be as follows: Figure 7 As shown. The terminal may include one or more processors 100 ( Figure 7 (Only one is shown in the image), memory 101, and computer program 102 stored in memory 101 and executable on one or more processors 100. For example, a program for measuring the moving distance of a dynamic object based on laser point clouds. When one or more processors 100 execute computer program 102, they can implement various steps in the embodiment of the method for measuring the moving distance of a dynamic object based on laser point clouds. Alternatively, when one or more processors 100 execute computer program 102, they can implement the functions of various modules / units in the embodiment of the system for measuring the moving distance of a dynamic object based on laser point clouds, without limitation herein.

[0094] In one embodiment, the processor 100 can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic components, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor.

[0095] In one embodiment, the memory 101 can be an internal storage unit of the electronic device, such as a hard disk or a memory of the electronic device. The memory 101 can also be an external storage device of the electronic device, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. Further, the memory 101 can include both the internal storage unit and the external storage device of the electronic device. The memory 101 is used to store computer programs and other programs and data required by the terminal. The memory 101 can also be used to temporarily store data that has been output or will be output.

[0096] Those skilled in the art can understand that, Figure 7 The block diagram shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the terminal to which the scheme of the present application is applied. The specific terminal can include more or less components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0097] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by a computer program instructing relevant hardware. The computer program can be stored in a non-volatile computer readable storage medium, and when executed, can include the processes of the above-mentioned embodiment methods. Any reference to memory, storage, operating database or other medium used in the embodiments of the present application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct RAM bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0098] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for measuring the moving distance of a dynamic object based on laser point clouds, characterized in that, The method includes: Acquire the current frame point cloud data of the moving target, and register the current frame point cloud data with a preset target point cloud template sequence to obtain the best matching template; Move the best matching template to the target position corresponding to the current frame point cloud data to obtain the reference point of the current frame point cloud data; Based on the reference point of the current frame point cloud data, it is determined whether the current frame point cloud data is the end frame point cloud data. If the current frame point cloud data is the end frame point cloud data, the movement distance of the moving target is obtained based on the reference point of the initial frame point cloud data and the reference point of the current frame point cloud data. The current frame point cloud data is registered with a preset target point cloud template sequence to obtain the best matching template, including: Obtain the point cloud dataset for each template in the target point cloud template sequence; Calculate the one-way Hausdorff distance between the current frame point cloud data and the point cloud dataset of each template; The template corresponding to the minimum value of the one-way Hausdorff distance is used as the best matching template; Moving the best matching template to the target position corresponding to the current frame point cloud data to obtain the reference point of the current frame point cloud data includes: Determine the highest point of the best matching template, determine the point in the current frame point cloud data that is closest to the highest point, and move the best matching template and the corresponding highest point to the closest point to obtain the point cloud dataset of the best matching template after the first move; Starting from the highest point of the best matching template after the first move, determine the highest point within a certain range in the current frame point cloud data, and continue to move the best matching template after the first move to the highest point within a certain range to obtain the point cloud dataset of the best matching template after the second move; Calculate the Hausdorff distance between the current frame point cloud data and the point cloud dataset of the best matching template, the point cloud dataset of the best matching template after the first move, and the point cloud dataset of the best matching template after the second move, respectively. The reference point of the current frame point cloud data is obtained based on the minimum value of the Hausdorff distance.

2. The method for measuring the moving distance of a dynamic object based on laser point clouds according to claim 1, characterized in that, The moving target is a landmark component or landmark location on the scanned device. When the method is applied to the measurement of the moving distance of the downhole hydraulic support, the moving target is the front position of the worktable of the downhole hydraulic support.

3. The method for measuring the moving distance of a dynamic object based on laser point clouds according to claim 1, characterized in that, The method for creating the target point cloud template sequence includes: Scan the movement process of the moving target throughout the entire motion cycle to obtain the point cloud flow of the moving target; The point cloud stream of the moving target is converted into single-frame point cloud data and saved. The single-frame point cloud data is then segmented to obtain the point cloud data of the moving target for each frame. The target point cloud template sequence is obtained based on the temporal and spatial distribution of the point cloud data of the moving target in different frames.

4. The method for measuring the moving distance of a dynamic object based on laser point clouds according to claim 1, characterized in that, Based on the reference points of the current frame point cloud data, determining whether the current frame point cloud data is the end frame point cloud data includes: Calculate the Euclidean distance between the reference point of the current frame point cloud data and the reference point of the initial frame point cloud data; Based on the Euclidean distance and the average distance calculated in advance according to the time window, it is determined whether the Euclidean distance meets the motion termination condition; If the motion termination condition is met, then the current frame point cloud data is determined to be the end frame point cloud data.

5. The method for measuring the moving distance of a dynamic object based on laser point clouds according to claim 1, characterized in that, The method further includes: A point-by-point analysis is performed on all points in the current frame point cloud data to obtain the optimal reference point of the current frame point cloud data, and the movement distance of the moving target is obtained based on the optimal reference point.

6. A dynamic object movement distance measurement system based on laser point clouds, characterized in that, The system is used to implement the steps of the dynamic object movement distance measurement method based on laser point clouds as described in any one of claims 1-5, and the system includes: The coarse registration module is used to acquire the current frame point cloud data of the moving target, register the current frame point cloud data with a preset target point cloud template sequence, and obtain the best matching template. The fine registration module is used to move the best matching template to the target position corresponding to the current frame point cloud data to obtain the reference point of the current frame point cloud data; The distance determination module is used to determine whether the current frame point cloud data is the end frame point cloud data based on the reference point of the current frame point cloud data. If the current frame point cloud data is the end frame point cloud data, the moving distance of the moving target is obtained based on the reference point of the initial frame point cloud data and the reference point of the current frame point cloud data.

7. A terminal, characterized in that, The terminal includes a memory, a processor, and a dynamic object movement distance measurement program based on laser point clouds stored in the memory and executable on the processor. When the processor executes the dynamic object movement distance measurement program based on laser point clouds, it implements the steps of the dynamic object movement distance measurement method based on laser point clouds as described in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a dynamic object movement distance measurement program based on laser point clouds, and the dynamic object movement distance measurement program based on laser point clouds implements the steps of the dynamic object movement distance measurement method based on laser point clouds as described in any one of claims 1-5 on the computer-readable storage medium.

Images