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    AJMRTC IMX219-83 Stereo Binocular Camera Module, Dual Sony IMX219 8MP Sensors, 83° Diagonal FOV, MIPI CSI Interface, 60mm Baseline, Low Distortion Lens for Raspberry Pi and Jetson Nano Stereo Depth Vi

    • 1. Dual independent Sony IMX219 8MP CMOS sensors, each outputs 3280×2464 high-resolution still images
    • 2. 83° diagonal wide-angle lens with distortion <1%, 60mm fixed baseline, ideal for stereo matching and depth calculation
    • 3. MIPI CSI-2 interface, works with dual CSI ports on Raspberry Pi CM carrier boards, Jetson Nano, Xavier NX and Orin series
    • 4. Rolling shutter design, supports synchronized capture for binocular disparity, stereo vision and 3D reconstruction algorithms
    • 5. Comes with calibration tutorials and sample codes for OpenCV, suitable for robotics obstacle avoidance and AI vision development
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    Overview
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    Any questions? Ask CelesteSM will help you find out quickly.

    AJMRTC IMX219-83 stereo binocular camera module carries two synchronized IMX219 8MP image sensors. The fixed 60mm baseline and low-distortion 83° lenses simplify stereo calibration. It connects via MIPI CSI-2 and requires a host board with dual CSI camera headers. Synchronous dual-image capture enables disparity map calculation, depth ranging and 3D vision. It is widely used for robot obstacle detection, SLAM, depth measurement, object recognition and embedded machine vision projects. Camera FPC cables are not included.


    Specifications

    1. Brand: AJMRTC
    2. Sensor: Dual Sony IMX219 CMOS, 1/4 inch
    3. Pixel: 8MP per sensor
    4. Max Resolution: 3280 × 2464
    5. Lens FOV: 83° diagonal /73° horizontal /50° vertical
    6. Baseline: 60 mm
    7. Distortion: <1%
    8. Interface: Dual MIPI CSI-2
    9. Shutter Type: Rolling shutter
    10. Compatible Host: Raspberry Pi CM series carrier boards, Jetson Nano, Xavier NX, Orin NX
    11. Power Supply: 3.3V
    12. Resources: Calibration guide, OpenCV demo codes
    13. Operating Temperature: 0 ~ 50
    14. Application: Stereo depth vision, SLAM, robot obstacle avoidance, 3D reconstruction, AI machine vision


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