LIDAR system with improved time-to-digital conversion circuit

Abstract

The invention relates to a LIDAR system with an improved time-to-digital conversion circuit. A light detection and ranging (LIDAR) system may include a laser and a plurality of single photon avalanche diodes (SPADs) that generate signals in response to laser light reflected from a target scene. The LIDAR system may be a direct time-of-flight system and may also include processing circuitry including a transform circuit, an integration circuit, and a decode circuit. The transformation circuit may transform the signal generated by the SPAD, such as to a lower dimensional space. For example, a transformation circuit may transform the signal into a vector in a two-dimensional complex plane. An integration circuit may combine the transformed signals to form an integrated value, such as by adding a vector. Decoding circuitry may use the integrated value to determine a time-of-flight of the external object.

Description

Technical Field

[0001] The present invention relates generally to imaging systems, and more particularly to LIDAR (Light Detection And Ranging) based imaging systems. Background Art

[0002] Conventional LIDAR imaging systems illuminate a target with light (usually a coherent laser pulse) and measure the return time reflected from the target to determine the distance to the target, and measure the light intensity to generate a three-dimensional image of the scene. The LIDAR imaging system includes a direct time-of-flight circuit and a laser that illuminates the target. The time-of-flight circuit can determine the flight time of the laser pulse (e.g., that has been reflected by the target) and thus determine the distance to the target. In a direct time-of-flight LIDAR system, the distance is determined for each pixel in the single-photon avalanche diode (SPAD) pixel array that forms the image sensor. BRIEF DESCRIPTION OF THE DRAWINGS

[0003] Figure 1 is a schematic diagram of an illustrative system including a LIDAR imaging system, according to some embodiments.

[0004] Figure 2 is a circuit diagram illustrating an exemplary single photon avalanche diode (SPAD) pixel, according to some embodiments.

[0005] Figure 3 is a diagram of an exemplary silicon photomultiplier according to some embodiments.

[0006] Figure 4 is a schematic diagram of an illustrative LIDAR processing circuit, according to some embodiments.

[0007] FIG. 5A to FIG. 5D is a diagram of illustrative transformations that may be used to transform a signal produced by a SPAD pixel, according to some embodiments.

[0008] Figure 6 is a diagram of exemplary transformation vectors produced by a SPAD pixel according to some embodiments.

[0009] Figure 7 is a diagram of exemplary integrated vectors combined with transformed vectors according to some implementations.

[0010] Figure 8 is a flow chart of illustrative steps that may be used to generate direct time-of-flight measurements, according to some embodiments. DETAILED DESCRIPTION

[0011] Embodiments herein relate to LIDAR systems with direct time-of-flight capabilities.

[0012] Some imaging systems include image sensors that sense light by converting impinging photons into charge carriers (electrons or holes) that accumulate (collect) in pixel photodiodes within the sensor array. After completing the accumulation period, the collected charge is converted into a voltage that is provided to the output terminals of the sensor. In complementary metal oxide semiconductor (CMOS) image sensors, the charge-to-voltage conversion is done directly in the pixel itself, and the analog pixel voltage is transferred to the output terminal through various pixel addressing and scanning schemes. The analog pixel voltage may also be subsequently converted to a digital equivalent on-chip and processed in various ways in the digital domain.

[0013] On the other hand, in the case of Figures 1 to 4 In light detection and ranging (LIDAR) devices such as the devices described above, the principle of photon detection is different. A LIDAR device may include a light source, such as a laser, that emits light toward a target object / scene. Each light sensing diode in a LIDAR device may be biased slightly above its breakdown point, and when incident photons from the laser, such as light reflected from a target object / scene, generate charge carriers (electrons and holes), the carriers induce an avalanche breakdown while generating additional carriers. Avalanche multiplication may produce a current signal that can be easily detected by a readout circuit associated with a single photon avalanche diode (SPAD). The avalanche process needs to be stopped (i.e., quenched) by reducing the diode bias below its breakdown point.

[0014] In LIDAR devices, SPAD pixels can be used to measure the time of flight (ToF) of photons from a synchronized light source to a scene object point and back to the sensor, which can be used to obtain a three-dimensional image of the scene. This method requires a time-to-digital conversion circuit to determine the amount of time that has passed since the laser was emitted, thereby determining the distance to the target object. For example, a histogram of the signal generated by each SPAD pixel can be created to determine the ToF. However, these histograms may require the storage of a large amount of data and therefore require a large amount of memory. Therefore, instead of storing the data that forms these histograms, each signal generated by the SPAD pixel can be transformed. The transformed values ​​can then be summed (or otherwise combined) to determine the ToF.

[0015] Figure 1 is a schematic diagram of an illustrative system including a LIDAR imaging system. Figure 1 The system 100 may be a vehicle system such as an active braking system or other vehicle safety system, a surveillance system, a medical imaging system, a general machine vision system, or any other desired type of system.

[0016] System 100 includes a LIDAR-based system 102, such as a LIDAR imaging system, sometimes referred to as a LIDAR module. LIDAR module 102 may be used to capture images of a scene and measure distances to obstacles (also referred to as targets) in the scene.

[0017] For example, in a vehicle safety system, information from the LIDAR module may be used by the vehicle safety system to determine environmental conditions around the vehicle. For example, the vehicle safety system may include systems such as a parking assistance system, an automatic or semi-automatic cruise control system, an automatic braking system, a collision avoidance system, a lane keeping system (sometimes referred to as a lane drift prevention system), or a pedestrian detection system. In at least some cases, the LIDAR module may form part of a semi-autonomous or autonomous unmanned vehicle.

[0018] The LIDAR module 102 may include a laser 104 that emits light 108 to illuminate an obstacle 110 (also referred to herein as a target or scene). The laser may emit light 108 of any desired wavelength, such as infrared light or visible light. Optics and beam steering equipment 106 may be used to direct a beam from the laser 104 toward the obstacle 110. The light 108 may illuminate the obstacle 110 and return to the LIDAR module 102 as a reflection 112. One or more lenses in the optics and beam steering 106 may focus the reflected light 112 onto a silicon photomultiplier (SiPM) 114 (sometimes referred to as a SiPM sensor 114).

[0019] SiPM 114 is a SPAD device. In other words, SiPM 114 may include multiple SPADs. In a SPAD device, a light sensing diode is biased above its breakdown point. When an incident photon generates a pair of charge carriers (electrons and holes), these carriers trigger an avalanche breakdown and generate additional carriers. Avalanche multiplication can produce a current signal that can be easily detected by a readout circuit associated with the SPAD. The avalanche process can be stopped (quenched) by reducing the diode bias below its breakdown point. Therefore, each SPAD may include a passive and / or active quenching circuit for stopping the avalanche. SPAD pixels can be used to measure the photon ToF from a synchronized light source such as a laser 104 to a scene object point and back to the sensor, which can be used to obtain a three-dimensional image of the scene. Figure 2 An illustrative example of a SPAD pixel that may be used in a silicon photomultiplier 114 is shown in FIG.

[0020] like Figure 2As shown, the SPAD device 202 includes a SPAD 204 coupled in series with a quenching circuit 206 between a first supply voltage terminal 208 (e.g., which may be a ground supply voltage terminal) and a second supply voltage terminal 210 (e.g., which may be a supply voltage terminal). During operation of the SPAD device 202, the supply voltage terminals 208 and 210 may be used to bias the SPAD 204 to a voltage above a breakdown voltage. The breakdown voltage is the maximum reverse voltage that can be applied without causing an exponential increase in leakage current in the diode. When the SPAD 204 is biased above the breakdown voltage in this manner, the absorption of a single photon may trigger a short but relatively large avalanche current by impact ionization.

[0021] Quenching circuit 206 (sometimes referred to as quenching element 206) can be used to reduce the bias voltage of SPAD 204 to a level below the breakdown voltage. Reducing the bias voltage of SPAD 204 below the breakdown voltage will stop the avalanche process and the corresponding avalanche current. There are many ways to form quenching circuit 206. Quenching circuit 206 can be a passive quenching circuit or an active quenching circuit. Once activated, a passive quenching circuit can automatically quench the avalanche current without external control or monitoring. For example, Figure 2 An example of using a resistor to form a quenching circuit 206 is shown. This is an example of a passive quenching circuit. After the avalanche is initiated, the resulting current quickly discharges the capacitance of the device, thereby reducing the voltage at the SPAD to near the breakdown voltage. The resistance associated with the resistor in the quenching circuit 206 may cause the final current to be lower than the current required to maintain itself. The SPAD can then be reset to above the breakdown voltage so that another photon can be detected.

[0022] This example of a passive quenching circuit is merely exemplary. An active quenching circuit may also be used in the SPAD device 202. The active quenching circuit may reduce the time it takes for the SPAD device 202 to reset. This may allow the SPAD device 202 to detect incident light at a faster rate than when a passive quenching circuit is used, thereby improving the dynamic range of the SPAD device. The active quenching circuit may adjust the SPAD quenching resistance. For example, before a photon is detected, the quenching resistance is set to a higher value, and then once a photon is detected and avalanche quenching occurs, the quenching resistance is minimized to reduce the recovery time.

[0023] The SPAD device 202 may also include a readout circuit 212. There are a variety of ways to form the readout circuit 212 to obtain information from the SPAD device 202. The readout circuit 212 may include a pulse counting circuit that counts the arriving photons. Alternatively or in addition, the readout circuit 212 may include a ToF circuit for measuring the ToF of the photons. The ToF information of the photons may be used to perform depth sensing.

[0024] In one example, the photons may be counted by an analog counter to form a light intensity signal as a corresponding pixel voltage. In other words, the pixel voltage may correspond to the light intensity on the SPAD device. The ToF signal may also be obtained by converting the photon flight time into a voltage. The example of the analog pulse counting circuit included in the readout circuit 212 is only exemplary. If necessary, the readout circuit 212 may include a digital pulse counting circuit. If necessary, the readout circuit 212 may also include an amplification circuit.

[0025] The readout circuit 212 coupled to the node between the diode 204 and the quenching circuit 206 is Figure 2 The examples in are merely exemplary. Readout circuit 212 may be coupled to any desired portion of the SPAD device. In some cases, quenching circuit 206 may be considered integral to readout circuit 212.

[0026] Because SPAD devices can detect single incident photons, SPAD devices can effectively image scenes with low light levels. Each SPAD can detect how many photons have been received in a given time period, for example, by using a readout circuit including a counting circuit. However, as described above, each time a photon is received and an avalanche current begins, the SPAD device must be quenched and reset before it is ready to detect another photon. As the incident light level increases, the reset time limits the dynamic range of the SPAD device. In particular, once the incident light level exceeds a given level, the SPAD device is triggered immediately after resetting. In addition, SPAD devices can be used in LIDAR systems to determine when light returns after reflecting from an external object.

[0027] Multiple SPAD devices may be grouped together to help increase the dynamic range. A group or array of SPAD devices may be referred to as a SiPM. Two SPAD devices, more than two SPAD devices, more than ten SPAD devices, more than one hundred SPAD devices, more than one thousand SPAD devices, or any other suitable number of SPAD devices may be included in a given SiPM. Figure 3 An example of multiple SPAD devices grouped together is shown in FIG.

[0028] Figure 3 2 is a circuit diagram of an exemplary group 220 of SPAD devices 202. A group of SPAD devices may be referred to as a SiPM. Figure 3 As shown, the silicon photomultiplier 220 may include a plurality of SPAD devices coupled in parallel between a first supply voltage terminal 208 and a second supply voltage terminal 210 . Figure 3N SPAD devices 202 are shown coupled in parallel as SPAD device 202 - 1 , SPAD device 202 - 2 , SPAD device 202 - 3 , SPAD device 202 - 4 , . . . , SPAD device 202 -N.

[0029] Each SPAD device may be referred to herein as a SPAD pixel 202. Although not specifically described herein, Figure 3 , but the readout circuitry for the SiPM measures the combined output current from all of the SPAD pixels in the SiPM. In this way, the dynamic range of an imaging system that includes SPAD pixels can be increased. However, if desired, each SPAD pixel may have a separate readout circuit. It is not guaranteed that each SPAD pixel has a triggered avalanche current when an incident photon is received. A SPAD pixel may have an associated probability of triggering an avalanche current when an incident photon is received. There is a first probability that an electron is generated when a photon reaches the diode, and then a second probability that the electron triggers an avalanche current. The total probability that a photon triggers an avalanche current may be referred to as the photon detection efficiency (PDE) of the SPAD. Therefore, grouping multiple SPAD pixels in a SiPM together enables a more accurate measurement of the incoming incident light. For example, if a single SPAD pixel has a PDE of 50% and receives one photon within a certain time period, there is a 50% probability that the photon will not be detected. Utilizing Figure 3 With the SiPM 220, two of the four SPAD pixels will detect a photon with a probability greater than 50%, thereby improving the image data provided for the time period and allowing for a more precise measurement of the incident light.

[0030] The example of multiple SPAD pixels with a common output in a SiPM is exemplary only. In the case where an imaging system includes a SiPM with a common output of all SPAD pixels, the imaging system may not have any resolution when imaging a scene. In other words, the SiPM is only able to detect photon flux at a single point. It may be advantageous to obtain image data on an array using SPAD pixels to allow a higher resolution reproduction of the imaged scene. In cases such as these, the SPAD pixels in a single imaging system may have a pixel-by-pixel readout capability. Alternatively, an array of SiPMs may be included in the imaging system, wherein each SiPM includes more than one SPAD pixel. The output from each pixel or from each SiPM may be used to generate image data of the imaged scene. The array may be capable of independent detection in a SiPM, a line array such as an array with a single row and multiple columns or a single column and multiple rows, or an array with more than ten rows and / or columns, more than one hundred rows and / or columns, or more than one thousand rows and / or columns, regardless of whether a single SPAD pixel or multiple SPAD pixels are used in the SiPM.

[0031] return Figure 1, the LIDAR module 102 may also include a transmitter 116 and a receiver 118. The LIDAR processing circuit 120 may control the transmitter 116 and the laser 104. The LIDAR processing circuit 120 may include processing circuits and storage devices, and may be configured to perform operations using hardware, firmware, and / or software such as dedicated hardware or circuits. Software code and other data for performing operations may be stored on a non-transitory computer-readable storage medium such as a tangible computer-readable storage medium in the processing circuit. Remote storage devices and other remote control circuits such as circuits on remote servers may also be used to store software code. Software code may sometimes be referred to as software, data, program instructions, computer instructions, instructions, or code. Non-transitory computer-readable storage media may include non-volatile memory such as non-volatile random access memory, one or more hard disk drives such as magnetic drives or solid-state drives, one or more removable flash drives or other removable media, or other storage devices. The software stored on the non-transitory computer readable storage medium may be executed on processing circuitry and / or processing circuitry of remote hardware such as a processor associated with one or more remote servers communicating via wired and / or wireless communication links. The processing circuitry may include an application specific integrated circuit with processing circuitry, one or more microprocessors, a central processing unit (CPU), or other processing circuitry.

[0032] The LIDAR processing circuit 120 may also receive data from the receiver 118 and the SiPM 114. Based on the data from the SiPM 114, the LIDAR processing circuit 120 may determine the distance to the obstacle 110. The LIDAR processing circuit 120 may communicate with the system processing circuit 101. The system processing circuit 101 may take corresponding measures, such as system-level measures, based on the information from the LIDAR module 102.

[0033] LIDAR processing circuit 120 may include time-to-digital converter (TDC) circuit 132 and autonomous dynamic resolution (ADR) circuit 134. Time-to-digital converter circuit 132 may use a time value, such as between a laser emitting light and SiPM 114 receiving a reflection, to obtain a digital value representing the distance to obstacle 110.

[0034] Reading out direct ToF LIDAR is implemented using multiple laser cycles to create a histogram in memory based on the timestamps generated by the SPAD and TDC. The peak of the histogram can then be used to determine the time it took the laser signal to travel to the target and back to the sensor. However, generating a histogram of all the timestamps can require a very large memory. Therefore, improvements in the TDC circuitry and ToF determination are needed. Figure 4An illustrative example of a LIDAR circuit that may be used to measure the distance to an obstacle, such as obstacle 110, is shown in FIG.

[0035] like Figure 4 As shown, it can be used as Figure 1 LIDAR circuit 400, which is part of LIDAR processing circuit 120, may first generate ToF measurement results 402. Figures 1 to 3 As discussed, ToF measurements 402 may be measurements generated by SPAD pixels 202 in SiPM 220. Specifically, the measurements may be based on a timestamp generated by SPAD pixels 202. Multiple SPAD pixels 202 in SiPM 220 may be triggered by light and generate ToF measurements 402. Specifically, each SPAD pixel 202 that detects light that has been reflected from an external object may generate a ToF measurement based on the time it takes for the light to be emitted and reflected back to the SPAD pixel 202. Additionally, each SPAD pixel 202 that detects stray light (e.g., ambient light) may also generate a ToF measurement.

[0036] The transformation circuit 404 may transform each ToF measurement 402. Specifically, each ToF measurement 402 may include information about the time at which light is incident on each SPAD pixel 202. The transformation circuit 404 may transform each ToF measurement 402 into a lower dimensional space. In general, the transformation circuit 404 may transform each ToF measurement 402 into any lower dimensional space, such as a space of approximate ToF measurements. Some exemplary transformations that may be applied to the ToF measurements 402 are shown in FIG. 5A to FIG. 5D middle.

[0037] like Figure 5A As shown, each ToF measurement result 402 can be transformed using a transformation 500. Transformation 500 can be a CORDIC (Coordinate Rotation Digital Computer) algorithm. In other words, each ToF measurement result 402 can be transformed into a circumference of a circle in a complex plane, which includes a real part and an imaginary part. Each ToF measurement result 402 can be transformed into a vector, such as vector 501. Vector 501 can have an angle measured counterclockwise from the real axis, hereinafter referred to as the amplitude angle θ505, which can reflect the ToF measurement result 402 (e.g., the ToF of the light received by the corresponding SPAD pixel 202). Vector 501 can be a unit vector, such as a Euclidean unit vector (wherein the vector x+iy satisfies ). Optionally, vector 501 may be any suitable vector of known length.

[0038] Transformation 500 may be a CORDIC algorithm and implemented using Equation 1,

[0039]

[0040] where x+iy is a vector resulting from transform 500 having a real part x and an imaginary part y, r is the timestamp received by a given SPAD pixel, and R is a maximum range, such as a power of 2 or other suitable maximum range (e.g., Figure 5A 32 in ). In this way, each ToF measurement result 402 can be transformed into a unit vector x+iy, which has an argument corresponding to the ToF.

[0041] like Figure 5B As shown, instead of using the transformation circuit 404 of the CORDIC transformation 500, a square transformation 502 may be used. In other words, each TOF measurement result 402 may be transformed into a point on the perimeter of a square on the complex plane. Specifically, the square transformation 502 may include a real part and an imaginary part. Each ToF measurement result 402 may be transformed into a vector with a constant Chebyshev distance (or other known length), such as vector 511. Vector 511 may have an argument θ 509 relative to the real axis. Argument 509 may reflect the ToF measurement result 402 (e.g., the ToF of the light received by the corresponding SPAD pixel 202).

[0042] The square transform 502 may be a conditional addition / subtraction algorithm that approximates the CORDIC transform 500. The square transform 502 may be implemented using Equation 2,

[0043]

[0044] where x+iy is a vector resulting from the square transform 502 having a real part x and an imaginary part y, r is the timestamp received by a given SPAD pixel, and R is a maximum range, such as a power of 2 or other suitable maximum range (e.g., Figure 5B 32 in ). As shown in Equation 2, the square transform 502 can be implemented as a conditional addition / subtraction based on the relative values ​​of r and R.

[0045] like Figure 5C As shown, instead of using the transform circuit 404 of the CORDIC transform 500 or the square transform 502, a diagonal transform 503 may be used. In other words, each ToF measurement 402 may be transformed into a point on the perimeter of a diagonal square. The diagonal square may be located on the complex plane and aligned so that its edges are 45° to the real and imaginary axes. The diagonal transform 503 may be a conditional addition / subtraction algorithm that approximates the CORDIC transform 500 and may be implemented using Equation 3.

[0046]

[0047] Where x+iy is a vector resulting from the diagonal transform 503 having a real part x and an imaginary part y, r is the timestamp received by a given SPAD pixel, and R is a maximum range, such as a power of 2 or other suitable maximum range (e.g., Figure 5C 32 in ). As shown in Equation 3, the diagonal transform 503 can be implemented as a conditional addition / subtraction based on the relative values ​​of r and R.

[0048] like Figure 5C As shown, instead of using the transform circuit 404 of the CORDIC transform 500, the square transform 502, or the diagonal transform 503, an octagonal transform 504 may be used. In other words, each TOF measurement result 402 may be transformed into a point on the perimeter of an octagon. The octagonal transform 504 may be a conditional addition / subtraction algorithm that approximates the CORDIC transform 500 and may be implemented using Equation 4.

[0049]

[0050] where I and Q are the real and imaginary parts, respectively, of the vector I+Qi (also referred to as x+iy) produced by the octagonal transform 504, n is the timestamp received by a given SPAD pixel, and N is a maximum range, such as a power of 2 or other suitable maximum range (e.g., Figure 5D 32 in the example of ). As shown in Equation 4, the octagon transformation 504 can be implemented as a conditional addition / subtraction based on the relative values ​​of n and N.

[0051] However, in general, the transformation circuit 404 may use any suitable transformation to transform the ToF measurement result 402 to a lower dimensional space, such as a transformation into a vector x+iy (or I+Qi for the octagonal transformation 504). As discussed, the ToF measurement result 402 may be transformed into a vector on a two-dimensional complex plane. Alternatively, the ToF measurement result 402 may be transformed into a grayscale encoded value or may be transformed into a grayscale encoded value after an error correction operation, for example.

[0052] Regardless of the transform used by transform circuit 404 to transform ToF measurement 402 to a lower dimensional space, the transformed value, such as vector x+iy, may be passed from transform circuit 404 to integration circuit 406, such as Figure 4 shown.

[0053] As each transformed value, such as vector x+iy, is generated by transform circuit 404, integration circuit 406 may add or otherwise combine the transformed values. FIG. 5A to FIG. 5D If one of the transformations in produces a vector x+iy for each ToF measurement result, the integration circuit 406 can add these vectors.

[0054] Because each complex vector has an argument that corresponds to the ToF measured by one of the SPADs, adding or otherwise combining the complex vectors produced for each SPAD pixel will produce a large vector whose argument will approximate the ToF of the external object. Specifically, there may be multiple vectors corresponding to SPAD pixel measurements of light that have been reflected from an external object. In other words, the argument of the vector may correspond to the correct ToF associated with the external object. In contrast, a vector corresponding to noise such as ambient light may have a random argument (e.g., a random ToF) in the transformed space. When these vectors are added together, such as by summing to zero, the vectors corresponding to the noise will mostly or completely cancel, while the vectors corresponding to the external object will dominate and estimate the ToF associated with the external object. Figure 6 An illustrative example of combined transformation vectors is shown in .

[0055] like Figure 6 As shown, vector integration 600 may include separate vectors 602A and 602B. Vectors 602A and 602B may each have axes 608 (e.g., real axes in the transformed space, such as Figure 5A and Figure 5B 604). Vector 602A may be a transformed value of ToF measurement 402 corresponding to light reflected from an external object, while vector 602B may be attributable to noise. By adding vector 602A and vector 602B, vector 602B will approximately cancel (e.g., sum to zero or approximately zero), while vector 602A may add to a vector of the same high magnitude having the same argument (e.g., the argument corresponding to the ToF associated with the external object).

[0056] like Figure 4 As shown, after all vectors have been integrated by the integration circuit 406, the decoding circuit 408 may convert the integrated value into a filtered ToF value 410. For example, the decoding circuit 408 may transform the integrated value into the filtered ToF value 410. Specifically, the decoding circuit 408 may convert the final x+iy vector back to ToF by calculating a ToF value or a range of ToF values ​​based on the argument of the integrated vector. In this way, the ToF associated with the external object may be determined. Figure 7 An illustrative example of determining a filtered ToF value is shown in FIG.

[0057] like Figure 7 As shown, the integrated vector 702 may be received by the decoding circuit 408. In other words, the integrated vector 702 may be a vector obtained by adding or otherwise combining all transformed vectors generated by the transform circuit 404. The integrated vector 702 may be at an angle θ704 to the axis 708. The axis 708 may correspond to a real axis in the transformed space, such as FIG. 5A to FIG. 5Dshown.

[0058] Decoding circuitry 408 may determine argument 704 of integrated vector 702. Based on argument 704, decoding circuitry 408 may determine a ToF or range of ToF that matches argument 704 (eg, by using the original transform used by transform circuitry 404). In this way, decoding circuitry 408 may determine the ToF from the integrated vector.

[0059] Decoding circuit 408 may determine argument 704 by using a reverse mapping (eg, based on the transform used by integration circuit 406). For example, if Figure 5C The diagonal transformation 503 of , the resulting integrated vector can be decoded by the decoding circuit using equation 5:

[0060]

[0061] Among them, n s is the argument; N is the maximum range (corresponding to R of Equation 3), such as a power of 2, or other suitable maximum range (e.g., Figure 5C 32); I and Q are the real and imaginary parts of the integrated vector x+iy (e.g., I+Qi), respectively.

[0062] As another example, if you use Figure 5D The octagonal transformation 504 of , the resulting integrated vector can be decoded by the decoding circuit using Equation 6:

[0063]

[0064] Among them, n s is the argument; N is the maximum range (corresponding to R of Equation 3), such as a power of 2, or other suitable maximum range (e.g., Figure 5C 32); I and Q are the real and imaginary parts of the integrated vector x+iy (e.g., I+Qi), respectively.

[0065] However, Equations 5 and 6 are merely illustrative examples of decoding equations that may be used to determine the argument of the integrated vector. In general, any suitable decoding equation may be used to determine the argument of the integrated vector based on the transform used by transform circuitry 404. In this manner, decoding circuitry 408 may determine the argument of the integrated circuit and determine ToF.

[0066] In addition to determining the ToF based on the argument 704 of the integrated vector 702, the length of the vector 702 may indicate a reliable result. For example, if the magnitude of the vector 702 is large, then a large number of the individual transformed vectors that are integrated to form the vector 702 have similar arguments and therefore have a ToF similar to the vector 702. In this way, the magnitude of the vector 702 may indicate reliability. In some embodiments, such as LIDAR processing circuitry 120 ( Figure 1 ) may discard measurements where the final integrated vector is below a threshold, such as a magnitude less than 5, 10, or 15. However, this is merely exemplary. A processing circuit such as LIDAR processing circuit 120 may retain or discard any desired measurement.

[0067] The integrated vector 702 may fall within the range of uncertainty illustratively shown by circle 706. Specifically, by combining the transformed vectors to form the integrated vector 702, the resulting ToF measurement is an approximation of the actual ToF. However, the process will always return a value (e.g., an integrated vector) that can be used to approximate the ToF using the argument of the integrated vector.

[0068] Based on the filtered ToF 410, the LIDAR processing circuit 120 or other circuitry may determine the distance to the external object.By transforming each SPAD measurement, the direct ToF LIDAR module may use less memory to determine the ToF to the external object. Figure 8 Illustrative steps that may be used to make this determination using a direct ToF system are shown in flowchart 800 .

[0069] like Figure 8 As shown, at step 810, such as Figure 1 A LIDAR system, such as the LIDAR module 102 of FIG. 1 , may emit light and detect reflections of the emitted light using SPAD pixels. In some embodiments, the LIDAR system may emit light toward an external object, such as by using beam steering or otherwise modifying the emitted light directed toward the external object, or may otherwise illuminate a scene including an object of interest. The SPAD pixels may generate measurements in response to light reflected from the external object, and these measurements may be used to calculate the ToF required for the emitted light to return to the LIDAR system.

[0070] At step 820, each SPAD measurement may be transformed to a lower dimensional space. Specifically, the SPAD measurement may include information about the ToF associated with the external object. The SPAD measurement may be transformed into a unit vector on a two-dimensional complex plane with an argument representing the SPAD measurement. As an example, one of FIG. 5A to FIG. 5DThe transforms in Equations 1 to 4 are used to transform the SPAD measurements onto a two-dimensional complex plane, or the SPAD measurements may be transformed into grayscale encoded values. However, in general, any suitable transform may be used to transform the SPAD measurements to a lower dimension.

[0071] At step 830, the transformed SPAD measurements may be combined (e.g., integrated). Specifically, the SPAD measurements may be added or otherwise combined. For example, if the SPAD measurements are converted into vectors on a two-dimensional complex plane, each transformed x+yi vector may be summed. Integration may be performed immediately after each transformed SPAD measurement is generated. In other words, each transformed SPAD measurement may be added to the previously integrated value. Optionally, integration may be performed after a desired number of transformed SPAD measurements have been generated (e.g., after every two, three, five, or other suitable number of SPAD measurements), or the transformed SPAD measurements may be summed after all SPAD measurements have been transformed. In this way, an integrated transformed SPAD measurement may be generated.

[0072] At step 840, the ToF may be determined based on the integrated transformed SPAD measurements. Specifically, the final integrated measurements may be converted (e.g., transformed) into a ToF value or value range. For example, if the raw SPAD measurements are transformed into vectors, each vector may be a unit vector with an amplitude corresponding to the SPAD measurement. Adding or otherwise combining the vectors generated for each SPAD pixel will approximate the ToF.

[0073] For example, there may be multiple transformed vectors with the same (or similar) magnitude that correspond to SPAD pixel measurements of light reflected from an external object. In contrast, vectors corresponding to noise, such as ambient light, may have varying (e.g., random) directions (ToF). When these vectors are added together, such as summing to zero, the vectors corresponding to the noise will mostly cancel, while the vectors corresponding to the external object will dominate and estimate the ToF associated with the external object. By transforming each SPAD measurement, the direct ToF LIDAR module can determine the ToF to the external object using less memory.

[0074] Those skilled in the art will appreciate that exemplary embodiments of the present invention may be practiced without some or all of these specific details. In other instances, well-known operations have not been described in detail to avoid unnecessarily obscuring embodiments of the present invention.

[0075] The above contents are merely exemplary, and various modifications may be made to the described embodiments. The above embodiments may be implemented individually or in any combination.

Claims

1. A light detection and ranging device, the light detection and ranging device comprising: a plurality of single photon avalanche diodes configured to produce measurements in response to light; a transformation circuit coupled to the single photon avalanche diode and configured to transform the measurement result from a first dimensional space to a second dimensional space lower than the first dimensional space; an integration circuit configured to combine the transformed measurements; and A decoding circuit is configured to determine a time of flight based on the integrated transformed measurements.

2. The light detection and ranging device of claim 1, wherein the second dimensional space comprises a two-dimensional complex plane, and wherein each of the transformed measurements comprises a vector on the two-dimensional complex plane.

3. The light detection and ranging device of claim 2, wherein the transformation circuit is configured to transform the measurement result into the perimeter of a square on the two-dimensional complex plane.

4. The light detection and ranging device according to claim 3, wherein the square is a diagonal square whose edges are at 45° to the real axis and the imaginary axis of the two-dimensional complex plane.

5. The light detection and ranging device of claim 2, wherein the transformation circuit is configured to transform the measurement result into a perimeter of an octagon on the two-dimensional complex plane.

6. The light detection and ranging device of claim 2, wherein the transformation circuit is configured to transform the measurement result into a circumference of a circle on the two-dimensional complex plane.

7. The light detection and ranging device of claim 2, wherein the transformation circuit is configured to transform the measurement result from the first dimensional space to the second dimensional space using a coordinate rotation digital computer (CORDIC) transformation.

8. The light detection and ranging device of claim 2, wherein the integration circuit is configured to combine the transformed measurements by summing the vectors produced by the transformation circuit to produce a summed vector.

9. The light detection and ranging device of claim 8, wherein the decoding circuit is configured to determine the time of flight based on the argument of the summed vector.

10. A method of operating a light detection and ranging device, the method comprising: emitting a laser and detecting reflections of the laser using a single photon avalanche diode pixel to produce a measurement; transforming each of the measurements from a first dimensional space to a second dimensional space lower than the first dimensional space to produce a transformed measurement; as well as The transformed measurements are combined.

11. The method of claim 10, wherein transforming each of the measurements comprises transforming each of the measurements into a vector in a two-dimensional complex plane to produce the transformed measurements. 12 . The method of claim 11 , wherein integrating the transformed measurements comprises adding the vectors to form an integrated vector.

13. The method according to claim 12, further comprising: After adding the vectors, the time of flight is determined based on the integrated vectors. The method of claim 13 , wherein determining the time of flight comprises determining a distance to an external object.

15. The method of claim 11, wherein transforming each of the measurements into a vector in a two-dimensional complex plane comprises transforming each of the measurements into a perimeter of a square on the two-dimensional complex plane.

16. The method of claim 11, wherein transforming each of the measurements into a vector in a two-dimensional complex plane comprises transforming each of the measurements into the perimeter of the square with edges at 45° to an axis of the two-dimensional complex plane.

17. The method of claim 11, wherein transforming each of the measurements into a vector in a two-dimensional complex plane comprises transforming each of the measurements into a perimeter of an octagon on the two-dimensional complex plane.

18. A light detection and ranging device, the light detection and ranging device being configured to produce a direct time-of-flight measurement in response to an external object, the light detection and ranging device comprising: a laser configured to emit light toward the external object; a plurality of single photon avalanche diodes configured to generate a signal in response to reflected light from the external object; a transform circuit configured to transform the generated signal to produce a transformed signal; an integrating circuit configured to combine the transformed signals to generate an integrated signal; and A decoding circuit is configured to generate the direct time-of-flight measurement based on the integrated signal.

19. The light detection and ranging device of claim 18, wherein the transformation circuit is configured to transform the generated signal from a first dimensional space to a second dimensional space lower than the first dimensional space.

20. The light detection and ranging device of claim 19, wherein the second dimensional space comprises a two-dimensional complex plane, wherein each of the transformed signals comprises a vector on the two-dimensional complex plane, and wherein the integration circuit is configured to generate the integrated signal by adding the vectors.

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