Distance calculation method, chip, camera and storage medium

Abstract

This invention relates to a distance calculation method, chip, camera, and storage medium. The method includes: transmitting a pulsed laser signal based on a laser emitter; receiving the pulsed laser signal reflected back from a target object based on a photoelectric sensor, and converting the received pulsed laser signal into an electrical signal, wherein the photoelectric sensor includes multiple pixels; acquiring a first statistical histogram of the electrical signal, the first statistical histogram being correlated with time bins and the number of times the photoelectric sensor is triggered for each time bin; acquiring a continuous increment curve based on the first statistical histogram; and calculating the real-time distance value of the target object based on any time bin in the continuous increment curve. This application can avoid the influence of differences in the reflectivity of different target objects on the measurement signal on the depth or distance measurement results, effectively improving the accuracy of depth or distance measurement.

Description

Technical Field

[0001] This invention relates to the field of image sensor technology, and in particular to a distance calculation method, chip, camera, and storage medium. Background Technology

[0002] With the continuous development of semiconductor imaging technology, semiconductor image sensors are widely used in various electronic devices such as digital cameras, mobile phones, medical imaging equipment, security inspection equipment, and rangefinders due to their advantages such as small size, low power consumption, and high sensitivity.

[0003] However, in traditional time-of-flight (TOF) cameras or lidar ranging systems, the image sensors of depth or distance measurement devices may be affected by factors such as low reflectivity of the target object and / or a long distance, resulting in different depth or distance measurement results when photographing two target objects with significantly different reflectivity at the same distance.

[0004] Therefore, how to avoid the influence of the difference in reflectivity of different target objects on the measurement signal on the depth or distance measurement results of traditional image sensors has become one of the technical problems that urgently need to be solved in the process of further improving the depth or distance measurement accuracy of image sensors. Summary of the Invention

[0005] Therefore, it is necessary to provide a distance calculation method, chip, camera, and storage medium to address the technical problems mentioned above, which can avoid the influence of differences in the reflectivity of different target objects on the measurement signal on the depth or distance measurement results, and effectively improve the accuracy of depth or distance measurement.

[0006] To achieve the above and other objectives, the first aspect of this application provides a distance calculation method, comprising:

[0007] Based on a laser transmitter to send pulsed laser signals;

[0008] The photoelectric sensor receives the pulsed laser signal reflected back from the target object and converts the received pulsed laser signal into an electrical signal. The photoelectric sensor includes multiple pixels.

[0009] Obtain a first statistical histogram of the electrical signal, the first statistical histogram being associated with a time bin and the number of times the photoelectric sensor corresponding to each time bin is triggered;

[0010] Obtain the continuous increment curve based on the first statistical histogram;

[0011] The real-time distance value of the target object is calculated based on any time bin in the continuous incremental curve.

[0012] In the distance calculation method described in the above embodiments, a pulsed laser signal is transmitted via a laser emitter, and the pulsed laser signal reflected back from the target object is received by a photoelectric sensor. The received pulsed laser signal is converted into an electrical signal. The photoelectric sensor includes multiple pixels to obtain a first statistical histogram of the electrical signal. The first statistical histogram is associated with time bins and the number of times the photoelectric sensor is triggered for each time bin. A continuous increment curve is obtained based on the first statistical histogram, thereby enabling the calculation of the real-time distance value of the target object based on any time bin in the continuous increment curve. Compared to the traditional method of calculating the depth or distance value of a target object by obtaining the peak value of the measurement signal reflected back by the target object, this application can avoid the influence of differences in the reflectivity of the measurement signal by different target objects on the depth or distance measurement results, effectively improving the accuracy of depth or distance measurement.

[0013] In one embodiment, obtaining the continuous increment curve based on the first statistical histogram includes:

[0014] Based on the number of times the photoelectric sensor is triggered in the nth time box, a first statistical histogram S0(n) is obtained, where n is a positive integer;

[0015] The continuous increment curve D0(n) is obtained from the first statistical histogram S0(n), where D0(n) = S0(n+1) – S0(n). The continuous increment curve D0(n) includes M consecutive segments greater than 0, where M ≥ 2 and M is a positive integer.

[0016] In one embodiment, obtaining the continuous increment curve D0(n) based on the first statistical histogram S0(n) includes:

[0017] The first statistical histogram S0(n) is subjected to a sliding filter to obtain the second statistical histogram H0(n);

[0018] The continuous increment curve D0(n) is obtained based on the second statistical histogram H0(n).

[0019] In one embodiment, calculating the real-time distance value of the target object based on any timebox in the continuous increment curve includes:

[0020] Obtain any time box in the continuous incremental curve as the rising edge time box t1 of the continuous incremental curve;

[0021] The real-time distance matrix S of the target object is calculated based on the rising edge time bin t1. 上 The real-time distance value matrix S 上 This includes the real-time distance values ​​of the target object captured by each pixel in the photoelectric sensor.

[0022] In one embodiment, calculating the real-time distance value of the target object based on any timebox in the continuous increment curve includes:

[0023] The starting time box in the continuous incremental curve is taken as the rising edge time box t1 of the continuous incremental curve.

[0024] In one embodiment, calculating the real-time distance value of the target object based on any timebox in the continuous increment curve further includes:

[0025] Obtain the peak time bin t of the first statistical histogram S0(n). max ;

[0026] According to the peak time bin t max Calculate the matching distance value S of the target object. MF ;

[0027] According to the matching distance value S MF and the real-time distance value S 上 Determine the precise distance value of the target object.

[0028] In one embodiment, the peak time bin t for obtaining the first statistical histogram S0(n) max Also includes:

[0029] After performing matched filtering on the first statistical histogram S0(n), the peak time bin t is then obtained. max .

[0030] In one embodiment, the step of matching the distance value S... MF and the real-time distance value S 上 Determining the precise distance value of the target object includes:

[0031] Obtain the peak time bin t max ;

[0032] Obtain the rising edge time bin t1;

[0033] If |t1-t max If |>threshold, then the matching distance value S MF Updated to the precise distance value of the target object, where threshold is a preset threshold and is a positive integer;

[0034] Conversely, the real-time distance value S is directly set. 上 This serves as the precise distance value to the target object.

[0035] In one embodiment, the threshold setting depends on the accuracy of the time-to-digital converter.

[0036] In one embodiment, obtaining the continuous increment curve based on the first statistical histogram further includes:

[0037] Determine the window size for the sliding filter;

[0038] The average number of times the photoelectric sensor was triggered in each time box within the window of the first statistical histogram is taken as the average number of times the sensor was triggered in each time box.

[0039] The second statistical histogram is obtained based on the average number of triggers corresponding to each time box.

[0040] In one embodiment, calculating the real-time distance value of the target object based on any timebox in the continuous increment curve further includes:

[0041] The real-time distance values ​​of the calibration obstacles are captured and acquired to obtain the calibration distance value matrix S' of the photoelectric sensor. 上 The actual distance to the calibration obstacle is D, and the calibration distance value matrix S' 上 This includes the real-time distance values ​​of obstacles captured by each pixel in the photoelectric sensor for calibration.

[0042] According to the calibration distance value matrix S' 上 And the offset matrix S of the photoelectric sensor is calculated based on the actual distance D. offset The offset matrix S offset This includes the offset of each pixel in the photoelectric sensor;

[0043] According to the offset matrix S offset and the real-time distance value S of the target object 上 Calculate the calibrated target object distance S.

[0044] A second aspect of this application provides a distance calculation chip, including a laser emitter, a photoelectric sensor, and a processor. The laser emitter is used to transmit pulsed laser signals; the photoelectric sensor is used to receive the pulsed laser signals reflected back from a target object and convert the received pulsed laser signals into electrical signals, wherein the photoelectric sensor includes a plurality of pixels; the processor is connected to the photoelectric sensor and is configured to:

[0045] Obtain a first statistical histogram of the electrical signal, the first statistical histogram being associated with a time bin and the number of times the photoelectric sensor corresponding to each time bin is triggered;

[0046] Obtain the continuous increment curve based on the first statistical histogram;

[0047] The real-time distance value of the target object is calculated based on any time bin in the continuous incremental curve.

[0048] In the distance calculation chip of the above embodiments, a photoelectric sensor receives a pulsed laser signal reflected back from a target object, converts the received pulsed laser signal into an electrical signal, and enables the processor to obtain a first statistical histogram based on the electrical signal. Based on the first statistical histogram, a continuous increment curve is obtained, thereby enabling the calculation of the real-time distance value of the target object based on any time bin in the continuous increment curve. Compared to the traditional method of calculating the depth or distance value of a target object by obtaining the peak value of the measurement signal reflected back from the target object, this application avoids the influence of differences in the reflectivity of different target objects on the measurement signal in the depth or distance measurement results, effectively improving the accuracy of depth or distance measurement.

[0049] In one embodiment, the processor is further configured to:

[0050] The first statistical histogram S0(n) is subjected to sliding filtering to obtain the second statistical histogram H0(n), where S0(n) is the number of times the photoelectric sensor is triggered in the nth time box, and n is a positive integer;

[0051] The continuous increment curve D0(n) is obtained from the second statistical histogram H0(n), where D0(n) = H0(n+1) – H0(n). The continuous increment curve D0(n) includes M consecutive segments greater than 0, where M ≥ 2 and M is a positive integer.

[0052] In one embodiment, the processor is further configured to:

[0053] Obtain M consecutive segments greater than 0 from the continuous incremental curve D0(n);

[0054] The starting point of the M segments greater than 0 is taken as the rising start time t1 of the incremental curve, or the starting point of any one of the M segments greater than 0 is taken as the rising start time t1 of the continuous incremental curve.

[0055] Calculate the real-time distance matrix S of the target object based on the ascent start time t1. 上 The real-time distance value matrix S 上 This includes the real-time distance values ​​of the target object captured by each pixel in the photoelectric sensor.

[0056] In one embodiment, the photoelectric sensor includes a silicon photomultiplier tube, which comprises a plurality of parallel single-photon avalanche diodes for receiving the pulsed laser signal reflected back from the target object and converting the received optical signal into an electrical signal.

[0057] In one embodiment, the photoelectric sensor includes a single-photon avalanche diode array and a time-to-digital converter. The time-to-digital converter is connected to the single-photon avalanche diode array and is used to receive the pulsed laser signal reflected back from the target object and to calculate the number of times the single-photon avalanche diode array is triggered by the received optical signal to generate the electrical signal.

[0058] A third aspect of this application provides a camera including a laser emitter, a photoelectric sensor, a memory, a processor, and a computer program stored in the memory and executable on the processor. The photoelectric sensor is connected to the processor, and the processor executes the computer program to implement the distance calculation method described in any embodiment of this application.

[0059] A fourth aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the distance calculation method described in any embodiment of this application.

[0060] In the camera or computer-readable storage medium of the above embodiments, a photoelectric sensor receives a pulsed laser signal reflected back from a target object. A processor converts the received pulsed laser signal into an electrical signal, obtains a first statistical histogram of the electrical signal, and acquires a continuous increment curve based on the first statistical histogram. This allows the processor to calculate the real-time distance value of the target object based on any time bin in the continuous increment curve. Compared to the traditional method of calculating the depth or distance value of a target object by acquiring the peak value of the measurement signal reflected back from the target object, this application avoids the influence of differences in the reflectivity of different target objects on the measurement signal in the depth or distance measurement results, effectively improving the accuracy of depth or distance measurement. Attached Figure Description

[0061] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other embodiments can be obtained based on these drawings without creative effort.

[0062] Figure 1 A schematic diagram of the waveform curves of the measurement electrical signals obtained by traditional depth or distance information acquisition devices from two different target objects at the same distance;

[0063] Figure 2 This is a flowchart illustrating a distance calculation method provided in the first embodiment of this application;

[0064] Figure 3 This is a flowchart illustrating a distance calculation method provided in the second embodiment of this application;

[0065] Figure 4 This is a flowchart illustrating a distance calculation method provided in the third embodiment of this application;

[0066] Figure 5 This is a flowchart illustrating a distance calculation method provided in the fourth embodiment of this application;

[0067] Figure 6 This is a flowchart illustrating a distance calculation method provided in the fifth embodiment of this application;

[0068] Figure 7 This is a schematic diagram of the structure of a camera provided in one embodiment of this application;

[0069] Figure 8 for Figure 7 The diagram shows a partial structural schematic of the camera shown. Detailed Implementation

[0070] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Preferred embodiments of the invention are shown in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to provide a thorough and complete understanding of the disclosure of the invention.

[0071] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.

[0072] When using the terms “including,” “having,” and “comprising” as described herein, another component may be added unless explicitly qualifying terms such as “only,” “consisting of,” etc. are used. Unless otherwise stated, singular terms may include plural forms and should not be construed as having a quantity of one.

[0073] In this application, unless otherwise expressly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0074] Traditional depth or distance information acquisition devices that use single-photon detectors or other photoelectric components exhibit differences in waveform when the intensity of the test light signal reflected back from the target object differs during depth or distance measurement. For example, when a depth information acquisition device photographs two target objects at the same distance with significantly different reflectivities, the waveforms of the corresponding two measurement electrical signals will differ considerably. Figure 1 As shown, this causes the depth information acquisition device to... Figure 1 The real-time distance values ​​obtained from the two measured electrical signals shown in the figure differ significantly, resulting in low measurement accuracy of the depth information acquisition device. To solve this technical problem, this application provides a distance calculation method, chip, camera, and storage medium as described in the following embodiments, which can avoid the influence of the difference in reflectivity of different target objects on the measurement signal on the depth or distance measurement results, and effectively improve the accuracy of depth or distance measurement.

[0075] Further, please refer to Figure 2 In one embodiment of this application, a distance calculation method is provided, comprising:

[0076] Step 22: Send a pulsed laser signal based on the laser transmitter;

[0077] Step 24: Receive the pulsed laser signal reflected back from the target object using a photoelectric sensor, and convert the received pulsed laser signal into an electrical signal. The photoelectric sensor includes multiple pixels.

[0078] Step 26: Obtain the first statistical histogram of the electrical signal, which is associated with the time bins and the number of times the photoelectric sensor corresponding to each time bin is triggered;

[0079] Step 28: Obtain the continuous increment curve based on the first statistical histogram;

[0080] Step 210: Calculate the real-time distance value of the target object based on any time bin in the continuous incremental curve.

[0081] For details, please continue to refer to Figure 2This method involves transmitting pulsed laser signals as measurement signals via a laser emitter and receiving the pulsed laser signals reflected back from the target object using a photoelectric sensor. The received pulsed laser signals are converted into electrical signals to obtain a first statistical histogram. This first statistical histogram is correlated with time bins and the number of times the photoelectric sensor is triggered for each time bin. A continuous increment curve is obtained based on the first statistical histogram, enabling the calculation of the real-time distance value of the target object from any time bin within the continuous increment curve. Compared to the traditional method of calculating the depth or distance value of a target object by acquiring the peak value of the measurement signal reflected back from the target object, this application avoids the influence of differences in the reflectivity of the measurement signal from different target objects on the depth or distance measurement results, effectively improving the accuracy of depth or distance measurement.

[0082] Further, please refer to Figure 3 In one embodiment of this application, a distance calculation method is provided, wherein obtaining the continuous incremental curve based on the first statistical histogram includes:

[0083] Step 281: Based on the number of times the photoelectric sensor is triggered in the nth time box, obtain the first statistical histogram S0(n), where n is a positive integer;

[0084] Step 282: Obtain the continuous increment curve D0(n) based on the first statistical histogram S0(n), where D0(n) = S0(n+1) – S0(n). The continuous increment curve D0(n) includes M consecutive segments greater than 0, where M ≥ 2 and M is a positive integer.

[0085] For details, please continue to refer to Figure 3 By obtaining a first statistical histogram S0(n) based on the number of triggers of the photoelectric sensor in the nth time box, where n is a positive integer, a continuous incremental curve D0(n) is obtained based on the first statistical histogram S0(n), where D0(n) = S0(n+1) – S0(n). The continuous incremental curve D0(n) includes M consecutive segments greater than 0, where M ≥ 2 and M is a positive integer. This allows the real-time distance value of the target object to be calculated based on any time box in the continuous incremental curve, thereby avoiding the influence of the difference in reflectivity of different target objects on the measurement signal on the depth or distance measurement results, and effectively improving the accuracy of depth or distance measurement.

[0086] Further, please refer to Figure 4 In one embodiment of this application, obtaining the continuous increment curve D0(n) based on the first statistical histogram S0(n) includes:

[0087] Step 2822: The first statistical histogram S0(n) is subjected to sliding filtering to obtain the second statistical histogram H0(n);

[0088] Step 2824: Obtain the continuous increment curve D0(n) based on the second statistical histogram H0(n).

[0089] For details, please continue to refer to Figure 4 A first statistical histogram S0(n), where n is a positive integer, can be obtained based on the number of triggers of the photoelectric sensor in the nth time bin. Then, a continuous increment curve D0(n) is obtained based on the first statistical histogram S0(n), where D0(n) = S0(n+1) – S0(n). The continuous increment curve D0(n) includes M consecutive segments greater than 0, where M ≥ 2 and M is a positive integer. Since the obtained first statistical histogram S0(n) generally contains noise signals, the accuracy of the real-time distance value of the target object calculated subsequently based on any time bin of the obtained continuous increment curve is relatively low. Therefore, firstly, a second statistical histogram H0(n) is obtained by sliding filtering the acquired first statistical histogram S0(n) to remove the influence of noise signals. Then, a continuous increment curve D0(n) is obtained based on the second statistical histogram H0(n), where D0(n) = H0(n+1) – H0(n). The continuous increment curve D0(n) includes M consecutive segments greater than 0, where M ≥ 2 and M is a positive integer. M consecutive segments greater than 0 are found on D0(n), and the starting time bins of these M segments are used as the rising edge time bins of the continuous increment curve. For example, if M = 5, if the number of triggers corresponding to 5 consecutive subsequent time bins is greater than the number of triggers corresponding to the preceding time bin, then the starting time bins of these 5 consecutive segments greater than zero are used as the rising edge time bins of the continuous increment curve. Alternatively, any one of these 5 consecutive segments greater than zero can be used as the rising edge time bin of the continuous increment curve to calculate the real-time distance value of the target object based on the rising edge time bins.

[0090] As an example, in one embodiment of this application, after obtaining the first statistical histogram, the window size of the sliding filter is determined, and then the average number of times the photoelectric sensor is triggered corresponding to each time box in the window of the first statistical histogram is used as the average number of triggers corresponding to each time box in turn, so as to obtain the second statistical histogram according to the average number of triggers corresponding to each time box, thereby improving the accuracy of the rising edge time box obtained based on the second statistical histogram, and further improving the accuracy of the real-time distance value of the target object calculated based on the rising edge time box.

[0091] Further, please refer to Figure 5 In one embodiment of this application, calculating the real-time distance value of the target object based on any time bin in the continuous increment curve includes:

[0092] Step 212: Obtain any time box in the continuous incremental curve as the rising edge time box t1 of the continuous incremental curve;

[0093] Step 214: Calculate the real-time distance matrix S of the target object based on the rising edge time bin t1. 上 The real-time distance value matrix S 上 This includes the real-time distance values ​​of the target object captured by each pixel in the photoelectric sensor.

[0094] For details, please continue to refer to Figure 5 By obtaining any timebox in the continuous incremental curve D0(n) as the rising edge timebox t1 of the continuous incremental curve, for example, M=3 can be set. If the number of times the next three consecutive timeboxes are triggered is greater than the number of times the previous three timeboxes are triggered, the starting timebox of these three consecutive segments with a value greater than zero can be used as the rising edge timebox of the continuous incremental curve. Alternatively, any one of these three consecutive timeboxes with a value greater than zero can be used as the rising edge timebox of the continuous incremental curve to calculate the real-time distance value of the target object based on the rising edge timebox.

[0095] Further, please refer to Figure 6 In one embodiment of this application, the step of calculating the real-time distance value of the target object based on any time bin in the continuous increment curve further includes:

[0096] Step 2112: Obtain the peak time bin t of the first statistical histogram S0(n). max ;

[0097] Step 2114, according to the peak time bin t max Calculate the matching distance value S of the target object. MF ;

[0098] Step 2116, based on the matching distance value S MF and the real-time distance value S 上 Determine the precise distance value of the target object.

[0099] For details, please continue to refer to Figure 6 The peak time bin t can be obtained after performing matched filtering on the first statistical histogram S0(n). max Peak time bin t max The corresponding trigger count is greater than the trigger count of the preceding adjacent timebox and greater than the trigger count of the following adjacent timebox. This is determined by the peak timebox t. max Calculate the matching distance value S of the target object. MF According to the matching distance value S MFand the real-time distance value S 上 Determine the precise distance value of the target object. For example, in one embodiment of this application, the peak time bin t can be obtained. max And obtain the rising edge time bin t1, if |t1-t max If |>threshold, then the matching distance value S MF Update the distance to the target object to a precise value, where threshold is a preset threshold and a positive integer; otherwise, directly update the real-time distance value S. 上 The threshold, as the precise distance value to the target object, depends on the accuracy of the time-to-digital converter. A matching distance value is calculated by acquiring the peak value of the measurement signal reflected back from the target object, and this matching distance value is used to determine the reliability of the real-time distance value of the target object calculated based on the rising edge timebox, thereby further improving the accuracy of distance measurement.

[0100] Furthermore, in one embodiment of this application, the step of calculating the real-time distance value of the target object based on any time bin in the continuous increment curve further includes:

[0101] Step 2162: Capture and acquire real-time distance values ​​of the calibration obstacle to obtain the calibration distance value matrix S' of the photoelectric sensor. 上 The actual distance to the calibration obstacle is D, and the calibration distance value matrix S' 上 This includes the real-time distance values ​​of obstacles captured by each pixel in the photoelectric sensor for calibration.

[0102] Step 2164, according to the calibration distance value matrix S' 上 And the offset matrix S of the photoelectric sensor is calculated based on the actual distance D. offset The offset matrix S offset This includes the offset of each pixel in the photoelectric sensor;

[0103] Step 2166, according to the offset matrix S offset and the real-time distance value S of the target object 上 The calibrated target object distance S is calculated. Specifically, the real-time distance values ​​of the calibration obstacle are captured and acquired to obtain the calibration distance value matrix S' of the photoelectric sensor. 上 The actual distance to the calibration obstacle is D, and the calibration distance value matrix S' 上 This includes capturing real-time distance values ​​of calibration obstacles by each pixel in the photoelectric sensor, based on the calibration distance value matrix S'. 上 And the offset matrix S of the photoelectric sensor is calculated based on the actual distance D. offset The offset matrix Soffset This includes the offset of each pixel in the photoelectric sensor, and then based on the offset matrix S offset and the real-time distance value S of the target object 上 Calculate the calibrated target object distance S, for example, using the formula S = S 上 +S offset Or S = S 上 -S offset The distance S of the calibrated target object is calculated to avoid the influence of the offset error of each pixel in the photoelectric sensor on the distance measurement result, thereby effectively improving the accuracy of the distance measurement.

[0104] Further, please refer to Figure 7 In one embodiment of this application, a distance calculation chip 200 is provided, including a laser emitter 201, a photoelectric sensor 202, and a processor 203. The laser emitter 201 is used to transmit pulsed laser signals; the photoelectric sensor 202 is used to receive the pulsed laser signals reflected back from a target object and convert the received pulsed laser signals into electrical signals, wherein the photoelectric sensor 202 includes multiple pixels; the processor 203 is connected to the photoelectric sensor 202 and is configured to:

[0105] Obtain a first statistical histogram of the electrical signal, the first statistical histogram being associated with a time bin and the number of times the photoelectric sensor corresponding to each time bin is triggered;

[0106] Obtain the continuous increment curve based on the first statistical histogram;

[0107] The real-time distance value of the target object is calculated based on any time bin in the continuous incremental curve.

[0108] For details, please continue to refer to Figure 7 The system receives pulsed laser signals reflected from a target object via a photoelectric sensor 202, converts these signals into electrical signals, and enables a processor 203 to obtain a first statistical histogram based on this signal. The processor 203 then obtains a continuous increment curve based on the first statistical histogram, allowing it to calculate the real-time distance to the target object from any time bin within the continuous increment curve. Compared to the traditional method of calculating the depth or distance of a target object by acquiring the peak value of the measurement signal reflected back from the target object, this application avoids the influence of differences in the reflectivity of different target objects on the measurement signal, effectively improving the accuracy of depth or distance measurements.

[0109] For further information, please continue to refer to [link / reference]. Figure 7 In one embodiment of this application, the processor 203 is further configured to:

[0110] Obtain M consecutive segments greater than 0 from the continuous incremental curve D0(n);

[0111] The starting point of the M segments greater than 0 is taken as the starting time t1 of the rise of the incremental curve, or the starting point of any one of the M segments greater than 0 is taken as the starting time t1 of the rise of the incremental curve.

[0112] The real-time distance S of the target object is calculated based on the ascent start time t1. 上 The real-time distance value matrix S 上 This includes the real-time distance values ​​of the target object captured by each pixel in the photoelectric sensor.

[0113] For details, please continue to refer to Figure 7 The processor 203 can first obtain a second statistical histogram H0(n) by sliding filtering the acquired first statistical histogram S0(n) to remove the influence of noise signals, where S0(n) is the number of triggers of the photoelectric sensor in the nth time box, and n is a positive integer. Then, the processor 203 obtains a continuous increment curve D0(n) based on the second statistical histogram H0(n), where D0(n) = H0(n+1) – H0(n). The continuous increment curve D0(n) includes M consecutive segments greater than 0, where M ≥ 2, and M is a positive integer. The processor 203 can find M consecutive segments greater than 0 on D0(n) and use the starting time box of these M segments as the rising edge time box of the continuous increment curve. For example, if M=5, and the number of times each of the next five consecutive time boxes is triggered is greater than the number of times each of the previous five time boxes is triggered, then the starting time box of these five consecutive segments with values ​​greater than zero is taken as the rising edge time box of the continuous increment curve. Alternatively, any one of these five consecutive segments with values ​​greater than zero can be taken as the rising edge time box of the continuous increment curve, so as to calculate the real-time distance value of the target object based on the rising edge time box.

[0114] For further information, please continue to refer to [link / reference]. Figure 7 In one embodiment of this application, the photoelectric sensor 205 includes a silicon photomultiplier tube (not shown), which includes a plurality of single-photon avalanche diodes connected in parallel for receiving the pulsed laser signal reflected back from the target object and converting the received optical signal into an electrical signal.

[0115] Further, please refer to Figure 8In one embodiment of this application, the photoelectric sensor 202 includes a single-photon avalanche diode array (SPAD) 11 and a time-to-digital converter (TDC) 12. The TDC 12 is connected to the SPAD array 11 and is used to receive the pulsed laser signal reflected back from the target object, and to calculate the number of times the SPAD array is triggered by the received light signal to generate an electrical signal. This allows the processor 203 to obtain the rising edge timebox of the continuous increment curve based on the electrical signal, and to calculate the real-time distance value of the target object based on the rising edge timebox. In this embodiment, it is preferable that the SPAD array 11 includes single-photon avalanche diodes arranged in a uniform array. A SPAD is a binary device that biases a PN junction under a bias voltage close to an avalanche. A small number of charge carriers excited by a weak light signal pass through the field region close to the avalanche and multiply in number due to collisional ionization, thus obtaining a larger electrical signal. Therefore, the SPAD only has two states: "with output signal" and "without output signal". In this embodiment, a time-to-digital converter 12 is used to record the number of times the SPAD array 11 is triggered, thereby converting the received optical signal, including the pulsed laser signal reflected back by the target object, into an electrical signal.

[0116] Furthermore, in one embodiment of this application, a camera is provided, including a laser emitter, a photoelectric sensor, a memory, a processor, and a computer program stored in the memory and executable on the processor. The photoelectric sensor is connected to the processor, and the processor executes the computer program to implement the distance calculation method described in any embodiment of this application.

[0117] Furthermore, in one embodiment of this application, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the distance calculation method described in any embodiment of this application.

[0118] In the camera or computer-readable storage medium of the above embodiments, a photoelectric sensor receives a pulsed laser signal reflected back from a target object, converts the received pulsed laser signal into an electrical signal, obtains a first statistical histogram of the electrical signal, and applies a sliding filter to the first statistical histogram to remove the influence of noise signals to obtain a second statistical histogram. Then, a continuous increment curve in the second statistical histogram is obtained, thereby enabling the calculation of the real-time distance value of the target object based on any time bin in the continuous increment curve. Compared to the traditional method of calculating the depth or distance value of a target object by obtaining the peak value of the measurement signal reflected back from the target object, this application avoids the influence of differences in the reflectivity of different target objects on the measurement signal in the depth or distance measurement results, effectively improving the accuracy of depth or distance measurement.

[0119] It should be understood that, although Figure 2-6 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 2-6 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but may be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but may be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0120] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0121] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0122] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this invention patent should be determined by the appended claims.

Claims

1. A distance calculation method characterized by, The method comprises: sending a pulsed laser signal based on a laser emitter; receiving the pulsed laser signal reflected back via a target object based on a photoelectric sensor, and converting the received pulsed laser signal into an electrical signal, wherein the photoelectric sensor comprises a plurality of pixels; obtaining a first statistical histogram of the electrical signal, the first statistical histogram being associated with time bins and the number of times the photoelectric sensor is triggered in each time bin; obtaining a continuous increment curve based on the first statistical histogram; the continuous increment curve comprises M consecutive segments greater than 0, M≥2, M being a positive integer; calculating a real-time distance value of the target object according to any time bin in the continuous increment curve; the real-time distance value comprises a real-time distance value matrix of each pixel in the photoelectric sensor shooting the target object; the calculation of the real-time distance value of the target object according to any time bin in the continuous increment curve further comprises: taking the starting time bin in the continuous increment curve as the rising edge time bin t1 of the continuous increment curve; According to the rising edge time box t1, a real-time distance value matrix S of the target object is calculated 上 The real-time distance value matrix S 上 includes the real-time distance value of each pixel in the photosensor shooting the target object; the real-time distance value of the calibration obstacle is shot and obtained to obtain the calibration distance value matrix S of the photosensor ， 上 The real distance of the calibration obstacle is D, and the calibration distance value matrix S ， 上 includes the real-time distance value of each pixel in the photosensor shooting the calibration obstacle; According to the calibration distance value matrix S ， 上 and the real distance D, an offset matrix S of the photosensor is calculated offset , the offset matrix S offset includes the offset of each pixel in the photosensor; According to the offset matrix S offset And the real-time distance value S of the target object 上 Calculate the calibrated target object distance S; the continuous increment curve based on the first statistical histogram includes: obtaining a first statistical histogram S0(n) according to the number of times the photoelectric sensor is triggered in the nth time bin, n being a positive integer; obtaining a continuous increment curve D0(n) according to the first statistical histogram S0(n), D0(n)= S0(n+1)–S0(n).

2. The distance calculation method according to claim 1, characterized by, According to the offset matrix S offset And the real-time distance value S of the target object 上 Calculate the calibrated target object distance S, comprising: According to the formula S = S 上 + S offset or S = S 上 - S offset The calibrated target object distance S is calculated.

3. The distance calculation method according to claim 2, characterized in that, The obtaining of the continuous increment curve D0(n) according to the first statistical histogram S0(n) comprises: sliding filtering the first statistical histogram S0(n) to obtain a second statistical histogram H0(n); obtaining the continuous increment curve D0(n) according to the second statistical histogram H0(n).

4. The distance calculation method of claim 1, wherein, The calculation of the real-time distance value of the target object according to any time bin in the continuous increment curve further comprises: obtaining a peak time bin t of the first statistical histogram S0(n) max ; According to the peak time box t max Calculate the matching distance value S of the target object MF ; According to the matching distance value S MF and the real-time distance value S 上 determining the accurate distance value of the target object.

5. The distance calculation method according to claim 4, characterized in that, said acquiring the peak time bin t of the first statistical histogram S0(n) max Further comprising: performing a peak time bin t after matched filtering of the first statistical histogram S0(n) max .

6. The distance calculation method of claim 4, wherein, The matching distance value S MF and the real-time distance value S 上 The accurate distance value of the target object is determined by: acquiring the peak time bin t max ; obtaining the rising edge time bin t1; If |t1-t max |>threshold, the matching distance value S MF is updated as the accurate distance value of the target object, wherein threshold is a preset threshold and is a positive integer. Conversely, the real-time distance value S is directly used as the accurate distance value of the target object. 上 Conversely, the real-time distance value S is directly used as the accurate distance value of the target object.

7. The distance calculation method according to claim 6, characterized in that, The setting of the threshold depends on the accuracy of the time-to-digital converter.

8. The distance calculation method according to any one of claims 1 to 3, characterized in that, The obtaining of the continuous increment curve based on the first statistical histogram further comprises: determining the window size of the sliding filter; taking the average number of times the photoelectric sensor is triggered in each time bin in the window in the first statistical histogram as the average number of times the photoelectric sensor is triggered in each time bin in turn; obtaining a second statistical histogram according to the average number of times the photoelectric sensor is triggered in each time bin.

9. A distance calculation chip, characterized by The method comprises: a laser emitter for sending a pulsed laser signal; a photoelectric sensor for receiving the pulsed laser signal reflected back via a target object, and converting the received pulsed laser signal into an electrical signal, wherein the photoelectric sensor comprises a plurality of pixels; a processor connected with the photoelectric sensor and configured to: obtain a first statistical histogram of the electrical signal, the first statistical histogram being associated with time bins and the number of times the photoelectric sensor is triggered in each time bin; obtaining a continuous increment curve based on the first statistical histogram; the continuous increment curve comprises M continuous segments greater than 0, M≥2, M being a positive integer; the obtaining of the continuous increment curve based on the first statistical histogram comprises: obtaining a first statistical histogram S0(n) according to the number of triggers of the photoelectric sensor in the nth time bin, n being a positive integer; obtaining a continuous increment curve D0(n) according to the first statistical histogram S0(n), D0(n)=S0(n+1)-S0(n); According to any time box in the continuous increment curve, the real-time distance value of the target object is calculated, further comprising: taking the starting time box in the continuous increment curve as the rising edge time box t1 of the continuous increment curve; calculating the real-time distance value matrix S of the target object according to the rising edge time box t1 上 , the real-time distance value matrix S 上 including the real-time distance value of each pixel in the photosensor shooting the target object; shooting and obtaining the real-time distance value of the calibration obstacle to obtain the calibration distance value matrix S ， 上 of the photosensor, the real distance of the calibration obstacle is D, and the calibration distance value matrix S ， 上 including the real-time distance value of each pixel in the photosensor shooting the calibration obstacle; according to the calibration distance value matrix S ， 上 and the real distance D, the offset matrix S offset of the photosensor is calculated, the offset matrix S offset including the offset of each pixel in the photosensor; according to the offset matrix S offset and the real-time distance value S 上 of the target object, the calibrated target object distance S is calculated; the real-time distance value includes the real-time distance value matrix of each pixel in the photosensor shooting the target object.

10. The distance calculation chip of claim 9, wherein, the processor is further configured to: obtain a first statistical histogram S0(n) according to the number of triggers of the photoelectric sensor in the nth time bin, n being a positive integer; sliding filter the first statistical histogram S0(n) to obtain a second statistical histogram H0(n); obtain a continuous increment curve D0(n) according to the second statistical histogram H0(n), D0(n)=H0(n+1)-H0(n), the continuous increment curve D0(n) comprising M continuous segments greater than 0, M≥2, M being a positive integer.

11. The distance calculation chip of claim 10, wherein, the processor is further configured to: obtain M continuous segments greater than 0 in the continuous increment curve D0(n), M≥2, M being a positive integer; take the starting point of the M continuous segments greater than 0 as the rising start time t1 of the increment curve, or take the starting point of any one of the M continuous segments greater than 0 as the rising start time t1 of the continuous increment curve; calculating a real-time distance value matrix S of the target object according to the rising start time t1 上 , the real-time distance value matrix S 上 includes real-time distance values of the target object shot by each pixel in the photoelectric sensor.

12. The distance calculation chip according to any of claims 9-11, characterized in that, the photoelectric sensor comprises: a silicon photomultiplier comprising a plurality of single-photon avalanche diodes connected in parallel, configured to receive the pulsed laser signal reflected by the target object and convert the received optical signal into an electrical signal.

13. The distance calculation chip according to any of claims 9-11, characterized by the photoelectric sensor comprises: a single-photon avalanche diode array; and a time-to-digital converter connected to the single-photon avalanche diode array, configured to receive the pulsed laser signal reflected by the target object and calculate the number of triggers of the single-photon avalanche diode array to generate the electrical signal.

14. A camera, comprising: a laser emitter, a photoelectric sensor, a memory, a processor, and a computer program stored in the memory and executable on the processor, the photoelectric sensor being connected to the processor, the processor implementing the steps of the method of any one of claims 1-8 when executing the computer program.

15. A computer readable storage medium having stored thereon a computer program, characterized in that, the computer program is executable on the processor to implement the steps of the method of any one of claims 1-8.

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