KR102150098B1 - A method and an apparatus for estimating a distance of a target by using an Laser Range-Gated Imaging sensor - Google Patents

Abstract

Acquiring a plurality of sample images while changing a point in time at which the shutter of the camera included in the LRGI sensor is exposed to the outside; Determining whether a target exists in each of the obtained sample images, and generating a binary sequence based on the determination result; Predicting a probability that a target will exist in an image acquired from an LRGI sensor by applying a maximum likelihood technique to the generated binary sequence; Determining a position of the target within the distance gate of the LRGI sensor based on the predicted probability; And estimating the distance of the target using the position of the distance gate and the position of the target within the distance gate. A method of estimating a distance of a target using an LRGI sensor is disclosed.

Description

TECHNICAL FIELD A method and an apparatus for estimating a distance of a target by using an Laser Range-Gated Imaging sensor.

The present disclosure relates to a method and apparatus for estimating a distance of a target using an LRGI sensor.

The LRGI (Laser Range-Gated Imaging) sensor is a device that emits a pulsed laser toward a target, receives light reflected from the target through a camera, and images a target based on the received light. The LRGI sensor can image only a target located at a specific distance by controlling the timing of firing the pulsed laser and exposing the shutter of the camera to the outside.

For example, FIG. 1 is a diagram illustrating an example of a process in which an LRGI sensor acquires an image. Referring to FIG. 1, the LRGI sensor may include a laser light source 110 and a camera 120. The LRGI sensor emits a pulsed laser using the laser light source 110, and after d seconds elapse, the shutter of the camera 120 may be exposed to the outside. Accordingly, a location at a distance z from the camera 120 may be imaged. The relationship between d and z may follow Equation 1 below.

에 따라 영상화되는 심도가 결정될 수 있다. 심도에 대응되는 특정한 거리 구간은 거리 게이트로 지칭될 수 있다. 카메라(120)의 셔터가 외부에 노출되는 시간

및 거리 게이트의 심도 s 간의 관계는 다음과 같은 수학식 2를 따를 수 있다.In Equation 1, c means the speed of light. Meanwhile, the time the shutter of the camera 120 is exposed to the outside

Depending on the image depth may be determined. A specific distance section corresponding to the depth may be referred to as a distance gate. Time when the shutter of the camera 120 is exposed to the outside

And the relationship between the depth s of the distance gate may follow Equation 2 below.

In order to continuously image the moving target 10 using the LRGI sensor, the target within the distance gate is controlled by adjusting the point d at which the shutter of the camera 120 is exposed to the outside after firing a pulsed laser using the laser light source 110. It is necessary to adjust the position of the street gate so that (10) is continuously included. For example, when the target 10 moves and tries to leave the street gate, the position of the street gate needs to be adjusted by changing the point d at which the shutter of the camera 120 is exposed to the outside. However, since a general LRGI sensor cannot know where the target 10 is located within the distance gate, the point d at which the shutter of the camera 120 is exposed to the outside cannot be changed immediately, and the moving target 10 is continuously tracked. And it can be difficult to image. Therefore, a technique of accurately estimating the distance of the target 10 using the LRGI sensor may be required.

Various embodiments provide a method and apparatus for estimating a distance of a target using an LRGI sensor. The technical problem to be achieved by the present disclosure is not limited to the technical problems as described above, and other technical problems may be inferred from the following embodiments.

As a means for solving the above-described technical problem, a method of estimating a distance of a target using a laser range-gated imaging (LRGI) sensor according to one aspect is to expose a shutter of a camera included in the LRGI sensor to the outside. Acquiring a plurality of sample images while changing a viewpoint; Determining whether the target is present in each of the acquired plurality of sample images, and generating a binary sequence based on the determination result; Predicting a probability that the target will exist in the image acquired from the LRGI sensor by applying a maximum likelihood technique to the generated binary sequence; Determining a location of the target within a distance gate of the LRGI sensor based on the predicted probability; And estimating a distance of the target using the location of the distance gate and the location of the target within the distance gate.

The time point at which the shutter of the camera for acquiring the plurality of sample images is exposed to the outside is obtained by adding a random variable j following a predefined random probability distribution having an average of 0 to an arbitrary time point d corresponding to the position of the distance gate. It can be changed according to the value d+j.

According to some embodiments, determining whether the target exists may include performing image binarization and morphology calculations based on preset thresholds for each of the acquired sample images; And determining whether the target exists based on a result of the image binarization and morphology operation.

In addition, in the generating of the binary sequence, when it is determined that the target is present in the first sample image among the acquired plurality of sample images, a value corresponding to the first sample image in the binary sequence is determined as 1 Step to do; And when it is determined that the target does not exist in the second sample image among the acquired plurality of sample images, determining a value corresponding to the second sample image as 0 in the binary sequence.

에 따라 결정될 수 있다.In one example, predicting the probability that the target will exist when the speed of the target is relatively slow may include modeling the existence of the target with a Bernoulli random variable; And applying the maximum likelihood method to the modeled result, wherein the predicted value p of the probability that the target exists in the image obtained from the LRGI sensor is the number of the plurality of sample images n, the plurality of sample images When r is the number of images in which the target exists, the equation

Can be determined according to.

, 상기 k번째 영상에서 상기 표적이 존재할 확률을

, 기 정의된 상수를

라고 할 때,

의 조건 하에서 수학식

을 최대화하는

로 결정될 수 있다.In another example, when the speed of the target is relatively high, predicting the probability of the presence of the target may include changing a predicted value of the probability of the presence of the target in the image obtained from the LRGI sensor for each of the plurality of sample images. Modeling to be performed, wherein the predicted value of the probability of the existence of the target in the image obtained from the LRGI sensor is the number of the plurality of sample images n, and whether the target is present in the k-th image among the plurality of sample images A function indicating whether

, The probability that the target exists in the k-th image

, A predefined constant

When I say,

Under the condition of

To maximize

Can be determined as

, 상기 거리 게이트의 심도를 s, 상기 거리 게이트 내 상기 표적의 위치를 m이라고 할 때, 다음과 같은 수학식

에 기초하여 상기 LRGI 센서로부터 획득되는 영상에 상기 표적이 존재할 확률(

) 및 상기 거리 게이트 내 상기 표적의 위치(m) 간의 관계가 결정될 수 있다.Meanwhile, a random probability distribution function corresponding to the random variable j

, When the depth of the distance gate is s and the position of the target in the distance gate is m, the following equation

The probability that the target exists in the image obtained from the LRGI sensor based on (

) And the position (m) of the target within the distance gate may be determined.

Estimating the distance of the target may include applying Kalman filtering to a value obtained by adding the position of the distance gate and the position of the target within the distance gate.

In addition, the method may further include adjusting the position of the distance gate by adjusting a time point at which the shutter of the camera included in the LRGI sensor is exposed to the outside based on the estimated distance of the target.

Further, the computer-readable recording medium according to another aspect may include a recording medium in which one or more programs including instructions for executing the above-described method are recorded.

In addition, an apparatus for estimating a distance of a target using an LRGI sensor according to another aspect comprises: a laser light source for emitting a pulsed laser toward the target; A camera for receiving the laser light reflected from the target by opening the shutter after the laser light source fires the pulsed laser toward the target and a predetermined time elapses; A memory in which at least one program is stored; And at least one processor estimating the distance of the target by executing the at least one program, wherein the at least one processor acquires a plurality of sample images while changing a point in time at which the shutter of the camera is exposed to the outside. And, by determining whether the target is present in each of the obtained plurality of sample images, generating a binary sequence based on the determination result, and by applying a maximum likelihood technique to the generated binary sequence, the LRGI Predict the probability of the presence of the target in the image obtained from the sensor, determine the position of the target within the distance gate of the LRGI sensor based on the predicted probability, the position of the distance gate and the target within the distance gate The distance of the target can be estimated using the location.

The present disclosure may provide a method and apparatus for estimating a distance of a target using an LRGI sensor. Specifically, the method and apparatus according to the present disclosure determine whether a target is present in each of a plurality of sample images, generate a binary sequence based on the determination result, and apply a maximum likelihood technique to the generated binary sequence. , It is possible to accurately estimate the distance of the target by predicting the probability that the target will exist in the image obtained from the LRGI sensor, and determining the position of the target within the distance gate of the LRGI sensor based on the predicted probability.

Accordingly, whether or not the target is about to leave the distance gate of the LRGI sensor can be predicted, and the position of the distance gate can be adjusted so that the target continues to be included in the distance gate. Thus, a moving target can be continuously tracked and imaged.

1 is a diagram illustrating an example of a process in which an LRGI sensor acquires an image.
2 is a flowchart illustrating a method of estimating a distance of a target using an LRGI sensor according to some embodiments.
3 is a graph illustrating a relationship between a position of a target in a distance gate and a probability of a target present in an image according to some embodiments.
4 is a block diagram showing a configuration of an apparatus for estimating a distance of a target using an LRGI sensor according to some embodiments.

Hereinafter, exemplary embodiments will be described in detail with reference to the accompanying drawings. It goes without saying that the following description is only for specifying the embodiments and does not limit or limit the scope of the invention. What can be easily inferred by experts in the art from the detailed description and examples is interpreted as belonging to the scope of the rights.

The terms “consisting of” or “including” used herein should not be construed as necessarily including all of the various elements or various steps described in the specification, and some of the elements or some steps It should be construed that they may not be included or may further include additional elements or steps.

In addition, terms including ordinal numbers such as'first' or'second' used in the present specification may be used to describe various elements, but the elements should not be limited by the terms. These terms are only used for the purpose of distinguishing one component from another component.

Terms used in the present specification have selected general terms that are currently widely used as possible while taking functions of the present invention into consideration, but this may vary depending on the intention or precedent of a technician working in the field, the emergence of new technologies, and the like. In addition, in certain cases, there are terms arbitrarily selected by the applicant, and in this case, the meaning of the terms will be described in detail in the description of the corresponding invention. Therefore, the terms used in the present invention should be defined based on the meaning of the term and the overall contents of the present invention, not a simple name of the term.

The present embodiments relate to a method and apparatus for estimating the distance of a target using an LRGI sensor, and detailed descriptions of matters widely known to those of ordinary skill in the art to which the following embodiments belong will be omitted. .

2 is a flowchart illustrating a method of estimating a distance of a target using an LRGI sensor according to some embodiments. The method of estimating the distance of the target using the LRGI sensor of FIG. 2 may be performed by the LRGI sensor itself or by a device including the LRGI sensor. Hereinafter, both the LRGI sensor and the device including the LRGI sensor are referred to as'devices'.

Referring to FIG. 2, in operation 210, the device may acquire a plurality of sample images while changing a point in time at which the shutter of the camera included in the LRGI sensor is exposed to the outside. In order for the device to image a target at a specific location using an LRGI sensor, the position of the distance gate must be properly adjusted. For example, when a user wants to position the distance gate at a desired location z, a time point at which the shutter of the camera included in the LRGI sensor is exposed to the outside may be calculated according to Equation 1 above.

The point at which the shutter of the camera for acquiring a plurality of sample images is exposed to the outside is d+, which is a value obtained by adding a random variable j following a predefined random probability distribution with an average of 0 to a point d corresponding to the position of the distance gate. It can be changed according to j. Accordingly, the probability that the target exists in the image obtained from the LRGI sensor may be changed according to the position of the target within the depth of the distance gate.

, 거리 게이트의 심도를 s, 거리 게이트 내 표적의 위치를 m이라고 할 때, 다음과 같은 수학식 3 에 기초하여 LRGI 센서로부터 획득되는 영상에 표적이 존재할 확률

및 거리 게이트 내 표적의 위치(m) 간의 관계가 결정될 수 있다.The random probability distribution function corresponding to the random variable j

, When the depth of the distance gate is s and the position of the target in the distance gate is m, the probability that the target exists in the image acquired from the LRGI sensor based on Equation 3 below.

And the relationship between the position (m) of the target in the distance gate may be determined.

는 평균이 0인 랜덤 확률 분포 함수일 수 있다. 이하 도 3을 참조하여 s=30m이고,

가 가우시안 랜덤 확률 분포 함수(

의 표준편차는 5.5m)인 경우를 가정할 때의 영상에 표적이 존재하는 확률

을 보다 상세히 설명한다.In Equation 3, m denotes the position of the target within the distance gate. If the nearest position from the LRGI sensor is 0 and the farthest position is s, m has a value in the range of (0,s).

May be a random probability distribution function with an average of 0. Hereinafter, s=30m with reference to FIG. 3,

Gaussian random probability distribution function (

The standard deviation of is 5.5m), the probability that the target exists in the image

It will be described in more detail.

3 is a graph illustrating a relationship between a position of a target in a distance gate and a probability of a target present in an image according to some embodiments.

이 달라짐을 알 수 있다. 따라서, 영상에 표적이 존재하는 확률

이 예측될 수 있다면, 앞의 수학식 3의 관계를 이용하여 거리 게이트 내 표적의 위치가 추정될 수 있다. Referring to the graph 300 of FIG. 3, the probability that the target exists in the image according to the position m of the target in the distance gate

You can see this change. Therefore, the probability that the target is present in the image

If this can be predicted, the position of the target in the distance gate can be estimated using the relationship of Equation 3 above.

Meanwhile, since the graph 300 is symmetrical around an intermediate point of the distance gate, two positions corresponding to one existence probability value may be determined. In this case, one of the two positions may be determined depending on whether the amount of change in the probability of the presence of the target increases or decreases. In addition, a position close to the predicted position may be determined by continuously tracking the position of the target using a tracking filter (eg, a Kalman filter).

가 가우시안 랜덤 확률 분포 함수(

의 표준편차는 5.5m)인 경우를 가정할 때의 예시에 불과할 뿐 이에 제한되는 것은 아니다. 거리 게이트 내 표적의 위치 및 영상에 표적이 존재하는 확률 간의 관계를 나타내는 그래프가 조건에 따라 다르게 설정될 수 있음은 해당 기술분야의 통상의 지식을 가진 자라면 쉽게 알 수 있다.Meanwhile, the graph 300 of FIG. 3 is s=30m,

Gaussian random probability distribution function (

The standard deviation of is only an example assuming 5.5m), but is not limited thereto. It can be easily understood by those of ordinary skill in the art that a graph indicating the relationship between the position of the target within the distance gate and the probability of the presence of the target in the image may be set differently depending on conditions.

Returning to FIG. 2 again, in step 220, the apparatus may determine whether a target exists in each of the acquired sample images, and may generate a binary sequence based on the determination result.

The apparatus may determine whether a target exists in each of a plurality of sample images acquired using a simple image processing technique. For example, the device may perform image binarization and morphology calculation based on a preset threshold for each of a plurality of acquired sample images, and determine whether a target exists based on image binarization and morphology calculation results. I can. Since the background signal is removed from the image acquired from the LRGI sensor and only the target signal remains, the existence of the target can be confirmed through image binarization and morphology calculation based on a preset threshold. However, it is not limited thereto.

When it is determined that the target is present in the first sample image among the plurality of acquired sample images, the apparatus determines a value corresponding to the first sample image as 1 in the binary sequence, and determines the second sample among the plurality of acquired sample images. When it is determined that the target does not exist in the image, a value corresponding to the second sample image in the binary sequence may be determined as 0.

In operation 230, the device may predict a probability that a target exists in an image obtained from an LRGI sensor by applying a maximum likelihood technique to the generated binary sequence.

라 할 때,

의 예측 값 p는 다음과 같은 수학식 4를 따르는 최대화 문제의 해를 구함으로써 계산될 수 있다.When the speed of the target is relatively slow, the device can model the existence of the target with a Bernoulli random variable, and apply a maximum likelihood technique to the modeled result. For example, in the k-th image, a function representing the presence and absence of a target as 1 and 0, respectively

When la,

The predicted value p of can be calculated by solving the maximization problem according to Equation 4 below.

In Equation 4, n denotes the number of a plurality of sample images, and r denotes the number of images in which a target exists among the plurality of sample images. The p that maximizes Equation 4 above may be derived according to Equation 5 below.

의 예측 값 p를 복수 개의 표본 영상들 각각에 대해 변경되도록 모델링할 수 있다. 예를 들어,

의 예측 값 p는 다음과 같은 수학식 6를 따르는 최대화 문제의 해를 구함으로써 계산될 수 있다.On the other hand, when the speed of the target is relatively fast, the prediction error may increase when the probability that the target exists in the image is predicted by using Equation 5 above. To prevent this, the device has the probability that the target exists in the image acquired from the LRGI sensor.

The predicted value p of may be modeled to be changed for each of a plurality of sample images. For example,

The predicted value p of can be calculated by solving the maximization problem according to Equation 6 below.

는 k번째 영상에서 표적이 존재할 확률을 의미하고,

는 기 정의된 상수를 의미할 수 있다.

에 의해 표적의 존재 확률의 최대 변화량이 제한될 수 있다. 도 3에서 살펴본 바와 같이, 표적의 존재 확률은 연속적으로 변하므로, 표적의 존재 확률의 최대 변화량을 제한함으로써

의 예측 값 p가 계산될 수 있다. 수학식 6의 비용함수(cost function)에 로그를 취하면 다음과 같은 수학식 7이 도출될 수 있다.In Equation 6

Means the probability that the target exists in the k-th image,

May mean a predefined constant.

The maximum amount of change in the probability of the existence of the target may be limited by. As shown in Figure 3, since the probability of the existence of the target continuously changes, by limiting the maximum change amount of the probability of the existence of the target

The predicted value p of can be calculated. If the logarithm of the cost function of Equation 6 is taken, Equation 7 as follows can be derived.

Equation 7 is a nonlinear optimization problem that can be solved by applying a nonlinear programming method.

, k번째 영상에서 표적이 존재할 확률을

, 기 정의된 상수를

라고 할 때,

의 조건 하에서 수학식

을 최대화하는

로 결정될 수 있다.In summary, the predicted value of the probability of the existence of a target in the image acquired from the LRGI sensor is the number of the plurality of sample images n, and the function indicating whether the target exists in the k-th image among the plurality of sample images.

, the probability that the target exists in the kth image

, A predefined constant

When I say,

Under the condition of

To maximize

Can be determined as

In step 240, the device may determine the position of the target within the distance gate of the LRGI sensor based on the predicted probability. In order to determine the position of the target in the distance gate of the LRGI sensor based on the predicted probability, the relationship between the position of the target in the distance gate as shown in FIG. 3 and the probability that the target exists in the image may be used.

In step 250, the device may estimate the distance of the target using the location of the distance gate and the location of the target within the distance gate. For example, the device may estimate the distance of the target by adding the position of the distance gate and the position of the target within the distance gate. In addition, the device may apply Kalman filtering to the sum of the position of the distance gate and the position of the target within the distance gate.

The device may adjust the position of the distance gate by adjusting the timing at which the shutter of the camera included in the LRGI sensor is exposed to the outside based on the estimated distance of the target. As the position of the distance gate is appropriately adjusted so that the target continues to be included in the distance gate, tracking and imaging of a moving target may be performed using an LRGI sensor.

Meanwhile, the method of estimating the distance of the target using the LRGI sensor may be recorded in a computer-readable recording medium in which one or more programs including instructions for executing the method are recorded. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks and magnetic tapes, optical media such as CD-ROMs and DVDs, and floptical disks. A magneto-optical media such as, and a hardware device specially configured to store and execute program instructions such as ROM, RAM, flash memory, and the like. Examples of program instructions include not only machine language codes such as those produced by a compiler, but also high-level language codes that can be executed by a computer using an interpreter or the like.

4 is a block diagram showing a configuration of an apparatus for estimating a distance of a target using an LRGI sensor according to some embodiments.

Referring to FIG. 4, the device 40 may include a laser light source 410, a camera 420, at least one processor 430, and a memory 440. The device 40 may be an LRGI sensor itself, or may be a device including an LRGI sensor. The apparatus 40 shown in FIG. 4 may process the method shown in FIG. 2 in time series. Accordingly, it can be seen that even though the contents are omitted below, the contents described above with respect to the method of FIG. 2 can be performed by the apparatus 40 of FIG. 4.

Meanwhile, only the configurations related to the present embodiment are shown in the apparatus 40 shown in FIG. 4. Therefore, it can be understood by those of ordinary skill in the art related to the present embodiment that other general-purpose components other than the components shown in FIG. 4 may be further included in the device 40.

The laser light source 410 may fire a pulsed laser toward a target.

The camera 420 may receive light reflected from the target by opening the shutter after the laser light source 410 emits a pulsed laser toward the target and an arbitrary time elapses. Also, the camera 420 may perform imaging based on the received light. The position of the distance gate may be adjusted as the timing at which the shutter of the camera 420 is exposed to the outside is adjusted, and the target at a specific position may be imaged as the position of the distance gate is appropriately adjusted.

At least one processor 430 may be implemented by one or a plurality of processors. For example, the at least one processor 430 may be implemented as an array of a plurality of logic gates, or may be implemented as a combination of a general-purpose microprocessor and a memory storing a program executable in the microprocessor.

At least one processor 430 may acquire a plurality of sample images while changing a point in time at which the shutter of the camera 420 is exposed to the outside. The time point at which the shutter of the camera 420 for obtaining a plurality of sample images is exposed to the outside is obtained by adding a random variable j following a predefined random probability distribution with an average of 0 to a random time point d corresponding to the position of the distance gate. It can be changed according to the value d+j.

The at least one processor 430 may determine whether a target exists in each of the acquired sample images, and generate a binary sequence based on the determination result.

The at least one processor 430 performs image binarization and morphology calculation based on a preset threshold for each of the acquired sample images, and determines whether a target exists based on the image binarization and morphology calculation results. I can.

In addition, when it is determined that a target exists in the first sample image among the plurality of acquired sample images, the at least one processor 430 determines a value corresponding to the first sample image as 1 in the binary sequence, and If it is determined that a target does not exist in the second sample image among the three sample images, a value corresponding to the second sample image in the binary sequence may be determined as 0.

The at least one processor 430 may predict a probability that a target exists in an image obtained from an LRGI sensor by applying a maximum likelihood technique to the generated binary sequence.

에 따라 결정될 수 있다.When the speed of the target is relatively slow, at least one processor 430 may model the existence of the target as a Bernoulli random variable, and may apply a maximum likelihood technique to the modeled result. The predicted value p of the probability of the presence of a target in the image acquired from the LRGI sensor is the number of the plurality of sample images n, and the number of images with the target among the plurality of sample images is r, the equation

Can be determined according to.

, k번째 영상에서 표적이 존재할 확률을

, 기 정의된 상수를

라고 할 때,

의 조건 하에서 수학식

을 최대화하는

로 결정될 수 있다.When the speed is relatively high, the at least one processor 430 may model a predicted value of a probability that a target exists in an image obtained from the LRGI sensor to be changed for each of the plurality of sample images. The predicted value of the probability of the presence of a target in the image acquired from the LRGI sensor is the number of a plurality of sample images n, and a function indicating whether a target exists in the k-th image among the plurality of sample images.

, the probability that the target exists in the kth image

, A predefined constant

When I say,

Under the condition of

To maximize

Can be determined as

) 및 거리 게이트 내 상기 표적의 위치(m) 간의 관계를 이용하여 거리 게이트 내 표적의 위치를 결정할 수 있다.The at least one processor 430 may determine the position of the target within the distance gate of the LRGI sensor based on the predicted probability. At least one processor 430 has a probability that a target exists in the image acquired from the LRGI sensor (

) And the location of the target in the distance gate (m) may be used to determine the location of the target in the distance gate.

, 상기 거리 게이트의 심도를 s, 상기 거리 게이트 내 상기 표적의 위치를 m이라고 할 때, 다음과 같은 수학식

에 기초하여 LRGI 센서로부터 획득되는 영상에 표적이 존재할 확률(

) 및 거리 게이트 내 상기 표적의 위치(m) 간의 관계가 결정될 수 있다. LRGI 센서로부터 획득되는 영상에 표적이 존재할 확률(

) 및 거리 게이트 내 상기 표적의 위치(m) 간의 관계를 나타내는 그래프(300)의 예시는 도 3에 도시되어 있다.For example, the random probability distribution function corresponding to the random variable j

, When the depth of the distance gate is s and the position of the target in the distance gate is m, the following equation

The probability that the target exists in the image acquired from the LRGI sensor based on (

) And the position (m) of the target in the distance gate may be determined. The probability that the target exists in the image acquired from the LRGI sensor (

) And a graph 300 showing the relationship between the position m of the target in the distance gate is shown in FIG. 3.

The at least one processor 430 may estimate the distance of the target using the location of the distance gate and the location of the target within the distance gate. For example, the at least one processor 430 may estimate a value obtained by adding the position of the distance gate and the position of the target within the distance gate as the distance of the target. The at least one processor 430 may apply Kalman filtering to the sum of the position of the distance gate and the position of the target within the distance gate.

In addition, the at least one processor 430 may adjust the position of the distance gate by adjusting a time point at which the shutter of the camera included in the LRGI sensor is exposed to the outside based on the estimated distance of the target. As the position of the distance gate is appropriately adjusted so that the target continues to be included in the distance gate, tracking and imaging of a moving target may be performed using an LRGI sensor.

The memory 440 is hardware that stores various types of data processed in the device 40. For example, the memory 440 may store data processed by the device 40 and data to be processed. In addition, the memory 440 may store applications, drivers, and the like to be driven by the device 40. For example, the memory 440 may store a plurality of sample images acquired from an LRGI sensor.

The memory 440 is a random access memory (RAM) such as dynamic random access memory (DRAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), and CD- ROM, Blu-ray or other optical disk storage, hard disk drive (HDD), solid state drive (SSD), or flash memory, and furthermore, other external storage devices that can be accessed by device 40. I can.

Although the embodiments have been described in detail above, the scope of the present invention is not limited thereto, and various modifications and improvements by those skilled in the art using the basic concept of the present invention defined in the following claims are also the scope of the present invention. Belongs to.

Claims (11)

In a method of estimating the distance of a target using a LRGI (Laser Range-Gated Imaging) sensor,
A plurality of sample images while changing the time point at which the shutter of the camera included in the LRGI sensor is exposed to the outside according to d+j, which is a value obtained by adding a random variable j following a predefined random probability distribution with an average of 0 to an arbitrary point d Obtaining them;
Determining whether the target is present in each of the acquired plurality of sample images, and generating a binary sequence based on the determination result;
By applying a maximum likelihood technique to the generated binary sequence, the shutter of the camera is released from the time point d.

Predicting a probability that the target will exist in the image to be acquired from the LRGI sensor when exposed by as many times as possible;
Determining a location of the target within a distance gate of the LRGI sensor based on the predicted probability; And
Estimating the distance of the target using the location of the distance gate and the location of the target within the distance gate,
The distance gate has a speed of light c,

Is,

When said, corresponds to a distance section from a distance z from the LRGI sensor to a distance by z+s, and the position of the distance gate corresponds to z. delete The method of claim 1,
The step of determining whether the target is present,
Performing an image binarization and morphology operation based on a preset threshold for each of the acquired sample images; And
And determining whether the target exists based on the image binarization and morphology operation results. The method of claim 1,
Generating the binary sequence,
Determining a value corresponding to the first sample image as 1 in the binary sequence when it is determined that the target is present in the first sample image among the plurality of acquired sample images; And
And determining a value corresponding to the second sample image as 0 in the binary sequence when it is determined that the target does not exist in the second sample image among the plurality of acquired sample images. The method of claim 1,
Predicting the probability that the target will exist,
If the speed of the target is relatively slow,
Modeling the presence or absence of a target with a Bernoulli random variable; And
Including the step of applying the maximum likelihood technique to the modeled result,
The exposure time from the time point d to the shutter of the camera

The predicted value p of the probability of the presence of the target in the image to be acquired from the LRGI sensor when exposed by n is the number of the plurality of sample images n, and the number of images in which the target exists among the plurality of sample images is r. When, the equation

Determined according to the method. The method of claim 1,
Predicting the probability that the target will exist,
If the speed of the target is relatively high,
The exposure time from the time point d to the shutter of the camera

And modeling such that a predicted value of the probability of the presence of the target in the image to be obtained from the LRGI sensor is given differently for each of the plurality of sample images,
The exposure time from the time point d to the shutter of the camera

The predicted value of the probability of the presence of the target in the image to be obtained from the LRGI sensor when exposed by n is the number of the plurality of sample images, and a function indicating whether the target is present in the k-th image among the plurality of sample images. To

, The probability that the target exists in the k-th image

, A predefined constant

When I say,

Under the condition of

To maximize

Determined by the way. The method of claim 1,
A random probability distribution function corresponding to the random variable j

, When the depth of the distance gate is s and the position of the target in the distance gate is m, the following equation

Based on

(That is, the shutter of the camera is released from the time point d to the exposure time

The method, wherein the relationship between the probability of the presence of the target in the image to be obtained from the LRGI sensor and m (i.e., the position of the target in the distance gate) is determined when exposed by the LRGI sensor. The method of claim 1,
Estimating the distance of the target,
And applying Kalman filtering to the sum of the location of the distance gate and the location of the target within the distance gate. The method of claim 1,
The above method,
The method further comprising adjusting the position of the distance gate by adjusting a time point at which the shutter of the camera included in the LRGI sensor is exposed to the outside based on the estimated distance of the target. A computer-readable recording medium on which one or more programs including instructions for executing the method of claim 1 are recorded. In the device for estimating the distance of the target using an LRGI sensor,
A laser light source for emitting a pulsed laser toward the target;
A camera for receiving light reflected from the target by opening a shutter after the laser light source fires the pulsed laser toward the target and a predetermined time elapses;
A memory in which at least one program is stored; And
At least one processor for estimating the distance of the target by executing the at least one program,
The at least one processor,
A plurality of sample images are obtained while changing the time point at which the shutter of the camera is exposed to the outside according to d+j, which is a value obtained by adding a random variable j following a predefined random probability distribution with an average of 0 to a random time point d,
It is determined whether the target is present in each of the obtained plurality of sample images, and a binary sequence is generated based on the determination result,
By applying a maximum likelihood technique to the generated binary sequence, the shutter of the camera is released from the time point d.

Predicts the probability that the target will exist in the image to be acquired from the LRGI sensor when exposed by
Determine the position of the target in the distance gate of the LRGI sensor based on the predicted probability,
Estimating the distance of the target using the position of the distance gate and the position of the target within the distance gate,
The distance gate has a speed of light c,

Is,

In the case of, the device corresponds to a distance section from a distance z from the LRGI sensor to a distance z + s, and the position of the distance gate corresponds to z.

Images