A 3D laser radar point cloud acquisition control method and system

Abstract

The present invention discloses a three-dimensional laser radar point cloud acquisition control method and system, which belongs to the field of laser radar measurement and control technology. The existing low-cost laser radar point cloud acquisition method has a large angle accumulation error and cannot meet the real-time angle tracking requirements under high-speed working conditions. A three-dimensional laser radar point cloud acquisition control method of the present invention, by creating a code disk design optimization model and a mutation signal capture model, designs a gap on the code disk of the three-dimensional laser radar, and optimizes the gap width of the code disk, so as to accurately capture the periodic mutation of the pulse signal, thereby effectively reducing the angle accumulation error and improving the angle synchronization accuracy, and is particularly suitable for real-time angle tracking requirements in high-speed scenarios. At the same time, the present invention can eliminate the jitter interference of the laser radar, and can perform spatiotemporal interpolation and fusion of multiple-circle acquisition data, so as to generate high-density point cloud data, thereby effectively improving the angular resolution and point cloud density.

Description

Technical Field

[0001] The present invention relates to a three-dimensional laser radar point cloud acquisition control method and system, belonging to the field of laser radar measurement and control technology. Background Art

[0002] A Chinese patent application (publication number: CN114627374A) discloses a point cloud acquisition system based on a laser radar and a pan-tilt platform, and an insulator identification and positioning method. The point cloud acquisition system includes a laser radar, a computer, and a pan-tilt platform. The laser radar is vertically installed at the center of the pan-tilt platform; the computer is connected to the laser radar and the pan-tilt platform respectively; the computer drives the laser radar to scan through ROS, and the computer simultaneously drives the pan-tilt platform to rotate at a fixed angle at a constant speed, thereby realizing scanning of the entire scene; after scanning, the laser radar transmits the scanned point cloud data to the computer via the Ethernet protocol for subsequent processing.

[0003] The above solution is a low-cost lidar point cloud acquisition method. To save costs, it adopts a fixed-angle sampling strategy, which leads to a large angle accumulation error and insufficient angle synchronization accuracy. In particular, it cannot adapt to the real-time angle tracking requirements in high-speed (greater than 10,000 RPM) scenarios.

[0004] Furthermore, repeated fixed-angle sampling in multiple scanning circles will cause the point cloud density of distant targets to decrease exponentially with increasing distance, making the point cloud relatively sparse, which in turn affects the promotion and use of 3D lidar in low-cost solutions.

[0005] The information disclosed in this Background Art is only for understanding the background of the present inventive concept and therefore it may include information that does not constitute prior art. Summary of the Invention

[0006] In response to the above problems or one of the above problems, the first purpose of the present invention is to provide a three-dimensional laser radar point cloud acquisition control method and system. By creating a code disk design optimization model and a mutation signal capture model, a gap is designed on the code disk of the three-dimensional laser radar, and the gap width of the code disk is optimized, so that the pulse signal period mutation can be accurately captured to obtain zero point position information, thereby effectively reducing the angle accumulation error and improving the angle synchronization accuracy, which is particularly suitable for real-time angle tracking requirements in high-speed scenarios; at the same time, by constructing a jitter interference elimination model, a sampling dynamic adjustment model and a spatiotemporal interpolation and fusion model, the jitter interference of the laser radar is eliminated, the angular resolution of the laser radar is calibrated, and through a multi-circle scanning accumulation mechanism, the multi-circle acquisition data is subjected to spatiotemporal interpolation and fusion to generate high-density point cloud data, thereby effectively improving the angular resolution and point cloud density, avoiding the situation where the point cloud density of distant targets decreases exponentially with increasing distance, and facilitating the promotion and use of three-dimensional laser radar in low-cost solutions.

[0007] In response to the above problem or one of the above problems, the second object of the present invention is to provide a three-dimensional lidar point cloud acquisition control method and system, which can achieve high-precision angle measurement without the use of high-precision angle encoders and high-speed signal processing units, and can ensure the real-time performance of the radar measurement system; at the same time, through frequency division interpolation, the angular resolution is effectively improved and the point cloud spacing of distant targets is reduced, thereby effectively reducing hardware costs and meeting the needs of consumer-grade robots for low-cost 3D radar.

[0008] To achieve one of the above purposes, the first technical solution of the present invention is:

[0009] A 3D laser radar point cloud acquisition control method includes the following steps:

[0010] Step 1: Using the previously created code disc design optimization model, based on the required information of the 3D laser radar zero point mark, a gap is designed on the code disc of the 3D laser radar, and the gap width of the code disc is optimized to obtain the gap design data;

[0011] Step 2: Using the previously created mutation signal capture model, based on the gap design data, capture the pulse signal period mutation caused by the gap and obtain the zero point position information;

[0012] Step 3: Based on the previously created jitter interference elimination model, according to the zero point position information and combined with the filtering algorithm, jitter interference elimination data is obtained to eliminate the jitter interference of the lidar;

[0013] Step 4: Using the previously created sampling dynamic adjustment model, angular resolution calibration is performed on the laser radar that has eliminated jitter interference to obtain sampling time adjustment information.

[0014] In step five, the spatiotemporal interpolation fusion model created in advance is used to adjust the information based on the sampling time. Through the multi-circle scanning accumulation mechanism, the multi-circle collected data is subjected to spatiotemporal interpolation fusion to generate high-density point cloud data.

[0015] After continuous exploration and experimentation, the present invention creates a code disk design optimization model and a mutation signal capture model, designs a gap on the code disk of the three-dimensional laser radar, and optimizes the gap width of the code disk, so as to accurately capture the periodic mutation of the pulse signal and obtain zero-point position information, thereby effectively reducing the angle accumulation error and improving the angle synchronization accuracy. It is particularly suitable for real-time angle tracking requirements in high-speed (greater than 10,000 RPM) scenarios; at the same time, by constructing a jitter interference elimination model, a sampling dynamic adjustment model and a spatiotemporal interpolation and fusion model, the jitter interference of the laser radar is eliminated, the angular resolution of the laser radar is calibrated, and through a multi-circle scanning accumulation mechanism, the multi-circle collected data is spatiotemporally interpolated and fused to generate high-density point cloud data, thereby effectively improving the angular resolution and point cloud density, avoiding the situation where the point cloud density of distant targets decreases exponentially with increasing distance, and facilitating the promotion and use of three-dimensional laser radar in low-cost solutions.

[0016] Furthermore, by optimizing the code disk design, the present invention can accurately capture the periodic mutations of the pulse signal and realize dynamic compensation of the motor speed; at the same time, it uses the point cloud spatiotemporal interpolation algorithm to improve the point cloud density, making it suitable for angle synchronization calibration and point cloud resolution enhancement scenarios under high-speed working conditions.

[0017] Furthermore, the present invention can achieve high-precision, low-cost angle measurement and ensure the real-time performance of the radar measurement system without the need for a high-precision angle encoder (such as a 2048-line magnetic encoder) and a high-speed signal processing unit (FPGA). Furthermore, through frequency division interpolation, the angular resolution can be effectively improved, and the point cloud spacing of distant targets can be reduced, thereby effectively reducing hardware costs and meeting the needs of consumer-grade robots for low-cost 3D radar.

[0018] As preferred technical measures:

[0019] Step 1: Using the previously created code disk design optimization model, based on the required information of the 3D laser radar zero point mark, a gap is designed on the code disk of the 3D laser radar, and the gap width of the code disk is optimized to obtain the gap design data:

[0020] Get the number of teeth and tooth position information of the code disk;

[0021] According to the number of teeth and tooth position information, several equally divided positions of the code disk are calculated;

[0022] Use one or more equally divided positions as the gap positions;

[0023] Collect the requirement information of the zero mark of the code disk, which includes the design requirement information and signal detection requirement information of the code disk;

[0024] Quantify the design requirement information and signal detection requirement information to obtain several variable parameters and construct multi-parameter constraint information;

[0025] Based on several variable parameters and multi-parameter constraint information, an objective function is constructed to optimize the gap width of the code disk; the objective function is solved to obtain the optimized gap width value;

[0026] The notch position and notch width values are summarized to obtain the notch design data.

[0027] As preferred technical measures:

[0028] The method for quantifying the design requirement information and signal detection requirement information to obtain several variable parameters is as follows:

[0029] Collect design requirement information, including mechanical parameters and dynamic parameters;

[0030] Acquiring signal detection requirement information, which includes optical parameters and environmental parameters;

[0031] Quantify the mechanical parameters to obtain the code disc diameter, total number of teeth and standard number of teeth;

[0032] Quantify the dynamic parameters to obtain the maximum operating speed and sensor response time;

[0033] Quantify the optical parameters to obtain the light source wavelength and spot diameter;

[0034] The environmental parameters are quantified to obtain the signal-to-noise ratio threshold.

[0035] As preferred technical measures:

[0036] The method for constructing multi-parameter constraint information is as follows:

[0037] According to the notch design requirements, set the spot angle and the angle drift compensation value caused by the rotation speed;

[0038] Based on the spot angle and angle drift compensation value, a minimum detection angle constraint is established so that the gap can cover the projection of the spot in the tangent direction of the code disk;

[0039] Obtain the maximum torque of the code disc and the yield strength of the material;

[0040] According to the maximum torque and material yield strength, establish mechanical strength safety constraints so that the gap width can meet the torsional strength requirements of the code disk;

[0041] Obtain the linear velocity of the code disk and the jitter tolerance value of the optical sensor;

[0042] Based on the linear velocity of the code disk and the jitter tolerance of the optical sensor, a signal width constraint is established so that the notch pulse can be distinguished from the normal tooth pulse.

[0043] The minimum detection angle constraint, mechanical strength safety constraint, and signal width constraint are summarized to obtain multi-parameter constraint information.

[0044] As preferred technical measures:

[0045] The objective function is constructed as follows:

[0046] Acquiring multi-parameter constraint information, including minimum detection angle constraint, mechanical strength safety constraint, and signal width constraint;

[0047] Construct a design basis expression based on mechanical strength safety constraints, signal width constraints, and the standard number of teeth;

[0048] Based on the minimum detection angle constraint, code disk diameter, and optical sensitivity weight, an expression for minimizing missed detection risk is constructed.

[0049] According to the material yield strength and material safety factor, an expression for suppressing stress concentration is constructed;

[0050] The design basis expression, the expression for minimizing missed detection risk and the expression for suppressing stress concentration are coupled to obtain the optimization objective function.

[0051] As preferred technical measures:

[0052] Step 2: Use the previously created mutation signal capture model to capture the pulse signal period mutation caused by the gap based on the gap design data. The method to obtain the zero point position information is as follows:

[0053] According to the signal attenuation under different working conditions and based on the speed, the pulse interval threshold is adjusted in real time;

[0054] Based on the pulse interval threshold, a pulse signal output by the photoelectric encoder is obtained;

[0055] Based on the notch design data, the edge detection circuit captures the pulse signal period mutation caused by the notch and obtains the edge signal;

[0056] According to the edge signal and the pulse interval threshold, the zero point position is determined to obtain the zero point position information;

[0057] The method for adjusting the pulse interval threshold in real time is as follows:

[0058] Capture the rising edge signal output by the photoelectric encoder;

[0059] The digital comparator identifies the effective edge time of the rising edge signal and obtains the timestamp;

[0060] Based on the output signal of the photoelectric encoder, the current linear speed is obtained;

[0061] According to the current linear velocity, minimum resolution distance, acceleration compensation factor and timestamp, a dynamic calculation formula for pulse interval is constructed; the dynamic calculation formula for pulse interval is solved to obtain the pulse interval threshold.

[0062] As preferred technical measures:

[0063] Step 3: Based on the previously created jitter interference cancellation model, the zero point position information is combined with a filtering algorithm to obtain the jitter interference cancellation data as follows:

[0064] Based on the real-time speed, number of encoder lines, and process tolerance, the speed timing is predicted and the expected pulse arrival time is calculated:

[0065] According to the expected pulse arrival time and the actual pulse time, the timing deviation is measured and the predicted value deviation is calculated:

[0066] According to the filtering algorithm, a multi-factor fusion decision formula is established using the predicted value deviation, timing deviation sensitivity coefficient, current signal-to-noise ratio and speed change rate suppression factor.

[0067] Solve the multi-factor fusion decision formula to obtain the dynamic confidence value;

[0068] Set up a filtering decision mechanism based on confidence intervals and dynamic confidence values;

[0069] When the dynamic confidence value is greater than or equal to the confidence threshold, a pulse signal is directly output;

[0070] When the dynamic confidence value is less than the confidence threshold, the pulse signal is de-jittered by a counter to obtain jitter interference elimination data.

[0071] As preferred technical measures:

[0072] Step 4: Use the previously created sampling dynamic adjustment model to calibrate the angular resolution of the laser radar after jitter interference has been eliminated. The method for obtaining the sampling time adjustment information is as follows:

[0073] Obtain the code disk pulse signal output by the photoelectric encoder;

[0074] After shaping the encoder pulse signal using a Schmitt trigger, the external interrupt of the microcontroller is triggered to obtain the time interval between adjacent encoder signals;

[0075] Calculate the real-time rotation speed of the 3D laser radar based on the time interval;

[0076] Calculate the corresponding angular velocity based on the real-time rotation speed;

[0077] Based on the target angular resolution, the radian resolution is calculated;

[0078] Calculate the sampling time based on the relationship between angular velocity and radian resolution;

[0079] Processing the sampling time and obtaining the timer count value based on the timer clock period and the pre-scaling coefficient;

[0080] According to the count value of the timer, the overflow time of the timer interrupt is updated in real time to determine the sampling time adjustment information.

[0081] As preferred technical measures:

[0082] Step 5: Using the previously created spatiotemporal interpolation fusion model, based on the sampling time adjustment information and through the multi-turn scanning accumulation mechanism, the multi-turn collected data is subjected to spatiotemporal interpolation fusion to generate high-density point cloud data as follows:

[0083] Obtain sampling time adjustment information, which includes a basic sampling period;

[0084] Calculate the required angular resolution based on the point cloud spacing requirements;

[0085] Calculate the number of sampling points per circle based on the angular resolution;

[0086] Determine the required frequency division coefficient based on the number of single-circuit sampling points;

[0087] Based on the frequency division coefficient, the basic sampling period is divided to generate multiple subdivision trigger moments;

[0088] Through the multi-circle scanning accumulation mechanism, and based on multiple subdivision trigger moments, the frequency division offset is superimposed at the same time to set the ranging trigger moment of each circle scan to cover multiple subdivision positions;

[0089] Based on the ranging trigger moment, several rounds of scanning are performed to ensure that all subdivided positions are completely covered, and multi-round collection data is obtained; the multi-round data is temporally and spatially interpolated and fused to generate high-density point cloud data.

[0090] To achieve one of the above purposes, the second technical solution of the present invention is:

[0091] A three-dimensional laser radar point cloud acquisition and control system, comprising:

[0092] one or more processors;

[0093] a storage device for storing one or more programs;

[0094] When the one or more programs are executed by the one or more processors, the one or more processors implement the above-mentioned three-dimensional lidar point cloud acquisition control method.

[0095] Compared with the existing technical solutions, the present invention has the following beneficial effects:

[0096] After continuous exploration and experimentation, the present invention creates a code disk design optimization model and a mutation signal capture model, designs a gap on the code disk of the three-dimensional laser radar, and optimizes the gap width of the code disk, so as to accurately capture the periodic mutation of the pulse signal and obtain zero-point position information, thereby effectively reducing the angle accumulation error and improving the angle synchronization accuracy. It is particularly suitable for real-time angle tracking requirements in high-speed (greater than 10,000 RPM) scenarios; at the same time, by constructing a jitter interference elimination model, a sampling dynamic adjustment model and a spatiotemporal interpolation and fusion model, the jitter interference of the laser radar is eliminated, the angular resolution of the laser radar is calibrated, and through a multi-circle scanning accumulation mechanism, the multi-circle collected data is spatiotemporally interpolated and fused to generate high-density point cloud data, thereby effectively improving the angular resolution and point cloud density, avoiding the situation where the point cloud density of distant targets decreases exponentially with increasing distance, and facilitating the promotion and use of three-dimensional laser radar in low-cost solutions.

[0097] Furthermore, by optimizing the code disk design, the present invention can accurately capture the periodic mutations of the pulse signal and realize dynamic compensation of the motor speed; at the same time, it uses the point cloud spatiotemporal interpolation algorithm to improve the point cloud density, making it suitable for angle synchronization calibration and point cloud resolution enhancement scenarios under high-speed working conditions.

[0098] Furthermore, the present invention can achieve high-precision, low-cost angle measurement and ensure the real-time performance of the radar measurement system without the need for a high-precision angle encoder (such as a 2048-line magnetic encoder) and a high-speed signal processing unit (FPGA). Furthermore, through frequency division interpolation, the angular resolution can be effectively improved, and the point cloud spacing of distant targets can be reduced, thereby effectively reducing hardware costs and meeting the needs of consumer-grade robots for low-cost 3D radar. BRIEF DESCRIPTION OF THE DRAWINGS

[0099] Figure 1 A schematic diagram of a flow chart of the cloud acquisition control method of the present invention;

[0100] Figure 2 A schematic structural diagram of the mechanical code disk of the present invention;

[0101] Figure 3 A schematic diagram for determining the zero point position of the present invention;

[0102] Figure 4 A schematic diagram of zero point detection performed in the present invention;

[0103] Figure 5 This is a timing diagram after performing n-round scanning in the present invention;

[0104] Figure 6A schematic diagram of a point cloud effect collected using the present invention. DETAILED DESCRIPTION

[0105] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0106] Rather, the present invention encompasses any alternatives, modifications, equivalents, and solutions that fall within the spirit and scope of the present invention as defined by the claims. Furthermore, to facilitate a better understanding of the present invention, certain specific details are described in detail below in the detailed description of the present invention. Those skilled in the art will be able to fully understand the present invention without these details.

[0107] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which the present invention pertains. The terms used herein are for the purpose of describing specific embodiments only and are not intended to limit the present invention.

[0108] like Figure 1 As shown, the first specific embodiment of the three-dimensional laser radar point cloud acquisition control method of the present invention is:

[0109] A 3D laser radar point cloud acquisition control method includes the following steps:

[0110] Based on the requirement information of the 3D laser radar zero point mark, a gap is designed on the code disk of the 3D laser radar, and the gap width of the code disk is optimized to obtain the gap design data;

[0111] Based on the notch design data, the pulse signal period mutation caused by the notch is captured to obtain the zero point position information;

[0112] According to the zero point position information and combined with the filtering algorithm, the jitter interference elimination data is obtained to eliminate the jitter interference of the laser radar;

[0113] Perform angular resolution calibration on the laser radar that has eliminated jitter interference to obtain sampling time adjustment information;

[0114] Based on the sampling time adjustment information and the multi-circle scanning accumulation mechanism, the multi-circle collected data is temporally and spatially interpolated and fused to generate high-density point cloud data.

[0115] The second specific embodiment of the three-dimensional laser radar point cloud acquisition control method of the present invention:

[0116] A 3D laser radar point cloud acquisition control method includes the following steps:

[0117] Step 1: Using the previously created code disk design optimization model, based on the requirement information of the mechanical code disk zero point mark, the gap width of the mechanical code disk is optimized to obtain the gap width value;

[0118] Step 2: Using the previously created mutation signal capture model, based on the gap width value, the pulse signal period mutation caused by the gap is captured to obtain the zero point position information;

[0119] Step 3: Based on the previously created jitter interference elimination model, according to the zero point position information and combined with the time window filtering algorithm, jitter interference elimination data is obtained to eliminate the jitter interference of the lidar;

[0120] Step 4: Using the previously created sampling dynamic adjustment model, angular resolution calibration is performed on the laser radar that has eliminated jitter interference to obtain sampling time adjustment information.

[0121] In step five, the spatiotemporal interpolation fusion model created in advance is used to adjust the information based on the sampling time, and through the multi-circle scanning accumulation mechanism, the multi-circle data is subjected to spatiotemporal interpolation and fusion to generate high-density point cloud data.

[0122] In this embodiment, in step 1, the gap width of the mechanical code disk is optimized based on the required information of the zero point mark of the mechanical code disk through the previously created code disk design optimization model. The method for obtaining the gap width value is as follows:

[0123] Based on the design requirements of the mechanical code disk zero point mark, combined with the signal detection robustness and high-speed dynamic response characteristics, a multi-parameter constrained code disk design optimization model is constructed. By quantifying mechanical, optical, and dynamic variables, a minimum safe gap width solution is output to ensure stable recognition at high speeds. The input parameters of this embodiment can be found in Table 1.

[0124] Table 1: Input parameters

[0125]

[0126] The multi-parameter constraints include minimum detectable angle constraint, mechanical strength safety constraint and signal width constraint.

[0127] Minimum detectable angle Constraints are used to ensure that the gap can cover the projection of the light spot in the tangent direction of the code disk. Its expression is as follows:

[0128]

[0129] Among them, the first term is the spot angle, and the second term is the angle drift compensation caused by the rotation speed.

[0130] Mechanical strength safety constraint to ensure gap width It can meet the requirements of the code disc's torsional strength, and its expression is as follows:

[0131]

[0132] in is the maximum torque, is the yield strength of the material.

[0133] The signal width constraint is used to ensure that the notch pulse can be significantly distinguished from the normal tooth pulse. Its expression is as follows:

[0134]

[0135] Where k is the safety factor, which is used to reserve design redundancy in the signal pulse width constraint, v is the linear velocity of the code disk, is the optical sensor jitter tolerance.

[0136] Based on multi-parameter constraints, the optimization objective function is constructed, and its expression is as follows:

[0137]

[0138] The first term of the expression is used to maintain the design benchmark, which is generally 2 times the tooth width; the second term of the expression is used to minimize the risk of missed inspection. is the optical sensitivity weight; the third term of the expression is used to suppress stress concentration, is the safety factor, It is work stress.

[0139] In this embodiment, a mechanical code disk with 11 teeth per circle is used, and a notch is set at the 12th equal division position of the mechanical code disk. The specific notch position can be found in Figure 2 ; The diameter of the code disk is D=50mm, and the speed is , the material yield strength is ,

[0140] The spot diameter is , the signal-to-noise ratio is SNR=20dB. The parameter calculation results can be seen in Table 2.

[0141] Table 2: Parameter calculation results

[0142]

[0143] Therefore, this embodiment achieves highly robust zero-point detection through gap width differences, enabling stable recognition even at high rotational speeds (greater than 10,000 RPM). Furthermore, the present invention utilizes a low-cost encoder to achieve zero-point recognition, reducing hardware costs by 60%.

[0144] In this embodiment, the method of zero point detection is as follows:

[0145] By taking advantage of the sudden change in pulse signal period caused by the gap, the edge signal is captured by the microcontroller MCU interrupt, and the jitter interference is eliminated by combining the time window filtering algorithm. The method is as follows:

[0146] The photoelectric encoder outputs a pulse signal, and the pulse signal interval threshold is 1.5 times the average period; then the edge detection circuit triggers the microcontroller MCU interrupt, and the zero point position is determined by the time interval threshold. Figure 3 The signal processing flow mainly includes the following steps:

[0147] The pulse interval threshold is adjusted in real time according to the rotational speed to adapt to the signal attenuation under different working conditions (such as conditions with enhanced electromagnetic interference during high-speed rotation).

[0148] A multi-level interrupt nesting mechanism is configured in the microcontroller MCU to ensure the real-time performance of the angle sampling task, with an interrupt response time of less than 1 microsecond. .

[0149] In this embodiment, the signal de-jitter filtering method is as follows:

[0150] For optical encoder signal processing, a code disk signal de-jitter filtering method and system based on speed prediction and confidence value judgment is adopted to address the defects of traditional code disk de-jitter solutions such as strong hardware dependence and insufficient dynamic adaptability. Specifically, it includes the following contents:

[0151] Using the speed time series prediction model, based on the real-time speed n, the unit is revolutions per minute RPM, the expected arrival time of the pulse is calculated , which is calculated as follows:

[0152]

[0153] in is the number of code disk lines, is the process tolerance.

[0154] Based on actual pulse time , measure the time series deviation and get the predicted value deviation , which is calculated as follows:

[0155]

[0156] Construct a multi-factor fusion decision formula and dynamically generate confidence values , which is calculated as follows:

[0157]

[0158] in, represents the timing deviation sensitivity coefficient; Indicates the current signal-to-noise ratio; Indicates the baseline signal-to-noise ratio, which is the quality baseline value measured by the system under ideal working conditions; Indicates the rate of change of speed; Indicates the speed change rate suppression factor, which depends on the encoder motor speed increase increment.

[0159] Based on the confidence value and confidence interval, a filtering decision mechanism is constructed, which includes the following contents:

[0160] When the confidence value When it is greater than or equal to 0.8, the signal does not need filtering and can be output directly.

[0161] When the confidence value When it is less than 0.8, the signal is filtered using a counter debounce method.

[0162] Furthermore, verification was performed based on a 12-line code disk at a rotation speed of 12000 RPM. The specific debouncing verification results can be seen in Table 3.

[0163] Table 3: Debounce Verification Results

[0164]

[0165] In this embodiment, the method for adjusting the laser pulse interval threshold is as follows:

[0166] Capture the rising edge of the sensor output signal in real time, identify the effective edge time through the digital comparator, and record the timestamp . And obtain the current linear speed through the sensor output signal Or angular velocity , calculate the instantaneous velocity vector.

[0167] Calculate pulse interval The dynamic formula is as follows:

[0168]

[0169] in, is the minimum resolvable distance (constrained by the spot diameter); is the acceleration compensation factor (to suppress the impact of speed mutation); is the sensor response time; Indicates the rate of change of speed; is the maximum permissible acceleration; is the response time correction factor.

[0170] In this embodiment, the method for dynamically reloading the timer is as follows:

[0171] After the edge event is detected, the pulse interval Write to the timer compare register to reload the initial value of the next cycle count; if the pulse interval Less than the dead time, the pulse queue buffer is enabled, and the trigger timing is scheduled according to priority. The dynamic overload effect can be seen in Table 4.

[0172] Table 4: Dynamic reload effects

[0173]

[0174] In this example, the method for dynamic angle calibration is as follows:

[0175] During the motor startup process, the speed reaches 70% of the rated speed, and under normal operating conditions, the motor speed fluctuates ( 5%), by adjusting the sampling interval in real time , ensuring that the angular resolution is strictly constant at , eliminating the angular resolution fluctuation problem caused by adjusting the sampling times in traditional solutions. The specific method is as follows:

[0176] Get the real-time speed (Unit: RPM), and convert the real-time speed into angular velocity (Unit: radians per second rad / s), the calculation formula is as follows:

[0177]

[0178] Set target angular resolution , and based on the target angular resolution, the radian resolution is obtained , which is calculated as follows:

[0179]

[0180] According to the relationship between angular velocity and resolution, the sampling time is derived , which is calculated as follows:

[0181]

[0182]

[0183] Due to the sampling time and speed The sampling interval is inversely proportional to the speed. When the speed increases, the sampling interval is shortened, and when the speed decreases, the interval is extended to ensure that the angle increment at each point is constant. .

[0184] In this embodiment, the method of real-time speed detection and dynamic calculation is as follows:

[0185] The output pulse of the photoelectric encoder is shaped by the Schmitt trigger and triggers the external interrupt of the microcontroller MCU (rising edge detection). The time interval between adjacent encoder signals is recorded. (excluding zero-point notch teeth).

[0186] Calculate real-time speed based on time interval , which is calculated as follows:

[0187]

[0188] The calculated adoption time Convert to the timer count value using the following formula , to update the sampling time, the calculation formula is as follows:

[0189]

[0190] in is the timer clock period, and PSC is the pre-scaling coefficient. The overflow time of the timer interrupt is updated in real time at the encoder trigger point to perform the laser ranging task to ensure the stability of the angular resolution, and to calibrate and recalculate the sampling interval at each rising edge. Figure 4 .

[0191] Then, the angular resolution stability test results can be obtained. %Angular resolution error under speed fluctuation %, significantly better than the traditional solution ( 5%), see Table 5.

[0192] Table 5: Angular resolution stability test results

[0193]

[0194] In this embodiment, the method for performing spatiotemporal interpolation fusion on multi-circle data to generate a high-density point cloud is as follows:

[0195] The frequency division interpolation mode is adopted. Without increasing the single-circle sampling frequency, the equivalent angular resolution is increased by multi-circle scanning accumulation and time interpolation. Improved angular resolution When the basic sampling period is one-nth of Perform n-frequency division (n=2, 4, 8) to generate n subdivision trigger moments. Through continuous n-circle scanning, each circle triggers ranging at a different time after frequency division, eventually covering all subdivision positions, and performing spatiotemporal interpolation fusion on multi-circle data to generate a high-density point cloud. It includes the following:

[0196] Set the frequency division trigger timing, which includes the basic sampling period , frequency division coefficient , .

[0197] When the angular resolution hour, ; Calculate the subdivision period based on the frequency division coefficient and the basic sampling period , which is calculated as follows:

[0198]

[0199] Generate multi-circle triggering moments according to the subdivision cycle, and scan in the kth circle Laser ranging trigger moment for:

[0200]

[0201] Scan in each revolution at the basic cycle Based on the superposition of the frequency offset , covering n subdivision positions. After n consecutive rounds of scanning, all subdivision positions are completely covered. m represents the serial number of the subdivision sampling cycle. The scanning effects of the first, second, third, fourth and cumulative four rounds can be seen in Figure 5 .

[0202] When the angular resolution When the number of sampling points in a single circle is calculated , which is calculated as follows:

[0203]

[0204] Based on the number of single-turn sampling points, calculate the equivalent number of points after n-frequency division , which is calculated as follows:

[0205]

[0206] When n=4, the equivalent angular resolution Equivalent to 0.15 degrees, the point cloud spacing 10 meters away is reduced from 10m to 2.5cm, effectively improving the equivalent angular resolution.

[0207] In summary, the present invention can achieve high-precision and low-cost angle measurement, and realize a dynamic resolution of 0.15 degrees on a single-turn 12-equally divided code disk with a rotation speed exceeding 10,000 RPM. By using a smaller number of code disk teeth, the interrupt overhead for the microcontroller MCU is effectively reduced, ensuring the real-time performance of the radar measurement system.

[0208] At the same time, the present invention can effectively improve the point cloud density. Through n-frequency interpolation, the angular resolution is increased to 0.15 degrees within 4 scan circles, and the point cloud spacing at 10 meters is reduced from 10cm to 2.5cm. The effect can be seen in Figure 6 , Figure 6 The red line in the figure represents the X-axis, the green line represents the Y-axis, and the blue line represents the Z-axis. The color scheme follows the correspondence between the three primary colors RGB and the three-dimensional rectangular coordinate axes.

[0209] Furthermore, the present invention has strong robustness and stabilizes the angle error within ±0.33% through a real-time speed compensation algorithm, making it suitable for scenarios with ±5% speed fluctuations.

[0210] An embodiment of a device applying the method of the present invention:

[0211] An electronic device comprising:

[0212] one or more processors;

[0213] a storage device for storing one or more programs;

[0214] When the one or more programs are executed by the one or more processors, the one or more processors implement the above-mentioned three-dimensional lidar point cloud acquisition control method.

[0215] A computer medium embodiment of the method of the present invention:

[0216] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the above-mentioned three-dimensional laser radar point cloud acquisition control method.

[0217] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, optical storage, etc.) containing computer-usable program code.

[0218] The present application is described in terms of flowcharts or / and block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process or / and block in the flowchart or / and block diagram, as well as the combination of processes or / and blocks in the flowchart or / and block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0219] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0220] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0221] The model in this application is an object that objectively describes the morphological structure with the help of physical or virtual representation. The object is not equal to the physical body and is not limited to physical and virtual. It can be a data processing function, software program, processing mode, usage method, operation method, workflow, application process, electronic hardware, circuit module, processing system, system imitation or simulation object.

[0222] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field can still modify or replace the specific implementation methods of the present invention. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be included in the scope of protection of the claims of the present invention.

Claims

1. A 3D laser radar point cloud acquisition control method, characterized by: The following steps are involved: Step 1: Using the previously created code disc design optimization model, based on the required information of the 3D laser radar zero point mark, a gap is designed on the code disc of the 3D laser radar, and the gap width of the code disc is optimized to obtain the gap design data; Step 2: Using the previously created mutation signal capture model, based on the gap design data, capture the pulse signal period mutation caused by the gap and obtain the zero point position information; Step 3: Based on the previously created jitter interference elimination model, according to the zero point position information and combined with the filtering algorithm, jitter interference elimination data is obtained to eliminate the jitter interference of the lidar; Step 4: Using the previously created sampling dynamic adjustment model, angular resolution calibration is performed on the laser radar that has eliminated jitter interference to obtain sampling time adjustment information. In step five, the spatiotemporal interpolation fusion model created in advance is used to adjust the information based on the sampling time. Through the multi-circle scanning accumulation mechanism, the multi-circle collected data is subjected to spatiotemporal interpolation fusion to generate high-density point cloud data.

2. The 3D laser radar point cloud acquisition and control method according to claim 1, wherein: Step 1: Using the previously created code disk design optimization model, based on the required information of the 3D laser radar zero point mark, a gap is designed on the code disk of the 3D laser radar, and the gap width of the code disk is optimized to obtain the gap design data: Get the number of teeth and tooth position information of the code disk; According to the number of teeth and tooth position information, several equally divided positions of the code disk are calculated; Use one or more equally divided positions as the gap positions; Collect the requirement information of the zero mark of the code disk, which includes the design requirement information and signal detection requirement information of the code disk; Quantify the design requirement information and signal detection requirement information to obtain several variable parameters and construct multi-parameter constraint information; Based on several variable parameters and multi-parameter constraint information, an objective function is constructed to optimize the gap width of the code disk; Solve the objective function to obtain the optimized gap width value; The notch position and notch width values are summarized to obtain the notch design data.

3. The 3D laser radar point cloud acquisition and control method according to claim 2, wherein: The method for quantifying the design requirement information and signal detection requirement information to obtain several variable parameters is as follows: Collect design requirement information, including mechanical parameters and dynamic parameters; Acquiring signal detection requirement information, which includes optical parameters and environmental parameters; Quantify the mechanical parameters to obtain the code disc diameter, total number of teeth and standard number of teeth; Quantify the dynamic parameters to obtain the maximum operating speed and sensor response time; Quantify the optical parameters to obtain the light source wavelength and spot diameter; The environmental parameters are quantified to obtain the signal-to-noise ratio threshold.

4. The 3D laser radar point cloud acquisition control method according to claim 2, wherein: The method for constructing multi-parameter constraint information is as follows: According to the notch design requirements, set the spot angle and the angle drift compensation value caused by the rotation speed; Based on the spot angle and angle drift compensation value, a minimum detection angle constraint is established so that the gap can cover the projection of the spot in the tangent direction of the code disk; Obtain the maximum torque of the code disc and the yield strength of the material; According to the maximum torque and material yield strength, establish mechanical strength safety constraints so that the gap width can meet the torsional strength requirements of the code disk; Obtain the linear velocity of the code disk and the jitter tolerance value of the optical sensor; Based on the linear velocity of the code disk and the jitter tolerance of the optical sensor, a signal width constraint is established so that the notch pulse can be distinguished from the normal tooth pulse. The minimum detection angle constraint, mechanical strength safety constraint, and signal width constraint are summarized to obtain multi-parameter constraint information.

5. The 3D laser radar point cloud acquisition control method according to claim 2, wherein: The objective function is constructed as follows: Acquiring multi-parameter constraint information, including minimum detection angle constraint, mechanical strength safety constraint, and signal width constraint; Construct a design basis expression based on mechanical strength safety constraints, signal width constraints, and the standard number of teeth; Based on the minimum detection angle constraint, code disk diameter, and optical sensitivity weight, an expression for minimizing missed detection risk is constructed. According to the material yield strength and material safety factor, an expression for suppressing stress concentration is constructed; The design basis expression, the expression for minimizing missed detection risk and the expression for suppressing stress concentration are coupled to obtain the optimization objective function.

6. The 3D laser radar point cloud acquisition control method according to claim 1, characterized in that: Step 2: Use the previously created mutation signal capture model to capture the pulse signal period mutation caused by the gap based on the gap design data. The method to obtain the zero point position information is as follows: According to the signal attenuation under different working conditions and based on the speed, the pulse interval threshold is adjusted in real time; Based on the pulse interval threshold, a pulse signal output by the photoelectric encoder is obtained; Based on the notch design data, the edge detection circuit captures the pulse signal period mutation caused by the notch and obtains the edge signal; According to the edge signal and the pulse interval threshold, the zero point position is determined to obtain the zero point position information; The method for adjusting the pulse interval threshold in real time is as follows: Capture the rising edge signal output by the photoelectric encoder; The digital comparator identifies the effective edge time of the rising edge signal and obtains the timestamp; Based on the output signal of the photoelectric encoder, the current linear speed is obtained; Construct a dynamic calculation formula for pulse interval based on the current linear velocity, minimum resolution distance, acceleration compensation factor and timestamp; The pulse interval dynamic calculation formula is solved to obtain the pulse interval threshold.

7. The 3D laser radar point cloud acquisition control method according to claim 1, wherein: Step 3: Based on the previously created jitter interference cancellation model, the zero point position information is combined with a filtering algorithm to obtain the jitter interference cancellation data as follows: Based on the real-time speed, number of encoder lines, and process tolerance, the speed timing is predicted and the expected pulse arrival time is calculated: According to the expected pulse arrival time and the actual pulse time, the timing deviation is measured and the predicted value deviation is calculated: According to the filtering algorithm, a multi-factor fusion decision formula is established using the predicted value deviation, timing deviation sensitivity coefficient, current signal-to-noise ratio and speed change rate suppression factor. Solve the multi-factor fusion decision formula to obtain the dynamic confidence value; Set up a filtering decision mechanism based on confidence intervals and dynamic confidence values; When the dynamic confidence value is greater than or equal to the confidence threshold, a pulse signal is directly output; When the dynamic confidence value is less than the confidence threshold, the pulse signal is de-jittered by a counter to obtain jitter interference elimination data.

8. The 3D laser radar point cloud acquisition control method according to claim 1, wherein: Step 4: Use the previously created sampling dynamic adjustment model to calibrate the angular resolution of the laser radar after jitter interference has been eliminated. The method for obtaining the sampling time adjustment information is as follows: Obtain the code disk pulse signal output by the photoelectric encoder; After shaping the encoder pulse signal using a Schmitt trigger, the external interrupt of the microcontroller is triggered to obtain the time interval between adjacent encoder signals; Calculate the real-time rotation speed of the 3D laser radar based on the time interval; Calculate the corresponding angular velocity based on the real-time rotation speed; Based on the target angular resolution, the radian resolution is calculated; Calculate the sampling time based on the relationship between angular velocity and radian resolution; Processing the sampling time and obtaining the timer count value based on the timer clock period and the pre-scaling coefficient; According to the count value of the timer, the overflow time of the timer interrupt is updated in real time to determine the sampling time adjustment information.

9. The 3D laser radar point cloud acquisition and control method according to claim 1, wherein: Step 5: Using the previously created spatiotemporal interpolation fusion model, based on the sampling time adjustment information and through the multi-turn scanning accumulation mechanism, the multi-turn collected data is subjected to spatiotemporal interpolation fusion to generate high-density point cloud data as follows: Obtain sampling time adjustment information, which includes a basic sampling period; Calculate the required angular resolution based on the point cloud spacing requirements; Calculate the number of sampling points per circle based on the angular resolution; Determine the required frequency division coefficient based on the number of single-circuit sampling points; Based on the frequency division coefficient, the basic sampling period is divided to generate multiple subdivision trigger moments; Through the multi-circle scanning accumulation mechanism, and based on multiple subdivision trigger moments, the frequency division offset is superimposed at the same time to set the ranging trigger moment of each circle scan to cover multiple subdivision positions; Based on the ranging trigger moment, several rounds of scanning are performed to ensure that all subdivided positions are completely covered, and multi-round collection data is obtained; the multi-round data is temporally and spatially interpolated and fused to generate high-density point cloud data.

10. A 3D laser radar point cloud acquisition and control system, characterized by: It includes: one or more processors; a storage device for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement a three-dimensional lidar point cloud acquisition control method as described in any one of claims 1-9.

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