A 2D laser radar point cloud data processing method

Abstract

This invention relates to the field of lidar technology, and in particular provides a method for processing 2D lidar point cloud data. The method includes: setting a distance threshold and an echo intensity threshold for data processing; acquiring a ring of point cloud data to obtain the actual measured distance and echo intensity of the lidar; retaining point cloud data with distances less than a preset distance, and setting data points with distances greater than a preset distance as invalid values, thus obtaining a new ring of data; determining the boundary of the target object based on the invalid values, and obtaining the boundary index value; setting a sliding window and a number of sliding steps based on the echo intensity characteristics, comparing the average echo intensity within adjacent windows, and if the difference is greater than a set threshold or reaches a set number of sliding steps, retaining the point cloud and exiting data processing; otherwise, deleting edge-widening points. This method effectively eliminates the widening problem at the edges of the target object, restoring the true contour of the scanned target object.

Description

Technical Field

[0001] This invention belongs to the field of lidar technology, specifically relating to a 2D lidar point cloud data processing method. Background Technology

[0002] 2D LiDAR boasts high ranging accuracy, fast scanning speed, and strong environmental interference resistance. The contour reconstruction accuracy of its point cloud data directly determines the accuracy of scene perception and measurement. Ideally, the LiDAR beam is an infinitely thin straight line whose cross-section can be considered as a geometric point. However, in practical applications, factors such as the inherent divergence angle of the laser, installation deviations, collimating lens surface errors, and manufacturing tolerances of structural components cause the emitted beam to diffuse, forming a light spot with a certain area. When the laser spot illuminates the edge of the target object, the weak echo generated by part of the spot is still captured by the receiver and identified as valid object point cloud, forming edge broadening points, resulting in the measured object size being larger than the actual physical size.

[0003] In existing technologies, conventional point cloud filtering methods can only filter out discrete outlier noise points and distant background points. They cannot accurately remove edge broadening points that are continuous with the main body of the object. This can easily lead to problems such as the accidental deletion of effective points and incomplete removal of broadening points, and thus cannot restore the true outline of the target object.

[0004] Therefore, there is an urgent need for a 2D lidar point cloud data processing method that can efficiently eliminate edge broadening. Summary of the Invention

[0005] In view of this, the present invention provides a 2D lidar point cloud data processing method to eliminate broadened points and restore the true outline of the scanned object.

[0006] The method includes:

[0007] S1. Preset distance threshold for 2D LiDAR point cloud data processing D and echo intensity threshold I ;

[0008] S2. Acquire complete point cloud data from a single-circle scan of the 2D LiDAR, and extract the actual measured distance corresponding to each scan point. d and echo intensity i ;

[0009] S3. Measure the actual distance of each scanning point. d With distance threshold D Compare and retain d < D The raw point cloud data will d ≥ D The scan points are set to invalid values ​​to generate a single-loop filtered point cloud dataset;

[0010] S4. Traverse the point cloud dataset after single-loop filtering, complete the target object segmentation through invalid values, and record the boundary index value of each target object after segmentation;

[0011] S5. Based on the characteristic that the echo intensity inside the object is greater than that at the edge, a sliding window is set starting from the boundary index value. X With maximum number of slides Y The system slides inward from both sides of the target object and compares the difference in the mean echo intensity of adjacent windows. If the difference is greater than the echo intensity threshold, the system will detect the difference. I Or reach the maximum number of slides Y If the remaining point cloud data is retained, the processing is terminated; otherwise, the corresponding edge expansion points are deleted.

[0012] Optionally, in step S1, the distance threshold D Using the reflective sticker specimen as a reference for calibration, after fixing the radar position, move the reflective sticker specimen to the position with the fewest widening points, and use the distance corresponding to this position as the distance threshold D;

[0013] echo intensity threshold I The calibration is based on the maximum fluctuation value of the echo intensity during the movement of the reflective sticker specimen.

[0014] Optionally, in step S2, the acquired single-loop point cloud data includes the scanning angle and actual measured distance corresponding to each scanning point. d With echo intensity i The total number of points in a single scan is determined by the radar scanning angle range and angular resolution.

[0015] Optionally, in step S3, each scan point of the single-circle point cloud is compared point by point according to the scanning angle order, and the sequence number of the scan point is determined. n Satisfying 1≤ n ≤ Total number of points scanned in a single loop, completing the filtering and invalid value assignment of the entire point cloud.

[0016] Optionally, step S4 further includes:

[0017] The left and right boundaries of the target object are marked based on the recorded boundary index values, and the number of valid scanned point clouds corresponding to the target object is calculated based on the boundary index values. N ,in N ≥2.

[0018] Optionally, in step S5, the sliding window... X The number of consecutive point clouds processed in a single operation. X ≥2;

[0019] Maximum number of slides Y This represents the maximum number of times the window can slide inwards towards the object. Y ≥2.

[0020] Optionally, in step S5, the average value of the echo intensity of all point clouds within a single sliding window is first calculated, and then the difference Δ between the average intensity values ​​of two adjacent sliding windows is calculated. i If Δ i <Echo intensity threshold I Determine all points within the current window as edge widening points and delete them.

[0021] Optionally, in step S5, the sliding processing logic of the left and right boundaries of the target object is consistent, and the sliding processing is carried out simultaneously from the left boundary to the right and from the right boundary to the left into the object.

[0022] Optionally, in step S5, the sliding window... X With maximum number of slides Y Based on the characteristic of 2D LiDAR point clouds being denser at close range and sparser at distant range, the algorithm adaptively adjusts with scanning distance.

[0023] As can be seen from the above technical solutions, the present invention has the following advantages:

[0024] This invention employs a progressive processing flow of initial screening using distance thresholds, invalid value boundary positioning, and fine screening using sliding window intensity. This process can accurately identify and eliminate continuous widening points on the edge of a target object, solving the problem of oversized measurements caused by laser beam diffusion, restoring the true physical contour of the target object, and improving the measurement accuracy and contour restoration of 2D LiDAR. By using a method of comparing the average difference of intensity between adjacent windows, it avoids the problem of poor adaptability of fixed intensity thresholds to targets with different reflectivities. Combined with dual stopping conditions, it avoids the accidental deletion of effective point clouds while eliminating widening points. Attached Figure Description

[0025] To more clearly illustrate the technical solution of the present invention, the accompanying drawings used in the description will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 This is a flowchart illustrating the 2D lidar point cloud data processing method in an embodiment of the present invention.

[0027] Figure 2 This is a schematic diagram of the object edge widening point in an embodiment of the present invention;

[0028] Figure 3 This is a schematic diagram of the laser radar scanning angle in an embodiment of the present invention;

[0029] Figure 4 This is a schematic diagram of distance data and echo intensity data in an embodiment of the present invention;

[0030] Figure 5 This is a schematic diagram of the data acquisition and processing flow in an embodiment of the present invention;

[0031] Figure 6 This is a diagram showing the effect of the lidar point cloud processing method in this embodiment of the invention. Detailed Implementation

[0032] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0033] It should be understood that the described embodiments are merely some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0034] The terminology used in the embodiments of this invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms “a,” “the,” and “the” used in the embodiments of this invention are also intended to include the plural forms unless the context clearly indicates otherwise.

[0035] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0036] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0037] Please see Figure 1 The diagram shows a flowchart of a 2D LiDAR point cloud data processing method, which includes the following steps:

[0038] S1. Preset distance threshold for 2D LiDAR point cloud data processing D and echo intensity thresholdI ;

[0039] S2. Acquire complete point cloud data from a single-circle scan of the 2D LiDAR, and extract the actual measured distance corresponding to each scan point. d and echo intensity i ;

[0040] S3. Measure the actual distance of each scanning point. d With distance threshold D Compare and retain d < D The raw point cloud data will d ≥ D The scan points are set to invalid values ​​to generate a single-loop filtered point cloud dataset;

[0041] S4. Traverse the point cloud dataset after single-loop filtering, complete the target object segmentation through invalid values, and record the boundary index value of each target object after segmentation;

[0042] S5. Based on the characteristic that the echo intensity inside the object is greater than that at the edge, a sliding window is set starting from the boundary index value. X With maximum number of slides Y The system slides inward from both sides of the target object and compares the difference in the mean echo intensity of adjacent windows. If the difference is greater than the echo intensity threshold, the system will detect the difference. I Or reach the maximum number of slides Y If the remaining point cloud data is retained, the processing is terminated; otherwise, the corresponding edge expansion points are deleted.

[0043] It should be noted that in existing technologies, when a laser shines on the edge of an object, the weak reflected light is captured by the receiver and used as point cloud data to output the actual object, making the measured object wider than its actual size. Figure 2 As shown, the true outline of the target object cannot be restored. This embodiment effectively eliminates the problem of widening at the edges of the target object, restoring the true outline of the scanned target object.

[0044] As a refinement and extension of the specific implementation of the above embodiments, in order to fully illustrate the specific implementation process in this embodiment, another 2D LiDAR point cloud data processing method is provided, which includes the following steps:

[0045] S1. Preset distance threshold for 2D LiDAR point cloud data processing D and echo intensity threshold I ;

[0046] In step S1, the distance threshold D Using the reflective sticker specimen as a reference for calibration, after fixing the radar position, move the reflective sticker specimen to the position with the fewest widening points, and use the distance corresponding to this position as the distance threshold D;

[0047] echo intensity threshold I The calibration is based on the maximum fluctuation value of the echo intensity during the movement of the reflective sticker specimen.

[0048] In this embodiment of the invention, a reflective sticker specimen is used as a reference. After fixing the radar position, the reflective sticker specimen is moved until the widening point is minimized. The distance at this position is used as the threshold D. At the same time, during the process of moving the reflective sticker, the intensity fluctuation range is statistically analyzed, and the maximum fluctuation value is used as the threshold I.

[0049] S2. Acquire complete point cloud data from a single-circle scan of the 2D LiDAR, and extract the actual measured distance corresponding to each scan point. d and echo intensity i ;

[0050] In step S2, the acquired single-loop point cloud data includes the scanning angle and actual measured distance corresponding to each scanning point. d With echo intensity i The total number of points in a single scan is determined by the radar scanning angle range and angular resolution.

[0051] In this embodiment of the invention, the 2D lidar can acquire angle, distance, and echo intensity information in a two-dimensional coordinate system, such as... Figure 3 As shown, taking the Keli LS3 2D lidar as an example, the scanning angle is 270°, with the starting and ending positions ranging from -45° to 225°. The angular interval between two adjacent laser scanning points is called the angular resolution, which is 0.1°. This means that distance and echo intensity are measured every 0.1°. The lidar rotates one revolution, measuring a total of 2701 points, forming one circle of point cloud data. Figure 4 As shown, 2701 points form a scanning plane, and each point represents distance data and echo intensity data measured at a specific angle.

[0052] S3. Measure the actual distance of each scanning point. d With distance threshold D Compare and retain d < D The raw point cloud data will d ≥ D The scan points are set to invalid values ​​to generate a single-loop filtered point cloud dataset;

[0053] In step S3, each scan point of the single-circle point cloud is compared point by point according to the scanning angle order, and the sequence number of the scan point is determined. n Satisfying 1≤ n ≤ Total number of points scanned in a single loop, completing the filtering and invalid value assignment of the entire point cloud.

[0054] In this embodiment of the invention, the point cloud distance data at each angle is measured in real time. d n The relationship with the preset distance D, (1≤n≤2701), is based on the measured actual distance. d n If the value is less than D, retain the original point cloud data for that point; otherwise, set the point to an invalid value and obtain a new point cloud dataset segmented according to a preset distance.

[0055] S4. Traverse the point cloud dataset after single-loop filtering, complete the target object segmentation through invalid values, and record the boundary index value of each target object after segmentation;

[0056] Step S4 also includes:

[0057] The left and right boundaries of the target object are marked based on the recorded boundary index values, and the number of valid scanned point clouds corresponding to the target object is calculated based on the boundary index values. N ,in N ≥2.

[0058] In this embodiment of the invention, a new circle of point cloud data is obtained and traversed. Object segmentation is performed using invalid values. The size of the target object, i.e., the number of point clouds scanned, is calculated based on the index value of the boundary of the segmented target object. N , N ≥2. Simultaneously, the left and right boundaries are marked based on the record's index value.

[0059] S5. Based on the characteristic that the echo intensity inside the object is greater than that at the edge, a sliding window is set starting from the boundary index value. X With maximum number of slides Y The system slides inward from both sides of the target object and compares the difference in the mean echo intensity of adjacent windows. If the difference is greater than the echo intensity threshold, the system will detect the difference. I Or reach the maximum number of slides Y If the remaining point cloud data is retained, the processing is terminated; otherwise, the corresponding edge expansion points are deleted.

[0060] In step S5, the sliding window X The number of consecutive point clouds processed in a single operation. X ≥2;

[0061] Maximum number of slides Y This represents the maximum number of times the window can slide inwards towards the object. Y ≥2.

[0062] First, calculate the average intensity of all point cloud echoes within a single sliding window. Then, calculate the difference Δ between the average intensity values ​​of two adjacent sliding windows. i If Δ i <Echo intensity threshold IDetermine all points within the current window as edge widening points and delete them.

[0063] The sliding logic of the left and right boundaries of the target object is consistent, and the sliding process is carried out simultaneously from the left boundary to the right and from the right boundary to the left into the object.

[0064] Sliding window X With maximum number of slides Y Based on the characteristic of 2D LiDAR point clouds being denser at close range and sparser at distant range, the algorithm adaptively adjusts with scanning distance.

[0065] In this embodiment of the invention, based on the characteristic that the echo intensity is high inside the object and low at the edge, a sliding window is used... and number of swipes The comparison is performed by sliding from both sides of the object inwards. If the difference between adjacent windows is greater than a set threshold... If the condition is met, all expanded points in the previous window will be deleted; otherwise, all points in the window will be retained, and data processing will exit. Figure 5 As shown, specifically including: taking left boundary data processing as an example, the sliding window settings Point cloud echo intensity data Calculate the average value. Calculate the difference in the mean intensity within adjacent windows. If it is less than the preset threshold If the condition is met, all points within the window are considered expandable points, and all points within the window are deleted. Continue sliding the window, repeating the above steps, until the difference in the average intensity between adjacent windows reaches a certain value. Greater than the preset threshold Or reach the set number of swipes. , If the condition is met, then data processing will stop.

[0066] It should be noted that this embodiment includes setting a distance threshold for data processing. and echo intensity threshold ;Acquire a ring of point cloud data to obtain the actual radar measurement distance. and echo intensity ;Reservation distance Less than the preset distance The point cloud data will be greater than The data points are set to invalid values ​​to obtain a new dataset; the boundary of the target object is determined based on the invalid values, and the boundary index value is obtained; based on the echo intensity characteristics, a sliding window and the number of sliding steps are set, and the average echo intensity within adjacent windows is compared. If the difference is greater than a set threshold, the data is considered invalid. Alternatively, after reaching a set number of slides, the point cloud is preserved and data processing is exited; otherwise, the widened edge points are deleted. This method effectively eliminates the widening problem at the edges of the target object, restoring the true contour of the scanned target object, such as... Figure 6 As shown.

[0067] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0068] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for processing 2D lidar point cloud data, characterized in that, The method includes the following steps: S1. Preset distance threshold for 2D LiDAR point cloud data processing D and echo intensity threshold I ; S2. Acquire complete point cloud data from a single-circle scan of the 2D LiDAR, and extract the actual measured distance corresponding to each scan point. d and echo intensity i ; S3. Measure the actual distance of each scanning point. d With distance threshold D Compare and retain d < D The raw point cloud data will d ≥ D The scan points are set to invalid values ​​to generate a single-loop filtered point cloud dataset; S4. Traverse the point cloud dataset after single-loop filtering, complete the target object segmentation through invalid values, and record the boundary index value of each target object after segmentation; S5. Based on the characteristic that the echo intensity inside the object is greater than that at the edge, a sliding window is set starting from the boundary index value. X With maximum number of slides Y The system slides inward from both sides of the target object and compares the difference in the mean echo intensity of adjacent windows. If the difference is greater than the echo intensity threshold, the system will detect the difference. I Or reach the maximum number of slides Y If the remaining point cloud data is retained, the processing is terminated; otherwise, the corresponding edge expansion points are deleted.

2. The 2D lidar point cloud data processing method according to claim 1, characterized in that, In step S1, the distance threshold D Using the reflective sticker specimen as a reference for calibration, after fixing the radar position, move the reflective sticker specimen to the position with the fewest widening points, and use the distance corresponding to this position as the distance threshold D; echo intensity threshold I The maximum fluctuation value of echo intensity during the movement of the retroreflective patch test piece is calibrated.

3. The 2D lidar point cloud data processing method according to claim 1, characterized in that, In step S2, the acquired single-loop point cloud data includes the scanning angle and actual measured distance corresponding to each scanning point. d With echo intensity i The total number of points in a single scan is determined by the radar scanning angle range and angular resolution.

4. The 2D lidar point cloud data processing method according to claim 1, characterized in that, In step S3, each scan point of the single-circle point cloud is compared point by point according to the scanning angle order, and the sequence number of the scan point is determined. n Satisfying 1≤ n ≤ Total number of points scanned in a single loop, completing the filtering and invalid value assignment of the entire point cloud.

5. The 2D lidar point cloud data processing method of claim 1, wherein, Step S4 also includes: The left and right boundaries of the target object are marked based on the recorded boundary index values, and the number of valid scanned point clouds corresponding to the target object is calculated based on the boundary index values. N ,in N ≥2.

6. The 2D lidar point cloud data processing method according to claim 1, characterized in that, In step S5, the sliding window X The number of consecutive point clouds processed in a single operation. X ≥2; Maximum number of slides Y This represents the maximum number of times the window can slide inwards towards the object. Y ≥2.

7. The 2D lidar point cloud data processing method according to claim 1, characterized in that, In step S5, first calculate the average value of the echo intensity of all point clouds within a single sliding window, and then calculate the difference Δ between the average intensity values ​​of two adjacent sliding windows. i If Δ i <Echo intensity threshold I Determine all points within the current window as edge widening points and delete them.

8. The 2D lidar point cloud data processing method of claim 1, wherein, In step S5, the sliding processing logic of the left and right boundaries of the target object is consistent, and the sliding processing is carried out simultaneously from the left boundary to the right and from the right boundary to the left into the object.

9. The 2D lidar point cloud data processing method of claim 1, wherein, In step S5, the sliding window X With maximum number of slides Y Based on the characteristic of 2D LiDAR point clouds being denser at close range and sparser at distant range, the algorithm adaptively adjusts with scanning distance.

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