A method and device for detecting dirt on a lidar light shield

Abstract

The present invention belongs to the technical field of lidar detection, and particularly relates to a method and device for detecting dirt on a lidar light shield, including: S1, dividing the light shield detection area of the lidar into several micro-areas; S2, emitting a first laser beam and receiving a first echo signal; S3, sequentially determining the dirt condition of each micro-area according to the pulse width of the first echo signal. If dirt may exist in the current micro-area, then continue to execute step S4; S4, emitting a second laser beam and receiving a second echo signal; S5, determining the dirt condition of the current micro-area according to the pulse width of the second echo signal. If dirt exists in the current micro-area, then determine the dirt type of the current micro-area; S6, processing the lidar according to the dirt type of each micro-area and sending out an alarm prompt. The method and device for detecting dirt on the lidar light shield of the present invention have the advantages of high detection accuracy and low false positive rate.

Description

Technical Field

[0001] The present invention belongs to the technical field of lidar detection, and particularly relates to a method and device for detecting dirt on a lidar light cover. Background Art

[0002] A lidar (LiDAR = Light Detection and Ranging) is a three-dimensional optical measurement system that is a radar system for detecting the position, speed, and other characteristic quantities of a target by emitting laser beams. Its working principle is to emit a detection signal (laser beam) to the target, and then compare the received signal (target echo) reflected from the target with the transmitted signal. After appropriate processing, relevant information about the target can be obtained, such as parameters like target distance, azimuth, altitude, speed, attitude, and even shape, so as to detect, track, and identify the target. The lidar has the advantages of high precision and high resolution, and also has the prospect of establishing a surrounding 3D model. With the rapid development of autonomous driving technology, lidars are widely used in vehicles. The function of an in-vehicle lidar is to sense the environment, assist the vehicle in planning routes, assist driving safety, and perform detailed positioning, etc.

[0003] Common lidars include components such as a laser emitting device, a laser receiving device, and a light cover (which can also be called a transmissive light cover, a light window). The light cover is an indispensable part of the laser emission and reception path, and its performance and functions mainly include the following aspects: 1. Protect the laser emitter and receiver: The window can prevent external elements such as dust, particulate matter, and dirt from entering the lidar system, thereby protecting the optical elements of the laser emitter and receiver from damage or contamination. 2. Transparency: The window must be transparent to ensure that the laser beam can pass through it, interact with the target object, and return to the receiver for measurement. 3. Optical performance: The optical performance of the window must match the requirements of the laser system to ensure that the laser beam does not distort or scatter when passing through the window. 4. Abrasion resistance and corrosion resistance: The window usually needs to have sufficient abrasion resistance and corrosion resistance to cope with the physical and chemical effects under different environmental conditions.

[0004] As a non-contact measurement device, a light shield is arranged on the emission path of a lidar transmitter. In practical applications, objects such as water mist and dust caused by weather conditions or vehicle exhaust adhere to the surface of the lidar light shield, resulting in situations such as dust, scratches, sewage, mud stains, insect corpses, and corrosion on the light shield. This causes signal attenuation of the signals emitted by the lidar and the echo signals collected, weakens the signal energy, and leads to a reduction or even complete loss of the performance of the lidar in ranging and measuring reflectivity. As a result, it is impossible to normally obtain the environmental information around the vehicle, leading to safety risks for the vehicle. Therefore, the cleanliness of the light shield directly affects the range and measurement accuracy of the lidar. To ensure the normal operation of the lidar, it is necessary to timely detect whether there is dirt on the lidar light shield and prompt the user to remove the dirt when there is dirt.

[0005] To avoid undetected lidar blind spots caused by pollutants during autonomous driving, the lidar system should be able to detect obstacles on the glass window within a short period of time and trigger system safety measures (e.g., system degradation, and turn on the glass window cleaning and heating systems to clean and dry the glass window), thereby avoiding safety risks. However, the existing lidar light shield dirt detection methods have low detection accuracy and are prone to false negatives. To improve the safety of vehicle driving, the current light shield dirt detection methods mainly improve the detection accuracy by reducing the trigger conditions for dirt alarms, such as setting relatively low or wide alarm trigger threshold conditions. Although this approach can improve the detection sensitivity and reduce the occurrence of false negatives to a certain extent, the false positive rate is also increased, resulting in the system frequently prompting lidar dirt and affecting the user experience.

[0006] Therefore, providing a lidar light shield dirt detection method and device with high detection accuracy is of great significance for promoting the application of lidar and the development of autonomous driving technology. Summary of the Invention

[0007] The objective of the present invention is to address the above-mentioned existing technical problems and provide a lidar light shield dirt detection method with high detection accuracy.

[0008] In view of this, the present invention provides a lidar light shield dirt detection method, including:

[0009] S1, dividing the light shield detection area of the lidar into several micro-regions;

[0010] S2, emitting a first laser beam and receiving a first echo signal generated by the first laser beam;

[0011] S3. Determine the contamination status of each micro-region in the lidar light shield in sequence according to the pulse width of the first echo signal. If the determination result is that the current micro-region may be contaminated, then proceed to step S4; if the determination result is that the current micro-region is not contaminated, then mark the current micro-region as a clean area.

[0012] S4. Transmit a second laser beam and receive the second echo signal generated by the second laser beam.

[0013] S5. Determine the contamination status of the current micro-region according to the pulse width of the second echo signal. If it is determined that the current micro-region is contaminated, then determine the type of contamination in the current micro-region, and then proceed to step S6; if it is determined that the current micro-region is not contaminated, then mark the current micro-region as a clean area.

[0014] S6. Process the lidar according to the contamination type of each micro-region and issue an alarm prompt.

[0015] Further, step S3 includes:

[0016] S31. Compare the pulse width W 1 of the first echo signal returned by each point in the lidar with a preset threshold W 阈1 in sequence. If W 1 ≤ W 阈1 , then determine that the current point is a clean point; if W 1 > W 阈1 , then determine that the current point is a contaminated point.

[0017] S32. Calculate the ratio K 1n of the number of contaminated points to the total number of points in each micro-region, and compare the ratio K 1n of the number of contaminated points to the total number of points with a preset threshold K 阈1 in sequence. If K 1n ≤ K 阈1 , then determine that the current micro-region is not contaminated and mark the current micro-region as a clean area; if K 1n > K 阈1 , then determine that the current micro-region may be contaminated.

[0018] Further, in steps S2 and S4, the intensity of the second laser beam is greater than the intensity of the first laser beam.

[0019] Further, step S5 includes:

[0020] S51. Obtain a preset pulse width threshold curve L 1 of the second echo signal and a maximum pulse width curve L 2 of the second echo signal;

[0021] S52. Process the pulse width threshold curve L 1 and the maximum pulse width curve L 2 to obtain the pulse width reference curve L 0 ;

[0022] S53. Determine the contamination condition of the current micro-region in sequence according to the pulse width W of the second echo signal returned by each point in the current micro-region 2 :

[0023] If it is determined that there is contamination in the current micro-region, determine the type of contamination in the current micro-region, and then continue to execute step S6;

[0024] If it is determined that there is no contamination in the current micro-region, mark the current micro-region as a clean area.

[0025] Further, the step S52 includes:

[0026] S521. Obtain the threshold W of the pulse width corresponding to the second echo signal and the maximum pulse width W 1 under different total usage times of the lidar according to the pulse width threshold curve L 2 and the maximum pulse width curve L 阈2 ; 阈3 ;

[0027] S522. Compare the relative magnitudes of the threshold W of the pulse width corresponding to the second echo signal and the maximum pulse width W 阈2 under different total usage times of the lidar in sequence, and take the smaller value of the W 阈3 and W 阈2 as the pulse width reference value W 阈3 corresponding to the total usage time of the lidar on the pulse width reference curve L 0 ; 基准 ;

[0028] S523. Fit the pulse width reference values W 基准 obtained in step S522 at each total usage time to obtain the pulse width reference curve L 0 .

[0029] Further, the step S53 includes:

[0030] S531. Obtain the current cumulative usage time t 累计 of the lidar, and obtain the value of W 0 corresponding to the current cumulative usage time t 累计 on the pulse width reference curve L 基准 , and at the same time obtain the maximum pulse width W 2 corresponding to the current cumulative usage time t 累计 on the maximum pulse width curve L 阈3 ;

[0031] S532. Compare the pulse widths W of the second echo signals returned by each point in sequence. 2 and W 基准 for their relative magnitudes. If W 2 ≤W 基准 , then determine that the current point is a clean point; if W 3 >W 基准 , then continue to execute step S533.

[0032] S533. Compare the pulse width W of the second echo signal returned by the current point 2 and W 阈3 for their relative magnitudes:

[0033] If W 2 ≤W 阈3 , then determine that the contamination situation of the current point is to be determined, and continue to execute step S534;

[0034] If W 3 >W 阈3 , then determine that the current point is a contaminated point, and continue to execute step S535 to determine the type of contamination of this point.

[0035] S534. Compare the intensity W of the second echo signal returned by the current point 2 with the intensity W of the first echo signal returned by the current point 1 . Calculate the difference △W between W 2 and W 1 . If △W < W 阈4 , then determine that the current point is a clean point; if △W ≥ W 阈4 , then determine that the current point is a contaminated point, and continue to execute step S535 to determine the type of contamination of this point.

[0036] S535. Obtain the natural change curve L of the preset pulse width of the second echo signal 3 , and determine whether the type of contamination of each point in the current microzone is type A contaminated point or type B contaminated point according to the pulse width W 2 of the second echo signal and the natural change curve L 3 of the pulse width.

[0037] S536. Calculate the ratio K of the number of contaminated points to the total number of points in the current microzone 2n , and compare the ratio K 2n of the number of contaminated points to the total number of points with the preset threshold K 阈2 :

[0038] If K 2n ≤K 阈2 , then determine that there is no contamination in the current microzone, and mark the current microzone as a clean area;

[0039] If K 2n > K 阈2 , it is determined that there is dirt in the current micro-region, and step S537 is continued;

[0040] where n represents the number of the micro-region, and its values are successively 1, 2, 3,..., n max , n max is the total number of micro-regions;

[0041] S537. Calculate the total number of type A dirt spots P 1 in the current dirty micro-region, and the total number of type B dirt spots P 2 ; Then compare the relative magnitudes of the value of P 1 / (P 1 + P 2 ) with the preset threshold P 阈 :

[0042] If P 1 / (P 1 + P 2 ) ≥ P 阈 , it is determined that the dirt in the current micro-region is caused by contaminants adhered to the photomask;

[0043] If P 1 / (P 1 + P 2 ) < P 阈 , it is determined that the dirt in the current micro-region is caused by natural aging of the photomask.

[0044] Furthermore, the step S535 includes:

[0045] S5351. First, obtain the value W 3 of the natural change of the pulse width corresponding to the current cumulative usage time t 累计 on the natural change curve L 阈4 of the pulse width;

[0046] S5352. Compare the relative magnitudes of the pulse width W 2 and W 阈4 of the second echo signal received by each dirt spot in the current micro-region in turn:

[0047] If the pulse width W 2 of the second echo signal ≥ α * W 阈4 , it is determined that the current point is dirt caused by contaminants adhered to the photomask, and the current point is recorded as a type A dirt spot;

[0048] If W 2 < α * W 阈4 , it is determined that the current point is dirt caused by natural aging of the photomask, and the current point is recorded as a type B dirt spot.

[0049] Further, the α is an adjustment coefficient, and the value of α is 0.8 to 1.0.

[0050] Further, the step S6 includes:

[0051] If there is a micro-region in the lidar where the mask is soiled due to natural aging, the intensity of the light emitted by the lidar is increased, and a mask aging warning is issued;

[0052] If there is a micro-region in the lidar where the mask is soiled due to adhered contaminants, a mask soiling warning and a flushing instruction are issued to clean the mask.

[0053] A lidar mask soiling detection device uses the above-mentioned lidar mask soiling detection method to detect the soiling condition of the lidar mask.

[0054] The beneficial effects of the present invention are:

[0055] 1. In the present invention, through the steps S2 and S3, the overall soiling of the lidar mask can be quickly detected. In this process, the setting standard of the clean area is relatively high, and the clean areas in the mask can be quickly and accurately screened out. For areas where soiling may occur and the soiling condition is difficult to define, further screening and detection analysis can be carried out. In this way, resource allocation can be optimized, the detection accuracy and efficiency can be improved, the detection accuracy can be enhanced, false detection and missed detection can be reduced, and the subsequent data processing volume can be reduced.

[0056] 2. The present invention first selects a first laser beam with a lower intensity for preliminary and overall detection, and then selects a second laser beam with a higher intensity for local and in-depth secondary screening of the suspected areas, which can optimize the energy consumption configuration and improve the detection accuracy.

[0057] 3. The pulse width reference curve L 0 , the pulse width natural change curve L 3 take into account the influences of various factors such as soiling, mask aging, and the maximum echo signal pulse width allowed for effective detection. Combining the pulse width reference curve L 0 and the pulse width natural change curve L 3 to judge the soiling condition of the mask is more accurate and comprehensive.

[0058] 4. The lidar mask soiling detection method described in the present invention classifies the soiling types of the mask and takes appropriate measures to deal with different types of mask soiling. In this way, the detection result can reflect the cause of the mask soiling and make the taken treatment measures more targeted. Description of the Drawings

[0059] Figure 1 is the flowchart of the method for detecting the dirt of the lidar light cover according to the present invention;

[0060] Figure 2 is the pulse width threshold curve L in the present invention 1 and the pulse width maximum curve L 2 schematic diagram;

[0061] Figure 3 is the pulse width threshold curve L in the present invention 1 and the pulse width maximum curve L 2 The pulse width reference curve L obtained by processing 0 schematic diagram;

[0062] Figure 4 is the schematic diagram of the natural change curve L of the pulse width according to the present invention 3 schematic diagram; Detailed implementation manners

[0063] Next, the technical solutions in the embodiments of the present application will be clearly described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art belong to the scope of protection of the present application.

[0064] In the description of the present application, it should be noted that the terms used here are only for describing the specific implementation manners, and are not intended to limit the exemplary embodiments according to the present application. For the sake of description, the technologies, methods, and devices known to those of ordinary skill in the relevant fields may not be discussed in detail, but in appropriate cases, the technologies, methods, and devices should be regarded as part of the authorization specification. In all the examples shown and discussed here, any specific value should be interpreted as merely exemplary, rather than as a limitation. Therefore, other examples of the exemplary embodiments may have different values. It should be noted that: similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further discussed in the subsequent drawings.

[0065] It should be noted that the terms "first", "second", etc. in the description and claims of this application are used to distinguish similar objects, rather than to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of this application can be implemented in an order other than those illustrated or described here, and the objects distinguished by "first", "second", etc. are usually of the same type, and the number of objects is not limited. For example, the first object can be one or more. In addition, "and / or" in the description and claims means at least one of the connected objects, and the character " / " generally indicates that the associated objects before and after are in an "or" relationship.

[0066] It should be noted that in this application, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such a process, method, article or device. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of additional identical elements in the process, method, article or device including that element. In addition, it should be pointed out that the scope of the methods and devices in the embodiments of this application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in a reverse order according to the functions involved. For example, the described methods may be performed in an order different from that described, and various steps may be added, omitted, or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0067] As Figure 1 shown, a method for detecting dirt on a lidar light shield includes the steps of:

[0068] S1, dividing the light shield detection area of the lidar into several micro-regions;

[0069] S2, emitting a first laser beam and receiving a first echo signal generated by the first laser beam;

[0070] S3, sequentially determining the dirt condition of each micro-region in the lidar light shield according to the pulse width of the first echo signal. If the determination result is that dirt may exist in the current micro-region, continue to execute step S4; if the determination result is that there is no dirt in the current micro-region, mark the current micro-region as a clean area;

[0071] S4, emitting a second laser beam and receiving a second echo signal generated by the second laser beam;

[0072] S5. Determine the contamination condition of the current micro-region according to the pulse width of the second echo signal. If it is determined that there is contamination in the current micro-region, then determine the type of contamination in the current micro-region, and then continue to execute step S6; if it is determined that there is no contamination in the current micro-region, then mark the current micro-region as a clean area.

[0073] S6. Process the lidar according to the contamination types of each micro-region and issue an alarm prompt.

[0074] Further, during the execution of the lidar dome contamination detection method, if it is determined that there is no contamination in the current micro-region, after marking the current micro-region as a clean area, end the detection of the current micro-region and perform the detection of the next micro-region.

[0075] During the use of the lidar, the contamination of the lidar dome, such as the coverage of pollutants such as dust, rain, snow, and insect corpses, will directly affect the light transmittance of the dome. These pollutants will scatter, absorb, or block part of the emitted light, making the emitted light unable to effectively penetrate the dome. When the emitted light is weakened or blocked, the system may need a longer time to accumulate enough signal intensity, resulting in an increase in the pulse width of the echo signal. In addition, the contamination may also cause reflections and scattering on the surface of the dome, and these reflected and scattered optical signals will be received by the receiving unit of the lidar and superimposed on the normal echo signal, thus increasing the complexity and pulse width of the echo signal. Therefore, the pulse width of the echo signal can reflect the contamination condition of the dome, enabling the present invention to obtain the contamination condition of the dome by detecting the pulse width of the echo signal.

[0076] Specifically, in the step S1, the micro-region can be in the shape of a sector, a rectangle, a triangle, etc. In the present invention, it is only necessary to divide the detection area of the lidar dome into several micro-regions with equal or approximately equal areas.

[0077] Preferably, to ensure the detection accuracy and at the same time reduce the data processing amount, in the step S1, the area of a single micro-region ≤ 1 / 5 of the total area of the detection area of the lidar dome.

[0078] As some examples of the present invention, during the lidar dome contamination detection described in the present invention, a beam transceiver device can be added to emit the emission light for contamination detection and receive it after the emission light passes through the dome; or the laser beam transceiver device and the lidar chip of the lidar itself can be used for contamination detection without adding an additional beam transceiver device.

[0079] Further, in the step S3, the process of sequentially determining the contamination conditions of each micro-region in the lidar dome according to the pulse width of the first echo signal is as follows:

[0080] S31. Sequentially compare the pulse width W of the first echo signal returned by each point in the lidar 1 with a preset threshold W 阈1 . If W 1 ≤W 阈1 , determine that the current point is a clean point; if W 1 >W 阈1 , determine that the current point is a dirty point;

[0081] S32. Calculate the ratio K of the number of dirty points to the total number of points in each micro-region 1n , and compare the ratio K of the number of dirty points to the total number of points 1n with a preset threshold K 阈1 . If K 1n ≤K 阈1 , determine that there is no dirt in the current micro-region and mark the current micro-region as a clean area; if K 1n >K 阈1 , determine that there may be dirt in the current micro-region and continue to execute step S4; where n represents the number of the micro-region, and its values are 1, 2, 3,..., n max , n max is the total number of micro-regions.

[0082] As some examples of the present invention, the values of the W 阈1 and K 阈1 can be set according to the requirements of detection accuracy, experimental values or empirical values. Generally, the value of the W 阈1 can be set to 1.05 - 1.2 times the width of the echo signal received after the first laser beam passes through a brand-new and clean photomask; the value of the K 阈1 can be set to more than 5 - 10%. In the present invention, the value of the K 阈1 can be set relatively low to meet the requirements of detection accuracy.

[0083] In the present invention, through steps S2 and S3, the overall dirt detection of the lidar photomask can be quickly performed. In this process, the setting standard of the clean area is relatively high, and the clean areas in the photomask can be quickly and accurately screened out. For areas where dirt may occur and the dirt situation is difficult to define, they can be obtained through further screening and detection analysis. In this way, resource allocation can be optimized, the detection accuracy and efficiency can be improved, the detection accuracy can be enhanced, misdetection and missed detection can be reduced, and the subsequent data processing volume can be reduced.

[0084] Furthermore, in steps S2 and S4, compared with the first laser beam and the second laser beam emitted, the frequency and / or intensity of the beam are different.

[0085] Preferably, the first laser beam and the second laser beam have the same frequency but different intensities; more preferably, the intensity of the second laser beam is greater than that of the first laser beam.

[0086] Compared with the first laser beam, the increase in the emission light intensity in the second laser beam causes the energy of the laser beam to increase accordingly. The high-energy laser beam is more likely to penetrate obstacles during propagation, such as contaminants on the photomask, and maintain a high energy level in the atmosphere. During the laser emission and reception process, the signal loss caused by the attenuation of the high-intensity emission light is small, the echo signal is stronger, and the signal-to-noise ratio is higher. Thus, the echo signal can more comprehensively reflect the complexity of the received echo signal and more fully reflect the changes in the echo signal.

[0087] More importantly, if there is dirt on the surface of the photomask, resulting in a large amount of reflected and scattered light on the surface of the photomask, these additional optical signals will be superimposed on the normal echo signal, thereby increasing the pulse width of the echo signal. In addition, if the dirt causes the surface of the photomask to become rough or uneven, it may also make the direction of the reflected light more dispersed, further increasing the pulse width of the echo signal. On the contrary, if the photomask is clean and in good surface condition, the increase in the emission light intensity may cause the intensity of the received echo signal to remain unchanged or increase slightly. At this time, if the response time of the receiving system is fast enough and the noise level is low, the pulse width of the echo signal may remain unchanged or decrease slightly. Therefore, increasing the intensity of the emission light is beneficial to expanding the difference in the pulse width of the echo signal between the dirty and clean states of the photomask surface, and thus can improve the accuracy of dirt detection.

[0088] However, increasing the beam intensity usually means an increase in energy consumption. In the present invention, first, a first laser beam with a lower intensity is selected for preliminary and overall detection, and then a second laser beam with a higher intensity is selected for local and in-depth secondary screening of the suspected area, which can optimize the energy consumption configuration and improve the detection accuracy.

[0089] Further, in the step S5, the process of determining the dirt condition of the current micro-region according to the pulse width of the second echo signal is as follows:

[0090] S51, obtain the preset pulse width threshold curve L of the second echo signal 1 and the maximum pulse width curve L of the second echo signal 2 ;

[0091] S52, process the pulse width threshold curve L 1 and the maximum pulse width curve L 2 to obtain the pulse width reference curve L 0 ;

[0092] S53, sequentially according to the pulse width W of the second echo signal returned by each point in the current micro-region 2 Determine the contamination condition of the current micro-region:

[0093] If it is determined that there is contamination in the current micro-region, then determine the type of contamination in the current micro-region, and then continue to execute step S6;

[0094] If it is determined that there is no contamination in the current micro-region, mark the current micro-region as a clean area.

[0095] Specifically, in the step S51, the pulse width threshold curve L 1 is a curve showing the change of the threshold of the pulse width of the second echo signal with the extension of the total usage time of the lidar. On the pulse width threshold curve L 1 the threshold of the pulse width of the second echo signal corresponding to different total usage times of the lidar is the boundary value for whether there is contamination on the mask. When the pulse width of the second echo signal is higher than the corresponding pulse width threshold, it indicates that there is contamination at that point on the mask; when the pulse width of the second echo signal is lower than the corresponding pulse width threshold, it indicates that the point on the mask is in a clean state.

[0096] Considering factors such as natural wear, aging, and corrosion, in the present invention, as Figure 2 shown, on the pulse width threshold curve L 1 the threshold of the pulse width of the second echo signal gradually increases with the extension of the total usage time of the lidar. The threshold of the pulse width of the second echo signal corresponding to each total usage time of the lidar can be set through experiments or experience.

[0097] In the present invention, the pulse width maximum curve L 2 is a curve showing the change of the maximum value of the pulse width of the second echo signal allowed to ensure that the lidar maintains a certain measurement accuracy with the extension of the total usage time of the lidar. When the pulse width of the second echo signal increases to a certain value, the pulse width of the echo signal makes it difficult to distinguish the signal from noise, and the signal-to-noise ratio decreases; at the same time, when the pulse width of the second echo signal increases to a certain value, in addition to the widening of the pulse width, it may also cause distortion of the shape of the echo signal, and this distortion may make the signal difficult to be accurately identified and processed, affecting the accuracy of detection. Therefore, in the present invention, on the pulse width maximum curve L 2 with the extension of the total usage time of the lidar, the maximum value of the pulse width of the second echo signal allowed can be a certain value, or it can be a value that gradually increases with the extension of the total usage time of the lidar, as Figure 2 shown. In the pulse width maximum curve L 2 the maximum value of the pulse width of the second echo signal corresponding to different total usage times of the lidar can be determined through experiments or according to experience, etc.

[0098] Generally, as Figure 2 shown, in the early stage of lidar use, that is, when the total lidar use time is short, the pulse width threshold corresponding to the same total lidar use time is less than the maximum value of the pulse width; as the lidar use time extends, the pulse width threshold corresponding to the same total lidar use time gradually increases, and since its increase rate is generally greater than the increase rate of the maximum pulse width value, therefore, in the later stage of lidar use, that is, when the total lidar use time is long, the pulse width threshold corresponding to the same total lidar use time is greater than the maximum value of the pulse width.

[0099] Furthermore, in the step S52, the process of processing the pulse width threshold curve L 1 and the maximum pulse width curve L 2 to obtain the pulse width reference curve L 0 is as follows:

[0100] S521, obtain the threshold W 1 of the pulse width corresponding to the second echo signal and the maximum pulse width W 2 under different total lidar use times according to the pulse width threshold curve L 阈2 and the maximum pulse width curve L 阈3 ;

[0101] S522, compare the relative sizes of the threshold W 阈2 of the pulse width corresponding to the second echo signal and the maximum pulse width W 阈3 under different total lidar use times in sequence, and take the smaller value of the W 阈2 and W 阈3 as the pulse width reference value W 0 corresponding to the total lidar use time in the pulse width reference curve L 基准 ;

[0102] S523, fit the pulse width reference values W 基准 obtained in step S522 at each total use time to obtain the pulse width reference curve L Figure 3 as shown in 0 .

[0103] The pulse width reference curve L 0 of the present invention takes into account the influences of various factors such as dirt, mask aging, and the maximum echo signal pulse width allowed for effective detection. By combining the pulse width reference curve L 0 to judge the dirt condition of the mask, it is more accurate and comprehensive.

[0104] Furthermore, in the step S53, according to the pulse width W 2The method for determining the contamination condition of the current micro-region is as follows:

[0105] S531, obtain the current cumulative usage time t of the lidar 累计 , and obtain the pulse width reference curve L 0 at the current cumulative usage time t 累计 corresponding W 基准 value, and at the same time obtain the maximum pulse width curve L 2 of the lidar at the current cumulative usage time t 累计 corresponding maximum pulse width W 阈3 ;

[0106] S532, compare the relative magnitudes of the pulse widths W 2 and W 基准 of the second echo signals returned by each point in sequence. If W 2 ≤W 基准 , then determine that the current point is a clean point; if W 3 >W 基准 , then continue to execute step S533;

[0107] S533, compare the relative magnitudes of the pulse width W 2 of the second echo signal returned by the current point and W 阈3 :

[0108] If W 2 ≤W 阈3 , then determine that the contamination condition of the current point is to be determined, and continue to execute step S534;

[0109] If W 3 >W 阈3 , then determine that the current point is a contaminated point, and continue to execute step S535 to determine the contamination type of this point;

[0110] S534, compare the intensity W 2 of the second echo signal returned by the current point with the intensity W 1 of the first echo signal returned by the current point, calculate the difference △W between W 2 and W 1 . If △W<W 阈4 , then determine that the current point is a clean point; if △W≥W 阈4 , then determine that the current point is a contaminated point, and continue to execute step S535 to determine the contamination type of this point;

[0111] S535, obtain the preset natural change curve L 3 of the pulse width of the second echo signal, and based on the pulse width W 2 of the second echo signal and the natural change curve L 3Determine whether the type of dirt at each point in the current micro-region is type A dirt stain or type B dirt stain;

[0112] S536. Calculate the ratio K of the number of dirt stains to the total number of points in the current micro-region 2n , and based on the ratio K of the number of dirt stains to the total number of points 2n compare it with the preset threshold K 阈2 as follows:

[0113] If K 2n ≤K 阈2 , it is determined that there is no dirt in the current micro-region, and the current micro-region is marked as a clean area;

[0114] If K 2n >K 阈2 , it is determined that there is dirt in the current micro-region, and step S537 is continued to be executed;

[0115] where n represents the number of the micro-region, and its values are successively 1, 2, 3,..., n max , n max is the total number of micro-regions;

[0116] S537. Calculate the total number of points P 1 of type A dirt stains and the total number of points P 2 of type B dirt stains in the current dirty micro-region; then compare the value of P 1 / (P 1 +P 2 ) with the relative size of the preset threshold P 阈 :

[0117] If P 1 / (P 1 +P 2 )≥P 阈 , it is determined that the cause of the dirt in the current micro-region is dirt caused by mask adhesion contaminants;

[0118] If P 1 / (P 1 +P 2 )<P 阈 , it is determined that the cause of the dirt in the current micro-region is dirt caused by natural aging of the mask.

[0119] As some examples of the present invention, the size of the preset threshold P 阈 can be set as needed, and its value can be a fixed value or a variable related to the total usage time of the lidar. Preferably, the value of the preset threshold P 阈 is 0.3 - 0.7.

[0120] Specifically, in the step S535, the pulse width natural change curve L 3For the case where the optical mask of the lidar is under pollution-free conditions, the general value or maximum value of the pulse width of the second echo signal changes due to the performance change of the optical mask caused by natural aging and corrosion. During actual use, due to reasons such as moisture, corrosive gases, dust, or sand grain friction in the air, the optical mask will inevitably experience a certain degree of aging and corrosion. The aging and corrosion of the optical mask may introduce additional optical path lengths, resulting in a longer time delay for the optical signal during transmission, which will widen the pulse width of the echo signal. This is inevitable and naturally existing. At this time, even if the optical mask is deeply cleaned, compared with a brand-new optical mask, there is still a certain degree of change in the pulse width of the second echo signal. Therefore, as Figure 4 shown, on the natural change curve L 3 of the pulse width, it shows that the general value or maximum value of the pulse width of the second echo signal has a natural tendency to increase with the extension of the total usage time.

[0121] Thus, in the present invention, by comparing the pulse width of the second echo signal with the natural change curve L 3 of the pulse width, the type of dirt can be obtained.

[0122] Furthermore, in the step S535, the process of determining the type of dirt at each point in the current micro-region according to the pulse width W 2 of the second echo signal and the natural change curve L 3 of the pulse width is as follows:

[0123] S5351, first obtain the value W 3 of the natural change of the pulse width corresponding to the current cumulative usage time t 累计 on the natural change curve L 阈4 of the pulse width;

[0124] S5352, sequentially compare the relative magnitudes of the pulse width W 2 and W 阈4 values of the second echo signal received at each dirt point in the current micro-region:

[0125] If the pulse width W 2 of the second echo signal is ≥ α * W 阈4 , it is determined that the current point is dirty due to contaminants adhered to the optical mask, and the current point is marked as a type-A dirt point;

[0126] If W 2 <α * W 阈4 , it is determined that the current point is dirty due to natural aging of the optical mask, and the current point is marked as a type-B dirt point;

[0127] Among them, the α is an adjustment coefficient, and the value of α is 0.8 to 1.0.

[0128] In the present invention, by detecting the types of dirt in each micro-region, it is possible to identify dirt caused by the natural aging of the photomask or dirt caused by contaminants adhering to the photomask, and adopt different treatment methods, making the dirt detection method more accurate and comprehensive, and less likely to produce false judgments and missed judgments.

[0129] Further, in the step S6, the process of processing the lidar according to the types of dirt in each micro-region is as follows:

[0130] If there is a micro-region in the lidar where dirt is caused by the natural aging of the photomask, the intensity of the light emitted by the lidar is increased, and a photomask aging warning is issued to remind the user to replace the photomask in a timely manner;

[0131] If there is a micro-region in the lidar where dirt is caused by contaminants adhering to the photomask, a photomask dirt warning and a flushing instruction are issued to clean the photomask.

[0132] Furthermore, when there is a micro-region in the lidar where dirt is caused by the natural aging of the photomask, the increase in the intensity of the light emitted by the lidar can be set according to experimental settings or the aging degree of the photomask.

[0133] Furthermore, in the step S6, after cleaning the photomask, it is necessary to execute again the lidar photomask dirt detection method of the present invention to confirm the cleaning condition of the cleaned photomask.

[0134] In addition, the present invention also provides a lidar photomask dirt detection device, and the lidar photomask dirt detection device uses the above-mentioned lidar photomask dirt detection method to detect the dirt condition of the lidar.

[0135] As some examples of the present invention, the lidar photomask dirt detection device includes:

[0136] A transmitting unit that can emit a laser beam for dirt detection;

[0137] A receiving unit that can receive the echo signal generated by the laser beam emitted by the transmitting unit;

[0138] A detection unit that can detect the echo signal received by the receiving unit to obtain information such as the pulse width of the echo signal;

[0139] A dirt judgment unit that can detect the dirt condition of the photomask according to information such as the pulse width of the echo signal detected by the detection unit.

[0140] In addition, the present invention also discloses a computer-readable storage medium, and the storage medium stores a computer program that can be loaded and executed by a processor to perform the above-mentioned lidar photomask dirt detection method.

[0141] The embodiments of the present application have been described above in conjunction with the accompanying drawings. Without conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application is not limited to the above specific embodiments. The above specific embodiments are merely illustrative rather than restrictive. Under the inspiration of the present application, those of ordinary skill in the art can also make many forms without departing from the purpose of the present application and the scope protected by the claims, and all of them fall within the protection scope of the present application.

Claims

1. A method for detecting contamination of a laser radar mask, characterized in that: include: S1, dividing the laser radar mask detection area into several micro-areas; S2, emitting a first laser beam, and receiving a first echo signal generated by the first laser beam; S3, determining the contamination status of each micro-area in the laser radar mask in turn according to the pulse width of the first echo signal, and if the determination result is that the current micro-area may be contaminated, proceeding to step S4; if the determination result is that the current micro-area is not contaminated, marking the current micro-area as a clean area; S4, emitting a second laser beam, and receiving a second echo signal generated by the second laser beam; S5, determining the contamination condition of the current micro-area according to the pulse width of the second echo signal, and if it is determined that the current micro-area is contaminated, determining the contamination type of the current micro-area, and then continuing to execute step S6; if it is determined that the current micro-area is not contaminated, marking the current micro-area as a clean area; S6, processing the laser radar according to the dirt type of each micro-area and issuing an alarm prompt; The step S5 comprises: S51, obtaining a preset pulse width threshold curve L1 of the second echo signal and a pulse width maximum curve L2 of the second echo signal; wherein the pulse width threshold curve L1 is a curve showing a change in the threshold of the pulse width of the second echo signal as the total use time of the laser radar increases; and the pulse width maximum curve L2 is a curve showing a change in the maximum value of the pulse width of the second echo signal allowed as the total use time of the laser radar increases; S52, processing the pulse width threshold curve L1 and the pulse width maximum value curve L2 to obtain a pulse width reference curve L0; S53, determining the contamination status of the current micro-area according to the pulse width W2 of the second echo signal returned from each point in the current micro-area: If it is determined that the current micro-area is contaminated, the contamination type of the current micro-area is determined, and then step S6 is continued; If it is determined that the current micro-area is not dirty, the current micro-area is marked as a clean area; The step S52 comprises: S521, obtaining the pulse width threshold W corresponding to the second echo signal under different total use time of the laser radar according to the pulse width threshold curve L1 and the pulse width maximum value curve L2. 阈2 The maximum pulse width W 阈3 ; S522, sequentially comparing the pulse width threshold W corresponding to the second echo signal under different total laser radar usage times 阈2 The maximum pulse width W 阈3 The relative size of W 阈2 and W 阈3 The smaller value is used as the pulse width reference value W corresponding to the total use time of the laser radar in the pulse width reference curve L0. 基准 ; S523, the pulse width reference value W at each total usage time obtained in step S522 基准 Fitting is performed to obtain the pulse width reference curve L0.

2. The laser radar mask contamination detection method according to claim 1, characterized in that: The step S3 comprises: S31, sequentially comparing the pulse width W1 of the first echo signal returned by each point in the laser radar with a preset threshold value W 阈1 For comparison, if W1≤W 阈1 , then the current point is determined to be a clean point; if W1>W 阈1 , then the current point is determined to be a dirty point; S32, calculate the ratio K of the number of dirty spots in each micro-area to the total number of spots 1n , and the ratio of dirty points to the total number of points K 1n With the preset threshold K 阈1 For comparison, if K 1n ≤K 阈1 , then it is determined that there is no dirt in the current micro-area, and the current micro-area is marked as a clean area; if K 1n >K 阈1 , it is determined that the current micro-area may be dirty.

3. The laser radar mask contamination detection method according to claim 1, characterized in that: In the steps S2 and S4, the intensity of the second laser beam is greater than the intensity of the first laser beam.

4. The laser radar mask contamination detection method according to claim 1, characterized in that: The step S53 comprises: S531, obtain the current cumulative usage time t of the laser radar 累计 , and obtain the current cumulative usage time t on the pulse width reference curve L0 累计 The corresponding W 基准 The value of the pulse width maximum value curve L2 is obtained at the same time, and the current cumulative usage time t 累计 The corresponding maximum pulse width W 阈3 ; S532, sequentially compare the pulse widths W2 and W of the second echo signal returned from each point. 基准 The relative size of 基准 , then the current point is determined to be a clean point; if W2>W 基准 , then continue to execute step S533; S533, compare the pulse widths W2 and W of the second echo signal returned from the current point. 阈3 Relative size: If W2≤W 阈3 , it is determined that the contamination status of the current point is to be determined, and the step S534 is continued; If W2>W 阈3 , the current point is determined to be a dirty point, and step S535 is continued to determine the dirt type of the point; S534, compare the pulse width W2 of the second echo signal returned from the current point with the pulse width W1 of the first echo signal returned from the current point, and calculate the difference △W between W2 and W1. If △W<W 阈4 , then the current point is determined to be a clean point; if △W ≥ W 阈4 , the current point is determined to be a dirty point, and step S535 is continued to determine the dirt type of the point; S535, obtaining a preset pulse width natural variation curve L3 of the second echo signal, and determining whether the dirt type of each point in the current micro-area is a type A dirt point or a type B dirt point according to the pulse width W2 of the second echo signal and the pulse width natural variation curve L3; S536, calculate the ratio K of the number of dirty points to the total number of points in the current micro-area 2n , and according to the ratio of dirty points to the total number of points K 2n With the preset threshold K 阈2 For comparison: If K 2n ≤K 阈2 , it is determined that there is no dirt in the current micro-area, and the current micro-area is marked as a clean area; If K 2n >K 阈2 , it is determined that the current micro-area is dirty, and step S537 is continued; Where n represents the number of the micro-area, and its values ​​are 1, 2, 3, ..., n. max , n max is the total number of micro-regions; S537, calculate the total number of type A dirt points P1 and the total number of type B dirt points P2 in the current dirt micro-area; then compare the value of P1 / (P1+P2) with the preset threshold value P 阈 Relative size: If P1 / (P1+P2)≥P 阈 , it is determined that the contamination of the current micro-area is caused by contaminants adhering to the mask; If P1 / (P1+P2)<P 阈 , it is determined that the contamination of the current micro-area is caused by the natural aging of the mask.

5. The method for detecting contamination of a laser radar mask according to claim 4, characterized in that: The step S535 includes: S5351, first obtain the current cumulative usage time t on the pulse width natural variation curve L3 累计 The corresponding pulse width naturally changes in value W 阈4 ; S5352, sequentially comparing the pulse widths W2 and W 阈4 Relative size of values: If the pulse width of the second echo signal W2 ≥ α*W 阈4 , it is determined that the current point is dirty due to contaminants adhering to the mask, and the current point is recorded as a type A dirty point; α is an adjustment coefficient, and the value of α is 0.8~1.0; If W2<α*W 阈4 , then the current point is determined to be dirty due to natural aging of the mask, and the current point is recorded as a type B dirty point.

6. The laser radar mask contamination detection method according to claim 1, characterized in that: The step S6 comprises: If there are micro-areas of dirt in the LiDAR due to natural aging of the light mask, the intensity of the light emitted by the LiDAR is increased and a light mask aging warning is issued; If a dirty micro-area appears in the laser radar due to contaminants adhering to the mask, a dirty mask warning and flushing instruction will be issued to clean the mask.

7. A laser radar mask dirt detection device, characterized in that: The laser radar mask contamination detection device uses the laser radar mask contamination detection method described in any one of claims 1 to 6 to detect the mask contamination condition of the laser radar.

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