WO2018086348A1 - Système de vision binoculaire stéréoscopique et procédé de mesure de profondeur - Google Patents

Système de vision binoculaire stéréoscopique et procédé de mesure de profondeur Download PDF

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WO2018086348A1
WO2018086348A1 PCT/CN2017/088492 CN2017088492W WO2018086348A1 WO 2018086348 A1 WO2018086348 A1 WO 2018086348A1 CN 2017088492 W CN2017088492 W CN 2017088492W WO 2018086348 A1 WO2018086348 A1 WO 2018086348A1
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pixel
dual port
port ram
image
coordinate
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PCT/CN2017/088492
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Chinese (zh)
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窦仁银
叶平
李嘉俊
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人加智能机器人技术（北京）有限公司
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C11/00—Photogrammetry or videogrammetry, e.g. stereogrammetry; Photographic surveying
    • G01C11/04—Interpretation of pictures
    • G01C11/06—Interpretation of pictures by comparison of two or more pictures of the same area
    • G01C11/08—Interpretation of pictures by comparison of two or more pictures of the same area the pictures not being supported in the same relative position as when they were taken

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  • the invention relates to the technical field of 3D sensing measurement, in particular to a binocular stereo vision system and a depth measuring method.
  • Binocular stereo vision is an important branch of computer vision, that is, two or one camera (CCD/CMOS) at different positions are moved or rotated to shoot the same scene, and the parallax of the two points in the two images is calculated.
  • the three-dimensional coordinate value of the point is calculated.
  • the measurement methods are currently mainly divided into active 3D measurement and passive 3D measurement.
  • the main principle of active 3D measurement is to generate structured coded light through the optical system, to obtain three-dimensional structure through decoding through imaging, and another TOF (Time offlight), which is a time-of-flight 3D imaging technology, mainly by measuring the transmitted beam and returning. The phase difference between the beams is measured for distance.
  • the main application scenarios for active 3D measurement are indoor somatosensory interaction or indoor robots. Outdoors, because of the sunlight, cause a lot of infrared light in the ambient light, which makes it impossible to produce effective measurements. Therefore, the passive binocular stereo system is a suitable choice and can also be extended to indoor use.
  • the current common scheme of binocular stereo vision system is divided into two types: one is binocular + high performance PC mode; the other is binocular + high performance GPU mode.
  • the main reason for adopting the above scheme is that the algorithm involved in passive binocular stereo vision is very complex, so a very powerful arithmetic unit is required.
  • the selection of the above computing system has the following disadvantages: high cost, difficulty in miniaturization, dynamic load of the computing unit makes real-time performance limited, and it is difficult to ensure consistent real-time performance.
  • the binocular stereo vision system and the depth measuring method provided by the invention
  • the method is implemented by FPGA platform, with high integration and fast processing speed, meeting the requirements of real-time, and bringing binocular stereo vision technology to a commercial level.
  • a depth measurement method for a binocular stereo vision system includes: generating a synchronization trigger signal, and transmitting the synchronization trigger signal to a left image acquisition unit and a right image acquisition unit;
  • the pixel points output by the left image acquisition unit and the right image acquisition unit are sequentially input to the first dual port RAM and the second dual port RAM, respectively; and the pixel points in the first dual port RAM are read for distortion and stereo correction Processing, the pixel value of the first corrected coordinate is input into the third dual port RAM, and at the same time, the pixel in the second dual port RAM is read for distortion and stereo correction processing, and the pixel value of the second corrected coordinate is input to the fourth.
  • a dual port RAM reading a pixel in the third dual port RAM and the fourth dual port RAM for stereo matching, obtaining a disparity of a pixel corresponding to the matching between the left image and the right image; and obtaining a pixel according to the parallax Corresponding real physical depth.
  • the depth measurement method for the binocular stereo vision system provided by the invention is implemented by using an FPGA platform, and the image acquisition preprocessing, correction and processing core output are integrated, and the concurrency and pipeline design of the FPGA are used to improve the whole processing.
  • the rate of the process meets the requirements of real-time.
  • the current frame rate is 720p@30fps, 480p@120fps, and higher performance parameters can be achieved while improving FPGA resources.
  • the solution of this embodiment achieves the level of consumption level available in both performance and cost.
  • the reading the pixel points in the first dual port RAM for distortion and stereo correction processing, and obtaining the pixel values of the first corrected coordinates into the third dual port RAM including: according to pre-calibrated internal parameters and distortion Parameter calculating a first original coordinate of the first correction coordinate in the left figure; reading a pixel value of a pixel adjacent to the coordinate of the first original image from the first dual port RAM; Converting a pixel value and a decimal point component of the first original image coordinate to obtain a pixel value of the first corrected coordinate, and inputting a third dual port RAM; and reading the second dual port RAM
  • the pixel is subjected to distortion and stereo correction processing, and the pixel value of the second corrected coordinate is input into the fourth dual port RAM, including: calculating the second corrected coordinate according to the pre-calibrated internal parameter and the distortion parameter, and corresponding to the second original in the right image Graph coordinates; reading pixel values of pixel points adjacent to the second original image coordinates from the second dual port RAM; performing double based on the read
  • performing bilinear interpolation according to the read pixel value and the decimal point component of the first original image coordinate to obtain the pixel value of the first corrected coordinate including: according to the read pixel value and the a decimal point component of the first original image coordinate is calculated by a fixed point calculation to obtain a bilinear difference value, and a pixel value of the first corrected coordinate is obtained; and the decimal point component is performed according to the read pixel value and the second original image coordinate Bilinear interpolation, obtaining the pixel value of the second correction coordinate, comprising: performing a bilinear difference value by using a fixed point calculation according to the read pixel value and the decimal point component of the second original image coordinate, to obtain the The pixel value of the two corrected coordinates.
  • the reading the pixel points in the third dual port RAM and the fourth dual port RAM for stereo matching, and obtaining the parallax of the pixel corresponding to the matching between the left image and the right image including: reading the The pixel in the third dual port RAM is convoluted to the left image by using the set convolution kernel to obtain the gradient information of the pixel in the left image, and at the same time, the pixel in the fourth dual port RAM is read.
  • the convolution operation is performed on the image of the right image by using the set convolution kernel to obtain the gradient information of the pixel points in the right image; according to the gradient information of the pixel points on the left image and the gradient information of the pixel points on the right image, it is found in the right image and left.
  • the pixels matched by the pixel points are used as the first matching cost result, and the left picture disparity is obtained according to the first matching cost result of the left picture pixel point and the right picture; meanwhile, according to the gradient information of the left picture pixel point and the gradient of the right picture pixel point Information, in the left picture, find the pixel point matching the pixel of the right picture as the second matching cost result, and obtain the right picture disparity according to the second matching cost result of the right picture pixel and the left picture; select the left picture disparity and the FIG output as parallax disparity.
  • the convolution kernel is a sobel gradient operator.
  • the pixel point matching the pixel point of the left figure is found as the first matching cost result in the right picture, including: calculating the left picture pixel point
  • the degree information is compared with the SAD value of the gradient information of all the pixels in the aggregate window in the right figure, and the pixel with the smallest SAD value is selected as the first matching cost result;
  • the gradient information of the pixel according to the left picture and the pixel of the right picture Gradient information, in the left picture, find the pixel point matching the pixel of the right picture as the second matching cost result, including: calculating the gradient information of the pixel of the right picture and the SAD value of the gradient information of all the pixels in the aggregate window in the left figure.
  • the pixel with the smallest SAD value is selected as the second matching cost result.
  • the obtaining the real physical depth corresponding to the pixel point according to the parallax comprises: performing floating point operation using DSP resources in the FPGA according to the focal length and the baseline in the calibration parameter and the parallax, and obtaining the true corresponding to the pixel point Physical depth.
  • the present invention provides a binocular stereo vision system, including: a left image acquisition unit, a right image acquisition unit, a processing unit, and a data output interface; the left image acquisition unit, the right image acquisition unit, and the data output interface are respectively connected to the processing unit; the left image acquisition unit includes a first lens and a first image sensor; the right image acquisition unit includes a second lens and a second image sensor; the processing unit includes: a binocular synchronization module, a data acquisition module, a distortion and stereo correction module, a stereo matching module, a depth calculation module, and An output interface module; the binocular synchronization module is configured to generate a synchronization trigger signal, and send the synchronization trigger signal to a left image acquisition unit and a right image acquisition unit; the data acquisition module is configured to acquire the left image acquisition unit And the pixel points output by the right image acquisition unit are sequentially input to the first dual port RAM and the second dual port RAM, respectively; the distortion and stereo correction module is configured to read pixel points in the
  • the binocular stereo vision system provided by the invention uses FPGA as an actual operation processing unit, integrates image transmission, correction and output interfaces, so that the binocular stereo vision system can be highly integrated, making miniaturization possible, using customized circuits.
  • the parallel acceleration and pipeline method ensure the minimum delay of the system and obtain high real-time performance.
  • the real-time performance is guaranteed because of the exclusiveness of computing resources.
  • a light filling unit is further included, the light filling unit is connected to the processing unit, and the light filling unit is configured to emit a fill light according to the control of the processing unit.
  • Fill light units can be used in low light or dark environments to achieve better image quality.
  • the light source of the light-filling unit is an infrared light source or a visible light source.
  • the method further includes a light brightness sensor, wherein the light brightness sensor is connected to the light filling unit, wherein the light brightness sensor is configured to detect ambient light brightness, and control the light filling unit to perform light filling according to the detection result.
  • a light brightness sensor is connected to the light filling unit, wherein the light brightness sensor is configured to detect ambient light brightness, and control the light filling unit to perform light filling according to the detection result.
  • a texture enhancement unit is further included, the texture enhancement unit being coupled to the processing unit, the texture enhancement unit for transmitting structured light in accordance with control of the processing unit.
  • the light source of the texture enhancement unit is an infrared light source or a visible light source.
  • an IR filter is installed between the first lens and the first image sensor, between the second lens and the second image sensor.
  • FIG. 1 is a flow chart of a depth measurement method for a binocular stereo vision system provided by the embodiment
  • 2 is a binocular synchronization module in a binocular stereo vision system provided by the embodiment
  • Figure 3 is a flow chart of the generation of a synchronous trigger signal
  • Figure 5 is a coordinate relationship between distortion and stereo correction processing
  • 6 is a distortion and stereo correction processing module in the binocular stereo vision system provided by the embodiment.
  • FIG. 8 is a stereo matching module in a binocular stereo vision system according to an embodiment
  • FIG. 9 is a depth calculation module in a binocular stereo vision system provided by the embodiment.
  • FIG. 10 is a structural block diagram of a binocular stereo vision system provided by the embodiment.
  • 11 is an output structure module in a binocular stereo vision system provided by the embodiment.
  • FIG. 12 is an imaging schematic diagram of a binocular stereo vision system
  • FIG. 13 is a schematic diagram of an application scenario of a binocular stereo vision system integrated in a human body wearable device according to the embodiment.
  • FIG. 14 is a schematic diagram of an application scenario of a binocular stereo vision system integrated on a drone according to the embodiment
  • FIG. 15 is a schematic diagram of an application scenario of a binocular stereo vision system integrated in a mobile robot according to an embodiment of the present invention.
  • a depth measurement method for a binocular stereo vision system includes:
  • step S1 a synchronization trigger signal is generated, and the synchronization trigger signal is sent to the left image acquisition unit and the right image acquisition unit.
  • the left image acquisition unit and the right image acquisition unit together form a binocular imaging system.
  • Step S2 Acquire pixel points output by the left image acquisition unit and the right image acquisition unit, and sequentially input the first dual port RAM and the second dual port RAM, respectively.
  • Step S3 reading pixel points in the first dual port RAM for distortion and stereo correction processing, obtaining pixel values of the first corrected coordinates, inputting the third dual port RAM, and simultaneously reading pixel points in the second dual port RAM. Distortion and stereo correction processing, the pixel value of the second corrected coordinate is input to the fourth dual port RAM.
  • step S4 the pixels in the third dual port RAM and the fourth dual port RAM are read for stereo matching, and the parallax of the pixel corresponding to the matching between the left image and the right image is obtained.
  • step S5 the real physical depth corresponding to the pixel point is obtained according to the parallax.
  • the depth measurement method for the binocular stereo vision system provided by the embodiment is implemented by using an FPGA platform, and the image acquisition preprocessing, correction, and processing core output are integrated, and the concurrency and pipeline design of the FPGA are used to improve the whole.
  • the processing rate meets the requirements of real-time performance.
  • the current frame rate is 720p@30fps, 480p@120fps, and higher performance parameters can be achieved while improving FPGA resources.
  • the solution of this embodiment achieves the level of consumption level available in both performance and cost.
  • step S1 is to generate a synchronization trigger signal for the left image acquisition unit and the right image acquisition unit, so that the CMOS sensors in the left image acquisition unit and the right image acquisition unit can start exposure simultaneously, and step S1 is implemented by using an FPGA hardware circuit.
  • the synchronization trigger signal is generated accurately and periodically according to the frame rate of the CMOS sensor.
  • the implementation of step S1 is as shown in FIG. 2, and a synchronous trigger signal is generated by a counter, and a high-frequency base clock is used as an input. When the enable signal is valid, counting starts, and when the upper limit is reached, a synchronous trigger signal is generated.
  • Synchronous trigger The specific flow generated by the number is shown in Figure 3. First, the counter is initialized to clear; then, the enable signal is detected. If the enable signal is 1, the counting starts; when the count value reaches the set upper limit T, the output synchronous trigger The signal, while the counter is cleared, enters the next round of counting.
  • step S2 is to obtain image data output by the CMOS sensor in the left image acquisition unit and the right image acquisition unit.
  • the specific implementation in the FPGA is as shown in FIG. 4, and each of CMOS1 and CMOS2 has its own pixel clock. Different clocks respectively input data into the respective FIFOs, and then sequentially output to the first dual port RAM and the second dual port RAM pixel by pixel under the same clock to achieve synchronization of the pixels of the CMOS1 and CMOS2 acquisition outputs.
  • the data collection method used in this embodiment is different from the traditional software-side frame buffer collection mechanism. The method used in this embodiment does not perform excessive image buffering (the traditional method needs to save one or more frames of images and then perform subsequent processing). The received pixel output is processed in the next step in time.
  • FIG. 12 is an imaging principle diagram of a binocular stereo vision system.
  • a point P(x, y, z) in space, the imaging coordinates in the left image acquisition unit and the right image acquisition unit are respectively P1 (x1, y1, z1).
  • P2 x2, y2, z2). Since the binocular imaging system is distorted during the imaging process, and the left image acquisition unit and the right image acquisition unit are difficult to parallelize the optical axis, the left and right images of the output are difficult to achieve planar alignment, so it is necessary to perform the stereo matching.
  • the distortion of step S3 and the stereo correction process are performed to ensure that the image is undistorted and satisfies the polar line constraint.
  • the distortion parameters and the external parameters of the binocular imaging system are obtained by double target calibration, and the following parameters are obtained after calibration:
  • f is the focal length
  • k is the radial distortion
  • p is the tangential distortion
  • x is the image lateral coordinate
  • y is the image longitudinal coordinate
  • ⁇ , ⁇ , and ⁇ are rotation amounts corresponding to the x, y, and z axes, respectively.
  • mapping relationship between the corrected image coordinates (u, v) and the pre-correction image (u", v") can be obtained.
  • the specific calculation steps are as follows:
  • Step2 rotate the image plane
  • x" x'(1+k 1 r 2 +k 2 r 2 +k 3 r 6 )+2plx'y'+p 2 (r 2 +2x' 2 )
  • (u, v) is an integer
  • (u", v") is a floating point number, as shown in Fig. 5
  • ⁇ , ⁇ are the fractional parts of u" and v", respectively, which can be corrected by bilinear interpolation.
  • the pixel value d at the image position (u, v) is calculated as:
  • A, B, C, and D are (u", v") coordinate positions corresponding to pixel values at four coordinate points of the neighbor.
  • the distortion correction and stereo correction processing of the left and right images are finally converted into coordinate mapping and bilinear interpolation operation, and the simultaneous and stereo correction processing of the left and right images greatly improves the processing efficiency.
  • the processing mode of the left figure as an example, the preferred mode of step S3 is:
  • Step S31 calculating the first correction coordinate according to the pre-calibrated internal parameter and the distortion parameter in the left figure Corresponding first original image coordinates;
  • Step S32 reading pixel values of pixel points adjacent to the coordinates of the first original image from the first dual port RAM;
  • Step S33 performing bilinear interpolation according to the read pixel value and the decimal point component in the first original image coordinate, obtaining the pixel value of the first corrected coordinate, and inputting the third dual port RAM.
  • the image buffer write logic module continuously writes the pixel points received by the front end to the dual port RAM, and when the image coordinate incrementing module receives the start signal, starts to trigger the real-time coordinate mapping calculation module to work, and calculates the first corrected coordinates of the corrected image one by one (or The second correction coordinate corresponds to the first original image coordinate (or the second original image coordinate) in the left image (or the right image), and simultaneously inputs the first original image coordinate into the coordinate address mapping module and the pixel value reading module, and further Reading the pixel value of the pixel adjacent to the coordinate of the first original image in the dual port RAM, and then the bilinear interpolation module is based on the obtained plurality of pixel values and the first original image coordinate (or the second original image coordinate) The decimal point component is interpolated to obtain the pixel value of the first corrected coordinate (or the second corrected coordinate), and so on, the pixel value corresponding to the next coordinate of the corrected image is calculated.
  • the speed of the image incrementing unit is limited by the writing speed of the front-end dual-
  • appropriate pixel points may be selected among adjacent points according to various algorithms, and generally 2 ⁇ 2 forms are used to take adjacent four pixel points. You can also use 3 ⁇ 3 forms. Using 2 ⁇ 2 form interpolation can generally ensure that the image is not excessively smooth after interpolation.
  • Figure 7 shows the FPGA circuit design of the entire real-time coordinate mapping calculation module.
  • the basic addition, subtraction, multiplication and division of the basic unit is used for calculation, which greatly increases the calculation rate.
  • the floating point calculation is converted into fixed point calculation, and the 6-bit fixed point is used to convert the floating point operation into an integer operation and a shift operation.
  • the specific calculation method is:
  • Tmp1 (A-B)*a+(B ⁇ 6)
  • the main function of the step S4 is to receive the corrected data of the front end for stereo matching, which specifically includes:
  • Step S41 reading a pixel point in the third dual port RAM, performing convolution operation on the image by using the set convolution kernel, obtaining gradient information of the pixel point in the left figure, and reading the pixel in the fourth dual port RAM.
  • Point use the set convolution kernel to convolute the image to obtain the gradient information of the pixel in the right image.
  • the left image and the right image are simultaneously convoluted, which greatly improves the processing efficiency.
  • the convolution kernel is preferably a sobel gradient operator.
  • the advantage of using the sobel gradient operator is that it is insensitive to absolute brightness according to its established feature description, so that the left and right cameras can still establish a good match in the case of different imaging brightness.
  • Step S42 according to the gradient information of the pixel point of the left picture and the gradient information of the pixel of the right picture, find the pixel point matching the pixel point of the left picture as the first matching cost result in the right picture, according to the left picture pixel point and the right picture
  • the first matching cost result obtains the left image disparity; meanwhile, according to the gradient information of the left pixel and the gradient information of the right pixel, the pixel matching the pixel of the right image is found in the left image as the second matching cost.
  • the right picture disparity is obtained from the second matching cost result of the right picture pixel and the left picture.
  • step S43 one of the left image disparity and the right image disparity is selected as the parallax output.
  • the overall design framework of the above stereo matching method is shown in FIG. 8.
  • the stereo matching algorithm requires a large storage space.
  • the stereo matching algorithm is designed for the FPGA, which greatly reduces the storage requirements.
  • the processing flow is:
  • the sobel gradient calculation is performed separately to obtain the gradient information of the pixel points in the left and right images.
  • the left-picture disparity is obtained according to the coordinate distance between the pixel point of the left picture and the first matching cost result of the right picture, and the right picture disparity is obtained by the same method.
  • one of the left-picture disparity and the right-picture disparity is selected as the parallax output, and the output is median-filtered.
  • the size and shape of the polymerization window can be selected according to actual needs.
  • the relative size relationship of the image is used instead of the absolute size in the feature point selection in the stereo matching process, so the applicability to the environment is stronger.
  • step S5 uses the DSP resources in the FPGA to perform floating-point operations, and is calculated as pipeline processing, so the resource consumption is only one multiplier and one divider.
  • the embodiment further provides a binocular stereo vision system, as shown in FIG. 10, including: a left image acquisition unit, a right image acquisition unit, a processing unit, and a data output interface;
  • the image acquisition unit, the right image acquisition unit, and the data output interface are respectively connected to the processing unit.
  • the left image acquisition unit includes a first lens and a first image sensor; the right image acquisition unit includes a second lens and a second image sensor.
  • the left image acquisition unit and the right image acquisition unit form a binocular imaging unit.
  • the first image sensor and the second image sensor may be selected from CMOS or CCD.
  • the processing unit includes: a binocular synchronization module, a data acquisition module, a distortion and stereo correction module, a stereo matching module, a depth calculation module, and an output interface module.
  • the binocular synchronization module is configured to generate a synchronization trigger signal and send the synchronization trigger signal to the left image acquisition unit and the right image acquisition unit.
  • the data acquisition module is configured to acquire pixel points output by the left image acquisition unit and the right image acquisition unit, and sequentially input the first dual port RAM and the second dual port RAM, respectively.
  • the distortion and stereo correction module is configured to read pixel points in the first dual port RAM for distortion and stereo correction processing, obtain pixel values of the first corrected coordinates, input the third dual port RAM, and simultaneously read the second dual port RAM. Distortion and stereo correction processing of the pixel points to obtain the second corrected coordinates The pixel value is entered in the fourth dual port RAM.
  • the stereo matching module is configured to read the pixel points in the third dual port RAM and the fourth dual port RAM for stereo matching, and obtain the parallax of the pixel corresponding to the matching between the left image and the right image.
  • the depth calculation module is configured to obtain a real physical depth corresponding to the pixel point according to the parallax.
  • the output interface module is used to select the output data, and configure different interfaces according to the protocol corresponding to the data output interface. As shown in Figure 11, the output interface module can select various original images, intermediate processed images, and final result output as needed, and can also customize different output interfaces to adapt to different data output interfaces, such as LVDS. , USB, etc.
  • the binocular stereo vision system provided by the embodiment uses an FPGA as an actual operation processing unit, and integrates an image transmission, correction, and output interface, so that the binocular stereo vision system can be highly integrated, making miniaturization possible, using customized The circuit performs calculations. On the one hand, it guarantees the minimum delay of the system through parallel acceleration and pipeline, and obtains high real-time performance. On the other hand, the real-time performance is guaranteed because of the exclusiveness of computing resources.
  • the binocular stereo vision system provided in this embodiment further includes a fill-in unit, the fill-light unit is connected to the processing unit, and the processing unit can control the fill-in unit to have insufficient external illumination. Illuminate to get better image quality.
  • the light source of the fill light unit may be in the near infrared band or in the visible light band.
  • the binocular stereo vision system provided by the embodiment further includes a texture enhancement unit, and the texture enhancement unit is connected with the processing unit, and the texture enhancement unit integrates the structure light projection module, and can be opened in the weak texture region, considering the weak texture region in the environment.
  • the structured light projection module emits structured light, such as stripes or random spots, which enhances the ambient light texture and improves the accuracy of the measurement.
  • an IR filter is installed between the first lens and the first image sensor and between the second lens and the second image sensor, and the IR filter functions to filter out the infrared light.
  • the first lens and the second lens are camera-specific lenses, and the near-infrared filtering film is not attached to the lens, so that the near-infrared can be transmitted; the IR filter can be used when the infrared filling unit and the infrared texture enhancing unit are not used.
  • the switch is turned on, turning off the IR filter switch when you need to use the near-infrared band to fill or replenish the texture.
  • the binocular stereo vision system provided by the embodiment has small volume and high real-time data processing, it can be integrated in various human body wearable devices.
  • FIG. 13 a schematic diagram of an application scenario in which a binocular stereo vision system of the present embodiment is integrated in a human body wearable device is provided.
  • the binocular stereo vision system is integrated in the glasses worn by the user, and is synchronized by two front cameras.
  • Shooting a rangefinder image of the ranging target The depth measurement method integrated in the binocular stereo vision system processes the ranging images of the two front camera synchronously shooting ranging targets, and obtains the distance between the ranging target and the imaging planes of the two front cameras. Combined with the focal length of the front camera and other parameters, the linear distance between the distance measuring target and the user is obtained, and the distance of each target in the human eye field is displayed in real time through the display on the glasses.
  • the binocular stereo vision system provided by the present example is small in size and high in real-time data processing, it can be integrated on the drone to add a three-dimensional visual perception function to the drone.
  • FIG. 14 a schematic diagram of an application scenario in which the binocular stereo vision system 100 of the present embodiment is integrated on a drone is provided, and the binoculars are placed in the flight direction of the drone to provide a flight direction obstacle perception for the drone.
  • Function, as shown in Fig. 14 in the front end of the UAV A binocular stereo vision system 100 the distance from the drone B can be obtained, and the binocular original image can be used as the front end of the visual odometer to provide mileage information for the flight.
  • the binocular stereo vision system 100 is placed in the downward viewing direction of the drone, and the functions of the drone height setting and topographic mapping can be completed based on the depth map.
  • binocular is one of the important sensors as the SLAM (Simultaneous Localization and Mapping).
  • the binocular stereo vision system provided in this example can be used as the front sensor of SLAM.
  • the depth map outputted in real time can be used as the basic data of the obstacle avoidance navigation and path planning of the robot 200.
  • the robot 200 obtains the depth map by the front-end binocular stereo vision system to obtain the obstacles around, and finally obtains the destination. Path 201 of 202.

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Abstract

L'invention concerne le domaine de la détection et de la mesure 3D, et plus particulièrement, un système de vision binoculaire stéréoscopique et un procédé de mesure de profondeur. Le procédé de mesure de profondeur adopté dans le système de vision binoculaire stéréoscopique consiste à: générer un signal de déclenchement synchrone, et transmettre celui-ci à une unité d'acquisition d'image gauche et à une unité d'acquisition d'image droite (S1); effectuer, en adoptant une architecture pipeline et parallèle d'un FPGA, et sur des pixels délivrés en sortie par l'unité d'acquisition d'image gauche et l'unité d'acquisition d'image droite, un processus de correction de distorsion et de correction stéréo (S3); obtenir une parallaxe des pixels correspondant à l'appariement de l'image gauche et de l'image droite et générée par stéréocorrespondance (S4); et obtenir, en fonction de la parallaxe, une profondeur physique réelle correspondant aux pixels (S5). Le système de vision binoculaire stéréoscopique et le procédé de mesure de profondeur adoptent une plateforme FPGA pour la mise en oeuvre, réalisant une intégration élevée et une vitesse de traitement rapide, satisfaisant une exigence en temps réel, et réalisant la commercialisation de la technologie de vision binoculaire stéréoscopique.
PCT/CN2017/088492 2016-11-09 2017-06-15 Système de vision binoculaire stéréoscopique et procédé de mesure de profondeur WO2018086348A1 (fr)

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CN201610987447.1A CN106525004A (zh) 2016-11-09 2016-11-09 双目立体视觉系统及深度测量方法

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