A single-line laser radar-based fixed target pose deviation monitoring method

Abstract

The application provides a fixed target pose deviation monitoring method based on single-line laser radar, belongs to the technical field of pose deviation monitoring, and comprises the following steps: point cloud data acquisition and preprocessing; point cloud clustering; target cluster screening and tracking; straight line fitting; pose deviation calculation and angle deviation output; the method has good lightweight advantage, and the hardware cost is much lower than that of multi-line radar or a complex vision system; the method has high precision and strong robustness: through multi-stage screening and random point filtering, the influence of environmental interference and noise points is effectively excluded, the processing time of each frame of data can be set to be in millisecond level, the demand of most real-time monitoring scenes can be met, the work is safe and reliable, and the method is suitable for popularization and application.

Description

Technical Field

[0001] This invention belongs to the field of pose deviation monitoring technology, and in particular, a fixed target pose deviation monitoring method based on single-line lidar. This method is suitable for scenarios where it is necessary to monitor the angular deviation generated by a fixed target object during movement or deformation in real time. Background Technology

[0002] In smart home and industrial automation scenarios, traditional solutions for monitoring the status of fixed targets (such as doors or robotic arms) often use contact sensors or simple infrared beam detectors. Contact sensors need to be physically installed on moving parts, which are prone to wear and tear and are complex to install; infrared beam detectors are easily interfered with by ambient light, dust and other factors, and can only provide a binary "on / off" state, unable to provide continuous angle information.

[0003] In recent years, non-contact monitoring methods based on vision or lidar have gradually emerged. However, vision-based solutions are greatly affected by lighting conditions and lack robustness; while solutions using multi-line lidar, although highly accurate, are costly, consume a lot of power, and have complex data processing, making them difficult to deploy on low-cost, resource-constrained embedded devices.

[0004] Therefore, there is an urgent need for a monitoring method that utilizes low-cost sensors, lightweight algorithms, and can provide high-precision continuous angular deviation information. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a method for monitoring the pose deviation of a fixed target based on a single-line lidar. It collects point cloud data of a fixed target using a single-line lidar and, through a core process design of "clustering → target selection and tracking → line fitting → angle comparison → deviation output," achieves real-time monitoring of the angle deviation of the fixed target. This method can be widely applied in fields such as smart homes, industrial inspection, and security monitoring, and has advantages such as low cost, high precision, and reliable operation, making it suitable for widespread adoption.

[0006] Firstly, a method for monitoring the pose deviation of a fixed target based on a single-line lidar includes the following steps: Step 1: Point cloud data acquisition and preprocessing; 1.1 Point Cloud Data Acquisition: A single-line lidar is used to collect point cloud data of the fixed target to be monitored. The point cloud data includes: angle information and distance value of each point. 1.2 Point Cloud Data Preprocessing: The point cloud data is preprocessed to remove invalid points; As an example, the single-line lidar is a 180° single-line lidar.

[0007] As an example, the invalid point refers to: ① The distance value is abnormal; ② The signal strength is too low, i.e., below the preset threshold.

[0008] Step 2: Point cloud clustering; 2.1 Based on the angle order and continuity rules of the point cloud data, point cloud clustering is performed, and point clouds that meet the threshold of the number of continuous points are divided into initial point cloud clusters; Step 3: Target cluster screening and tracking; 3.1 Select candidate target clusters that meet preset conditions from the initial point cloud clusters; 3.2.1 If it is the first frame of data: select the candidate target cluster with the most points as the target cluster of the current frame; 3.2.2 If it is not the first frame of data: calculate the distance between the center point of the current candidate target cluster and the center point of the target cluster in the previous frame, and select the candidate target cluster with the closest distance as the target cluster of the current frame; As an example, the preset condition refers to: the number of points in the point cloud cluster is greater than or equal to a set value, and the center angle of the point cloud cluster is within a preset angle range directly in front of the single-line lidar.

[0009] Step 4: Line Fitting; 4.1 Perform straight line fitting on the selected target cluster of the current frame, and record the angle parameters of the fitted straight line as the reference angle. ; Step 5: Calculate pose deviation and output angle deviation; 5.1 Acquire point cloud data for subsequent frames in real time, repeat the above steps, and obtain the real-time fitted line angle for each frame. ; 5.2 Calculate the angle deviation value for each frame And output the angle deviation value.

[0010] As an example, when the angle deviation value Δθ is greater than a preset threshold, an alarm signal is triggered and output.

[0011] As an example, the continuity rule is as follows: ① The angle difference is less than or equal to the set value; ② The difference between the measured distances is less than or equal to the set value; If any condition is not met, it is determined to be the boundary of different targets, the current cluster ends and a new point cloud clustering operation begins.

[0012] As an example, the specific operations of the line fitting include: ① Randomly select N points (e.g., 2 points) from the candidate target cluster as initial sampling points and fit an initial linear model; ② Repeat the sampling M times to obtain multiple initial straight line models; ③ Set a distance threshold (e.g., 30mm) and determine points whose distance from the line to the point is less than or equal to the threshold as interior points; ④ The least squares method is used to perform fine fitting on the interior points to obtain the final fitted line.

[0013] Secondly, this application discloses an electronic device comprising: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to execute a method for monitoring the pose deviation of a fixed target based on a single-line lidar.

[0014] Thirdly, this application discloses a non-transitory computer-readable storage medium that, when the instructions in the storage medium are executed by the processor of an electronic device, enables the electronic device to perform a fixed target pose deviation monitoring method based on a single-line lidar.

[0015] Fourthly, this application discloses a computer program product that, when the instructions in the computer program product are executed by the processor of an electronic device, enables the electronic device to perform a fixed target pose deviation monitoring method based on a single-line lidar.

[0016] The beneficial effects of this invention are: (1) Lightweight advantages: The algorithm process of using 180° single-line lidar is simplified (only four core steps: clustering, screening, fitting, and comparison), and the hardware cost is much lower than that of multi-line lidar or complex vision systems. (2) High accuracy and robustness: Through multi-level screening (region, number of points, continuity) and noise filtering, the influence of environmental interference and noise points is effectively eliminated, ensuring high accuracy of line fitting and angle calculation; (3) Strong real-time performance: The processing time for each frame of data can be set in milliseconds, which can meet the needs of most real-time monitoring scenarios. Attached Figure Description

[0017] Fig. 1 This is an overall flowchart of a fixed target pose deviation monitoring method based on single-line lidar according to the present invention.

[0018] Fig. 2 This is a schematic diagram of an embodiment 1 of the method for monitoring the pose deviation of a fixed target based on a single-line lidar according to the present invention.

[0019] Fig. 3 This is a schematic diagram of linear fitting and angular deviation in a fixed target pose deviation monitoring method based on single-line lidar according to the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application. Figs. 1 to 3 As shown.

[0021] Reference Fig. 1 As shown, in the first aspect, a method for monitoring the pose deviation of a fixed target based on a single-line lidar includes the following steps: Step 1: Point cloud data acquisition and preprocessing; 1.1 Point Cloud Data Acquisition: A single-line lidar is used to collect point cloud data of the fixed target to be monitored. The point cloud data includes: angle information and distance value of each point. 1.2 Point Cloud Data Preprocessing: The point cloud data is preprocessed to remove invalid points; As an example, the single-line lidar is a 180° single-line lidar.

[0022] As an example, the invalid point refers to: ① The distance value is abnormal; ② The signal strength is too low, i.e., below the preset threshold.

[0023] Step 2: Point cloud clustering; 2.1 Based on the angle order and continuity rules of the point cloud data, point cloud clustering is performed, and point clouds that meet the threshold of the number of continuous points are divided into initial point cloud clusters; Step 3: Target cluster screening and tracking; 3.1 Select candidate target clusters that meet preset conditions from the initial point cloud clusters; 3.2.1 If it is the first frame of data: select the candidate target cluster with the most points as the target cluster of the current frame; 3.2.2 If it is not the first frame of data: calculate the distance between the center point of the current candidate target cluster and the center point of the target cluster in the previous frame, and select the candidate target cluster with the closest distance as the target cluster of the current frame; As an example, the preset condition refers to: the number of points in the point cloud cluster is greater than or equal to a set value, and the center angle of the point cloud cluster is within a preset angle range directly in front of the single-line lidar.

[0024] Step 4: Line Fitting; 4.1 Perform straight line fitting on the selected target cluster of the current frame, and record the angle parameters of the fitted straight line as the reference angle. ; Step 5: Calculate pose deviation and output angle deviation; 5.1 Acquire point cloud data for subsequent frames in real time, repeat the above steps, and obtain the real-time fitted line angle for each frame. ; 5.2 Calculate the angle deviation value for each frame And output the angle deviation value.

[0025] As an example, when the angle deviation value Δθ is greater than a preset threshold, an alarm signal is triggered and output.

[0026] As an example, the continuity rule is as follows: ① The angle difference is less than or equal to the set value; ② The difference between the measured distances is less than or equal to the set value; If any condition is not met, it is determined to be the boundary of different targets, the current cluster ends and a new point cloud clustering operation begins.

[0027] As an example, the specific operations of the line fitting include: ① Randomly select N points (e.g., 2 points) from the candidate target cluster as initial sampling points and fit an initial linear model; ② Repeat the sampling M times to obtain multiple initial straight line models; ③ Set a distance threshold (e.g., 30mm) and determine points whose distance from the line to the point is less than or equal to the threshold as interior points; ④ The least squares method is used to perform fine fitting on the interior points to obtain the final fitted line.

[0028] Secondly, this application discloses an electronic device comprising: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to execute a method for monitoring the pose deviation of a fixed target based on a single-line lidar.

[0029] Thirdly, this application discloses a non-transitory computer-readable storage medium that, when the instructions in the storage medium are executed by the processor of an electronic device, enables the electronic device to perform a fixed target pose deviation monitoring method based on a single-line lidar.

[0030] Fourthly, this application discloses a computer program product that, when the instructions in the computer program product are executed by the processor of an electronic device, enables the electronic device to perform a fixed target pose deviation monitoring method based on a single-line lidar.

[0031] To better illustrate the design principles of this invention, specific embodiments are provided below: Example 1: Refer to Fig. 2 As shown; A method for monitoring the pose deviation of a fixed target based on a single-line lidar includes the following steps: Step 1: Point cloud data acquisition and preprocessing; 1.1 A 180° single-line lidar 101 is used, with its scanning plane facing and covering the fixed target 102 to be monitored.

[0032] The 180° single-line lidar emits a laser beam in real time to scan the surface of the fixed target 102 and receives the reflected signal to acquire a frame of point cloud data.

[0033] The point cloud data includes: angle information and distance value (distance from the origin of the lidar to the target point) for each point. Among them, the 180° scanning range 103 of the 180° single-line lidar can ensure complete coverage of the monitoring area and avoid monitoring blind spots.

[0034] 1.2 Point Cloud Preprocessing: The collected raw point cloud data is initially denoised to remove invalid points, according to the following rules: (1) Abnormal distance values ​​(e.g., blind zone data less than 100mm, or data greater than the effective range of 6000mm). (2) The signal strength is too low (e.g., signal strength value < 10, dimensionless relative value, quantization range 0~100, signal strength value itself has no standard physical unit, and is a specific normalized digital value).

[0035] Step 2: Point cloud clustering; 2.1 Clustering Rules: Using the origin of the 180° single-line lidar as the reference, consecutive point cloud points are extracted sequentially according to the angular order of the point cloud data. A threshold for the number of consecutive points is set (e.g., 8 points). Point clouds that satisfy "number of consecutive points ≥ threshold" are divided into initial point cloud clusters, thus achieving preliminary clustering of the point clouds.

[0036] 2.2 Continuity Determination: Two adjacent point cloud points are considered continuous if they simultaneously meet the following two conditions: (1) The angle difference between two adjacent point cloud points is less than or equal to a set value (e.g., angle difference ≤ 1°). (2) The difference between the measured values ​​is less than or equal to a set value (e.g., the difference between the measured values ​​is ≤20cm).

[0037] If any condition is not met, it is determined to be the boundary of different targets, the current cluster ends and a new point cloud clustering operation begins.

[0038] Step 3: Target cluster screening and tracking; 3.1 Selection criteria: From the initial point cloud clusters obtained in step 2, select clusters that simultaneously meet the following conditions as candidate target clusters.

[0039] (1) All points in the point cloud cluster are continuous points (satisfying the continuous point determination rule in step 2.2), and the number of points in the point cloud cluster is ≥5 (set value); the setting value of 5 here can ensure the effectiveness of the point cloud cluster and avoid the formation of false point cloud clusters by a single or a small number of noisy points; (2) The center angle of the point cloud cluster is within the preset angle range of 104° in front of the 180° single-line lidar. The preset angle here is 20° in front, and the range can be finely adjusted according to the actual monitoring needs.

[0040] 3.2 Tracking target point cloud clusters; (1) If it is the first frame of data, the candidate cluster with the most points is selected as the target cluster.

[0041] (2) If it is not the first frame, calculate the distance between the center point of all current candidate clusters and the center point of the target cluster in the previous frame, and select the candidate cluster with the closest distance as the target cluster of the current frame to ensure the consistency of the target.

[0042] Step 4: Line fitting, refer to Fig. 3 As shown; (1) Randomly select 2 points from the target cluster in the current frame as initial sampling points, fit an initial straight line, and repeat sampling 50~200 times (the number of iterations can be adjusted according to real-time requirements, taking into account both accuracy and speed) to obtain multiple initial straight line models; (2) Inner point screening: Calculate the distance from each point in the target cluster to each initial straight line model, set the distance threshold to 30mm, and determine the points whose measured distance value of the collected point cloud points is greater than the distance threshold as potential noise points, and the points whose measured distance value of the point cloud points is less than or equal to the distance threshold as inner points; (3) Line Fitting: The least squares method is used to fit the inner points after noise filtering to obtain the fitted line 106 corresponding to the target cluster. The angle 107 of the fitted line of the target cluster (the angle between the fitted line and the normal 105 of the lidar origin) is recorded. During the fitting process, the inner point set is used for fine fitting to improve the calculation accuracy of the line angle and ensure the stability of the reference angle.

[0043] Step 5: Calculate pose deviation and output angle deviation; 5.1 Reference Setting: For the first frame of valid data, the reference angle is... (For example, 90°) is set as the reference state.

[0044] 5.2 Frame Angle Deviation Calculation: The lidar acquires point cloud data for each frame in real time. Steps 1-4 are repeated to cluster, filter targets, and fit lines for each frame of point cloud data, resulting in a corresponding fitted line 108 for each frame. The angle between the normal 109 of the real-time fitted line and the corresponding fitted line 108 is the real-time fitted line angle 110. (Symbol: 110) (i is the frame number, i=1,2,3,...). The real-time angle of each frame... relative to the reference angle For comparison, the angular deviation value 111 is calculated, with the symbol: ; 5.3 Numerical output: Real-time output of angle deviation value 111, symbol: Δθ. When Δθ > preset threshold (e.g. 0.5°), it is determined that there is an angle deviation in the movement of the target object, and the actual angle deviation value 111 is issued.

[0045] It should be noted that, for the sake of simplicity, the method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, because according to this application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions involved are not necessarily required by this application.

[0046] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0047] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0048] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

[0049] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this application can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0050] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0051] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0052] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0053] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0054] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0055] The above description is only a preferred embodiment of the present invention. It should be understood that the above description of the embodiments is only for the purpose of helping to understand the method and core idea of ​​the present invention, and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, etc. made within the idea and principle of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for monitoring the pose deviation of a fixed target based on a single-line lidar, characterized in that, Includes the following steps: Step 1: Point cloud data acquisition and preprocessing; point cloud data of the fixed target to be monitored is acquired using a single-line lidar; the point cloud data is preprocessed to remove invalid points; Step 2: Point cloud clustering; Based on the angular order and continuity rules of the point cloud data, point cloud clustering is performed, and point clouds that meet the threshold for the number of consecutive points are divided into initial point cloud clusters; Step 3: Target cluster screening and tracking; Select candidate target clusters that meet preset conditions from the initial point cloud clusters; If it is the first frame of data: select the candidate target cluster with the most points as the target cluster of the current frame; if it is not the first frame of data: calculate the distance between the center point of the current candidate target cluster and the center point of the target cluster of the previous frame, and select the candidate target cluster with the closest distance as the target cluster of the current frame. Step 4: Line Fitting; Perform line fitting on the selected target clusters in the current frame, and record the angle parameters of the fitted line as the reference angle. ; Step 5: Pose deviation calculation and angle deviation output; real-time acquisition of point cloud data for subsequent frames, repeating the above steps to obtain the real-time fitted straight line angle for each frame. ; Calculate the angle deviation value for each frame. And output the angle deviation value.

2. The method for monitoring the pose deviation of a fixed target based on a single-line lidar according to claim 1, characterized in that, The point cloud data includes: the angle information and distance value of each point.

3. The method for monitoring the pose deviation of a fixed target based on a single-line lidar according to claim 1, characterized in that, The single-line lidar is a 180° single-line lidar.

4. The method for monitoring the pose deviation of a fixed target based on a single-line lidar according to claim 1, characterized in that, The invalid point refers to: ① The distance value is abnormal; ② The signal strength is too low, i.e., below the preset threshold.

5. A method for monitoring the pose deviation of a fixed target based on a single-line lidar according to claim 1, characterized in that, The preset conditions refer to the following: the number of points in the point cloud cluster is greater than or equal to a set value, and the center angle of the point cloud cluster is within a preset angle range directly in front of the single-line lidar.

6. The method for monitoring the pose deviation of a fixed target based on a single-line lidar according to claim 1, characterized in that, When the angle deviation value Δθ is greater than a preset threshold, an alarm signal is triggered and output.

7. The method for monitoring the pose deviation of a fixed target based on a single-line lidar according to claim 1, characterized in that, The continuity rule is as follows: ① The angle difference is less than or equal to the set value; ② The difference between the measured distances is less than or equal to the set value; If any condition is not met, it is determined to be the boundary of different targets, the current cluster ends and a new point cloud clustering operation begins.

8. A method for monitoring the pose deviation of a fixed target based on a single-line lidar according to claim 1, characterized in that, The specific operations for the linear fitting include: ① Randomly select N points from the candidate target cluster as initial sampling points and fit an initial linear model; ② Repeat the sampling M times to obtain multiple initial straight line models; ③ Set a distance threshold and determine points whose distance from a point to a line is less than or equal to the threshold as interior points; ④ The least squares method is used to perform fine fitting on the interior points to obtain the final fitted line.

9. An electronic device, characterized in that, include: A processor, and a memory for storing processor-executable instructions; wherein the processor is configured to perform the monitoring method according to any one of claims 1-8.

10. A non-transitory computer-readable storage medium, characterized in that, When the instructions in the storage medium are executed by the processor of the electronic device, the electronic device is able to perform the monitoring method according to any one of claims 1-8.

Images