Time-of-flight measurement method, device, storage medium and lidar

Abstract

The embodiment of the application discloses a time-of-flight measurement method and device, a storage medium and a laser radar, and relates to the field of measurement. First, the laser radar is set to a calibration mode to prepare for subsequent accurate calibration. A signal is emitted to a known distance calibration object at 0 phase gear, and a reflection signal intensity value is obtained. A first time-of-flight response model coefficient is fitted by using a plurality of data points. The operation is repeated at 90 phase gear, and a second model coefficient is fitted. The factors such as theta, phi and R are calculated by using the fitted coefficients. Then, the laser radar is switched to a measurement mode, and intensity values of a to-be-measured object at 0 phase and 90 degrees phase are obtained. According to the intensity values and the calculated factors, cosine values and sine values are calculated. Finally, the time of flight is calculated according to the sine values and the cosine values. Through the steps of model establishment and factor calculation, high-precision time-of-flight calculation is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of measurement, and in particular to a time-of-flight measurement method and device, a storage medium and a laser radar. BACKGROUND

[0002] i-TOF (indirect time-of-flight) laser radars have been widely used in many fields such as autonomous driving, robot navigation, industrial detection, etc. due to their significant advantages of low cost, compact design and high spatial resolution. The working principle is to calculate the time-of-flight of an object by emitting a modulated laser signal and measuring the phase difference between the reflected signal and the emitted signal, and then to obtain the distance information of the object.

[0003] However, in actual operation, the measurement accuracy and stability of the i-TOF system are severely restricted by various error factors. In terms of systematic errors, there are problems such as offset, wiggling error and fixed pattern noise (FPN). Among them, the complex coupling phenomenon between wiggling error and FPN is particularly prominent, which leads to time-of-flight response non-uniformity (TFRNU) and greatly affects the accuracy of the measurement results. In terms of random errors, readout noise and photon noise also interfere with the measurement results.

[0004] Currently, in order to solve the problem of wiggling error, existing technologies mainly start from two directions of hardware optimization and software compensation. In terms of hardware optimization, methods such as waveform modulation and duty cycle adjustment are adopted. For example, by adjusting the shape and duty cycle of the laser emission waveform, it is attempted to improve the stability of the laser signal and thus reduce the generation of wiggling error. In terms of software compensation, look-up table and B-spline interpolation techniques are widely used. The look-up table stores error compensation values under different conditions in advance, and queries and compensates in actual measurement; the B-spline interpolation uses mathematical methods to fit and correct the measurement data.

[0005] For the FPN problem, delay line self-compensation circuit and deep learning algorithm are common improvement means. The delay line self-compensation circuit adjusts and compensates the signal in time by introducing a delay line structure to reduce the influence of FPN. The deep learning algorithm learns the characteristics and rules of FPN by using a large amount of training data, and then intelligently compensates in the measurement process.

[0006] However, these existing methods have many problems in terms of accuracy, complexity and applicability.

[0007] In terms of accuracy, existing methods have limited effectiveness, only reducing the root mean square error (RMSE) of the system error from tens of millimeters to 5-10 millimeters. This is significantly different from the theoretical sub-millimeter accuracy limit of the i-TOF system. This means that in practical applications, the measurement results still have large errors and cannot meet the requirements for high-precision measurement.

[0008] In terms of complex coupling, the sources of FPN are extremely complex, involving multiple hardware components and signal processing stages of the lidar. Moreover, the multi-source coupling mechanism between FPN and oscillation error is not yet fully understood. Due to insufficient understanding of this coupling mechanism, existing algorithms struggle to achieve comprehensive error suppression and cannot fundamentally solve the TFRNU problem.

[0009] Regarding high manufacturing costs, existing solutions place stringent requirements on CMOS manufacturing processes to improve measurement accuracy. For example, achieving more precise signal modulation and processing necessitates higher-precision manufacturing processes, which undoubtedly increases chip manufacturing costs and process complexity. Simultaneously, complex hardware optimization and software compensation methods also increase the overall system cost and development difficulty, limiting the application of i-TOF lidar in cost-sensitive fields.

[0010] Therefore, there is an urgent need for a new technical solution that can effectively solve the error problem in i-TOF lidar, improve measurement accuracy, reduce manufacturing costs, and promote the widespread application of i-TOF lidar in more fields. Summary of the Invention

[0011] This application provides a method, apparatus, storage medium, and lidar for measuring time of flight, which can solve the problems of high cost and large error in existing lidar time of flight measurements. The technical solution is as follows:

[0012] In a first aspect, embodiments of this application provide a method for measuring flight time, the method comprising:

[0013] Set the lidar to calibration mode;

[0014] In the 0-phase setting, a measurement laser signal is emitted towards a calibration object at a known distance, and the calibration intensity value I of the corresponding reflected laser signal is obtained. AC-C Using multiple sets of data points (t) FT I AC-C The coefficients m of the first flight time response model were fitted. AC1 and m AC2 The first flight time response model is: m AC1 *cos(2π*t FT / T)+m AC2*sin(2π*t FT / T), where T represents the period of the measured laser signal, and the measured laser signal is a sinusoidal amplitude modulated laser signal, t FT Indicates the flight time of the calibrated object;

[0015] At the 90° phase setting, a measurement laser signal is emitted towards a calibration object at a known distance, and the calibration intensity value I of the reflected laser signal is obtained. BD-C Based on multiple sets of data points (t) obtained during the calibration process FT I BD-C The coefficients m of the second flight time response model were fitted. BD1 and m BD2 The second flight time response model is expressed as: m BD1 *cos(2π*t FT / T)+m BD2 *sin(2π*t FT / T);

[0016] Calculate the factors θ, φ, and R using the fitted coefficients;

[0017] θ=arctan[(m AC1 ^2+m AC2 ^2)^1 / 2] / [(m BD1 ^2+m BD2 ^2)^1 / 2];

[0018] R=(m AC1 ^2+m AC2 ^2+m BD1 ^2+m BD2 ^2)^1 / 2;

[0019] φ=arctan(m AC1 / m AC2 =arctan(m BD1 / m BD2 );

[0020] Switch the lidar to measurement mode and acquire the measured intensity value I of the object under test at the 0 phase setting. AC The measured intensity value I at a 90-degree phase BD ;

[0021] Based on the above measured strength value I AC Measured strength value I BD Calculate the cosine and sine values ​​of cos(2π*t) based on θ, φ, and R respectively. FT / T) and sin(2π*t) FT / T): ;

[0022] Calculate the flight time t based on the calculated sine and cosine values. FT , .

[0023] Secondly, embodiments of this application provide a time-of-flight measuring device, the device comprising:

[0024] The setting unit is used to set the lidar to calibration mode;

[0025] The fitting unit is used to emit a measurement laser signal to a calibration object at a known distance via the transmitter in the 0-phase mode, and to acquire the calibration intensity value I of the corresponding reflected laser signal. AC-C Using multiple sets of data points (t) FT I AC-C The coefficients m of the first flight time response model were fitted. AC1 and m AC2 The first flight time response model is: m AC1 *cos(2π*t FT / T)+m AC2 *sin(2π*t FT / T), where T represents the period of the measured laser signal, and the measured laser signal is a sinusoidal amplitude modulated laser signal, t FT Indicates the flight time of the calibrated object;

[0026] The fitting unit is also used to emit a measurement laser signal to a calibration object at a known distance via a transmitter at a 90-degree phase setting, and to acquire the calibration intensity value I of the reflected laser signal. BD-C Based on multiple sets of data points (t) obtained during the calibration process FT I BD-C The coefficients m of the second flight time response model were fitted. BD1 and m BD2 The second flight time response model is expressed as: m BD1 *cos(2π*t FT / T)+m BD2 *cos(2π*t FT / T);

[0027] The calculation unit is used to calculate factors θ, φ, and R using the fitted coefficients;

[0028] θ=arctan[(m AC1 ^2+m AC2 ^2)^1 / 2] / [(m BD1 ^2+m BD2 ^2)^1 / 2];

[0029] R=(m AC1 ^2+m AC2^2+m BD1 ^2+m BD2 ^2)^1 / 2;

[0030] φ=arctan(m AC1 / m AC2 =arctan(m BD1 / m BD2 );

[0031] The measurement unit is used to switch the lidar to measurement mode and acquire the measured intensity value I of the object under test at the 0-phase setting. AC The measured intensity value I at a 90-degree phase BD ;

[0032] The calculation unit is also used to calculate the measured strength value I as described above. AC Measured strength value I BD Calculate the cosine and sine values ​​of cos(2π*t) based on θ, φ, and R respectively. FT / T) and sin(2π*t) FT / T): ;

[0033] The calculation unit is also used to calculate the flight time t based on the calculated sine and cosine values. FT , .

[0034] Thirdly, embodiments of this application provide a computer storage medium storing a plurality of instructions adapted for loading by a processor and executing the above-described method steps.

[0035] Fourthly, embodiments of this application provide a lidar, which may include: a processor and a memory; wherein the memory stores a computer program adapted to be loaded by the processor and executed by the above-described method steps.

[0036] The beneficial effects of the technical solutions provided in some embodiments of this application include at least the following:

[0037] By establishing a time-of-flight response model, quantifying the amplitude deviation and phase delay caused by FPN (Fixed Pattern Noise) and oscillation error, and correcting them using weighted normalization techniques, high-precision time-of-flight calculation can be achieved. Attached Figure Description

[0038] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0039] Figure 1 This is a schematic diagram of the system architecture provided in the embodiments of this application;

[0040] Figure 2 This is a schematic flowchart of the flight time measurement method provided in the embodiments of this application;

[0041] Figure 3 This is a schematic diagram of the structure of a flight time measuring device provided in this application;

[0042] Figure 4 This is a schematic diagram of the structure of a computer program provided in this application;

[0043] Figure 5 This is a schematic diagram of the structure of a lidar provided in this application. Detailed Implementation

[0044] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0045] Figure 1 An exemplary system architecture is shown that can be applied to the flight time measurement method or flight time measurement device of this application.

[0046] like Figure 1 As shown, the system architecture may include: an object under test 101 and a lidar 102. The lidar 102 includes a controller, a driver chip, a transmitter, and a receiver. The controller performs control functions, such as controlling the drive voltage and drive current of the driver chip. The transmitter emits a sinusoidal detection signal, and the receiver receives the reflected signal from the object under test and measures the intensity of the reflected laser signal. The receiver is typically a matrix with multiple pixel units. The phase difference between the sinusoidal detection signal and the reflected signal is Δφ. The controller calculates the indirect flight time based on the phase difference, thereby calculating the distance to the object under test.

[0047] The following will be combined with the appendix Figure 2 This application provides a detailed description of the flight time measurement method provided in its embodiments. The flight time measurement device in these embodiments can be... Figure 1 The lidar shown.

[0048] Please seeFigure 2 This is a flowchart illustrating a method for measuring flight time, as provided in this application embodiment. Figure 2 As shown, the method described in this application embodiment may include the following steps:

[0049] S201. Set the lidar to calibration mode.

[0050] LiDAR systems typically have different operating modes. Through specific control commands or hardware settings, the LiDAR can be switched from normal measurement mode to calibration mode. In calibration mode, some parameter settings and data processing methods differ from those in normal measurement mode to facilitate subsequent calibration operations. For example, parameters such as laser emission power and receiver sensitivity may be adjusted to better suit the calibration process.

[0051] For example, the operator can select the "calibration mode" option through the software interface that comes with the lidar. The software will send the corresponding control signals to the lidar. After receiving the signals, the lidar's internal control circuit will adjust the relevant parameters and enter the calibration mode.

[0052] S202. In the 0-phase setting, emit a measurement laser signal towards a calibration object at a known distance, and obtain the calibration intensity value I of the corresponding reflected laser signal. AC-C Using multiple sets of data points (t) FT I AC-C The coefficients m of the first flight time response model were fitted. AC1 and m AC2 .

[0053] In the 0-phase setting, the lidar emits a sinusoidal measurement laser signal according to the set measurement laser signal period T. The 0-degree phase indicates that the phase difference between the emitted measurement laser signal and the received reflected signal is 0 degrees. When the measurement laser signal reaches a calibration object at a known distance, it will be reflected. The lidar's receiving module will receive the reflected signal and measure the calibration intensity value I of the reflected laser signal. AC-C At the same time, based on the known distance to the calibrated object, the corresponding flight time t can be calculated. FT By repeatedly changing the position of the calibration object (i.e., changing the flight time t), FT ), obtain multiple sets (t) FT I AC-C The data points are then used. A mathematical fitting method (such as least squares) is then applied to these data points in the first flight time response model m. AC1 *cos(2π*t FT / T)+m AC2 *sin(2π*t FT In / T), solve for the coefficient m.AC1 and m AC2 .

[0054] For example: Assuming the period of the measured laser signal is T = 10 ns, and the distance to the calibrated object is known to be 1 m, according to the speed of light c = 3 × 10⁻⁶, 8 m / s, the flight time t can be calculated FT =2×1 / c≈6.67ns. At this distance, the lidar emits a measurement laser signal and acquires the calibrated intensity value I of the reflected signal. AC-C Then, the calibration object is moved to different distances such as 2m and 3m, and the above operation is repeated to obtain multiple sets (t). FT I AC-C ) data points. The least squares method is used to fit these data points to obtain m. AC1 and m AC2 The value of .

[0055] In some possible embodiments of this application, the calibration object is set on a reflector outside the lidar, and the laser signal is measured to reach the calibration object after being reflected multiple times by the reflector.

[0056] The calibration object is a high-reflectivity reflector specially installed outside the lidar. This reflector has uniform and stable reflection characteristics, and its surface is specially treated to effectively reflect the laser beam emitted by the lidar back along its original path. This ensures that the lidar can accurately receive the reflected signal, thus providing a reliable and clear reference for the lidar calibration. The size of the reflector is customized according to the actual calibration requirements and the performance parameters of the lidar. Its shape is usually a regular rectangle or circle, which facilitates installation and placement in a suitable calibration location.

[0057] Meanwhile, to adapt to different calibration requirements, the calibration object may have certain adjustment mechanisms. For example, it can be moved within a limited range using a fine-tuning mechanism to simulate different flight time conditions. However, this adjustment is carried out within a known and controllable range to ensure that the position after each adjustment can be accurately measured and recorded.

[0058] During calibration, the lidar emits measurement laser signals towards the internal calibration object at both the 0-phase and 90-phase settings. Because the calibration object is inside the lidar, the signal propagation path is relatively stable, reducing the impact of external environmental factors (such as air disturbances and interference from other objects) on the signal. This makes the acquired reflected signal intensity values ​​more accurate and reliable, which is beneficial for the subsequent precise fitting of the coefficients of the first and second time-of-flight response models.

[0059] In some possible embodiments of this application, before the lidar performs the fitting work of the first time-of-flight response model and the second time-of-flight response model, the collected multiple sets of data points (t) are processed. FT I AC-C ) and multiple sets of data points (t) FT I BD-C Data cleaning is a crucial step in ensuring the quality of model fitting.

[0060] The primary task of data cleaning is to identify and process outlier data. In actual measurement processes, due to various interference factors, some data points that do not conform to normal patterns may be collected. For example, when measuring the calibration intensity value I of a reflected signal... AC-C and I BD-C At times, it may be subject to sudden high-intensity electromagnetic interference from the outside, causing the received signal strength value to be abnormally high or low. For flight time t FT and t FT The measured value may deviate from the true value due to temporary malfunctions inside the lidar or brief obstruction by external objects.

[0061] To identify these outliers, statistical analysis can be used. Calculate the mean and standard deviation of the data points, and set a reasonable range, typically centered on the mean and bounded by a certain number of standard deviations. If a data point exceeds this range, it is considered outlier. For example, for a set of (t... FT I AC-C ) data points, calculate I AC-C The mean and standard deviation of I, when a certain I AC-C A data point is marked as an outlier when the difference between its value and the mean exceeds three times the standard deviation.

[0062] Besides identifying outliers, data cleaning also includes data consistency checks. During multiple measurements, since the measurement conditions should be relatively stable, the data should exhibit a certain degree of coherence and consistency. If some data points show significant discrepancies in their trends compared to other data points in the same group, further analysis and processing are necessary. For example, when continuously measuring calibration objects at different distances, the flight time t increases with distance. FT The reflected signal strength value I should be gradually increased. AC-C The change should be gradually reduced. If a data point shows the opposite trend, its accuracy needs to be checked.

[0063] For identified outlier data and data points with consistency issues, you can choose to remove or correct them. If the outlier data is caused by temporary interference and is few in number, it can be removed directly, and the remaining normal data can be used for model fitting. For some data points that may have slight biases, reasonable corrections can be made based on the patterns of surrounding data points.

[0064] S203. At the 90° phase setting, emit a measurement laser signal towards a calibration object at a known distance, and acquire the calibration intensity value I of the reflected laser signal. BD-C Based on multiple sets of data points (t) obtained during the calibration process FT I BD-C The coefficients m of the second flight time response model were fitted. BD1 and m BD2 .

[0065] In this process, the lidar is switched to the 90-degree phase setting, where the phase difference between the measured laser signal and the received reflected signal is 90 degrees. Similarly, the measurement laser signal is emitted towards a calibration object at a known distance, and the reflected signal is received and its intensity value I is measured. BD-C The corresponding flight time t is calculated based on the distance to the calibrated object. FT By repeatedly changing the position of the calibration object, multiple sets of (t) were obtained. FT I BD-C Data points. Substitute these data points into the second flight time response model: m BD1 *cos(2π*t FT / T)+m BD2 *sin(2π*t FT In / T), the coefficient m is solved using mathematical fitting methods. BD1 and m BD2 .

[0066] For example: At the 90° phase setting, keeping the laser signal period T = 10 ns constant, place the calibration object at a distance of 1.5 m and calculate the flight time t. FT =2×1.5 / c=10ns, obtain the reflected signal intensity value I at this time. BD-C Change the distance of the calibration object to different distances, such as 2.5m, 3.5m, etc., and obtain multiple sets of (t) FT I BD-C Data points, fitted to obtain m BD1 and m BD2 The value of .

[0067] In some possible embodiments of this application, multiple sets of data points are obtained using multiple calibration objects at different distances.

[0068] Specifically, during the calibration process, the lidar measures the calibration object at multiple different distances. For the first time-of-flight response model, in the 0-phase setting, the controller directs the lidar to perform four different signal transmissions and receptions at certain time intervals and steps. The reflected signal intensity value obtained in each measurement is assigned different weights based on its measurement accuracy, environmental interference factors, etc.

[0069] For example, under certain measurement conditions, where ambient light interference is low and signal reception quality is high, the acquired data will be given a larger weight; conversely, data acquired under conditions of high interference will have a relatively smaller weight. This approach allows for more rational use of measurement data and reduces the impact of data with large errors on model fitting.

[0070] For the second time-of-flight response model, at the 90-phase setting, the controller precisely controls the variation of the calibration object and the measurement process, obtains the reflected signal intensity values ​​under different conditions, and assigns corresponding weights according to the actual situation.

[0071] The coupling error between oscillation and FPN (Fixed Pattern Noise) is a significant factor affecting the model fitting accuracy. Oscillation may originate from factors such as the mechanical vibration of the lidar itself or the instability of laser emission, while FPN is noise generated due to the inherent characteristics of hardware such as the receiver. This application effectively separates the influence of oscillation and FPN on the measurement results through multiple measurements and weighted processing. Because oscillation and FPN behave differently under different examples, their coupling effect can be identified and eliminated through multi-distance measurements and weighted analysis, thereby improving the accuracy of model fitting.

[0072] S204. Calculate the factors θ, φ, and R using the fitted coefficients.

[0073] Where θ = arctan[(m AC1 ^2+m AC2 ^2)^1 / 2] / [(m BD1 ^2+m BD2 ^2)^1 / 2];

[0074] R=(m AC1 ^2+m AC2 ^2+m BD1 ^2+m BD2 ^2)^1 / 2;

[0075] φ=arctan(m AC1 / m AC2 =arctan(m BD1 / m BD2 ).

[0076] Based on the coefficients m obtained from the previous fitting... AC1 m AC2 m BD1 and m BD2 Calculate θ, φ, and R according to the given formulas. θ is calculated by taking the square root of the coefficient combinations at 0° and 90° phase settings, and then finding their arctangent values. R is calculated by taking the square root of the sum of the squares of all coefficients. φ is calculated by taking the square root of m at 0° and 90° phase settings. AC1 With m AC2 m BD1 With m BD2 The arctangent of the ratio is theoretically equal to the two arctangent values.

[0077] S205. Switch the lidar to measurement mode and acquire the measured intensity value I of the object under test at the 0 phase setting. AC The measured intensity value I at a 90-degree phase BD .

[0078] The process involves switching the lidar from calibration mode back to measurement mode via control commands. In measurement mode, the lidar emits measurement laser signals towards the object under test at both the 0-phase and 90-phase settings, receives the reflected signals, and measures the corresponding intensity value I. AC and I BD For example, a lidar unit has physical buttons for switching between calibration and measurement modes. After successful calibration, the user is prompted, either by voice or by text on the display screen.

[0079] It should be noted that the lidar can automatically enter calibration mode after being powered on, or it can enter measurement mode according to the user's selection. If it enters measurement mode directly, the lidar will use the previously stored model parameters for measurement.

[0080] For example: The operator selects "Measurement Mode" through the software interface. After the lidar switches to this mode, it first emits a measurement laser signal at the 0-phase setting, receives and measures the intensity of the reflected signal to obtain I. AC Then switch to the 90-phase setting and repeat the above operation to obtain I. BD .

[0081] S206, Based on the above measured strength value I AC Measured strength value I BD Calculate the cosine and sine values ​​of cos(2π*t) based on θ, φ, and R respectively. FT / T) and sin(2π*t) FT / T).

[0082] Among them, using the obtained IAC I BD The previously calculated θ, φ, and R are then used to perform calculations using specific mathematical formulas. These formulas are derived from the previously established model and calibrated parameters, with the aim of extracting the cosine and sine values ​​related to flight time from the measured intensity values. .

[0083] S207. Calculate the flight time t based on the calculated sine and cosine values. FT .

[0084] Among them, based on the trigonometric relationships, it is known that cos(2π*t) FT / T) and sin(2π*t) FT The value of / T) can be calculated using mathematical methods such as the arctangent function, which is 2π*t. FT The value of / T is then used to calculate the flight time t. FT : .

[0085] Furthermore, the laser pulses emitted by the lidar are reflected when they encounter the object being measured, and the reflected laser is captured by the lidar's receiving module. Time of flight t FT It is the time interval from laser emission to reception.

[0086] Because the laser travels back and forth between the lidar and the object being measured, the actual propagation distance is twice the distance the lidar travels to the object. Based on the fundamental formula that distance equals velocity multiplied by time, let the distance the lidar travels to the object be D. Then we can obtain the formula D = c × t. FT / 2.

[0087] This application has the following specific beneficial effects:

[0088] By fitting the first and second time-of-flight response models with multiple sets of data points at 0-phase and 90-phase settings, a mathematical model capable of describing the measurement characteristics of lidar was successfully established. This concretizes the previously abstract concepts of FPN and oscillation error, enabling the quantification of amplitude deviation and phase delay caused by these errors.

[0089] By using the fitted coefficients to calculate factors such as θ, φ, and R, the measurement data can be indirectly adjusted and corrected. This can effectively compensate for errors in the measurement process and make the calculation results closer to the true values.

[0090] Through the aforementioned series of model building, factor calculations, and weighted normalization corrections, high-precision time-of-flight (TOF) calculations were finally achieved. Accurate TOF calculation is crucial for i-TOF lidar to acquire accurate distance information, and this technical solution effectively addresses the impact of FPN and sway errors on measurement accuracy.

[0091] The following are embodiments of the apparatus described in this application, which can be used to execute the embodiments of the method described in this application. For details not disclosed in the apparatus embodiments of this application, please refer to the embodiments of the method described in this application.

[0092] Please see Figure 3 This illustration shows a schematic diagram of a time-of-flight measurement device provided in an exemplary embodiment of this application, hereinafter referred to as device 3. Device 3 can be implemented as all or part of a lidar system through software, hardware, or a combination of both. Device 3 includes: a setting unit 301, a fitting unit 302, a calculation unit 303, and a measurement unit 304.

[0093] Setting unit 301 is used to set the lidar to calibration mode;

[0094] Fitting unit 302 is used to emit a measurement laser signal to a calibration object at a known distance via a transmitter in the 0-phase mode, and to acquire the calibration intensity value I of the corresponding reflected laser signal. AC-C Using multiple sets of data points (t) FT I AC-C The coefficients m of the first flight time response model were fitted. AC1 and m AC2 The first flight time response model is: m AC1 *cos(2π*t FT / T)+m AC2 *sin(2π*t FT / T), where T represents the period of the measured laser signal, and the measured laser signal is a sinusoidal signal, t FT Indicates the flight time of the calibrated object;

[0095] The fitting unit 302 is also used to emit a measurement laser signal to a calibration object at a known distance via a transmitter at a 90° phase setting, and to acquire the calibration intensity value I of the reflected laser signal. BD-C Based on multiple sets of data points (t) obtained during the calibration process FT I BD-C The coefficients m of the second flight time response model were fitted. BD1 and m BD2 The second flight time response model is expressed as: m BD1 *cos(2π*t FT / T)+m BD2 *cos(2π*tFT / T), t FT This is expressed as the flight time of the calibrated object;

[0096] Calculation unit 303 is used to calculate factors θ, φ, and R using the fitted coefficients;

[0097] θ=arctan[(m AC1 ^2+m AC2 ^2)^1 / 2] / [(m BD1 ^2+m BD2 ^2)^1 / 2];

[0098] R=(m AC1 ^2+m AC2 ^2+m BD1 ^2+m BD2 ^2)^1 / 2;

[0099] φ=arctan(m AC1 / m AC2 =arctan(m BD1 / m BD2 );

[0100] Measurement unit 304 is used to switch the lidar to measurement mode and acquire the measured intensity value I of the object under test at the 0 phase setting. AC The measured intensity value I at a 90-degree phase BD ;

[0101] The calculation unit 303 is also used to calculate the measured intensity value I as described above. AC Measured strength value I BD Calculate the cosine and sine values ​​of cos(2π*t) based on θ, φ, and R respectively. FT / T) and sin(2π*t) FT / T): ;

[0102] The calculation unit 303 is also used to calculate the flight time t based on the calculated sine and cosine values. FT , .

[0103] In one or more possible embodiments, calibration mode and measurement mode are switched based on physical buttons.

[0104] In one or more possible embodiments, the calibration object is disposed inside the lidar.

[0105] In one or more possible embodiments, multiple sets of data points are acquired based on multiple calibrated objects at different distances.

[0106] In one or more possible embodiments, the distance D = c × t to the object under test is calculated based on the calculated time of flight. FT / 2.

[0107] In one or more possible embodiments, before model fitting, multiple sets of data points (t) are processed. FT I AC-C ) and multiple sets of data points (t) FT I BD-C Data cleaning is performed.

[0108] It should be noted that the device 3 provided in the above embodiments, when performing the flight time measurement method, is only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the above functions. In addition, the flight time measurement device and the flight time measurement method embodiments provided in the above embodiments belong to the same concept, and the implementation process is detailed in the method embodiments, which will not be repeated here.

[0109] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0110] See Figure 4 This application provides a computer storage medium, which can be a disk, optical disk, magnetic tape, or USB flash drive, etc. The computer storage medium can store multiple instructions (i.e., computer programs), which are adapted to be loaded and executed by a processor as described above. Figure 2 The method steps of the illustrated embodiment can be found in the following documentation for detailed execution. Figure 1 The specific details of the illustrated embodiments will not be elaborated here.

[0111] This application also provides a computer program product that stores at least one instruction, which is loaded and executed by the processor to implement the flight time measurement method as described in the above embodiments.

[0112] Please see Figure 5 The diagram below provides a structural schematic of a lidar according to an embodiment of this application. Figure 5 As shown, the lidar 500 may include: at least one processor 501, at least one receiver 504, a transmitter 503, a memory 505, and at least one communication bus 502.

[0113] The communication bus 502 is used to enable communication between these components.

[0114] The transmitter 503 is used to transmit a sine wave signal, which can be an infrared signal, a laser signal, or an ultrasonic signal.

[0115] The receiver 504 is used to receive the reflected signal based on the transmitted sinusoidal signal, and there is a certain phase difference between the transmitted signal and the reflected signal.

[0116] The processor 501 may include one or more processing cores. The processor 501 connects to various parts within the terminal 500 using various interfaces and lines, and performs various functions and processes data of the terminal 500 by running or executing instructions, programs, code sets, or instruction sets stored in the memory 505, and by calling data stored in the memory 505. Optionally, the processor 501 may be implemented using at least one hardware form selected from Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), and Programmable Logic Array (PLA).

[0117] The memory 505 may include random access memory (RAM) or read-only memory. Optionally, the memory 505 may include a non-transitory computer-readable storage medium. The memory 505 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 505 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 505 may also be at least one storage device located remotely from the aforementioned processor 501. Figure 5 As shown, the memory 505, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and application programs.

[0118] exist Figure 5 In the lidar 500 shown, the processor 501 can be used to call the application program stored in the memory 505 and specifically execute, such as Figure 2 The method shown can be referred to for details. Figure 2 As shown, it will not be elaborated further here.

[0119] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory, or random access memory, etc.

[0120] The above-disclosed embodiments are merely preferred embodiments of this application and should not be construed as limiting the scope of this application. Therefore, any equivalent variations made in accordance with the claims of this application shall still fall within the scope of this application.

Claims

1. A method for measuring flight time, characterized in that, include: Set the lidar to calibration mode; In the 0-phase setting, a measurement laser signal is emitted towards a calibration object at a known distance, and the calibration intensity value I of the corresponding reflected laser signal is obtained. AC-C Using multiple sets of data points (t) FT I AC-C The coefficients m of the first flight time response model were fitted. AC1 and m AC2 The first flight time response model is: m AC1 *cos(2π*t FT T)+m AC2 *sin(2π*t FT / T), where T represents the period of the measured laser signal, and the measured laser signal is a sinusoidally modulated laser signal with amplitude t FT The time of flight of light to and from an object is determined by measuring the distance d from the object and the speed of light c. FT =2*d / c; At the 90° phase setting, a measurement laser signal is emitted towards a calibration object at a known distance, and the calibration intensity value I of the reflected laser signal is obtained. BD-C Based on multiple sets of data points (t) obtained during the calibration process FT I BD-C The coefficients m of the second flight time response model were fitted. BD1 and m BD2 The second flight time response model is expressed as: m BD1 *cos(2π*t FT / T)+m BD2 *sin(2π*t FT / T); Calculate the factors θ, φ, and R using the fitted coefficients; θ=arctan[(m AC1 ^2+m AC2 ^2)^1 / 2] / [(m BD1 ^2+m BD2 ^2)^1 / 2]; R=(m AC1 ^2+m AC2 ^2+m BD1 ^2+m BD2 ^2)^1 / 2; φ=arctan(m AC1 / m AC2 )=arctan(m BD1 / m BD2 ); Switch the lidar to measurement mode and acquire the measured intensity value I of the object under test at the 0 phase setting. AC The measured intensity value I at a 90-degree phase BD ; Based on the above measured strength value I AC Measured strength value I BD Calculate the cosine and sine values ​​of cos(2π*t) based on θ, φ, and R respectively. FT / T) and sin(2π*t) FT / T): ; Calculate the flight time t based on the calculated sine and cosine values. FT , .

2. The method according to claim 1, characterized in that, The calibration mode and measurement mode can be switched using physical buttons.

3. The method according to claim 1, characterized in that, The calibration object is a reflector plate set outside the lidar.

4. The method according to claim 1, characterized in that, Multiple sets of data points are obtained from calibration objects at different distances.

5. The method according to claim 1, characterized in that, The distance d = c × t to the object under test is calculated based on the time of flight. FT / 2.

6. The method according to claim 1, characterized in that, Before model fitting, multiple sets of data points (t) were analyzed. FT I AC-C ) and multiple sets of data points (t) FT I BD-C Data cleaning is performed.

7. A device for measuring flight time, characterized in that, include: The setting unit is used to set the lidar to calibration mode; The fitting unit is used to emit a measurement laser signal to a calibration object at a known distance via the transmitter in the 0-phase mode, and to acquire the calibration intensity value I of the corresponding reflected laser signal. AC-C Using multiple sets of data points (t) FT I AC-C The coefficients m of the first flight time response model were fitted. AC1 and m AC2 The first flight time response model is: m AC1 *cos(2π*t FT / T)+m AC2 *sin(2π*t FT / T), where T represents the period of the measured laser signal, and the measured laser signal is a sinusoidally modulated laser signal. FT This is expressed as the flight time of the calibrated object; The fitting unit is also used to emit a measurement laser signal to a calibration object at a known distance via a transmitter at a 90-degree phase setting, and to acquire the calibration intensity value I of the reflected laser signal. BD-C Based on multiple sets of data points (t) obtained during the calibration process FT I BD-C The coefficients m of the second flight time response model were fitted. BD1 and m BD2 The second flight time response model is represented as m BD1 *cos(2π*t FT / T)+m BD2 *sin(2π*t FT / T), t FT The time of flight of light to and from an object is determined by measuring the distance d from the object and the speed of light c. FT =2*d / c; The calculation unit is used to calculate factors θ, φ, and R using the fitted coefficients; θ=arctan[(m AC1 ^2+m AC2 ^2)^1 / 2] / [(m BD1 ^2+m BD2 ^2)^1 / 2]; R=(m AC1 ^2+m AC2 ^2+m BD1 ^2+m BD2 ^2)^1 / 2; φ=arctan(m AC1 / m AC2 )=arctan(m BD1 / m BD2 ); The measurement unit is used to switch the lidar to measurement mode and acquire the measured intensity value I of the object under test at the 0-phase setting. AC The measured intensity value I at a 90-degree phase BD ; The calculation unit is also used to calculate the measured strength value I as described above. AC Measured strength value I BD Calculate the cosine and sine values ​​of cos(2π*t) based on θ, φ, and R respectively. FT / T) and sin(2π*t) FT / T): ; The calculation unit is also used to calculate the flight time t based on the calculated sine and cosine values. FT , .

8. A computer storage medium, characterized in that, The computer storage medium stores a plurality of instructions, which are adapted to be loaded by a processor and executed as method steps as claimed in any one of claims 1 to 6.

9. A lidar, characterized in that, include: A processor and a memory; wherein the memory stores a computer program adapted to be loaded by the processor and executed the method steps as claimed in any one of claims 1 to 6.

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