Overview
The RDK ROS2 robot car is an educational robot car based on the RDK X3/X5 development board, designed with Mecanum wheels for flexible omnidirectional movement. It supports ROS2 system development and SLAM mapping navigation, and combines camera vision with TOF lidar for navigation and obstacle avoidance. Development can be done with Jupyter Lab Python programming and deep learning algorithms for real-time perception and interaction.
For technical questions before or after purchase, contact support@rcdrone.top or visit https://rcdrone.top/.
Key Features
- Based on RDK board; TogetherROS (ROS2); FreeRTOS
- AI visual recognition; OpenCV image processing
- Mecanum wheel movement (360°); 4-wheel independent drive; motor with hall encoder
- TOF radar mapping navigation; depth camera 3D scanning (configuration dependent)
- HD MIPI camera
- Aluminum alloy body; innovative chassis; stacked structure with tidy internal wiring
- Multiple control methods: mobile app, PS2 controller, computer keyboard, Jupyter Lab control, and ROS system control
- Mapping Navigation APP supports iOS/Android and supports ROS1/ROS2
Configuration Options (as listed)
| Standard Version | Depth camera: No |
| Advanced Version | Depth camera: Astra Pro Plus depth camera |
| HD camera | 8MP HD MIPI camera |
| Lidar | MS200 TOF Lidar |
| ROS controller | RDK X3 (4GB) / RDK X5 (8GB) |
Functions (from the product function list)
Lidar
- Introduction and use of lidar
- Lidar obstacle avoidance
- Lidar tracking
- Lidar guard
- Patrol
- Gamapping mapping
- Cartographer mapping
- Navigation and avoiding
- APP mapping and navigation
Depth camera
- Depth camera usage
- Camera internal reference
- ROS+OpenCV
- AR vision
- Color tracking
- KCF object tracking
MIPI camera
- Camera drive
- Color HSV value adjustment
- Color recognition
- Color tracking
- Color follow
- QR code recognition
- Face detection
- Object detection
- Visual line patrol-OpenCV
- Visual line patrol-Deep Learning
- Human tracking
- Gesture control
ROS Master (RDK X3/X5)
- Virtual Machine
- Linux Fundamentals
- Remote connection
- Multi-machine communication
- Bind device ID
- Image back up
OpenCV
- Getting Started with OpenCV
- OpenCV Geometric transformation
- OpenCV image processing
- OpenCV image beautification
- QR code creation and recognition
Robot course
- PID algorithm theory
- Robot information release
- Robot control
- Robot state estimation
- Robot calibration
- Robot trajectory tracking
- URDF model
MS200 TOF Lidar (standard) Specifications
Adopt TOF ranging method; resist 30KLux strong light irradiation; support indoor and outdoor mapping navigation; measurement radius can reach 12M; measurement blind area is only 20cm; measurement error within 2M is ±20mm; sampling frequency is 4500/S; scanning frequency is 7-15HZ; support 360° omni-directional scanning.
| Principle of distance measurement | TOF |
| Range | 0.03m~12.0m (90% reflectivity) |
| Measurement accuracy | Typical value: ±10mm (0.2m~2.0m); ±20mm (2.0m~12.0m). Maximum value: ±40mm (0.2m~12.0m) |
| Measurement accuracy | Typical value: <=4mm (0.2m~2.0m); <15mm (2.0m~12.0m). Maximum value: <=15mm (0.2m~2.0m); <30mm (2.0m~12.0m) |
| Data information | distance, angle, intensity, timestamp |
| Scan angle | 360° |
| Dot frequency | 4,500 points/second |
| Spinning speed | 7~15Hz, default 10Hz (configurable, 1Hz interval) |
| Angular resolution | 0.8°@10Hz (angle resolution and dot frequency related) |
| Eye Safety Level | Class 1 IEC60825-1:2014 |
| Anti-ambient light performance | 30,000Lux |
| Working power | DC 5.0±0.5V |
| Operating temperature | -10°C~50°C (Typical value 25°C) |
| Storage temperature | -30°C~70°C (Typical value 25°C) |
| Product Size | 37.7*37.5*33.0 (length×width×height), unit: mm |
| Net weight | about 40g |
| Certified | RoHS2.0, REACH, CE, FCC |
| Degree of protection | IP5X |
Astra Pro Plus Depth Camera (optional) Specifications
The Astra Pro Plus depth camera supports manual adjustment of the pitch angle. It supports depth image data processing and 3D navigation and mapping.
| 3D technology | ORBBECR monocular structured light |
| Working range | 0.6-8m |
| Accuracy (Depth) | 1m: ±3mm |
| Field of View (FOV) (depth) | H 58.4° x V 45.8° |
| Resolution@frame rate (depth) | 640x480@30FPS; 320x240@30FPS; 160x120@30FPS |
| Resolution@Frame Rate (RGB) | 1920x1080@30FPS; 1280x720@30FPS; 640x480@30FPS |
| Deep processing chip | MX6000 |
| Close protection | support |
| Field of view (RGB) | H66.1° V40.2° |
| UVC (RGB) | support |
| Supported operating systems | Android / Linux / Windows |
| Data interface | USB2.0 |
| Size (mm) | 164.85*48.25*40 |
| Microphone | two-channel stereo |
| Power consumption | <2.5W |
| Safety | Class1 laser |
| Operating temperature | 10°C-40°C |
ROS2 Versions (system images)
- RDK X3 version car: Using ROS2 Foxy
- RDK X5 version car: Using ROS2 humble
Video
Manuals
Tutorial link: http://www.yahboom.net/study/RDK-X3-Robot
Details

Built on the RDK X3/X5 platform, the Yahboom RDK ROS2 Robot is designed for SLAM learning and omnidirectional mecanum movement.

Core capabilities include TogetherROS (ROS2), OpenCV vision, TOF radar mapping navigation, and a durable aluminum alloy chassis.

Multiple upgrade paths support both RDK X3 and RDK X5 configurations for robotics development and expansion.

Choose the standard kit or step up to the advanced version with an Astra Pro Plus depth camera while keeping the same lidar and HD MIPI camera foundation.

I/O-rich RDK X3 control board supports HDMI, Ethernet, USB, CSI camera connection, and a 40‑pin interface for robotics integration.

RDK X5 adds expanded connectivity and performance options for ROS2 projects, including high-speed interfaces for peripherals.

A quick comparison helps match the controller to your workload, from basic ROS learning to higher-demand AI perception tasks.

Prebuilt ROS2 system images simplify setup, with options aligned to common ROS2 releases for RDK X3 and RDK X5.

MS200 TOF lidar enables obstacle avoidance behaviors and supports mapping workflows like Gmapping and Cartographer.

Vision features cover detection, tracking, gestures, and QR recognition to support interactive ROS2 perception demos.

Optional depth sensing extends 3D mapping and tracking, while multiple control methods cover app, rocker controller, and keyboard.

A consolidated function list outlines lidar navigation, camera vision modules, ROS master setup, and the included learning curriculum.

Control the robot from mobile apps, a computer keyboard, JupyterLab, or directly through the ROS system for flexible workflows.






The Yahboom RDK ROS2 robot stack combines an RDK control board, ROS expansion boards, MIPI camera, lidar, and a 7.4V battery pack in a layered chassis with mecanum wheels.

Yahboom RDK ROS2 Robot tutorials are organized with course folders and direct study links for RDK-X3 and RDK-X5 setup and development.

The Yahboom RDK ROS2 learning materials include a ROS2 basic course and English‑subtitled video tutorials alongside a driver board course with step‑by‑step modules.

The Yahboom RDK ROS2 Robot course bundle covers LiDAR mapping and navigation plus MIPI and depth camera tracking, recognition, and ROS/OpenCV basics, with supporting documentation included.

The Yahboom RDK ROS2 Robot course catalog covers setup, ROS2 fundamentals, mapping and navigation, plus camera and vision modules.

The Yahboom RDK ROS2 robot platform comes in multiple standard and advanced style configurations with mecanum wheels for omnidirectional movement.

Yahboom RDK ROS2 robot parameters list Ubuntu + ROS2 options (Foxy/Humble), 4GB RAM, DC 7.4V power, and a 236.11 × 181.10 × 184.9 mm footprint.

The Yahboom RDK X3 ROS2 robot kit includes a mecanum-wheel chassis with a camera module, expansion board, antenna, and wiring accessories for assembly.
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