Noise filtering methods, devices, ranging systems, and storage media

Abstract

This invention provides a noise filtering method, apparatus, ranging system, and storage medium, relating to the field of lidar ranging technology. The method includes: setting multiple measurement cycles, each measurement cycle incrementing or decreasing sequentially at equal intervals with a preset standard measurement cycle as the arithmetic mean; cyclically measuring the object to be measured in the sequentially increasing or decreasing order of each measurement cycle; collecting and calculating data in each measurement cycle to obtain a return value corresponding to each measurement cycle; filtering each return value using a gradient filtering algorithm to determine the distance of the object to be measured that exceeds the measurement range of the previous measurement cycle and is displayed in the current measurement cycle, which is then used as the data to be filtered; filtering the data to be filtered to obtain point cloud data of the object to be measured after noise removal. This invention solves the problem of point cloud noise caused by mutual interference between adjacent lidar measurement cycles.

Description

Technical Field

[0001] This invention relates to the field of lidar ranging technology, and more specifically, to a noise filtering method, apparatus, ranging system, and storage medium. Background Technology

[0002] Current lidar or laser rangefinders use a method of emitting a laser and then collecting the returned laser. Therefore, if the interval between consecutive measurements is short, the laser that returned later (from a greater distance) in the previous measurement will return within the current measurement cycle, resulting in measurement noise. Summary of the Invention

[0003] In view of this, the purpose of the present invention is to provide a noise filtering method, device, ranging system and storage medium to solve the point cloud noise problem caused by mutual interference between adjacent measurement cycles of lidar.

[0004] To achieve the above objectives, the technical solutions adopted in the embodiments of the present invention are as follows:

[0005] In a first aspect, embodiments of the present invention provide a noise filtering method applied to the control unit of a lidar in a ranging system, the method comprising:

[0006] Multiple measurement cycles are set, and each measurement cycle is sequentially increased or decreased at equal intervals with the arithmetic average of the preset standard measurement cycle as the standard measurement cycle.

[0007] The object to be measured is cyclically measured in an increasing or decreasing order of the measurement cycles. Data is collected and calculated in each measurement cycle to obtain a return value corresponding to each measurement cycle. Each return value represents the distance of the object to be measured in the current measurement cycle, and / or the distance of the object to be measured that is outside the measurement range of the previous measurement cycle and is displayed in the current measurement cycle, wherein the measurement range is obtained based on the corresponding measurement cycle.

[0008] The return values ​​are filtered using a gradient filtering algorithm to determine the distance of the object to be measured that is outside the measurement range of the previous measurement cycle and is displayed in the current measurement cycle, and this distance is used as the data to be filtered out.

[0009] The data to be filtered out is then filtered to obtain the point cloud data of the object to be measured after noise removal.

[0010] In an optional implementation, after the step of setting multiple measurement periods, the method further includes:

[0011] The corresponding measurement range is calculated based on each measurement period and the speed of light.

[0012] In an optional implementation, the standard measurement cycle is set according to the range of the lidar.

[0013] In an optional implementation, the step of acquiring and calculating data in each measurement cycle to obtain a return value corresponding to each measurement cycle includes:

[0014] Data is collected during each of the aforementioned measurement cycles to obtain multiple return points;

[0015] Based on each measurement cycle and the measurement range corresponding to each measurement cycle, calculate the distance of the return point corresponding to each measurement cycle in the current measurement cycle to obtain the return value corresponding to each measurement cycle;

[0016] The distance displayed by the return point in the current measurement cycle represents the distance of the return point within the measurement range corresponding to the current measurement cycle, and / or the distance exceeding the measurement range corresponding to the previous measurement cycle and displayed in the current measurement cycle.

[0017] In an optional implementation, the step of filtering each of the return values ​​using a gradient filtering algorithm to determine the distance of the object to be measured that exceeds the measurement range corresponding to the previous measurement cycle and is displayed in the current measurement cycle, as data to be filtered out, includes:

[0018] Set gradient thresholds according to each measurement period;

[0019] Calculate the difference between each of the return values ​​and the return values ​​corresponding to the two adjacent other measurement periods;

[0020] Each of the aforementioned differences is compared with the gradient threshold;

[0021] If the difference is greater than the gradient threshold, the return value is determined as the distance of the object to be measured that is outside the measurement range corresponding to the previous measurement cycle and is displayed in the current measurement cycle, as measured in the previous measurement cycle, and is used as the data to be filtered out.

[0022] If the difference is less than the gradient threshold, the returned value is determined as data to be filtered.

[0023] The data to be filtered is used to determine the distance of the object to be measured that is outside the measurement range of the previous measurement cycle and is displayed in the current measurement cycle, and these distances are used as data to be filtered out.

[0024] In an optional implementation, the step of filtering the data to be filtered to determine the distance of the object to be measured that exceeds the measurement range corresponding to the previous measurement cycle and is displayed in the current measurement cycle, as the data to be filtered out, includes:

[0025] Determine whether the two other return values ​​adjacent to each of the data to be filtered are distances to the object to be measured that are outside the measurement range corresponding to the previous measurement cycle and are displayed in the current measurement cycle;

[0026] If so, the data to be screened will be used as the data to be filtered out.

[0027] Secondly, embodiments of the present invention provide a noise filtering device applied to the control unit of a lidar in a ranging system, the device comprising:

[0028] The parameter setting module is used to set multiple measurement cycles, and each measurement cycle increases or decreases sequentially with the arithmetic average interval of a preset standard measurement cycle as the interval.

[0029] The measurement module is used to cyclically measure the object to be measured in an increasing or decreasing order according to each measurement cycle. Data is collected and calculated in each measurement cycle to obtain a return value corresponding to each measurement cycle. Each return value represents the distance of the object to be measured in the current measurement cycle, and / or the distance of the object to be measured that exceeds the measurement range corresponding to the previous measurement cycle and is displayed in the current measurement cycle, wherein the measurement range is obtained based on the corresponding measurement cycle.

[0030] The filtering module is used to filter each of the return values ​​using a gradient filtering algorithm to determine the distance of the object to be measured that is outside the measurement range corresponding to the previous measurement cycle and is displayed in the current measurement cycle, and is used as the data to be filtered out.

[0031] The data processing module is used to filter out the data to be filtered out in order to obtain the point cloud data of the object to be measured after filtering out noise.

[0032] Thirdly, embodiments of this application provide a ranging system including a lidar, the lidar including a laser, a rotating device and a control unit, the control unit being electrically connected to the laser and the rotating device;

[0033] The laser is used to emit laser light towards the object to be measured;

[0034] The rotating device is used to deflect the laser emitted by the laser to scan the object to be measured;

[0035] The control unit is used to control the operation of the laser and the rotating device, and is also used to perform the noise filtering method provided by the first aspect embodiment and / or possible implementations in combination with the first aspect embodiment, so as to obtain the point cloud data of the object to be measured after noise filtering.

[0036] Fourthly, embodiments of this application provide a control unit, including a memory and a processor;

[0037] The memory is used to store computer programs;

[0038] The processor is used to execute the computer program to implement the noise filtering method provided as described in the first aspect embodiment and / or in combination with possible implementations of the first aspect embodiment.

[0039] Fifthly, embodiments of this application provide a computer-readable storage medium, wherein the computer program, when executed by a processor, implements the noise filtering method provided by the first aspect embodiment and / or some possible implementations in combination with the first aspect embodiment.

[0040] The beneficial effects of the embodiments of the present invention include, for example:

[0041] The noise filtering method, apparatus, ranging system, and storage medium provided in this invention, by setting multiple measurement cycles to change according to a certain pattern (i.e., each measurement cycle increases or decreases sequentially at equal intervals with the arithmetic mean of a preset standard measurement cycle), discretizes the data entering the next measurement cycle from a distance according to a certain pattern. By filtering the continuously acquired return values ​​through a gradient filtering algorithm, the distance of the object to be measured that exceeds the corresponding measurement range of the previous measurement cycle and is displayed in the current measurement cycle is determined as the data to be filtered out. This solves the point cloud noise problem caused by mutual interference between adjacent measurement cycles of lidar, eliminates cross-cycle noise, and improves the quality of point cloud data.

[0042] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. Attached Figure Description

[0043] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.

[0044] Figure 1An exemplary structural block diagram of a ranging system provided by an embodiment of the present invention is shown;

[0045] Figure 2 An exemplary structural block diagram of a lidar control unit in a ranging system provided by an embodiment of the present invention is shown;

[0046] Figure 3 A flowchart illustrating a noise filtering method provided by an embodiment of the present invention is shown;

[0047] Figure 4 The second schematic flowchart of a noise filtering method provided by an embodiment of the present invention is shown;

[0048] Figure 5 The third schematic flowchart of a noise filtering method provided by an embodiment of the present invention is shown;

[0049] Figure 6 The fourth schematic flowchart of a noise filtering method provided by an embodiment of the present invention is shown;

[0050] Figure 7 The fifth illustration shows a flowchart of a noise filtering method provided by an embodiment of the present invention;

[0051] Figure 8 An exemplary structural block diagram of a noise filtering device provided in an embodiment of the present invention is shown.

[0052] Icons: 100-Distance measuring system; 110-LiDAR; 111-Laser; 112-Control unit; 1121-Memory; 1122-Processor; 1123-Communication interface; 120-Rotation device; 300-Noise filtering device; 301-Parameter setting module; 302-Measurement module; 303-Filtering module; 304-Data processing module. Detailed Implementation

[0053] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0054] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0055] It should be noted that relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0056] It should be noted that, where there is no conflict, the features in the embodiments of the present invention can be combined with each other.

[0057] Currently, traditional ranging systems generally consist of lidar or laser rangefinders and a rotating device. Lidar or laser rangefinders also include a laser, a laser detection circuit, and a control unit. When measuring an object, the distance is often calculated by measuring the time between the laser emission and return. When it is necessary to detect a series of objects, the rotating device needs to be controlled to perform continuous single measurements at the required measurement positions.

[0058] Based on this, embodiments of the present invention provide a ranging system 100. Please refer to [link / reference]. Figure 1 The ranging system 100 includes a lidar 110, which includes a laser 111, a rotating device 120, and a control unit 112. The control unit 112 is electrically connected to the laser 111 and the rotating device 120.

[0059] Among them, laser 111 is used to emit laser light towards the object to be measured.

[0060] The rotating device 120 is used to deflect the laser emitted by the laser 111 to scan the object to be measured.

[0061] The control unit 112 is used to control the operation of the laser 111 and the rotating device 120. The control unit 112 is also used to calculate the distance by measuring the time between laser emission and return after the laser 111 emits a laser, so as to obtain the point cloud data of the object to be measured.

[0062] During the measurement process, when a high point cloud density is required, the measurement frequency needs to be increased, which will reduce the measurement cycle. When the measurement is performed in this way, the measurement distance will be smaller. Furthermore, if the time for the laser to return from the previous measurement is long, exceeding the previous measurement cycle before entering the current measurement cycle, point cloud noise will be generated.

[0063] For example, when using a lidar 110 for ranging, if the measurement method is to measure once every 1µs, and continuously measure one hundred points:

[0064] Based on the speed of light and the optical path, the ranging range can be obtained using the following formula:

[0065] d=v*t / 2

[0066] (Formula 1)

[0067] Where v is the speed of light and t is the measurement period, the measurement range for each measurement can be calculated to be within 150m. When the object to be measured exceeds 150m, it will be used as the object to be measured in the next measurement after being acquired in the previous measurement. For example, for a 200m surface, it will be displayed as a 50m surface in the next measurement. This will create over-period noise, affecting the quality of the point cloud data and impacting the accuracy of the measurement.

[0068] Based on this, embodiments of the present invention provide a noise filtering method to solve the above problems.

[0069] See also Figure 2 , Figure 2 An exemplary structural block diagram of the control unit 112 of the lidar 110 in a ranging system 100 provided by an embodiment of the present invention is shown, as follows: Figure 2 As shown, the control unit 112 includes a memory 1121, a processor 1122, and a communication interface 1123. The memory 1121, processor 1122, and communication interface 1123 are electrically connected to each other directly or indirectly to realize data transmission or interaction. For example, these components can be electrically connected to each other through one or more communication buses or signal lines.

[0070] The memory 1121 can be used to store software programs and modules. The processor 1122 executes various functional applications and data processing by executing the software programs and modules stored in the memory 1121. The communication interface 1123 can be used to communicate with other node devices for signaling or data.

[0071] The memory 1121 may be, but is not limited to, random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), etc.

[0072] The processor 1122 can be an integrated circuit chip with signal processing capabilities. The processor 1122 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0073] The following description uses the control unit 112 of the lidar 110 in the ranging system 100 as the execution subject to illustrate the noise filtering method provided in this embodiment of the invention. Please refer to [link / reference]. Figure 3 , Figure 3 A flowchart illustrating a noise filtering method provided by an embodiment of the present invention is shown. Figure 3 As shown, the noise filtering method described above is applied to the control unit 112 of the lidar 110 in the ranging system 100. The method may include the following steps:

[0074] S210, set multiple measurement cycles, each measurement cycle is equal to the arithmetic mean of the preset standard measurement cycle, and increases or decreases sequentially at equal intervals.

[0075] S220, the object to be measured is cyclically measured in an increasing or decreasing order according to each measurement cycle. Data is collected and calculated in each measurement cycle to obtain the return value corresponding to each measurement cycle. Each return value represents the distance of the object to be measured in the current measurement cycle, and / or the distance of the object to be measured that is outside the measurement range of the previous measurement cycle and is displayed in the current measurement cycle.

[0076] The measurement range is obtained based on the corresponding measurement period.

[0077] S230: The return values ​​are filtered using a gradient filtering algorithm to determine the distances of the objects to be measured that are outside the measurement range of the previous measurement cycle and are displayed in the current measurement cycle. These distances are then used as data to be filtered out.

[0078] S240 filters out the data to be filtered to obtain point cloud data of the object to be measured after noise removal.

[0079] The above steps realize the process of setting multiple measurement cycles to change according to a certain pattern, measuring the object to be measured based on the multiple measurement cycles that change according to the pattern, calculating the return value, and processing the return value to filter out noise.

[0080] Step S210 involves setting multiple measurement periods that vary according to a set pattern. For example, if the preset standard measurement period is 1 µs, then multiple measurement periods that increase or decrease sequentially at equal intervals with the preset standard measurement period as the arithmetic mean can be 0.8 µs, 1 µs, and 1.2 µs. In other words, the multiple measurement periods must be distributed around the preset standard measurement period as the center value to ensure regular changes within the range of the standard measurement period.

[0081] Based on this, step S220 can be continued, measuring the object to be measured in a cyclical manner with increasing or decreasing order of each measurement cycle, collecting and calculating data in each measurement cycle to obtain the return value corresponding to each measurement cycle.

[0082] For example, based on the multiple measurement cycles of 0.8µs, 1µs, and 1.2µs set above, the object to be measured is measured in a cyclical manner, repeating 0.8µs, 1µs, 1.2µs, 0.8µs, 1µs, 1.2µs... Data is collected and calculated in each measurement cycle to obtain the corresponding return value for each measurement cycle. For example, return values ​​d1, d2, and d3 are obtained in the 0.8µs measurement cycle; d4, d5, and d6 are obtained in the 1µs measurement cycle; and d7, d8, and d9 are obtained in the 1.2µs measurement cycle. The collected data generally represents the first two and last data collected during the same measurement cycle.

[0083] The return value corresponding to each measurement cycle is actually the distance of the object to be measured in the current measurement cycle, and / or the distance of the object to be measured that is outside the measurement range of the previous measurement cycle and is displayed in the current measurement cycle. If the return value corresponding to each measurement cycle is set to dn, then the return value can be obtained based on the following formula:

[0084] dn = D - v*tn / 2

[0085] (Formula 2)

[0086] Where D is the actual distance of the object to be measured when it exceeds the measurement range of the previous measurement cycle, v is the speed of light, and tn is the previous measurement cycle.

[0087] For example, if the standard measurement period is set to 1µs as mentioned above, and multiple measurement periods are set to 0.8µs, 1µs, and 1.2µs, and the actual distance of the object to be measured is 200m, then v / 2 = 150m. Therefore, the return value obtained in the 0.8µs measurement period can be 200m - 0.8 * 150m = 80m, the return value obtained in the 1µs measurement period can be 200m - 1 * 150m = 50m, and the return value obtained in the 1.2µs measurement period can be 200m - 1.2 * 150m = 20m.

[0088] After obtaining the return value corresponding to each measurement cycle, step S230 can be executed to filter each return value using a gradient filtering algorithm to determine the distance of the object to be measured that is outside the measurement range of the previous measurement cycle and is displayed in the current measurement cycle, and this distance is used as the data to be filtered out.

[0089] In this embodiment of the invention, the process of filtering each return value using a gradient filtering algorithm can involve setting a gradient threshold and determining whether each return value represents a distance to the object to be measured that exceeds the measurement range of the previous measurement cycle and is displayed in the current measurement cycle, thus serving as data to be filtered out. Specifically, the gradient threshold can be compared with the difference between each return value and two return values ​​corresponding to other adjacent measurement cycles. If the difference is greater than the gradient threshold, the return value is determined to be a distance to the object to be measured that exceeds the measurement range of the previous measurement cycle and is displayed in the current measurement cycle, thus serving as data to be filtered out.

[0090] Furthermore, the maximum gradient threshold in the above gradient threshold can be calculated based on the determined interval value of each measurement period and Formula 1 mentioned above. For example, based on the multiple measurement periods of 0.8us, 1us, and 1.2us set above, the interval value of each measurement period is 0.2. Therefore, t=0.2 can be substituted into Formula 1 mentioned above to calculate the maximum gradient threshold when each measurement period is 0.8us, 1us, and 1.2us.

[0091] Based on the above gradient threshold calculation method, if the calculated maximum gradient threshold is 30m, and the return values ​​obtained above are 80m, 50m, and 20m, and if the gradient threshold set based on the maximum gradient threshold is 20m, then when judging the return value 50m, the difference between the return value 50m and its two adjacent return values ​​80m and 20m is 80-50=30m and 50-20=30m. Both of these differences are greater than the gradient threshold of 20m, so the return value 50 is determined to be the distance of the object to be measured that exceeds the corresponding measurement range of the previous measurement cycle and is displayed in the current measurement cycle. Therefore, the return value 50 can be used as the data to be filtered out. The method for judging the return values ​​80m and 20m is the same as the above method, and will not be repeated here.

[0092] Furthermore, if judging each return value by setting a gradient threshold fails to determine the distance of the object to be measured that is outside the measurement range of the previous measurement cycle and is displayed in the current measurement cycle, then further determination is needed based on filtering conditions. For example, the filtering condition can be set as follows: if the return values ​​of two adjacent measurement cycles are both distances of the object to be measured that are outside the measurement range of the previous measurement cycle and are displayed in the current measurement cycle, then that return value can be used as data to be filtered out. Based on this filtering condition, the data to be filtered out can be further obtained.

[0093] Based on the above settings, step S240 can be executed to filter out all the data to be filtered out, so as to obtain the point cloud data of the object to be measured after filtering out noise.

[0094] The noise filtering method provided in this invention sets multiple measurement cycles to change according to a certain pattern (i.e., each measurement cycle increases or decreases sequentially at equal intervals with the arithmetic mean of a preset standard measurement cycle). This discretizes data that enters the next measurement cycle from a distance according to a certain pattern. By filtering the continuously acquired return values ​​through a gradient filtering algorithm, the distance of the object to be measured that exceeds the corresponding measurement range of the previous measurement cycle and is displayed in the current measurement cycle is determined and filtered out. This solves the problem of point cloud noise caused by mutual interference between adjacent measurement cycles of lidar, eliminates cross-cycle noise, and improves the quality of point cloud data.

[0095] Optionally, after setting multiple measurement cycles, it is also necessary to calculate the corresponding measurement range based on each measurement cycle. This process can be achieved through the following steps:

[0096] exist Figure 3 Based on this, please refer to Figure 4 , Figure 4This is a second schematic flowchart of a noise filtering method provided by an embodiment of the present invention. After the step of setting multiple measurement cycles in step S210, the noise filtering method further includes:

[0097] S211, the corresponding measurement range is calculated based on each measurement cycle and the speed of light.

[0098] The above steps realize the process of calculating the corresponding measurement range based on each measurement cycle and the speed of light.

[0099] For example, if the standard measurement period set above is 1µs, then according to Formula 1 above, the measurement range d corresponding to this standard measurement period can be calculated to be 150m.

[0100] Optionally, the standard measurement cycle is set according to the range of the lidar.

[0101] In this embodiment of the invention, the standard measurement cycle can be set comprehensively based on the environment in which the lidar is used for ranging and the range of the lidar.

[0102] Optionally, the specific process of acquiring and calculating data in each measurement cycle in step S220 to obtain the return value corresponding to each measurement cycle can be implemented through the following steps:

[0103] exist Figure 4 Based on this, please refer to Figure 5 , Figure 5 This is illustrated in the third flowchart of a noise filtering method provided by an embodiment of the present invention. Step S220, which involves acquiring and calculating data in each measurement cycle to obtain the return value corresponding to each measurement cycle, includes:

[0104] S221, data is collected in each measurement cycle to obtain multiple return points.

[0105] S222, calculate the distance of the return point in each measurement cycle to the current measurement cycle based on each measurement cycle and the measurement range corresponding to each measurement cycle, so as to obtain the return value corresponding to each measurement cycle.

[0106] The distance displayed by the return point in the current measurement cycle represents the distance of the return point within the corresponding measurement range of the current measurement cycle, and / or the distance beyond the corresponding measurement range of the previous measurement cycle that is displayed in the current measurement cycle.

[0107] The above steps realize the process of collecting data for each measurement cycle and calculating the distance of the corresponding return point in the current measurement cycle.

[0108] For example, if the standard measurement period is set to 1µs, and multiple measurement periods are set to 0.8µs, 1µs, and 1.2µs, and the actual distance of the object to be measured is 200m, then according to Formula 2 above, the return value obtained in the 0.8µs measurement period is 200m - 0.8 * 150m = 80m, the return value obtained in the 1µs measurement period is 200m - 1 * 150m = 50m, and the return value obtained in the 1.2µs measurement period is 200m - 1.2 * 150m = 20m.

[0109] Optionally, in step S230, each return value is filtered using a gradient filtering algorithm to determine the distance of the object to be measured that is outside the measurement range of the previous measurement cycle and is displayed in the current measurement cycle. The specific process for filtering out this data can be implemented through the following steps:

[0110] exist Figure 4 Based on this, please refer to Figure 6 , Figure 6 The fourth step of the flowchart of a noise filtering method provided by an embodiment of the present invention is shown. Step S230 involves filtering each return value using a gradient filtering algorithm to determine the distance of the object to be measured that exceeds the measurement range of the previous measurement cycle and is displayed in the current measurement cycle, and using this distance as the data to be filtered. This step includes:

[0111] S231, set the gradient threshold according to each measurement cycle.

[0112] S232, calculate the difference between each return value and the corresponding return values ​​of the two adjacent other measurement cycles.

[0113] S233, compare each difference with the gradient threshold respectively.

[0114] S234, if the difference is greater than the gradient threshold, the return value is determined as the distance of the object to be measured that is outside the measurement range of the previous measurement cycle and is displayed in the current measurement cycle, and is used as the data to be filtered out.

[0115] S235: If the difference is less than the gradient threshold, the returned value is determined as the data to be filtered.

[0116] S236, Filter the data to be filtered to determine the distance of the object to be measured that is outside the measurement range of the previous measurement cycle and is displayed in the current measurement cycle, and use it as the data to be filtered out.

[0117] The above steps implement the process of filtering each return value using a gradient filtering algorithm to determine the distance of the object to be measured that is outside the corresponding measurement range of the previous measurement cycle and is displayed in the current measurement cycle, and use this distance as the data to be filtered out.

[0118] For example, if the gradient threshold set according to each measurement cycle is 20m, and the return values ​​obtained based on step S220 are 80m, 50m, 20m, 80m, 50m, and 20m, when the return value 50m is used for judgment, the difference between the return value 50m and its two adjacent return values ​​80m and 20m is 80-50=30m and 50-20=30m. Both of the above differences are greater than the gradient threshold, so the return value 50 is determined to be the distance of the object to be measured that is outside the measurement range of the previous measurement cycle and is displayed in the current measurement cycle.

[0119] For example, if the gradient threshold set for each measurement cycle is 40m, and the return values ​​obtained in step S230 are 80m, 50m, 20m, 80m, 50m, and 20m, when judging the return value 50m, the difference between the return value 50m and its two adjacent return values ​​80m and 20m is 80-50=30m and 50-20=30m. Both of these differences are less than the gradient threshold, so the return value 50 is determined to be the data to be filtered. At this time, the data to be filtered needs to be further filtered to determine whether the data to be filtered is the distance of the object to be measured that is outside the measurement range of the previous measurement cycle and is displayed in the current measurement cycle.

[0120] It should be noted that the gradient threshold selection in step S231 is based on the settings for each measurement cycle. For each measurement cycle and the selection of the gradient threshold, the greater the change in the measurement cycle, the greater the dispersion of the point where the distant object to be measured enters the next cycle. The greater the dispersion, the larger the range of selectable gradient thresholds. Furthermore, a larger gradient threshold range better prevents the filtering out of large curved surfaces, i.e., prevents large curved surfaces from being mistakenly treated as points spanning multiple cycles and filtered out.

[0121] Optionally, the process of filtering the data to be filtered in step S236 to determine the distances of the objects to be measured that are outside the measurement range of the previous measurement cycle and are displayed in the current measurement cycle can be implemented through the following steps:

[0122] exist Figure 4 Based on this, please refer to Figure 7 , Figure 7The fifth step of the flowchart of a noise filtering method provided by an embodiment of the present invention is shown. Step S236 involves filtering the data to be filtered to determine the distances of the objects to be measured that are outside the measurement range of the previous measurement cycle and are displayed in the current measurement cycle, and these distances are used as the data to be filtered.

[0123] S2361, determine whether the two other return values ​​adjacent to each data to be filtered are the distances of the objects to be measured that are outside the corresponding measurement range of the previous measurement cycle and are displayed in the current measurement cycle.

[0124] S2362, if so, then the data to be screened will be treated as the data to be filtered out.

[0125] If not, the data to be filtered remains unchanged.

[0126] The above steps enable further filtering of the identified data to be filtered, ultimately yielding all the data to be removed.

[0127] For example, if the gradient threshold is 40m based on the previous text, and the return values ​​obtained in step S220 are 80m, 50m, 20m, 80m, 50m, and 20m, and return value 50 is determined to be the data to be filtered in step S235, then it is necessary to determine whether the two other return values ​​80m and 20m adjacent to return value 50 are distances to the object to be measured that are outside the corresponding measurement range of the previous measurement cycle and are displayed in the current measurement cycle. If 80m and 20m are confirmed in step S235 to be distances to the object to be measured that are outside the corresponding measurement range of the previous measurement cycle and are displayed in the current measurement cycle, then return value 50 can be used as the data to be filtered out. When filtering out the data to be filtered out in the final step, return values ​​80m, 50m, and 20m need to be filtered out.

[0128] Based on the above noise removal methods, a noise removal device is presented below to execute the process steps in the above implementation methods and achieve the corresponding technical effects.

[0129] Specifically, Figure 8 For an exemplary structural block diagram of a noise filtering device 300 provided in an embodiment of the present invention, please refer to [link / reference]. Figure 8 The device is used in the control unit 112 of the lidar 110 in the ranging system 100. The device includes: parameter setting module 301, measurement module 302, screening module 303 and data processing module 304.

[0130] The parameter setting module 301 is used to set multiple measurement cycles, each of which increases or decreases sequentially at equal intervals with the arithmetic average of a preset standard measurement cycle.

[0131] The measurement module 302 is used to cyclically measure the object to be measured in an increasing or decreasing order according to each measurement cycle. Data is collected and calculated in each measurement cycle to obtain the return value corresponding to each measurement cycle. Each return value represents the distance of the object to be measured in the current measurement cycle, and / or the distance of the object to be measured that is outside the measurement range of the previous measurement cycle and is displayed in the current measurement cycle. The measurement range is obtained based on the corresponding measurement cycle.

[0132] The filtering module 303 is used to filter each return value using a gradient filtering algorithm to determine the distance of the object to be measured that is outside the measurement range of the previous measurement cycle and is displayed in the current measurement cycle, and this distance is used as the data to be filtered out.

[0133] The data processing module 304 is used to filter out the data to be filtered out in order to obtain point cloud data of the object to be measured after noise removal.

[0134] Based on the same inventive concept, this embodiment of the invention also provides a ranging system 100, which includes a lidar 110. The lidar includes a laser 111, a rotating device 120, and a control unit 112. The control unit 112 is electrically connected to the laser 111 and the rotating device 120.

[0135] Among them, laser 111 is used to emit laser light towards the object to be measured.

[0136] The rotating device 120 is used to deflect the laser emitted by the laser 111 to scan the object to be measured.

[0137] The control unit 112 is used to control the operation of the laser 111 and the rotating device 120. The control unit 112 is also used to execute the noise filtering method provided in the above embodiment to obtain point cloud data of the object to be measured after noise filtering.

[0138] Based on the same inventive concept, embodiments of the present invention also provide a computer-readable storage medium storing a computer program thereon, which, when executed by processor 1122, implements the noise filtering method provided in the above embodiments.

[0139] The steps executed by the aforementioned computer program during runtime will not be described in detail here; please refer to the explanation of the noise filtering method above.

[0140] In the several embodiments provided in this application, it should be understood that the disclosed apparatus, systems, and methods can also be implemented in other ways. The apparatus and system embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0141] In addition, the functional modules in the various embodiments of the present invention can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0142] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0143] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A noise filtering method, characterized in that, A control unit for a lidar system used in a ranging system, the method comprising: Multiple measurement cycles are set, and each measurement cycle is sequentially increased or decreased at equal intervals with the arithmetic average of the preset standard measurement cycle as the standard measurement cycle. The object to be measured is cyclically measured in an increasing or decreasing order of the measurement cycles. Data is collected and calculated in each measurement cycle to obtain a return value corresponding to each measurement cycle. Each return value represents the distance of the object to be measured in the current measurement cycle, and / or the distance of the object to be measured that is outside the measurement range of the previous measurement cycle and is displayed in the current measurement cycle, wherein the measurement range is obtained based on the corresponding measurement cycle. The return values ​​are filtered using a gradient filtering algorithm to determine the distances of the object to be measured that are outside the measurement range of the previous measurement cycle and are displayed in the current measurement cycle, and these distances are used as data to be filtered out. The data to be filtered out is then filtered to obtain the point cloud data of the object to be measured after noise removal.

2. The noise filtering method according to claim 1, characterized in that, After the step of setting multiple measurement cycles, the method further includes: The corresponding measurement range is calculated based on each measurement period and the speed of light.

3. The noise filtering method according to claim 1, characterized in that, The standard measurement cycle is set according to the range of the lidar.

4. The noise filtering method according to claim 2, characterized in that, The step of collecting and calculating data in each of the measurement cycles to obtain the return value corresponding to each measurement cycle includes: Data is collected during each of the aforementioned measurement cycles to obtain multiple return points; Based on each measurement cycle and the measurement range corresponding to each measurement cycle, calculate the distance of the return point corresponding to each measurement cycle in the current measurement cycle to obtain the return value corresponding to each measurement cycle; The distance displayed by the return point in the current measurement cycle represents the distance of the return point within the measurement range corresponding to the current measurement cycle, and / or the distance exceeding the measurement range corresponding to the previous measurement cycle and displayed in the current measurement cycle.

5. The noise filtering method according to claim 4, characterized in that, The step of filtering each of the return values ​​using a gradient filtering algorithm to determine the distances of the object to be measured that are outside the measurement range of the previous measurement cycle and are displayed in the current measurement cycle, and which are then used as data to be filtered out, includes: Set gradient thresholds according to each measurement period; Calculate the difference between each of the return values ​​and the corresponding return values ​​of the two adjacent other measurement periods; Each of the aforementioned differences is compared with the gradient threshold; If the difference is greater than the gradient threshold, the return value is determined as the distance of the object to be measured that is outside the measurement range corresponding to the previous measurement cycle and is displayed in the current measurement cycle, and is used as the data to be filtered out. If the difference is less than the gradient threshold, the returned value is determined as data to be filtered. The data to be filtered is used to determine the distance of the object to be measured that is outside the measurement range of the previous measurement cycle and is displayed in the current measurement cycle, and these distances are used as data to be filtered out.

6. The noise filtering method according to claim 5, characterized in that, The step of filtering the data to be filtered to determine the distance of the object to be measured that exceeds the measurement range corresponding to the previous measurement cycle and is displayed in the current measurement cycle, and which is used as the data to be filtered out, includes: Determine whether the two other return values ​​adjacent to each of the data to be filtered are distances to the object to be measured that are outside the measurement range corresponding to the previous measurement cycle and are displayed in the current measurement cycle; If so, the data to be screened will be used as the data to be filtered out.

7. A noise filtering device, characterized in that, A control unit for a lidar system used in a ranging system, the device comprising: The parameter setting module is used to set multiple measurement cycles, each of which is sequentially increased or decreased at equal intervals with the arithmetic average of a preset standard measurement cycle. The measurement module is used to cyclically measure the object to be measured in an increasing or decreasing order according to each measurement cycle. Data is collected and calculated in each measurement cycle to obtain a return value corresponding to each measurement cycle. Each return value represents the distance of the object to be measured in the current measurement cycle, and / or the distance of the object to be measured that exceeds the measurement range corresponding to the previous measurement cycle and is displayed in the current measurement cycle, wherein the measurement range is obtained based on the corresponding measurement cycle. The filtering module is used to filter each of the return values ​​using a gradient filtering algorithm to determine the distance of the object to be measured that is outside the measurement range corresponding to the previous measurement cycle and is displayed in the current measurement cycle, and is used as the data to be filtered out. The data processing module is used to filter out the data to be filtered out in order to obtain the point cloud data of the object to be measured after filtering out noise.

8. A ranging system, characterized in that, The system includes a lidar, which comprises a laser, a rotating device, and a control unit, wherein the control unit is electrically connected to the laser and the rotating device. The laser is used to emit laser light towards the object to be measured; The rotating device is used to deflect the laser emitted by the laser to scan the object to be measured; The control unit is used to control the operation of the laser and the rotating device, and is also used to perform the noise filtering method as described in any one of claims 1-6 to obtain point cloud data of the object to be measured after noise filtering.

9. A control unit, characterized in that, Including memory and processor; The memory is used to store computer programs; The processor is used to execute the computer program to implement the noise filtering method as described in any one of claims 1-6.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the noise filtering method as described in any one of claims 1-6.

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