Laser radar and method and device for detecting contamination of laser radar and storage medium

Abstract

The application provides a laser radar and a light cover contamination detection method, device and storage medium thereof. The light cover contamination detection method comprises: receiving echoes under different horizontal field angles respectively; for the echoes under each horizontal field angle, calculating environmental noise in the echoes; and determining whether the light cover is contaminated according to the intensity of the environmental noise under each horizontal field angle and the intensity variation rate of the environmental noise. The technical scheme of the application can realize detection of light cover contamination without adding additional light sources and light receiving devices, reduce the complexity of the structure of the laser radar and the complexity of light cover contamination detection, and can also increase the contamination detection range and ensure the comprehensiveness of contamination detection.

Description

Technical Field

[0001] This invention relates to the field of lidar technology, and in particular to a lidar and its method, apparatus, and storage medium for detecting dirt on the photomask. Background Technology

[0002] LiDAR is a crucial sensor for autonomous driving. A typical LiDAR system includes a laser emitter, a laser receiver, and a photomask (also called a light-transmitting cover). The photomask is an indispensable part of the laser emission and reception path, protecting the internal optical and circuit components and isolating stray light. As a non-contact measurement device, the cleanliness of the photomask directly affects the LiDAR's measurement range and accuracy. Therefore, detecting contamination on the photomask surface is an essential aspect of LiDAR use and maintenance.

[0003] In existing technologies, to detect dirt on the photomask, an additional photomask dirt detection system needs to be installed in the lidar. The photomask dirt detection system can emit multi-pulses and determine whether dirt is present on the photomask by observing the echoes of the multi-pulses from the photomask.

[0004] However, existing detection methods that utilize photomask echoes rely on the laser emitted by the radar itself, resulting in a small coverage area for dirt detection. Summary of the Invention

[0005] This invention provides a lidar and its photomask dirt detection method, device, and storage medium, which do not rely on the laser emitted by the lidar itself, and can also increase the dirt detection range and ensure the comprehensiveness of dirt detection.

[0006] In a first aspect, embodiments of the present invention provide a method for detecting dirt on a lidar radome. The method includes: receiving echoes at different horizontal field of view angles; calculating environmental noise in the echoes at each horizontal field of view angle; and determining whether the lidar is dirty based on the intensity of the environmental noise at each horizontal field of view angle and the rate of change of the intensity of the environmental noise.

[0007] Optionally, receiving echoes at different horizontal field of view angles includes: receiving echoes of beams emitted by the lidar at different horizontal field of view angles, wherein the echoes include the ambient noise and the reflected beams of the beams emitted by the lidar; or, when the lidar is not emitting a beam, receiving echoes at different horizontal field of view angles, wherein the echoes include ambient noise.

[0008] Optionally, determining the dirt status of the photomask based on the intensity and rate of change of the environmental noise at each horizontal field of view includes: if the intensity of the environmental noise at at least one horizontal field of view is lower than a first preset value and the rate of change of the environmental noise is greater than a second preset value, then the photomask is determined to be dirty.

[0009] Optionally, determining whether the photomask is dirty based on the intensity and rate of change of the ambient noise at each horizontal field of view includes: determining an ambient noise curve based on the intensity of the ambient noise at each horizontal field of view; and determining whether the photomask is dirty based on the magnitude and rate of change of the ambient noise curve.

[0010] Optionally, determining whether the photomask is dirty based on the amplitude and rate of change of the ambient noise curve includes: if the amplitude of the ambient noise curve at at least one horizontal field of view is lower than a first preset value, and the rate of change of the ambient noise curve at the at least one horizontal field of view is greater than a second preset value, then it is determined that the photomask is dirty.

[0011] Optionally, the ambient noise curve having an amplitude lower than a first preset value at at least one horizontal field of view, and the rate of change of the ambient noise curve at at least one horizontal field of view being greater than a second preset value, includes: the ambient noise curve having a falling edge and a rising edge at at least one horizontal field of view, the rate of change of ambient noise at both the falling edge and the rising edge being greater than the second preset value, and the amplitude of the ambient noise curve at the horizontal field of view between the falling edge and the rising edge being lower than the first preset value, then it is determined that the photomask is dirty.

[0012] Optionally, the step of determining that the photomask is dirty if the intensity of at least one horizontal field of view is lower than a first preset value and the rate of change of the environmental noise is greater than a second preset value further includes: the angle range of the horizontal field of view during which the environmental noise with an intensity lower than the first preset value is sustained is less than a fourth preset value, in which case the photomask is determined to be dirty.

[0013] Optionally, determining that the photomask is dirty if the intensity of the ambient noise at at least one horizontal field of view is lower than a first preset value and the rate of change of the ambient noise is greater than a second preset value further includes: if the intensity of the ambient noise in the ambient noise curve has a maximum value and the rate of change of the ambient noise in the direction away from the maximum value is lower than a third preset value, then the photomask is determined to be dirty.

[0014] Optionally, the second preset value is determined by: calculating the average rate of change of environmental noise at each horizontal field of view; and calculating the product of the average rate and a preset multiple to obtain the second preset value.

[0015] Optionally, the photomask contamination detection method further includes: when it is determined that the photomask is contaminated, determining the position of the contaminant on the photomask based on the horizontal field of view of the contaminant and the current vertical field of view.

[0016] Optionally, the photomask contamination detection method further includes: when it is determined that the photomask is contaminated, determining the degree of contamination based on the intensity of ambient noise at the horizontal field of view where the contamination is located.

[0017] Optionally, the calculation of the ambient noise in the echo includes: for each horizontal field of view, determining the echo within a preset time range after the lidar emits a beam at the horizontal field of view, and calculating the average value of all ambient light measurements in the echo as the ambient noise; or, for each horizontal field of view, filtering out echoes with light intensity less than a preset threshold, and calculating the average value of the filtered echoes as the ambient noise.

[0018] Optionally, determining the dirt status of the photomask based on the intensity and rate of change of the ambient noise at each horizontal field of view includes: if it is determined that the photomask is dirty at the same horizontal field of view based on the intensity and rate of change of the ambient noise at each horizontal field of view within several consecutive detection cycles, then the photomask is determined to be dirty.

[0019] Secondly, embodiments of the present invention also disclose a photomask contamination detection device for lidar. The photomask contamination detection device includes: a receiving module for receiving echoes at different horizontal field of view angles; an environmental noise calculation module for calculating the environmental noise in the echoes at each horizontal field of view angle; and a judgment module for determining whether the photomask is contaminated based on the intensity and rate of change of the environmental noise at each horizontal field of view angle.

[0020] Thirdly, embodiments of the present invention also disclose a lidar, the lidar including the aforementioned photomask dirt detection device for lidar.

[0021] Optionally, the lidar further includes a prompting module, which is used to output a prompting message when the judgment module determines that the photomask is dirty.

[0022] Fourthly, embodiments of the present invention also disclose a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, performs the steps of the method for detecting dirt on a photomask for lidar.

[0023] Fifthly, embodiments of the present invention also disclose a terminal device, including a memory and a processor, wherein the memory stores a computer program that can run on the processor, and the processor executes the steps of the method for detecting dirt on a photomask for lidar when running the computer program.

[0024] Compared with the prior art, the technical solution of the embodiments of the present invention has the following beneficial effects:

[0025] Since the change in ambient noise is uniform, gradual, and symmetrical across different horizontal field-of-view angles when the photomask is clean, but changes in intensity and rate of change when the photomask is dirty, this invention determines the presence of dirt on the photomask by receiving echoes and determining the intensity and rate of change of ambient noise in the echoes at various horizontal field-of-view angles. This invention enables photomask dirt detection without the need for additional light sources and light receiving devices, reducing the structural complexity of lidar and the complexity of photomask dirt detection. Furthermore, it avoids reliance on probe lasers, overcoming the problem that the detected photomask area must be covered by the emitted laser spot, increasing the detection range and ensuring comprehensive dirt detection.

[0026] Furthermore, if the ambient noise intensity of the ambient noise curve has a maximum value, and the rate of change of ambient noise in directions away from the maximum value is lower than a third preset value, then it is determined that the photomask is dirty. This invention, by adding the determination of the maximum ambient noise value and the rate of change of ambient noise in directions away from the maximum value, can avoid misjudgments of photomask dirt caused by partial coverage of the lidar photomask due to tree shade or other obstructions, thus ensuring the accuracy of photomask dirt detection.

[0027] Furthermore, if, based on the intensity and rate of change of the environmental noise at various horizontal field-of-view angles over several consecutive detection cycles, it is determined that the photomask is contaminated at the same horizontal field-of-view angle, then the presence of contamination in the photomask is confirmed. This invention, by determining the presence of contamination only upon detection at the same horizontal field-of-view angle over several consecutive detection cycles, further avoids false detections and ensures the robustness of photomask contamination detection. Attached Figure Description

[0028] Figure 1 This is a schematic diagram showing how dirt on the photomask can block the path of emitted light.

[0029] Figure 2 This is a schematic diagram showing how dirt on the photomask can block the light receiving path.

[0030] Figure 3 This is a schematic diagram of an optical path for detecting dirt on a photomask in the prior art;

[0031] Figure 4 This is a schematic diagram of the principle of photomask contamination detection in existing technology;

[0032] Figure 5 This is a flowchart of a photomask contamination detection method provided in an embodiment of the present invention;

[0033] Figure 6 This is a schematic diagram of a photomask being contaminated according to an embodiment of the present invention;

[0034] Figure 7 This is a schematic diagram of environmental noise when the photomask is dirty, provided by an embodiment of the present invention;

[0035] Figure 8 This is a schematic diagram of a lidar echo under a horizontal field of view provided in an embodiment of the present invention;

[0036] Figure 9 This is a flowchart illustrating a method for detecting dirt on a photomask according to an embodiment of the present invention.

[0037] Figure 10 This is a schematic diagram of the structure of a photomask contamination detection device provided in an embodiment of the present invention;

[0038] Figure 11 This is a schematic diagram of another photomask contamination detection device provided in an embodiment of the present invention. Detailed Implementation

[0039] Please refer to Figure 1 Dirt on the photomask of a lidar can block the light path of the emitted laser, preventing it from properly detecting the surrounding environment. This causes the lidar to lose its detection capability in areas with contaminated photomasks, resulting in "no-line" detection in some areas. Please refer to... Figure 2 Dirt on the photomask can also block the receiving optical path, causing the received laser to fail to reach the laser receiving unit normally. This will also reduce the echo energy received by the radar, thus reducing the radar's detection capability and causing the calculation of the object's reflectivity to be lower, resulting in a decrease in the quality of the detection results.

[0040] The main effects of dirt on the lidar radome on the lidar point cloud are as follows:

[0041] 1) The power of the detection beam incident on the target is reduced, resulting in a decrease in rangefinding capability and a decrease in reflectivity;

[0042] 2) After being reflected by the target, the power of the beam incident on the radar receiver is reduced, resulting in a decrease in range measurement capability and a decrease in reflectivity.

[0043] 3) The direction of the beam emitted from the radar radome changes, resulting in incorrect position information for some point clouds;

[0044] 4) Dirt reflections may cause additional near-field noise.

[0045] like Figure 3 As shown, a conventional solution for detecting mask contamination is to add a mask contamination detection system to the lidar unit. This system includes a transmitting unit and a receiving unit. The transmitting unit emits a probe beam, and the receiving unit receives the echo of the mask contamination against the probe beam, detecting the presence of contamination based on this echo. For details, please refer to... Figure 4 The presence of mask contamination can be determined by the fact that the corresponding multi-mask echoes meet the encoding conditions and the leading edge time is less than t1.

[0046] However, existing detection methods using photomask echoes rely on lasers emitted by the radar itself, resulting in a small detection coverage area for dirt.

[0047] This invention's technical solution is based on the difference in environmental noise between the presence and absence of dirt on the photomask. It determines the presence of dirt on the photomask by receiving echoes and determining the intensity of environmental noise at various horizontal field-of-view angles, as well as the rate of change of that intensity. This invention enables photomask dirt detection without requiring additional light sources and light receiving devices, reducing the structural complexity of lidar and the complexity of photomask dirt detection. Furthermore, it avoids reliance on probe lasers, overcoming the challenge that the detected photomask area must be covered by the emitted laser spot, thus increasing the detection range and ensuring comprehensive dirt detection.

[0048] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0049] Figure 5 This is a flowchart of a photomask contamination detection method provided in an embodiment of the present invention.

[0050] The above-mentioned method for detecting dirt on the photomask can be executed by the lidar side, for example, by using the processor inside the lidar to execute the various steps of the method, or by using a terminal device connected to the lidar to execute the various steps of the method.

[0051] Specifically, the method for detecting dirt on a photomask may include the following steps:

[0052] Step 101: Receive echoes from different horizontal field of view angles;

[0053] Step 102: For the echo at each horizontal field of view, calculate the ambient noise in the echo;

[0054] Step 103: Determine whether the photomask is dirty based on the intensity of the ambient noise at each horizontal field of view and the rate of change of the ambient noise intensity.

[0055] It should be noted that the sequence number of each step in this embodiment does not represent a limitation on the execution order of each step.

[0056] It is understood that, in specific implementations, the photomask contamination detection method can be implemented using software programs, which run within a processor integrated into the chip or chip module. This method can also be implemented using a combination of software and hardware; this application does not impose any limitations on this approach.

[0057] In the specific implementation of step 101, the echo detection circuit of the lidar itself can be reused to receive the echo, without the need to add an additional light source and light receiving device.

[0058] In a non-limiting embodiment, the lidar emits a beam during rotation. In step 101, echoes of the beams emitted by the lidar at different horizontal field of view angles are received. The received echoes include ambient noise and reflected beams from the lidar's emitted beam. In this case, the echoes within a preset time range after the lidar emits a beam at a horizontal field of view angle can be determined, and the average value of all ambient light measurements in the echoes can be calculated as the ambient noise. Alternatively, for each horizontal field of view angle, echoes with light intensities less than a preset threshold can be selected, and the average value of the selected echoes can be calculated as the ambient noise.

[0059] In another non-limiting embodiment, the lidar does not emit a beam but only receives one. In this case, echoes are received at different horizontal field-of-view angles in step 101, and the received echoes contain only ambient noise. Since the echoes contain only ambient noise, the ambient noise can be calculated as the ambient noise level.

[0060] In this embodiment of the invention, environmental noise can refer to ambient visible light. The intensity of environmental noise incident on the lidar receiver is affected by the relative positions of the lidar and the ambient light source. The variation of environmental noise across different horizontal field-of-view angles of the lidar is uniform, gradual, and symmetrical. When dirt appears on the photomask, it affects any light entering the lidar receiver, including the probe laser and ambient visible light, causing a significant change in the environmental noise received by the receiver. Specifically, when dirt appears on the photomask, the intensity of environmental noise exhibits a "deep pit" falling edge in the horizontal field-of-view dimension, disrupting the "symmetry" of the environmental noise. Therefore, the presence of dirt at different locations can be identified by detecting the gradualness and symmetry of the environmental noise variation at different horizontal field-of-view angles.

[0061] Specifically, within the same optomechanical system, the relative positions of the ambient light source and the lidar are fixed at a given moment. For a single detection channel of the lidar, the ambient noise should also be fixed, exhibiting a symmetrical trend relative to the direction of the ambient light source. The ambient noise attenuates smoothly in directions away from the axis of symmetry. In the case of multiple ambient light sources, it can be viewed as the superposition of multiple waveforms, without disrupting the smoothness and symmetry of the changes, which will not be elaborated upon here. However, if dirt appears on the photomask, since the energy of ambient light illuminating a certain area of ​​the photomask is constant, dirt will reflect and absorb some of the energy, causing a significant attenuation of the energy entering the radar receiver. This disrupts the smoothness and symmetry of the ambient noise changes.

[0062] Furthermore, in the specific implementation of steps 102 and 103, the environmental noise in the echo under each horizontal field of view is determined, and the presence of dirt on the photomask is determined based on the intensity of the environmental noise under each horizontal field of view and the rate of change of the intensity of the environmental noise.

[0063] This invention does not rely on the laser radar's own emission; the detection effect remains unaffected even when the laser radar is not emitting light or its emission intensity is very low. Furthermore, this invention solves the problem that photomask contamination detection depends on the detection laser, and the detected photomask area must be covered by the emitted laser spot. For laser radars where the area covered by the emitted laser spot is smaller than the area covered by the received laser spot, the detection range is further expanded, ensuring that the contamination detection coverage area, which affects the radar detection effect, reaches 100%.

[0064] In a non-limiting embodiment of the present invention, if the intensity of environmental noise at at least one horizontal field of view is lower than a first preset value and the rate of change of environmental noise is greater than a second preset value, then the photomask is determined to be dirty.

[0065] In this embodiment of the invention, the first preset value can be preset based on experience. For example, the first preset value can be one-tenth of the normal ambient light intensity in the environment where the lidar is located. Furthermore, the first preset value can also be set to different values ​​depending on the detection time. For example, the first preset value used during the daytime detection time is different from the first preset value used at nighttime detection time, and the first preset value used during the daytime detection time can be greater than the first preset value used at nighttime detection time.

[0066] Specifically, the second preset value can also be preset based on experience, or calculated by: calculating the average value of the rate of change of environmental noise under each horizontal field of view; calculating the product of the average value and the preset multiple, as the second preset value.

[0067] The rate of change of environmental noise can be calculated by comparing the environmental noise intensity value at the current horizontal field of view with the environmental noise intensity value at the previous horizontal field of view. For example, it can be the ratio of the difference in environmental noise intensity between the two horizontal field of view to the angular difference between the two horizontal field of view. Alternatively, the rate of change of environmental noise can also be the ratio of the change in environmental noise intensity (i.e., the difference) within a preset time range after the lidar emits a beam at the current horizontal field of view to the length of that preset time range.

[0068] In a non-limiting embodiment of the present invention, an environmental noise curve is determined based on the intensity of the environmental noise at various horizontal field of view angles; and the presence of dirt on the photomask is determined based on the magnitude and rate of change of the environmental noise curve.

[0069] Furthermore, if the amplitude of the ambient noise curve at at least one horizontal field of view is lower than a first preset value, and the rate of change of the ambient noise curve at at least one horizontal field of view is greater than a second preset value, then it is determined that the photomask is dirty.

[0070] For details, please refer to Figure 6 For the detection channel of a lidar, the lidar can collect environmental noise intensity at different horizontal field of view angles within a single 360° rotation cycle (a single frame of data from a rotating mechanical lidar includes a horizontal field of view of 360°; for lidars with other structures, the angle of a single detection cycle can be adjusted accordingly). After completing a single detection cycle, the environmental noise at each horizontal field of view angle within that detection cycle can be obtained.

[0071] like Figure 7 As shown, the environmental noise at various horizontal field-of-view angles within a detection cycle can be plotted as an environmental noise curve. When the photomask is contaminated, the environmental noise exhibits a "deep pit" waveform at the horizontal field-of-view angle where the contaminant is located, specifically as shown in the figure. Figure 7As shown by the dashed line. At a certain horizontal field of view, if the ambient noise is too low (i.e., below the first preset value) and there are obvious falling and rising edges, i.e., the positions where the ambient noise curve drops sharply and rises steeply, and its rate of change is significantly higher than a certain multiple of the average rate of change of the entire horizontal field of view (i.e., the second preset value), then it can be determined that there is dirt in the photomask area corresponding to that horizontal field of view (i.e., a field of view of 270 degrees).

[0072] In another specific embodiment, if the horizontal field of view angle range of the environmental noise with an intensity lower than the first preset value is less than the fourth preset value, then it is determined that the photomask is dirty.

[0073] In this embodiment of the invention, the range of horizontal field of view angles during which environmental noise with an intensity lower than the first preset value persists is less than the fourth preset value, that is... Figure 7 The shallow width of the deep pit waveform shown by the dashed line reduces the misjudgment of dirt caused by large shadows from trees or other obstructions.

[0074] It should be noted that the specific value of the fourth preset value can be set according to the actual application scenario, and the embodiments of the present invention do not impose any restrictions on this.

[0075] In a non-limiting embodiment of the present invention, the environmental noise curve has a falling edge and a rising edge at at least one horizontal field of view, the rate of change of environmental noise at both the falling edge and the rising edge is greater than a second preset value, and the amplitude of the environmental noise curve at the horizontal field of view between the falling edge and the rising edge is lower than a first preset value, and the intensity of environmental noise of the environmental noise curve has a maximum value, and the rate of change of environmental noise in the direction away from the maximum value is lower than a third preset value, then it is determined that the photomask is dirty.

[0076] In the embodiments of the present invention, please refer to Figure 7 The following two conditions are used to determine the presence of dirt in a single detection cycle: ① At a certain horizontal field of view, the ambient noise is too low (below the first preset value), and there are obvious falling and rising edges, and the rate of change is significantly higher than a certain multiple of the average rate of change of the entire horizontal field of view (the second preset value); ② The ambient noise has a maximum value and decreases gradually in the direction away from the maximum value (the rate of change is less than the third preset value).

[0077] In a non-limiting embodiment of the present invention, for the case of a lidar emitting a beam, the echo includes reflected beam and ambient noise. The echo of the lidar within a preset time range after emitting the beam at the horizontal field of view is determined, and the average value of all ambient light measurements in the echo is calculated as the ambient noise.

[0078] In practice, the start time of the preset time range can be determined based on the detection range of the lidar, and the duration of the preset time range can be determined based on empirical values, such as 200 ns. Specifically, the flight time corresponding to the maximum detection distance set by the lidar is the start time of the preset time range. Light obtained beyond this time, i.e., beyond this maximum distance, can be considered ambient light. Therefore, the average light intensity over a period of time after this time can be calculated as the ambient noise.

[0079] like Figure 8 As shown, Figure 7 The ambient noise at a certain angle is calculated using the echo map of the lidar at that angle. Specifically, the method involves calculating the average value of all ambient light measurements within a preset time range after the lidar emits its beam, such as 500ns-700ns, as the mean. Figure 7 The environmental noise at this angle is formed by connecting the calculated environmental noise at various angles within one cycle of the lidar. Figure 7 The environmental noise curve shown.

[0080] In another non-limiting embodiment of the present invention, the environmental noise can also be calculated in the following way: for each horizontal field of view, echoes with light intensity less than a preset threshold are selected, and the average value of the selected echoes is calculated as the environmental noise.

[0081] In a non-limiting embodiment of the present invention, further, if it is determined that the photomask is dirty at the same horizontal field of view based on the intensity and rate of change of the ambient noise at each horizontal field of view within a number of consecutive detection cycles, then it is determined that the photomask is dirty.

[0082] In this embodiment of the invention, if dirt is detected in a certain horizontal field of view within a single detection cycle, and dirt is detected in the same horizontal field of view in several consecutive detection cycles, then the horizontal field of view is considered to be dirty. It should be noted that the specific number of detection cycles can be set according to actual needs.

[0083] In a non-limiting embodiment of the present invention, in addition to determining whether there is dirt in the photomask of the lidar, the location and degree of dirt can also be determined.

[0084] In practice, when it is determined that the photomask is dirty, the position of the dirt on the photomask is determined based on the horizontal field of view of the dirt and the current vertical field of view.

[0085] In this embodiment, the horizontal field of view where the dirt is located can refer to the horizontal field of view when the environmental noise is abnormal at the time the dirt is determined, for example... Figure 7The horizontal field of view shown is 270 degrees. The vertical field of view of a lidar is usually a preset fixed value. Therefore, when dirt is found at a certain horizontal field of view, a region on the photomask can be determined based on the horizontal and vertical field of view. This region is the location of the dirt on the photomask.

[0086] In practice, when it is determined that the photomask is dirty, the degree of dirtiness is determined based on the intensity of the ambient noise at the horizontal field of view where the dirt is located.

[0087] In this embodiment, the lower the intensity of the environmental noise, the greater the degree of dirtiness; conversely, the higher the intensity of the environmental noise, the less dirty the degree of dirtiness.

[0088] In a specific application scenario of this invention, please refer to Figure 9 The present invention also discloses a flowchart of a method for detecting dirt on a photomask.

[0089] In step 901, echoes at different horizontal field of view angles are received.

[0090] In step 902, it is determined whether the lidar emits a beam. If it does, step 903 or step 904 is executed; otherwise, step 905 is executed.

[0091] In step 903, the echo of the lidar after emitting a beam at a horizontal field of view within a preset time range is determined, and the average value of all ambient light measurements in the echo is calculated as the ambient noise.

[0092] In step 904, for each horizontal field of view, echoes with light intensity less than a preset threshold are selected, and the average value of the selected echoes is calculated as environmental noise.

[0093] In step 905, the intensity of the echo is calculated as ambient noise.

[0094] In step 906, it is determined whether the ambient noise is lower than the first preset value at a certain horizontal field of view, and whether there are obvious falling and rising edges, and the rate of change is significantly higher than a certain multiple of the average rate of change of the entire horizontal field of view. If so, step 907 is executed; otherwise, step 910 is executed.

[0095] In step 907, it is determined whether the environmental noise has a maximum value and the rate of change in the direction away from the maximum value is less than a third preset value. If so, step 908 is executed; otherwise, step 910 is executed.

[0096] In practice, steps 901 to 907 can be executed within the same detection cycle. By executing steps 901 to 907, it is possible to detect whether there is dirt in the horizontal field of view within the current detection cycle.

[0097] In step 908, it is determined whether the photomask is dirty at the same horizontal field of view within several consecutive detection cycles. If so, step 909 is executed; otherwise, step 910 is executed.

[0098] In step 909, it is determined that the photomask is dirty.

[0099] In step 910, it is determined that the photomask is free of dirt.

[0100] Please refer to Figure 10 This invention also discloses a photomask contamination detection device for lidar. The photomask contamination detection device 100 may include:

[0101] Receiver module 1001 is used to receive echoes under different horizontal field of view angles;

[0102] The environmental noise calculation module 1002 is used to calculate the environmental noise in the echo for each horizontal field of view.

[0103] The judgment module 1003 is used to determine whether the photomask is dirty based on the intensity and rate of change of the environmental noise at each horizontal field of view.

[0104] Further, please refer to Figure 11 The photomask dirt detection device 100 may also include a prompting module 1004, which is used to output a prompting message when the judgment module determines that the photomask is dirty.

[0105] For more information on the working principle and operation mode of the aforementioned photomask contamination detection device 100, please refer to [link / reference needed]. Figures 5 to 9 The relevant descriptions in the corresponding embodiments are not repeated here.

[0106] In specific implementations, the aforementioned photomask contamination detection device may correspond to a chip in a lidar or terminal device that has a photomask contamination detection function, such as a SOC (System-On-a-Chip), baseband chip, etc.; or correspond to a chip module in a lidar or terminal device that includes a photomask contamination detection function; or correspond to a chip module with a data processing function chip; or correspond to a lidar or terminal device.

[0107] Regarding the modules / units included in the various devices and products described in the above embodiments, they can be software modules / units, hardware modules / units, or a combination of both. For example, for various devices and products applied to or integrated into a chip, all of their modules / units can be implemented using hardware methods such as circuits, or at least some modules / units can be implemented using software programs that run on a processor integrated within the chip, while the remaining (if any) modules / units can be implemented using hardware methods such as circuits; for various devices and products applied to or integrated into a chip module, all of their modules / units can be implemented using hardware methods such as circuits, and different modules / units can be located in the same component (e.g., chip, circuit module, etc.) or different components of the chip module, or at least some modules / units can be implemented using hardware methods such as circuits. The components can be implemented using software programs that run on the processor integrated within the chip module. The remaining (if any) modules / units can be implemented using hardware methods such as circuits. For various devices and products applied to or integrated into the terminal, each of its components / units can be implemented using hardware methods such as circuits. Different modules / units can be located in the same component (e.g., chip, circuit module, etc.) or in different components within the terminal. Alternatively, at least some modules / units can be implemented using software programs that run on the processor integrated within the terminal, while the remaining (if any) modules / units can be implemented using hardware methods such as circuits.

[0108] This invention also discloses a storage medium, which is a computer-readable storage medium storing a computer program thereon. When the computer program is executed, it can perform the steps of the aforementioned method. The storage medium may include ROM, RAM, a magnetic disk, or an optical disk, etc. The storage medium may also include non-volatile memory or non-transitory memory, etc.

[0109] This invention also discloses a lidar, which may include a memory and a processor. The memory stores a computer program that can run on the processor. When the processor runs the computer program, it can perform the steps of the aforementioned method. Alternatively, the lidar may include the aforementioned photomask contamination detection device.

[0110] Furthermore, the lidar also includes a prompting module, which is used to output a prompting message when the judgment module determines that the photomask is dirty.

[0111] This invention also discloses a terminal device, which may include a memory and a processor. The memory stores a computer program that can run on the processor. When the processor runs the computer program, it can perform the steps of the aforementioned method. Alternatively, the terminal device may include the aforementioned photomask contamination detection device.

[0112] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this article indicates that the preceding and following related objects have an "or" relationship.

[0113] In the embodiments of this application, "multiple" refers to two or more.

[0114] The descriptions of "first," "second," etc., appearing in the embodiments of this application are for illustrative purposes and to distinguish the objects being described. They have no order and do not indicate any special limitation on the number of devices in the embodiments of this application, nor do they constitute any limitation on the embodiments of this application.

[0115] In this application, the term "connection" refers to various connection methods, such as direct connection or indirect connection, to achieve communication between devices. This application does not impose any limitations on this.

[0116] It should be understood that in the embodiments of this application, the processor can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0117] It should also be understood that the memory in the embodiments of this application can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0118] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of this application are generated, in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center containing one or more sets of available media. It should be understood that in the various embodiments of this application, the sequence number of each process does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0119] In the several embodiments provided in this application, it should be understood that the disclosed methods, apparatuses, and systems can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for example, the division of units is merely a logical functional division, and other division methods may exist in actual implementation; for example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, and the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0120] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0121] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can be physically comprised separately, or two or more units can be integrated into one unit. The integrated unit described above can be implemented in hardware or in the form of hardware plus software functional units.

[0122] The integrated unit implemented as a software functional unit described above can be stored in a computer-readable storage medium. This software functional unit, stored in a storage medium, includes several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute some steps of the methods described in the various embodiments of the present invention.

[0123] While the present invention has been disclosed above, it is not limited thereto. Any person skilled in the art can make various modifications and alterations without departing from the spirit and scope of the invention; therefore, the scope of protection of the present invention should be determined by the scope defined in the claims.

Claims

1. A method for detecting dirt on a lidar mask, characterized in that, include: Echoes were received from different horizontal field-of-view angles. For the echo at each horizontal field of view, calculate the ambient noise in the echo; The presence of dirt on the photomask is determined based on the intensity of the ambient noise at various horizontal field of view angles and the rate of change of the ambient noise intensity.

2. The method for detecting dirt on a photomask according to claim 1, characterized in that, The echoes received at different horizontal field of view angles include: The echoes of the beams emitted by the lidar at different horizontal field of view are received respectively, and the echoes include the ambient noise and the reflected beams of the beams emitted by the lidar. Alternatively, when the lidar is not emitting a beam, the echoes at different horizontal field of view angles are received, and the echoes include ambient noise.

3. The method for detecting dirt on a photomask according to claim 1, characterized in that, The step of determining whether the photomask is dirty based on the intensity of the environmental noise at various horizontal field of view angles and the rate of change of the environmental noise intensity includes: If the intensity of the ambient noise at at least one horizontal field of view is lower than a first preset value, and the rate of change of the ambient noise is greater than a second preset value, then it is determined that the photomask is dirty.

4. The method for detecting dirt on a photomask according to claim 1, characterized in that, The step of determining whether the photomask is dirty based on the intensity of the environmental noise at various horizontal field of view angles and the rate of change of the environmental noise intensity includes: An environmental noise curve is determined based on the intensity of the environmental noise at each horizontal field of view; the magnitude and rate of change of the environmental noise curve are used to determine whether the photomask is dirty.

5. The method for detecting dirt on a photomask according to claim 4, characterized in that, The step of determining whether the photomask is dirty based on the amplitude and rate of change of the environmental noise curve includes: If the amplitude of the ambient noise curve at at least one horizontal field of view is lower than a first preset value, and the rate of change of the ambient noise curve at at least one horizontal field of view is greater than a second preset value, then it is determined that the photomask is dirty.

6. The method for detecting dirt on a photomask according to claim 5, characterized in that, The amplitude of the environmental noise curve at at least one horizontal field of view is lower than a first preset value, and the rate of change of the environmental noise curve at at least one horizontal field of view is greater than a second preset value, including: If the environmental noise curve has a falling edge and a rising edge at at least one horizontal field of view, and the rate of change of environmental noise at both the falling edge and the rising edge is greater than the second preset value, and the amplitude of the environmental noise curve at the horizontal field of view between the falling edge and the rising edge is lower than the first preset value, then it is determined that the photomask is dirty.

7. The method for detecting dirt on a photomask according to claim 6, characterized in that, The step of determining that the photomask is dirty if the intensity of the ambient noise at at least one horizontal field of view is lower than a first preset value and the rate of change of the ambient noise is greater than a second preset value further includes: If the intensity of the environmental noise is lower than the first preset value and the range of the horizontal field of view of the environmental noise is less than the fourth preset value, then it is determined that the photomask is dirty.

8. The method for detecting photomask contamination according to any one of claims 5 to 7, characterized in that, The step of determining that the photomask is dirty if the intensity of the ambient noise at at least one horizontal field of view is lower than a first preset value and the rate of change of the ambient noise is greater than the second preset value further includes: If the intensity of the ambient noise on the ambient noise curve has a maximum value, and the rate of change of the ambient noise in a direction away from the maximum value is lower than a third preset value, then it is determined that the photomask is dirty.

9. The method for detecting photomask contamination according to any one of claims 3, 5 to 7, characterized in that, The second preset value is determined in the following way: Calculate the average rate of change of the environmental noise at each horizontal field of view; Calculate the product of the average value and the preset multiple, and use it as the second preset value.

10. The method for detecting dirt on a photomask according to claim 1, characterized in that, Also includes: When it is determined that the photomask is dirty, the position of the dirt on the photomask is determined based on the horizontal field of view of the dirt and the current vertical field of view.

11. The method for detecting photomask contamination according to claim 1, characterized in that, Also includes: When it is determined that the photomask is dirty, the degree of dirtiness is determined based on the intensity of the ambient noise at the horizontal field of view where the dirt is located.

12. The method for detecting photomask contamination according to claim 1, characterized in that, The calculation of the ambient noise in the echo includes: For each horizontal field of view, the echo of the lidar within a preset time range after the laser beam is emitted at the horizontal field of view is determined, and the average value of all ambient light measurements in the echo is calculated as the ambient noise. Alternatively, for each horizontal field of view, echoes with light intensity less than a preset threshold are selected, and the average value of the selected echoes is calculated as the environmental noise.

13. The method for detecting dirt on a photomask according to claim 1, characterized in that, The step of determining whether the photomask is dirty based on the intensity of the environmental noise at various horizontal field of view angles and the rate of change of the environmental noise intensity includes: If, based on the intensity and rate of change of the ambient noise at various horizontal field of view within several consecutive detection cycles, it is determined that the photomask is dirty at the same horizontal field of view, then the photomask is determined to be dirty.

14. A device for detecting dirt on a lidar radome, characterized in that, include: The receiving module is used to receive echoes from different horizontal field of view angles. An environmental noise calculation module is used to calculate the environmental noise in the echo for each horizontal field of view. The judgment module is used to determine whether the photomask is dirty based on the intensity and rate of change of the ambient noise at various horizontal field of view angles.

15. A lidar, characterized in that, Includes the photomask dirt detection device for lidar as described in claim 14.

16. The lidar according to claim 15, characterized in that, Also includes: The prompting module is used to output a prompt message when the judgment module determines that the photomask is dirty.

17. A computer-readable storage medium having a computer program stored thereon, characterized in that, The computer program, when executed by a processor, performs the steps of the method for detecting dirt on a lidar as described in any one of claims 1 to 13.

18. A terminal device, comprising a memory and a processor, wherein the memory stores a computer program executable on the processor, characterized in that, When the processor runs the computer program, it performs the steps of the method for detecting dirt on a lidar as described in any one of claims 1 to 13.

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