CN115601446A - Linearity optimization method, device and electronic equipment of iToF camera - Google Patents

Abstract

The application relates to a linearity optimization method and device for an iToF camera. The method comprises the following steps: adjusting, by the image sensor, a duty cycle of the emitted light waveform; adjusting a delay value at a preset step length under the current duty ratio, and acquiring a virtual real phase and a target measurement phase corresponding to a corresponding virtual real distance after adjusting the delay value each time; after the delay value of the preset period is adjusted, acquiring linear parameters of the current duty ratio according to all the acquired virtual real phases and the corresponding target measurement phases; when the current duty ratio is smaller than a preset threshold value, acquiring the duty ratio corresponding to the optimal value in all the linear parameters as the optimal duty ratio; and taking the optimal duty ratio as a setting parameter of the image sensor to finish the nonlinear calibration of the iToF camera and realize the linearity optimization of the iToF camera. The method and the device can reduce the nonlinearity of the system, save the calibration cost and improve the linearity of the image sensor measurement.

Description

Linearity optimization method, device and electronic equipment of iToF camera

technical field

The present application relates to the technical field of ToF distance measurement, in particular to a linearity optimization method, device, and electronic equipment of an iToF camera.

Background technique

Binocular ranging, structured light and Time-of-Flight (ToF) are the three mainstream 3D imaging technologies today. Among them, ToF has been gradually applied due to its simple principle, simple and stable structure, and long measurement distance. In the fields of gesture recognition, 3D modeling, unmanned driving and machine vision. The working principle of ToF technology is: using an external light source (VCSEL or LED, etc.) The time difference or phase difference between the emitted light and reflected light is used to obtain the depth/distance of the object from the camera. Among them, the method of calculating the distance by the time difference is called the pulsed method (Pulsed ToF), and the method of calculating the distance by the phase difference is called the continuous-wave method (Continuous-Wave ToF).

Indirect Time-of-Flight (iToF for short) refers to the indirect measurement of the time-of-flight of light by measuring the phase shift. As shown in FIG. 1 , the iToF camera controls the light emitting module 12 to actively emit a modulated light signal through a modulation module (modulation) 11; The photosensitive pixel array unit 13 of the image sensor samples; and then calculates the distance of the target object according to the phase shift (Phase shift) of the emitted light and reflected light. The light-emitting module 12, such as VCSEL, infrared emitter (IR emitter) or LED, etc., is usually driven by a modulated square wave generated by the sensor, but since the light waveform gradually approaches a sine wave with the increase of the modulation frequency, the square wave High-order harmonics in the wave will bring periodic errors to the measurement, as shown in Figure 2. There is a wiggling error in the measurement process due to the presence of aliased harmonics in the relevant waveform, as shown in FIG. 3 .

Establishing a nonlinear error lookup table directly through calibration can better correct the swing error in principle; however, since multiple distance measurements are required and each measurement requires multiple averages to remove random noise well, it will bring calibration costs increase. The non-linear error caused by multiple harmonics can be compensated by multiple measurements (greater than four phases); but since iToF cameras are usually global exposure, if a depth map is obtained through multiple measurements, it is equivalent to a depth map The map is elongated in time, which will cause smears for some moving objects; in addition, due to the increase in the measurement data required for a depth map, the load on the system is also increased, resulting in an increase in the dynamic power consumption of the system.

Therefore, how to reduce the calibration cost of iToF cameras and improve the linearity of measurement is an urgent technical problem to be solved.

Contents of the invention

The purpose of this application is to provide a linearity optimization method, device, and electronic equipment for an iToF camera, which are used to solve the problems of high calibration cost and high dynamic power consumption of the system in the existing iToF camera, so as to save iToF camera calibration. At the same time of reducing the cost, quickly select the appropriate duty cycle and improve the linearity of the measurement.

In order to achieve the above purpose, the present application provides a method for optimizing the linearity of an iToF camera, including the following steps: adjusting the duty cycle of the emitted light waveform through the image sensor of the iToF camera; Delay value, and after each adjustment of the delay value, obtain the virtual real phase corresponding to the corresponding virtual real distance and the target measurement phase; after completing the delay value adjustment of the preset cycle, according to all obtained virtual real phases The target measurement phase obtains the linear parameter of the current duty cycle; when the current duty cycle is less than the preset threshold value, obtains the duty cycle corresponding to the optimal value of all linear parameters as the optimal duty cycle; and uses the maximum The optimal duty cycle is used as a setting parameter of the image sensor to complete the nonlinear calibration of the iToF camera and realize the linearity optimization of the iToF camera.

In some embodiments, the method further includes: selecting a plurality of image sensors, and obtaining an optimal duty cycle of each of the image sensors; obtaining a target duty cycle according to all the optimal duty cycles, wherein, The target duty cycle is a median or average value of all the optimal duty cycles; and backfilling the target duty cycle into settings of all the image sensors.

In order to achieve the above object, the present application also provides a linearity optimization device for an iToF camera, including: an adjustment module, used to adjust the duty cycle of the emitted light waveform through the image sensor; a first acquisition module, used for the current duty cycle Next, adjust the delay value with the preset step length, and obtain the virtual real phase corresponding to the corresponding virtual real distance and the target measurement phase after each adjustment of the delay value; the second acquisition module is used to complete the delay of the preset period After the time value is adjusted, obtain the linear parameters of the current duty cycle according to all obtained virtual real phases and corresponding target measurement phases; the third obtaining module is used to obtain all linear parameters when the current duty cycle is less than the preset threshold The duty ratio corresponding to the optimal value in is used as the optimal duty ratio; and an optimization module is used to use the optimal duty ratio as the setting parameter of the image sensor to complete the nonlinear calibration of the iToF camera and realize the iToF Camera linearity optimization.

In some embodiments, the second acquiring module is further used to calculate the absolute value of the difference between the virtual real phase and the target measured phase obtained after each adjustment of the delay value, and calculate all the phases within the preset period The sum of the absolute values of the differences, using the sum as the linear parameter of the current duty ratio; the third acquisition module is further used to obtain the minimum value of the sums corresponding to all duty ratios as the optimal value.

To achieve the above object, the present application also provides an electronic device, including a memory, a processor, and a computer-executable program stored on the memory and operable on the processor, and the processor executes the computer program. The steps of implementing the linearity optimization method of the iToF camera as described in this application can be implemented when the program is executable.

The linearity optimization method and device of the iToF camera provided by this application, the application adjusts the duty cycle of the optical waveform through the image sensor, which can suppress the aliasing harmonic component to improve the linearity of the image sensor measurement, reduce the nonlinearity of the system, and save Reduce the calibration time required for wiggling correction when calculating the phase, saving calibration costs. Furthermore, the delay is added by the delay unit to simulate the real distance movement, and the optimal duty cycle is found through constraint adjustment to minimize the nonlinearity and improve the linearity of the image sensor measurement. Randomly select several modules in the same batch of image sensor modules to perform duty cycle optimization search at the same time, determine the optimal solution according to the median or average value of the optimal duty cycle of all image sensor modules, and backfill to this In the setting of all image sensor modules in a batch, the nonlinear calibration of all image sensor modules in the batch can be completed, which is conducive to improving the calibration accuracy and saving the calibration time of the same batch of image sensor modules .

Description of drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings that need to be used in the description of the embodiments. Apparently, the drawings in the following description are only some embodiments of the present application, and those skilled in the art can also obtain other drawings according to these drawings without creative efforts.

Figure 1 is a schematic diagram of the iToF imaging principle;

Figure 2 shows the periodic error caused by high-order harmonics;

Fig. 3 is the swing error existing in the measurement process;

Fig. 4 is a schematic diagram of the steps of the linearity optimization method of the iToF camera provided by an embodiment of the present application;

FIG. 5 is a phase distribution diagram under different duty ratios provided by an embodiment of the present application;

Figure 6 is a schematic diagram of the deviation between modulation and demodulation;

7 is a schematic diagram of simulating a virtual calibration board by a delay circuit;

FIG. 8 is a flowchart of a linearity optimization method for an iToF camera provided by an embodiment of the present application;

FIG. 9 is a structural block diagram of an iToF camera linearity optimization device provided by an embodiment of the present application.

detailed description

The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings. Apparently, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts belong to the scope of protection of this application.

An embodiment of the present application provides a linearity optimization method for an iToF camera.

Please refer to Figures 4 to 7 together, where Figure 4 is a schematic diagram of the steps of an iToF camera linearity optimization method provided by an embodiment of the present application, and Figure 5 is a phase distribution diagram under different duty ratios provided by an embodiment of the present application , FIG. 6 is a schematic diagram of the deviation between modulation and demodulation, and FIG. 7 is a schematic diagram of simulating a virtual calibration board through a delay circuit.

As shown in Figure 4, the linearity optimization method of the iToF camera described in this embodiment includes the following steps: S1, adjust the duty cycle of the emitted light waveform through the image sensor of the iToF camera; Set the step length to adjust the delay value, and obtain the virtual real phase corresponding to the corresponding virtual real distance and the target measurement phase after each adjustment of the delay value; S3. After completing the delay value adjustment of the preset cycle, according to all acquired The virtual real phase and the corresponding target measurement phase obtain the linear parameter of the current duty cycle; S4. When the current duty cycle is less than the preset threshold, obtain the duty cycle corresponding to the optimal value of all linear parameters as the optimal duty cycle Duty ratio; and S5, using the optimal duty ratio as a setting parameter of the image sensor to complete the nonlinear calibration of the iToF camera and realize the linearity optimization of the iToF camera.

Regarding step S1, the duty cycle of the emitted light waveform is adjusted by the image sensor of the iToF camera. Specifically, the light-emitting module is usually driven by a square wave generated by the image sensor of the iToF camera. The high-order harmonics in the square wave are also the main factor causing the nonlinearity of the measurement. The nonlinearity presents periodic changes (as shown in Figure 2 ). As the modulation frequency increases, the optical waveform gradually approaches a sine wave, and the optical waveform affects the linearity of the calculated phase. Therefore, by adjusting the duty cycle of the optical waveform through the image sensor, the aliased harmonic components can be suppressed to improve the linearity of the image sensor measurement, reduce the nonlinearity of the system, and save the need for wiggling correction when calculating the phase. Calibration time saves calibration cost. Furthermore, the delay is added by the delay unit to simulate the real distance movement, and the optimal duty cycle is found through constraint adjustment to minimize the nonlinearity and improve the linearity of the image sensor measurement.

The modulation waveform is close to a sine wave, and the following calculation formula can be used to calculate the phase according to the sine wave:

phase = arctan(I/Q);

Among them, I=(Q ₃ -Q ₄ ), Q=(Q ₁ -Q ₂ ); Q ₁ is the measured phase when the phase delay (phase delay) is 0°, and Q ₂ is when the phase delay is 90° Q ₃ is the measured phase when the phase delay is 180°, and Q ₄ is the measured phase when the phase delay is 270°. I and Q are orthogonal vectors, and ideally I and Q are distributed on a circle. The optical waveform affects the linearity of the calculated phase. Therefore, adjusting the duty cycle of the optical waveform through the image sensor can reduce the nonlinearity of the system. By adjusting the duty cycle, as the duty cycle decreases, the I and Q distributions are closer to a circle, as shown in Figure 5.

The following calculation formula can be used to obtain the depth according to the phase:

d=c*(phase/(2*f*phase_max))+d_max*n;

Among them, c is the speed of light, phase is the measurement phase, phase_max is a phase period (for example, 2π), f is the modulation frequency of the emitted light, d_max=(c/2)*(1/f), n is the preset number of frames and n ∈ [0, 1...].

Regarding step S2, the delay value is adjusted with a preset step under the current duty cycle, and the virtual real phase corresponding to the corresponding virtual real distance and the target measurement phase are obtained after each adjustment of the delay value. Specifically, the preset step size may be any one of π/4, π/8, and π/16. In order to improve the calibration accuracy, the preset step size can also be a smaller value; the delay value adjustment step can be set according to the comprehensive consideration of calibration accuracy and calibration time.

Due to reasons such as circuits and manufacturing processes, ideally there is a certain fixed skew (skew) in modulation and demodulation, as shown in FIG. 6 . These fixed deviations can be repaired by adding a delay unit in the modulation or demodulation path and setting a certain delay parameter. Therefore, the real distance is simulated by setting the delay value, that is, a virtual real distance is generated, as shown in FIG. 7 . Furthermore, the virtual real phase corresponding to the corresponding virtual real distance and the target measurement phase can be acquired.

In some embodiments, the step of obtaining the virtual real phase corresponding to the corresponding virtual real distance and the target measurement phase after each adjustment of the delay value in step S2 further includes: 1) Adding a delay unit to the modulation or demodulation path , and simulate the real distance by setting the delay value; 2) form a current virtual real distance after each adjustment of the delay value, wherein, the current virtual real distance corresponds to a virtual real phase; and 3) acquire the preset frame depth image , and performing a time-domain average on the measurement phases of the pixel center points of all the depth images to obtain a corresponding average measurement phase as the target measurement phase corresponding to the current virtual real distance.

The measurement phase of each pixel in the depth image acquired by the image sensor contains a certain amount of random noise. Since the transfer function of each pixel is consistent, the time-domain average that satisfies the preset number of times converges to a fixed value. Therefore, the random noise is removed by performing time-domain averaging on the measured phases of the pixel center points of all the depth images. Among them, the number of frames of the depth image to be captured can be set according to the calibration accuracy requirements for temporal averaging.

For example, m frames of depth images are captured by the image sensor, and the random noise converges to a fixed value (for example, 0) after the time-domain average is performed on the measured phases of the pixel center points of the m frames of depth images. Specifically, the obtained average measurement phase satisfies the following formula:

phase _i = phase+ε _m ;

Among them, phase _i is the i-th measured phase of the pixel center point, phase is the theoretical measurement value of the pixel point after the random noise is removed, and ε _m is the fixed value to which the random noise converges after m times of time-domain averaging. Preferably, based on the normal distribution form of noise, ε _m =0.

Regarding step S3, after the delay value adjustment of the preset period is completed, the linear parameter of the current duty cycle is obtained according to all obtained virtual real phases and corresponding target measurement phases. Specifically, the preset period is 2π (that is, a complete period). Under the current duty cycle, the linear parameter of the current duty cycle can be obtained by adjusting the delay value to obtain the virtual real phase of a complete cycle and the corresponding target measurement phase.

In some embodiments, the step of obtaining the linear parameter of the current duty cycle according to all obtained virtual real phases and corresponding target measurement phases described in step S3 further includes: 1) obtaining The absolute value of the difference between the virtual real phase and the target measured phase; and 2) calculating the sum of all the absolute values of the differences within the preset period, and using the sum as a linear parameter of the current duty cycle.

After the delay value adjustment of the preset period is completed under the current duty cycle, a set of measured phase measure_avg[i] and the virtual real phase real[i] under the virtual real distance are obtained. The linear parameter duty_cycle of the current duty cycle can be obtained by the following formula:

duty_cycle = sum(abs(measure_avg[i]-real[i])).

Regarding step S4, when the current duty cycle is smaller than the preset threshold, the duty cycle corresponding to the optimal value among all the linear parameters is obtained as the optimal duty cycle. Specifically, the system presets a duty ratio threshold, that is, a minimum duty ratio that can be set by the system. By obtaining all the linear parameters before adjusting to the preset threshold and constraining the optimal solution, the optimal duty cycle of the system can be obtained.

In some embodiments, the constraint condition for obtaining the optimal duty cycle is the sum of the absolute values of the differences between the virtual real phase and the target measured phase at each duty cycle. Specifically, the optimal solution of the duty cycle can be obtained by the following formula:

duty-cycle _optimal = arg_min(sum(abs(measure_avg[i]-real[i]))).

That is, when the absolute value of the deviation between the target measurement phase and the virtual real phase is the smallest under a certain duty cycle, the duty cycle is the optimal duty cycle.

Regarding step S5, the optimal duty cycle is used as the setting parameter of the image sensor to complete the nonlinear calibration of the iToF camera and realize the linearity optimization of the iToF camera. Specifically, after obtaining the optimal duty cycle, setting the duty cycle parameter of the image sensor to the optimal duty cycle can minimize the nonlinearity and improve the linearity of the image sensor measurement. In this embodiment, the traditional wiggling calibration is replaced by adjusting the duty cycle of the emitted light waveform, which saves the calibration time required for wiggling correction when calculating the phase.

In some embodiments, the method further includes: 1) selecting a plurality of image sensors, and obtaining the optimal duty cycle of each of the image sensors; 2) obtaining the target duty cycle according to all the optimal duty cycles , wherein the target duty cycle is the median or average value of all the optimal duty cycles; and 3) backfilling the target duty cycle into the settings of all the image sensors. Specifically, considering the influence of random error factors in a single image sensor module, multiple image sensor modules are selected to search for duty cycle optimization at the same time, and according to the optimal duty ratio of all image sensor modules Value or average value to determine the optimal solution to complete the nonlinear calibration, which is conducive to improving the calibration accuracy.

Following the above embodiment, the step of selecting a plurality of image sensors further includes: randomly selecting a plurality of image sensors from the same batch of image sensors; and backfilling the target duty cycle to all the image sensors The step of setting further includes: backfilling the target duty cycle into the settings of all image sensors of the batch. Specifically, in the same batch of image sensor modules, several modules are randomly selected for duty cycle optimization search at the same time, and the optimal solution is determined according to the median or average value of the optimal duty cycle of all image sensor modules, Backfilling in the settings of all the image sensor modules of the batch can complete the non-linear calibration of all the image sensor modules of the batch, saving the calibration time of the image sensor modules of the same batch.

The flow of the linearity optimization method for the iToF camera of the present application will be further explained below in conjunction with FIG. 8 . The specific process of this embodiment is: 1) select n image sensor modules from the same batch of modules; 2) adjust the duty cycle; 3) adjust the delay value in steps of π/4; 4) capture m frames For the depth image, time-domain average is performed on the measurement phases of the pixel center points of all the depth images to obtain the corresponding average measurement phase, which is used as the target measurement phase corresponding to the current virtual real distance, that is, to obtain the current delay value corresponding to the center point Measure the phase; 5) Obtain the measurement phase corresponding to the center point under different delay values, that is, obtain the array measure_avg[i]; 6) Obtain the virtual real phase according to the virtual real distance corresponding to the delay value, that is, obtain real[i]; 7 ) to determine whether the current delay value is less than or equal to the preset period (for example, 2π), that is, to determine delay<=2π; if delay<=2π, return to continue the delay value increase and subsequent operations; if delay>2π, the current duty cycle The adjustment of the delay value under the ratio is completed; 8) With Sum(abs(measure_avg[i])-real[i])) as the constraint condition, the minimum duty ratio under all duty ratios is the desired one.

According to the above content, it can be seen that the present application adjusts the duty cycle of the optical waveform through the image sensor, which can reduce the nonlinearity of the system, suppress the aliasing harmonic components to improve the linearity of the image sensor measurement, reduce the nonlinearity of the system, and save Reduce the calibration time required for wiggling correction when calculating the phase, saving calibration costs. Furthermore, the delay is added by the delay unit to simulate the real distance movement, and the optimal duty cycle is found through constraint adjustment to minimize the nonlinearity and improve the linearity of the image sensor measurement. Randomly select several modules in the same batch of image sensor modules to perform duty cycle optimization search at the same time, determine the optimal solution according to the median or average value of the optimal duty cycle of all image sensor modules, and backfill to this In the setting of all image sensor modules in a batch, the nonlinear calibration of all image sensor modules in the batch can be completed, which is conducive to improving the calibration accuracy and saving the calibration time of the same batch of image sensor modules .

Based on the same inventive concept, the present application also provides an iToF camera linearity optimization device. The linearity optimization device of the iToF camera provided can use the linearity optimization method of the iToF camera as shown in FIG. 4 to complete the linearity optimization of the iToF camera.

Please refer to FIG. 9 , which is a structural block diagram of an iToF camera linearity optimization device provided by an embodiment of the present application. As shown in FIG. 9 , the linearity optimization device of the iToF camera includes: an adjustment module 101 , a first acquisition module 102 , a second acquisition module 103 , a third acquisition module 104 and an optimization module 105 .

Specifically, the adjustment module 101 is used to adjust the duty cycle of the emitted light waveform through the image sensor. The first acquiring module 102 is used to adjust the delay value with a preset step under the current duty cycle, and acquire the virtual real phase and the target measurement phase corresponding to the corresponding virtual real distance after each delay value adjustment. The second acquisition module 103 is configured to acquire the linear parameter of the current duty cycle according to all acquired virtual real phases and corresponding target measurement phases after the delay value adjustment of the preset period is completed. The third obtaining module 104 is configured to obtain a duty cycle corresponding to an optimal value among all linear parameters as an optimal duty cycle when the current duty cycle is smaller than a preset threshold. The optimization module 105 is used to use the optimal duty cycle as the setting parameter of the image sensor to complete the nonlinear calibration of the iToF camera and realize the linearity optimization of the iToF camera.

In some embodiments, the second acquisition module 103 is further configured to calculate the absolute value of the difference between the virtual real phase and the target measured phase acquired after each delay value adjustment, and calculate all phases within the preset period. The sum of the absolute values of the differences is used as the linear parameter of the current duty cycle. Correspondingly, the third obtaining module 104 is further configured to obtain a minimum value among sum values corresponding to all duty ratios as the optimal value.

Based on the same inventive concept, the present application also provides an electronic device, including a memory, a processor, and a computer-executable program stored on the memory and operable on the processor; the processor executes the computer The steps of realizing the linearity optimization method of the iToF camera as shown in FIG. 4 when the executable program is executed.

It is within the realm of the present application contemplation that embodiments may be described and illustrated in terms of means for performing one or more of the described functions. These modules (which may also be referred to herein as units, etc.) may be physically realized by analog and/or digital circuits, such as logic gates, integrated circuits, microprocessors, microcontrollers, memory circuits, passive electronic components, active electronic components , optical components, hardwired circuitry, etc., and may optionally be driven by firmware and/or software. Circuitry may be implemented, for example, in one or more semiconductor chips. The circuitry constituting a module may be implemented by dedicated hardware, or by a processor (e.g., one or more programmed microprocessors and associated circuits), or by dedicated hardware that performs some of the functions of the module and a computer that performs other functions of the module. combination of processors. Each module of an embodiment may be physically divided into two or more interacting and separate modules without departing from the scope of the inventive concept. Likewise, modules of the embodiments may be physically combined into more complex modules without departing from the scope of the inventive concept.

In general, a term can be understood, at least in part, from its usage in context. For example, the term "one or more" as used herein may be used to describe a feature, structure or characteristic in the singular or may be used to describe a feature, structure or combination of features in the plural, depending at least in part on the context. In addition, the term "based on" may be understood as not necessarily intended to express an exclusive set of factors, but may instead, again depending at least in part on the context, allow for the presence of other factors not necessarily expressly described.

It should be noted that the terms "including" and "having" and their variants involved in the documents of this application are intended to cover non-exclusive inclusion. The terms "first", "second", etc. are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence unless the context clearly dictates, and it should be understood that the data so used are interchangeable under appropriate circumstances . In addition, the embodiments in the present application and the features in the embodiments can be combined with each other under the condition of no conflict. Also, in the above description, descriptions of well-known components and techniques are omitted to avoid unnecessarily obscuring the concepts of the present application. In the above-mentioned various embodiments, each embodiment focuses on the difference from other embodiments, and the same/similar parts between the various embodiments can be referred to each other.

The above description is only the preferred implementation mode of the present application, and it should be pointed out that for those of ordinary skill in the art, without departing from the principle of the application, some improvements and modifications can also be made, and these improvements and modifications should also be regarded as For the scope of protection of this application.

Claims (10)

1. a linearity optimization method of an iToF camera, is characterized in that, comprises the steps: Adjust the duty cycle of the emitted light waveform through the image sensor of the iToF camera; Adjust the delay value with a preset step under the current duty cycle, and obtain the virtual real phase corresponding to the corresponding virtual real distance and the target measurement phase after each adjustment of the delay value; After the delay value adjustment of the preset period is completed, the linear parameter of the current duty cycle is obtained according to all obtained virtual real phases and corresponding target measurement phases; When the current duty cycle is less than the preset threshold, obtain the duty cycle corresponding to the optimal value among all the linear parameters as the optimal duty cycle; and Using the optimal duty cycle as the setting parameter of the image sensor, the nonlinear calibration of the iToF camera is completed, and the linearity optimization of the iToF camera is realized. 2. The linearity optimization method of an iToF camera according to claim 1, wherein the preset step size is any one of π/4, π/8, π/16; the preset period is 2π. 3. The linearity optimization method of an iToF camera according to claim 1, wherein the step of obtaining the virtual real phase corresponding to the corresponding virtual real distance and the target measurement phase after each adjustment of the delay value further comprises : Add a delay unit in the modulation or demodulation path, and simulate the real distance by setting the delay value; A current virtual real distance is formed after each delay value is adjusted, wherein the current virtual real distance corresponds to a virtual real phase; and Obtain a preset frame depth image, and perform time-domain averaging on the measurement phases of the pixel center points of all the depth images to obtain a corresponding average measurement phase, which is used as the target measurement phase corresponding to the current virtual real distance. 4. The linearity optimization method of an iToF camera according to claim 1, wherein the step of obtaining the linear parameter of the current duty cycle further comprises : Calculating the absolute value of the difference between the virtual real phase obtained after each delay value adjustment and the target measured phase; and Calculate the sum of the absolute values of all the differences within the preset period, and use the sum as a linear parameter of the current duty cycle. 5. the linearity optimization method of iToF camera according to claim 4, is characterized in that, the described step of obtaining the duty cycle corresponding to the optimal value in all linear parameters as optimal duty cycle further comprises: A minimum value among sum values corresponding to all duty cycles is obtained as the optimal value. 6. the linearity optimization method of iToF camera according to claim 1, is characterized in that, described method further comprises: selecting a plurality of image sensors, and obtaining the optimal duty cycle of each image sensor; Obtaining a target duty cycle according to all the optimal duty cycles, wherein the target duty cycle is a median or an average value of all the optimal duty cycles; and The target duty cycle is backfilled into settings for all of the image sensors. 7. the linearity optimization method of iToF camera according to claim 6, is characterized in that, The step of selecting a plurality of image sensors further includes: randomly selecting a plurality of image sensors from the same batch of image sensors; The step of backfilling the target duty cycle into the settings of all the image sensors further includes: backfilling the target duty cycle into the settings of all the image sensors in the batch. 8. A linearity optimization device for an iToF camera, comprising: An adjustment module, configured to adjust the duty cycle of the emitted light waveform through the image sensor; The first acquisition module is used to adjust the delay value with a preset step under the current duty cycle, and obtain the virtual real phase corresponding to the corresponding virtual real distance and the target measurement phase after each adjustment of the delay value; The second acquisition module is used to acquire the linear parameter of the current duty cycle according to all acquired virtual real phases and corresponding target measurement phases after the delay value adjustment of the preset period is completed; The third acquisition module is used to acquire the duty cycle corresponding to the optimal value among all the linear parameters as the optimal duty cycle when the current duty cycle is less than the preset threshold; and The optimization module is used to use the optimal duty cycle as the setting parameter of the image sensor to complete the nonlinear calibration of the iToF camera and realize the linearity optimization of the iToF camera. 9. The device of claim 8, wherein: The second acquisition module is further used to calculate the absolute value of the difference between the virtual real phase and the target measurement phase acquired after each delay value adjustment, and calculate the absolute value of all the differences within the preset period And value, with described and value as the linear parameter of current duty ratio; The third acquiring module is further configured to acquire a minimum value among sum values corresponding to all duty ratios as the optimal value. 10. An electronic device, comprising a memory, a processor, and a computer-executable program stored on the memory and operable on the processor, wherein when the processor executes the computer-executable program The step of realizing the linearity optimization method of an iToF camera as described in any one of claims 1-7.

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