Yahboom AI VIEW Binocular Structured Light 3D Depth Camera for ROS1/ROS2 Robots, USB3.0 Type‑C RGBD

Yahboom

$243.80 USD

特價售罄

已包含稅額。結帳時計算運費。

Version separate camera camera + bracket

數量

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Overview

The Yahboom AI VIEW is a binocular structured light 3D depth camera for ROS robot development. It combines binocular vision with structured light projection to calculate depth via left/right image matching and triangulation, supporting 3D reconstruction and depth sensing in complex environments. The compact body size is listed as 68.3 × 25.3 × 19.0 mm, with a measurement range of 0.25–2.5 m and resistance to strong light for indoor use scenarios.

Key Features

Binocular structured light depth sensing; ranging principle: active binocular stereo vision
ROS support: ROS1 & ROS2; compatible with ROS1/ROS2 systems and SDK support is noted
Built-in depth engine chip: MX6000
Smaller blind area: as low as 0.25 m (close-range measurement; suitable for robot end effector positioning)
Anti-glare capability (strong-light resistance); usage note: “Please use it in indoor”
Cross-platform operating systems listed: Android / Linux / Windows8/10
Example platforms and scenarios shown: Raspberry Pi, Jetson, PC, programming education, robot, 3D face recognition, 3D object measurement, sensory games, smart devices

Specifications

Product name	AI VIEW
Model	Astra SV1301S U3
Baseline	40 mm
Ranging principle	Active binocular stereo vision
Depth range	0.25–2.5 m
Relative accuracy	±5 mm @ 1000 mm
Absolute accuracy (multi-distance calibration not enabled)	±4 mm @ 200 mm; ±20 mm @ 900 mm; ±80 mm @ 2500 mm
Absolute accuracy (multi-distance calibration enabled)	±4 mm @ 200 mm; ±14 mm @ 900 mm; ±60 mm @ 2500 mm
Power consumption (typical)	Average 2.2 W; Standby 0.9 W; Peak 5 W
Power note	USB2.0 maximum drive current must reach 1 A; depth 640 × 400 @ 60 FPS mode average power consumption 2.9 W
Depth map resolution	USB2.0 mode: 1280 × 800 @ 7 FPS; 640 × 400 @ 30 FPS USB3.0 mode: 1280 × 800 @ 30 FPS; 640 × 400 @ 60 FPS
Color circle resolution	USB2.0 mode: 1280 × 720 @ 7 FPS; 640 × 480 @ 30 FPS USB3.0 mode: 1920 × 1080 @ 30 FPS; 1280 × 720 @ 30 FPS; 640 × 480 @ 30 FPS; 640 × 480 @ 60 FPS 5M (still photo)
Frame rate	Frame rate dynamic adjustment
Depth FOV	H67.9° V45.3° D78° ±3°
Color FOV	H71.5° V56.7° D84°
Depth engine	MX6000
Data transmission	USB3.0 Type-C
Power supply mode	USB3.0 Type-C
Supported operating systems	Android / Linux / Windows8/10
ROS support	ROS1 & ROS2
Operating temperature	10°C to 40°C
Applicable scenarios (listed)	Indoor Indoor/Outdoor (cloudy)
Safety (listed)	Class1 Laser
Overall size (listed)	Length 68.3 mm; Width 25.25 mm; Thickness 19 mm Also listed: 65.3 mm × 22.5 mm × 12.3 mm
Weight (listed)	45.7 g Also listed: 29.2 g
Mechanical drawing notes (unit: mm)	Front: 68.30 (W) × 25.25 (H); side thickness 19; mounting note: M3 threaded hole; additional drawing dimensions shown: 59.90, 45, 17

Software / SDK Notes (as listed)

“[SDK] Provide better RGBD camera development experience” (Orbbec SDK): cross-platform (Windows, Android, Linux) for structured light, binocular, iToF and other 3D sensing cameras
Functions listed: hardware setting orientation and control; access/control/data reading of sensors; frame synchronization and alignment control; point cloud data acquisition; filtering and other algorithm capabilities; support for different systems and wrappers; display tool Orbbec Viewer
Viewer note: supports switching between Chinese and English
Basic functions listed: view device information; obtain basic data streams; perform device control
Advanced functions listed: data frame synchronization and alignment; obtain point cloud data; data recording and playback

Optional Accessories

Optional angle-adjustable bracket for robot: 120° adjustable angle (Up 30°, Down 90°)
“3D model will be provided” (listed alongside the camera and angle adjustable bracket)

Applications

3D reconstruction and environment modeling (indoor)
3D visual mapping, navigation and surveying (as listed)
Close-range measurement (blind area as low as 0.25 m)
Object recognition, target detection, and tracking workflows (as listed in course topics)

Tutorials

Tutorial link (official study page): http://www.yahboom.net/study/AIVIEW_Camera

Depth camera usage course (topics listed)

Camera usage instructions / Linux basics (listed): Introduction to Linux system; Ubuntu file system; Ubuntu common commands; Ubuntu common editors; Ubuntu software operation commands; virtual machine installation; SSH remote control; VNC remote control; transfer files remotely; driver library and communication; static IP and hotspot mode; bind device ID; capacity expansion and resource; update system software sources; set root user password; sudo free password; connect to WiFi network; view system version; customized service management; back up system image
OpenCV course (listed): Opencv Source CV Introduction; image reading and display; picture writing; picture quality; pixel operation; picture scaling; picture cutting; picture translation; picture mirroring; affine transformation; picture rotation; perspective transformation; grayscale processing; image binarization; edge detection; line segment drawing; rectangle and circle drawing; text and picture drawing
ROS1 basic course (listed): ROS introduction; ROS installation; ROS common command tools; ROS workspace; ROS function package; ROS node; ROS topic publisher; ROS topic subscribers; ROS service client; ROS service server; ROS action client; ROS action server; ROS custom message reception; ROS-launch file; ROS-TF transformation; ROS parameter service; ROS-rviz use; ROS-rqt tool usage; topic message recording and; urdf model introduction; gazebo introduction; ROS distributed communication
ROS1 Mediapipe course (listed): hand detection; posture detection; overall detection; face detection; face recognition; face effects; 3D object recognition; brush; finger control; gesture recognition
ROS1 + OpenCV application (listed): camera calibration; QR code
Additional ROS + OpenCV topics (listed): 3. Human pose estimation; 4. Target Detection; 5. ROS+Opencv basics; 6. Face recognition; 7. harris corner detection; 8. Target tracking algorithm; 9. Contour moment; 10. Polygon outline; 11. Discrete fourier transform algorithm; 12. Edge detection algorithm; 13. Face detection algorithm; 14. Optical flow detection algorithm; 15. Contour detection; 16. General contour detection; 17. Feature point tracking; 18. HLS color filtering; 19. Hough circle detection; 20. Hough linear detection; 21. HSV color filtering; 22. LK optical flow algorithm; 23. Human detection algorithm; 24. Phase dependent displacement; 25. Image pyramid sampling algorithm; 26. RGB color filtering; 27. Clear background detection; 28. Simplified optical flow algorithm; 29. Simple filter; 30. Threshold image processing; 31. Watershed segmentation algorithm; 32. Data conversion and point cloud; 33. AR vision; 34. AR QR code; 35. Color recognition; 36. Object tracking
ROS2 basic course (listed): Introduction to ROS2; ROS2 install Humble; ROS2 development environment; ROS2 workspace; ROS2 function package; ROS2 node; ROS2 topic communication; ROS2 service communication; ROS2 action communication; ROS2 custom interface message; ROS2 parameter service case; ROS2 meta-function package; ROS2 distributed communication; ROS2 DDS; ROS2 time related API; ROS2 common command tools; ROS2 rviz2 use; ROS2 rqt toolbox; ROS2 Launch startup file; ROS2 recording and playback tool; ROS2 URDF model; ROS2 Gazebo simulation platform; ROS2 TF2 coordinate transformation
ROS2 OpenCV courses (listed): ROS+opencv application; QR code creation and recognition; AR vision
ROS2 mediapipe course (listed): hand detection; posture detection; overall detection; face detection; personal insurance identification
ROS2 depth camera series courses (listed): depth camera usage; camera internal parameter calibration; color tracking; KCF object tracking; ORB_SLAM2 basics; ORB_SLAM2 PCL mapping; ORB_SLAM2 Octomap mapping

For pre-sales compatibility questions or post-sales support, contact support@rcdrone.top or visit https://rcdrone.top/.

Details

Yahboom AI VIEW binocular structured light 3D depth camera for ROS robots, front view

AI VIEW combines binocular stereo and structured light to deliver fast RGB‑D depth sensing for ROS robot projects.

AI VIEW depth camera application icons: ROS1/ROS2, Raspberry Pi, Jetson, PC, 3D measurement

Works across common robot platforms including Raspberry Pi, Jetson, and PC for mapping, measurement, and perception tasks.

AI VIEW depth camera specification sheet with dimensions, ports, and performance table

Detailed mechanical drawings and core specs help with enclosure design and robot integration planning.

Binocular structured light depth principle illustration comparing monocular vs binocular layout

Binocular structured light uses left/right matching and triangulation to improve depth perception accuracy.

Mini size comparison graphic for AI VIEW depth camera mounted on a small robot arm

Compact form factor fits easily on robot arms and mobile platforms where space and weight matter.

Depth field-of-view graphic for AI VIEW camera with 0.25–2.5 m range and wide angles

Wide depth FOV supports close-range perception and broader scene capture for navigation and tracking.

Close-range blind area graphic indicating minimum depth distance of 0.25 m for AI VIEW

A short 0.25 m minimum range helps with near-field sensing such as end-effector positioning.

Depth range 2.5 m example with point cloud / mapping visualization on a desktop interface

Depth output can be used for 3D visual mapping workflows such as point clouds and environment reconstruction.

Anti-glare capability graphic for AI VIEW depth camera with indoor use note

Designed to better resist strong light, with recommended use in indoor environments for best results.

ROS1 and ROS2 support graphic with AI VIEW depth camera next to a laptop display

ROS1 and ROS2 support helps streamline integration into existing robot software stacks.

Orbbec SDK interface screenshot for RGBD camera configuration, streams, and point cloud tools

SDK tools provide device configuration, stream capture, and point cloud data access for development.

Angle-adjustable bracket accessory mounting the AI VIEW depth camera on a wheeled robot chassis

An optional adjustable bracket allows flexible mounting angles during robot prototyping and testing.

Second specifications sheet for AI VIEW binocular depth camera with dimension drawings and table

Package contents graphic listing AI VIEW depth camera and angle-adjustable mounting bracket

Available as the camera alone or bundled with an angle-adjustable bracket for easier installation.

Depth camera usage course overview chart for AI VIEW tutorials and learning path

Step-by-step course materials cover common RGB‑D topics from basic setup to advanced functions.

Tutorial link page layout for AI VIEW camera with ROS1 and ROS2 folders and example cases

ROS2 basic course and Raspberry Pi Docker course folder list for AI VIEW camera tutorials

Case experience grid: hand detection, ORB-SLAM2 mapping, ARTags, OpenCV, depth map, color tracking

Example projects include SLAM mapping, AR tags, OpenCV processing, and depth-map applications for learning.

Compatibility graphic for Yahboom AI VIEW RGBD 3D depth camera showing support for Raspberry Pi, Jetson, and ROS robots

The AI VIEW binocular structured-light RGBD depth camera is presented as compatible with Raspberry Pi, NVIDIA Jetson, and ROS1/ROS2 robot platforms.

Compatibility graphic for Yahboom AI VIEW 3D depth camera SDK with ROS1/ROS2, C/C++, Java, Python, Windows, Linux, Android, Unity

The SDK includes wrappers for ROS1/ROS2 and common languages and platforms like C/C++, Java, Python, Windows, Linux, Android, and Unity.

Yahboom AI VIEW binocular structured light 3D depth camera with dual lenses and USB-C port on black housing

The Yahboom AI VIEW structured light RGB-D camera features a compact black enclosure with dual front lenses and a USB-C connection for PC or robot setups.