Distance calibration method based on depth camera, distance measurement method and related equipment

Abstract

The embodiment of the invention provides a distance calibration method based on a depth camera, a distance measurement method and related equipment, which are used for reducing quantization errors. Comprising the following steps: for any pixel, in a fixed length corresponding to an Mth time box, adjusting the distance between a depth camera and a target object at a predetermined step length from an initial point to an end point; wherein one histogram is obtained at one distance, and N histograms are obtained at N distances; using a super-resolution algorithm to calculate one piece of histogram information to obtain an ultra-precision time box in the Mth time box, and obtaining N ultra-precision distances through the N ultra-precision time boxes; wherein the ultra-precision time box is related to photon numbers corresponding to an (M-1) th time box, an M th time box and an (M + 1) th time box in a histogram; calculating the difference between the N ultra-precision distances and the actual distance between the corresponding depth camera and the target object to obtain N ultra-precision error values; and averaging the N ultra-precision error values to obtain an ultra-precision error mean value.

Description

Technical Field

[0001] Embodiments of the present application relate to the technical field of depth cameras, and in particular, to a distance calibration method, a distance measurement method, and related devices based on a depth camera. Background Art

[0002] With the development and application of artificial intelligence (AI) and 3D technology, 3D imaging technology has begun to be popularized. In the existing 3D technology principle, direct time of flight (DTOF) calculates the distance by using the time difference between the beam emission and the sensor reception. Large area array DTOF obtains a depth image through the distances of each pixel in the sensor.

[0003] However, in the design of the chip, due to the circuit delay and different delay times between pixels, it is necessary to calibrate the fixed deviation of the entire pixel. And due to the limited time resolution bin of the time-to-digital converter (TDC), there will be quantization errors between bins. Summary of the Invention

[0004] Embodiments of the present application provide a distance calibration method, a distance measurement method, and related devices based on a depth camera, which are used to reduce quantization errors.

[0005] In a first aspect of the embodiments of the present application, a distance calibration method based on a depth camera is provided, including:

[0006] For any pixel, within a fixed length corresponding to the Mth time bin, adjust the distance between the depth camera and the target object from the initial point to the end point at a predetermined step size; wherein, a histogram is obtained at one distance, and N histograms are obtained at N distances;

[0007] Using a super-resolution algorithm, calculate the information of the one histogram to obtain one super-precision time bin within the Mth time bin, and obtain N super-precision distances through N super-precision time bins; wherein, the super-precision time bin is related to the number of photons corresponding to the (M - 1)th time bin, the Mth time bin, and the (M + 1)th time bin within the one histogram;

[0008] By calculating the difference between the N super-precision distances and the actual distances between the depth camera and the target object corresponding thereto, N super-precision error values are obtained;

[0009] Average the N super-precision error values to obtain a super-precision error mean value.

[0010] Optionally, the method further includes:

[0011] If there are W pixels, obtain W super-precision error means corresponding to the W pixels; where W is a positive integer greater than or equal to 2.

[0012] Optionally, the fixed length corresponding to any one time bin is associated with a time-to-digital converter (TDC) in the depth camera.

[0013] Optionally, the super-resolution algorithm includes the centroid method.

[0014] Optionally, the calculation formula of the centroid method is where TOF_BIN is the super-precision time bin, h1 is the number of photons corresponding to the (M - 1)th time bin, h2 is the number of photons corresponding to the Mth time bin, h3 is the number of photons corresponding to the (M + 1)th time bin, and x is the Mth time bin.

[0015] Optionally, for any one pixel, the super-precision error means of all its super-precision time bins are the same.

[0016] The second aspect of the embodiments of the present application provides a distance measurement method based on a depth camera, including:

[0017] When measuring a target object, obtain a ranging histogram of any one pixel;

[0018] Inside the measurement time bin, according to the super-resolution algorithm, obtain a measurement super-precision time bin; where the measurement time bin corresponds to the peak value of the ranging histogram;

[0019] Find the super-precision error mean corresponding to any one pixel, and calculate the difference between the measurement super-precision time bin and the super-precision error mean; where the difference is the depth value of any one pixel; the super-precision error mean is obtained by any one of the distance calibration methods described in the first aspect.

[0020] Optionally, the super-resolution algorithm includes the centroid method.

[0021] The third aspect of the embodiments of the present application provides an electronic device, including: a depth camera, and performing the distance calibration method and the distance measurement method based on the depth camera described in the first aspect and the second aspect.

[0022] The fourth aspect of the embodiments of the present application provides a computer-readable storage medium, where the computer-readable storage medium includes instructions, and when the instructions run on a depth camera, the depth camera is caused to perform the distance calibration method and the distance measurement method based on the depth camera described in the first aspect and the second aspect.

[0023] A fifth aspect of the embodiments of the present application provides a computer program product, which includes instructions that, when running on a depth camera, cause the depth camera to execute the distance calibration method and distance measurement method based on the depth camera described in the first aspect and the second aspect.

[0024] As can be seen from the above technical solutions, the embodiments of the present application have the following advantages: Through a distance calibration method based on a depth camera disclosed in the embodiments of the present application, a finer-grained distance value is obtained by using a super-resolution algorithm, thereby reducing the quantization error caused by the limitation of the TDC resolution of the depth camera itself. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments recorded in the present application, and those of ordinary skill in the art can also obtain other drawings based on these drawings.

[0026] Figure 1 It is a schematic flowchart of a distance calibration method based on a depth camera disclosed in the embodiments of the present application;

[0027] Figure 2 It is a schematic flowchart of a distance measurement method based on a depth camera disclosed in the embodiments of the present application;

[0028] Figure 3 It is a schematic logic diagram of the resolution of a time-to-digital converter disclosed in the embodiments of the present application;

[0029] Figure 4 It is a schematic logic diagram of a super-resolution algorithm disclosed in the embodiments of the present application;

[0030] Figure 5 It is a schematic diagram of the relationship between the number of photons corresponding to a time bin when the distance moves disclosed in the embodiments of the present application;

[0031] Figure 6 It is a schematic diagram of the relationship between the actual distance, the ultra-precision error value, and the calibrated distance disclosed in the embodiments of the present application;

[0032] Figure 7 It is a schematic diagram of the relationship between another actual distance and the ultra-precision error value disclosed in the embodiments of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0033] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative efforts shall fall within the scope of protection of the present application.

[0034] Please refer to Figure 1 , Figure 1 which is a schematic flowchart of a distance calibration method based on a depth camera disclosed in an embodiment of the present application. It includes steps 101 - 104.

[0035] 101. For any pixel, within the fixed length corresponding to the M - th time bin, adjust the distance between the depth camera and the target object from the initial point to the end point at a predetermined step size.

[0036] As mentioned in the background technical problems above, since the resolution of the Bin width of the TDC will affect the ranging accuracy, and a single Bin width corresponds to a distance period, there will still be oscillations in the depth accuracy obtained within this distance period. Therefore, in order to reduce the oscillation amplitude, it is necessary to collect different distances within the distance period according to the minimum distance resolution. During the acquisition process of the depth camera, since the distance calibration methods between different pixels are similar, in this embodiment, a single pixel is used as an example for illustration. In other words, other pixels can also complete pixel calibration according to the distance calibration method described in this embodiment.

[0037] Specifically, the DTOF module can be placed parallel to the target object first, and at the same time, the echo intensity is controlled to ensure acquisition without pileup. It should be noted that because too strong echo energy will cause the Pileup phenomenon (multiple particles or signals reach the detector in a very short time, resulting in signal overlap and making it difficult to distinguish individual events), which will affect the ranging accuracy of the SPAD to a certain extent. Therefore, during the distance calibration process, it is necessary to ensure that the echo energy does not cause Pileup, that is, the depth camera needs to control the energy intensity of the transmitted signal. For the convenience of description, this will not be elaborated further hereinafter.

[0038] Specifically, for any pixel, within the fixed length corresponding to a certain time bin, taking the time length of a single Bin of the TDC (the time interval measured by the TDC) / distance width (the spatial distance calculated from the time length, usually calculated from the time length and the known speed, such as the speed of light) as a distance period, and then using the set minimum distance resolution as the preset step size to collect data. For example, please refer to Figure 3 , Figure 3 which is a logical schematic diagram of the resolution of a time - to - digital converter disclosed in an embodiment of the present application.Figure 3 As shown, the abscissa is the label of the Time Bin, and the ordinate is the number of photons. Among them, one Bin of the TDC is 1 ns, and when converted to distance resolution, it is 150 mm, that is, 1 bin = 150 mm. It should also be noted that the time bin is a commonly used concept in time measurement and signal processing, which refers to dividing the continuous time axis into discrete time intervals (or "bins"), and each interval corresponds to a specific time range. The width of the time bin (i.e., the time length of each interval) depends on the specific application requirements and measurement accuracy.

[0039] Furthermore, in this embodiment, reference can be continued to Figure 3 , in order to use the super-resolution algorithm to reduce the quantization error, it is then necessary to calibrate the distance within a fixed length. For example, in one implementable technical solution, within the fixed length (150 mm) corresponding to the Mth time bin (3 bins), from the starting point to the end point (for example, from the starting point 301 mm corresponding to bin3 to the end point 450 mm), and then gradually adjust the distance between the depth camera and the target object in steps of 1 mm. Since the step size is 1 mm and the fixed length is 150 mm during the gradual movement of the distance, N (in the example, corresponding to 150) histograms can be collected at N (in the example, corresponding to 150) distances. Thus, the distance calibration is completed.

[0040] In other implementable technical solutions, it is also possible to determine which Bin position the signal emitted by the depth camera is located in. Specifically, it can be based on the position of the TOF where the maximum number of photons (count) is located. For example, in Figure 3 , it is the 3rd bin in the figure, and at this time, the target object is located between 301 - 450 mm.

[0041] 102. Use the super-resolution algorithm to calculate a histogram information to obtain a super-precision time bin within the Mth time bin, and obtain N super-precision distances through N super-precision time bins.

[0042] Then, the super-resolution algorithm can be calculated for the Bin obtained by the TDC, so as to calculate one of the histogram information and obtain a super-precision time bin within the Mth time bin. Further, since the time bin corresponds to 3 bins, furthermore, this super-precision time bin corresponds to the M - 1th, Mth, and M + 1th time bins in the histogram, and each time bin corresponds to the number of photons collected.

[0043] In one specific embodiment, reference can be made to Figure 5 , Figure 5 This is a schematic diagram of the relationship between the number of photons corresponding to the time bin when the distance moves in the embodiment of the present application. From Figure 5It can be seen that the highest peak corresponds to the signal peak. The number of photons at the signal peak (the M-th time bin) is h2, the number of photons in the bin before the signal peak (the (M - 1)-th time bin) is h1, and the number of photons in the bin after the signal peak (the (M + 1)-th time bin) is h3. When the distance gradually moves (for example, from the initial point 301 mm corresponding to bin3 to the end point 450 mm), the relative relationship among h1, h2, and h3 shows a periodic movement, and h2 > h1 (or h3). Since the relative relationship among h1, h2, and h3 shows a periodic change in terms of the time length / distance length of multiple bins, the super-resolution algorithm (centroid method) will introduce periodic errors.

[0044] Furthermore, the method shown in step 102 can also be used to calculate the N histogram information, and then N super-precision time bins can be obtained. The specific steps are not elaborated here.

[0045] At this time, reference can be made to Figure 4 , Figure 4 which is a logical schematic diagram of a super-resolution algorithm disclosed in an embodiment of the present application.

[0046] As can be seen from Figure 4 , the abscissa is the label of the time bin, and the ordinate is the number of photons. Among them, using the centroid method, there is

[0047]

[0048] TOF_BIN is the label of the super-precision time bin, h1 is the number of photons corresponding to the (M - 1)-th time bin, h2 is the number of photons corresponding to the M-th time bin, h3 is the number of photons corresponding to the (M + 1)-th time bin, and x is the label of the M-th time bin.

[0049] Thus, in Figure 4 , it is -0.5 + 3 = 2.5. Furthermore, 2.5 * 150 mm = 375 mm.

[0050] 103. By calculating the difference between the N super-precision distances and the actual distance between the depth camera and the target object corresponding thereto, N super-precision error values are obtained.

[0051] During the process of adjusting the distance between the depth camera and the target object at a predetermined step size, the actual distance between the current depth camera and the target object can be determined. At the same time, by combining the difference between the N super-precision distances obtained in step 102 and the corresponding actual distance, N super-precision error values can be obtained.

[0052] In one specific embodiment, since N is 150, then there are 150 points on the curve at this time, and the super-precision distances are connected together to form a curve. For easy understanding, reference can be made to Figure 6 , Figure 6Schematic diagram of the relationship between the actual distance, the ultra-precision error value, and the calibrated distance disclosed in the embodiments of the present application. It can be seen from Figure 6 that for a certain pixel point, the error offset value at 300 mm is 200 mm = 500 - 300, the error offset value at a distance of 375 mm is 300 mm = 675 - 375, and the error offset value at a distance of 450 mm is 200 mm = 650 - 450.

[0053] If the error value at a single distance is used as the compensation value, 1) taking the error value of 200 mm at 300 mm as the compensation value, at a distance of 300 mm, the compensated value of this pixel point is 500 - 200 = 300 mm, and the error is 0; at a distance of 375 mm, the compensated value of this pixel point is 675 - 200 = 475 mm, and the error is 475 - 375 = 100; at a distance of 450 mm, the compensated value of this pixel point is 650 - 200 = 450 mm, and the error is 0. 2) Similarly, taking the error value of 300 mm at 375 mm as the compensation value, at a distance of 300 mm, the compensated value of this pixel point is 500 - 300 = 200 mm, and the error is 200 - 300 = -100 mm; at a distance of 375 mm, the compensated value of this pixel point is 675 - 300 = 375 mm, and the error is 0; at a distance of 450 mm, the compensated value of this pixel point is 650 - 300 = 350 mm, and the error is 350 - 450 = -100. Therefore, if the error value at a single distance is used as the calibrated compensation value, the error range will be enlarged. Further, reference can be made to Figure 7 . In Figure 7 , the abscissa is the actual distance, and the ordinate is the difference between the distance measured by TOF and the actual distance, that is, the error value offset value, which is the difference between the TOF distance (ultra-precision distance) and the actual distance. Through experimental measurement and calibration of multiple distances, it can be known that the changing trends of N ultra-precision error values in the Mth time bin and in other time bins are the same.

[0054] In one of the feasible technical solutions, the ultra-precision error value can also be obtained by traversing the depth information within the distance period of each pixel point and taking the difference from the actual distance.

[0055] Furthermore, combining the experimental data, it can be known that within one time bin, when choosing the error at a single distance for compensation, it is easy for the error range to be within [0, 100] or [-100, 0] at the full distance (for example, 300 - 450 mm). However, when choosing to average the error values at multiple distances and using the error mean for compensation, the error range at the full distance can be controlled within [-50, 50], indicating that after compensation, the output TOF distance is closer to the actual distance.

[0056] 104. Average the N ultra-precision error values to obtain the ultra-precision error mean value.

[0057] After obtaining the ultra-precision error value corresponding to each pixel point, the average value of the N ultra-precision error values can be calculated to obtain the ultra-precision error mean value, and this ultra-precision error mean value is used as the error calibration value (compensation) of the depth camera, thereby completing the distance calibration.

[0058] In one specific embodiment, for a pixel, the ultra-precision error mean values of all its time bins are the same, and the details are not elaborated here.

[0059] In other implementable technical solutions, the maximum ultra-precision error value and the minimum ultra-precision error value of each pixel point within a distance period can also be obtained, and then the average value of the maximum ultra-precision error value and the minimum ultra-precision error value is calculated as the ultra-precision error value, and then the ultra-precision error mean value is calculated.

[0060] Through a distance calibration method based on a depth camera disclosed in this embodiment, the error performance between the tof distance and the actual distance is obtained through multi-distance tests within a period, then the errors are averaged, and then the measured value by tof is subtracted from the error mean value, thereby reducing the quantization error brought by the ultra-precision time bins calculated by the ultra-precision algorithm and improving the measurement accuracy.

[0061] In practical applications, combined with Figure 1 the embodiments shown, accurate measurement of the distance can be achieved. For easy understanding, please refer to Figure 2 , Figure 2 which is a schematic flowchart of a distance measurement method based on a depth camera disclosed in an embodiment of the present application. It includes steps 201 - step 203.

[0062] 201. When measuring a target object, obtain the ranging histogram of any one pixel.

[0063] It should be noted that in steps 201 - step 203 of this embodiment, there is no need to perform distance calibration on the depth camera again, and the distance calibration results can be referred to Figure 1 the embodiments shown.

[0064] Specifically, when the depth camera performs depth measurement on the target object, the ranging histograms of multiple pixels can be obtained. Since the processing methods of each pixel are similar, in this embodiment, any one of the pixels is described in detail.

[0065] Specifically, a depth camera can scan a scene using infrared light or laser light, record the depth information of each pixel, and generate a depth map. A depth map is a two-dimensional array in which each element represents the depth value of the corresponding pixel. These values ​​are usually in millimeters or centimeters. Determine the pixel area that needs to be analyzed, which can be a single pixel or an area.

[0066] 202. Inside the measurement time box, a measurement super-precision time box is obtained according to the super-resolution algorithm.

[0067] Since the peak value of the range histogram corresponds to the measurement time bin (bin3), a measurement super-precision time bin bin2.5 can be obtained according to the super-resolution algorithm within the measurement time bin (for specific acquisition methods, please refer to Figure 1 Step 102 is shown in the embodiment).

[0068] It should be noted that the super-resolution algorithm is the centroid method. The specific method for measuring super-precision time can be found in Figure 1 Step 102 is an embodiment shown.

[0069] 203. Find the ultra-precision error mean value corresponding to the pixel, and calculate the difference between the measured ultra-precision time bin and the ultra-precision error mean value.

[0070] After obtaining the measured ultra-precision time bin of any pixel miniature, the ultra-precision error mean of the pixel can be found, and then the difference between the measured ultra-precision time bin and the ultra-precision error mean, i.e. the compensated tof value, can be calculated. It should be noted that since the units of the measured ultra-precision time bin and the ultra-precision error mean are different, it is necessary to convert the measured ultra-precision time bin into units, specifically using the resolution of the TDC of the depth camera as a reference. For example, combined with Figure 1 In the embodiment shown, assuming that the ultra-precision time bin is 2.5 bins, 2.5*150 mm=375 mm. The difference between the ultra-precision error mean and 375 mm can be calculated.

[0071] By using a distance measurement method based on a depth camera disclosed in this embodiment, a super-resolution algorithm is used to obtain a distance value with finer granularity, thereby reducing the quantization error caused by the limitation of the time resolution of the depth camera itself.

[0072] It should be understood that although the steps in the flowcharts involved in the above-described embodiments are shown sequentially in the direction of the arrows, these steps are not necessarily executed sequentially in the order indicated by the arrows. Unless clearly stated herein, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-described embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or in turn with at least a part of other steps or steps or stages in other steps.

[0073] The embodiments of the present application also provide a computer-readable storage medium. The computer-readable storage medium includes instructions. When the instructions run on a computer, the computer is caused to execute the foregoing Figure 1 distance calibration method and distance measurement method based on a depth camera in the illustrated embodiments.

[0074] The embodiments of the present application also provide a computer program product containing instructions. When the computer program product runs on a computer, the computer is caused to execute the foregoing Figure 1 distance calibration method and distance measurement method based on a depth camera in the illustrated embodiments.

[0075] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the above-described systems, devices, and units can refer to the corresponding processes in the foregoing method embodiments, and will not be described herein again.

[0076] In several embodiments provided by the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, indirect couplings or communication connections of devices or units, and can be in electrical, mechanical, or other forms.

[0077] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place, or can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0078] In addition, each functional unit in various embodiments of the present application may be integrated into one processing unit, may exist separately as individual physical units, or two or more units may be integrated into one unit. The above-mentioned integrated units may be implemented in the form of hardware or in the form of software functional units.

[0079] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it may be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, may be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present application. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM, read-only memory), random access memories (RAM, random access memory), magnetic disks, or optical discs.

Claims

1. A distance calibration method based on a depth camera, characterized in that: The method comprises: For any pixel, within the fixed length corresponding to the Mth time bin, the distance between the depth camera and the target object is adjusted from the initial point to the end point with a predetermined step length; wherein, a histogram is obtained at one distance, and N histograms are obtained at N distances; Using a super-resolution algorithm, the one histogram information is calculated to obtain a super-precision time bin in the Mth time bin, and N super-precision distances are obtained through N super-precision time bins; wherein the super-precision time bin is related to the number of photons corresponding to the M-1th time bin, the Mth time bin, and the M+1th time bin in the one histogram; Obtaining N ultra-precision error values ​​by calculating the difference between the N ultra-precision distances and the corresponding actual distances between the depth camera and the target object; The N ultra-precision error values ​​are averaged to obtain an ultra-precision error mean.

2. The calibration method according to claim 1, characterized in that: The method further comprises: If there are W pixels, W ultra-precision error means corresponding to the W pixels are obtained; wherein W is a positive integer greater than or equal to 2.

3. The calibration method according to claim 1, characterized in that: The fixed length corresponding to any time bin is associated with a time-to-digital converter TDC located in the depth camera.

4. The calibration method according to claim 1, characterized in that: The super-resolution algorithm includes a centroid method.

5. The calibration method according to claim 4, characterized in that: The calculation formula of the centroid method is: Among them, the TOF_BIN is the ultra-precision time bin, the h1 is the number of photons corresponding to the M-1th time bin, the h2 is the number of photons corresponding to the Mth time bin, the h3 is the number of photons corresponding to the M+1th time bin, and the x is the Mth time bin.

6. The calibration method according to claim 1, characterized in that: For any one pixel, the mean values ​​of the super-precision errors of all super-precision time bins are the same.

7. A distance measurement method based on a depth camera, characterized in that: The measuring method comprises: When measuring the target object, the distance measurement histogram of any pixel is obtained; Inside the measurement time bin, a measurement super-precision time bin is obtained according to a super-resolution algorithm; wherein the measurement time bin corresponds to the peak value of the range-finding histogram; Find the ultra-precision error mean corresponding to any one of the pixels, and calculate the difference between the measured ultra-precision time bin and the ultra-precision error mean; wherein the difference is the depth value of any one of the pixels; the ultra-precision error mean is obtained by any one of the distance calibration methods described in claims 1-6.

8. The measuring method according to claim 1, characterized in that: The super-resolution algorithm includes a centroid method.

9. An electronic device, characterized in that: The method comprises a depth camera, wherein the depth camera executes the distance calibration method or the distance measurement method described in claims 1 to 8.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium comprises instructions, and when the instructions are executed on a depth camera, the depth camera performs the distance calibration method or the distance measurement method as described in claims 1 to 8.

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