The Yahboom microROS ROS2 Visual SLAM Self-Balancing Robot Car Vision Kit is a research and robotics education platform built around STM32, ESP32-S3, ROS2 Humble, T-mini Plus LiDAR, and a ROS-WiFi camera. This configuration is the Vision Kit and includes both LiDAR and camera hardware for SLAM navigation, computer vision, control-algorithm learning, and multi-robot experiments. The robot body is pre-assembled before shipment. Functional modules and accessories still require installation and configuration by the user. Designed for Robotics Education and Research This platform is intended for universities, laboratories, robotics courses, ROS2 learning, algorithm development, and research projects that require a mobile self-balancing robot with LiDAR and vision capabilities. ROS2 Humble and microROS learning SLAM mapping and autonomous navigation Computer vision and human-machine interaction PID, Kalman filter, complementary filter, and LQR control learning Robot kinematics, balance control, and mathematical modeling Multi-robot navigation and synchronous control STM32 and embedded robotics development microROS Wireless Architecture with ROS2 Humble The robot uses an STM32F103RCT6 as the main motion controller and an ESP32-S3-WROOM-1U-N4R2 for communication. Sensor data can be transmitted through microROS wireless communication to the official PC virtual-machine development environment running Ubuntu 22.04 and ROS2 Humble. This architecture allows computationally heavier ROS2 functions such as visualization, mapping, navigation, and vision processing to run on the host development environment while the onboard controllers handle real-time robot control and communication. ROS2 Visualization, Simulation and Robot Modeling The development materials cover ROS2 fundamentals and commonly used robotics tools including RViz2, URDF, TF2, and Gazebo. These tools can be used to understand robot coordinate systems, visualization, simulation, robot models, and ROS2 communication workflows. LiDAR SLAM Mapping and Navigation The Vision Kit includes a T-mini Plus LiDAR for ROS2 mapping and navigation experiments. Supported learning content includes LiDAR data processing, mapping, localization, navigation, obstacle avoidance, following, patrol, and path planning. Development examples include Gmapping, Cartographer, and Navigation2, allowing users to compare different approaches to SLAM and autonomous navigation. AI Vision with ROS-WiFi Camera The ROS-WiFi camera is included in this Vision Kit. It supports computer-vision learning and interactive robot applications using technologies such as OpenCV and MediaPipe. Example vision applications include: QR code motion control Palm following Gesture-based robot control Face tracking Human posture tracking Self-Balancing and Control Algorithm Learning The platform is designed not only for application-level ROS2 development but also for learning the control theory behind a self-balancing robot. Available learning topics include PID control, cascade PID, PI, PD, Kalman filtering, complementary filtering, LQR, attitude calculation, and self-balancing mathematical modeling. Multi-Robot Experiments The learning materials also include multi-robot applications such as synchronous navigation and synchronous control, making the platform suitable for classroom demonstrations and experiments involving multiple mobile robots. Mechanical Performance and Onboard Functions The two-wheel self-balancing chassis uses two 520 encoder DC reduction motors and an MPU6050 6-axis IMU. The manufacturer specifies a maximum payload of 4 kg when the robot is operated in the dedicated load mode. Load-mode note: the dedicated load mode is intended for operation with a heavy payload. Running the robot in load mode without the intended load may cause shaking. The manufacturer also demonstrates climbing performance of approximately 30° under suitable conditions. Actual performance depends on surface conditions, payload, balance configuration, battery state, and other operating factors. Technical Specifications Configuration Vision Kit Robot Type Two-wheel self-balancing robot car ROS Version ROS2 Humble Main Controller STM32F103RCT6 Communication Controller ESP32-S3-WROOM-1U-N4R2 LiDAR T-mini Plus Camera ROS-WiFi Camera IMU MPU6050 6-axis sensor Motor 520 encoder DC reduction motor ×2 Encoder AB-phase incremental Hall encoder Reduction Ratio 1:30 Motor Speed 333 ± 10 rpm Programming Languages C, Python Program Download Serial port, ST-Link Battery 2200 mAh battery pack Maximum Payload 4 kg in dedicated load mode Product Net Weight Approx. 1.06 kg Body Material Metal + PCB + acrylic Official Development Environment PC virtual machine with Ubuntu 22.04 + ROS2 Humble Development Resources Yahboom provides development resources for this platform including learning tutorials, example code, hardware information, manuals, virtual-machine images, ROS2 learning environments, apps, and a 3D model. View Official Development Resources Open-Source Scope Most example and application code provided for the platform is open for learning and development. However, the firmware running on the ESP32 communication chip is not open source. This distinction should be considered when evaluating the platform for projects that require full firmware-level access to every onboard controller. Assembly and Delivery The main robot chassis is pre-assembled before shipment. This is not a from-scratch mechanical assembly kit. However, functional modules such as the LiDAR, camera, brackets, and other accessories may require installation and configuration before use. Package Contents – Vision Kit Pre-assembled self-balancing robot car body T-mini Plus LiDAR ROS-WiFi camera Camera damping hinge / bracket Camera hinge mounting plate STM32 control board ESP32 communication board MPU6050 module Ultrasonic module Ultrasonic mounting plate OLED display module 12.6 V battery pack Power adapter 1 m USB Type-C cable Connection wires Velcro Screws and copper standoffs Screwdriver User manual Apps and Remote Control The manufacturer provides two mobile applications for different functions: Mapping / Navigation App: Android and iOS Remote Control App: Android System Requirements The official development workflow uses a PC virtual-machine environment based on Ubuntu 22.04 + ROS2 Humble. According to the manufacturer, the official development solution does not support macOS. Users planning to integrate the robot into a different host environment should evaluate software compatibility before purchase. University and Institutional Purchasing OpenELAB supports purchasing workflows for universities, laboratories, research institutions, and companies. Available procurement support can include formal quotations, proforma invoices, VAT-inclusive quotations or invoices where applicable, volume pricing, lead-time confirmation, bank transfer or corporate payment, and vendor-registration documentation. Documents such as company registration information, bank account confirmation, tax documents, and certificate-of-origin documentation can be provided when applicable to the order. Use the Request a Quote function on this product page if your institution requires a quotation, purchasing documentation, quantity pricing, or lead-time confirmation. Technical Support and Warranty For technical or after-sales questions, contact OpenELAB first. When manufacturer-level assistance is required, OpenELAB will coordinate with Yahboom as appropriate. Warranty and after-sales handling follow the applicable Yahboom and OpenELAB order terms. FAQ Is this the Standard Kit or Vision Kit? This listing is for the Vision Kit. It includes the T-mini Plus LiDAR and ROS-WiFi camera. Is the ROS-WiFi camera optional? No. The ROS-WiFi camera is included in this Vision Kit. Does the robot arrive fully assembled? The main robot body is pre-assembled before shipment, but functional modules and accessories still require installation and configuration. Can the robot really carry 4 kg? The manufacturer specifies a maximum payload of 4 kg when using the dedicated load mode. Actual performance depends on the operating conditions and correct configuration. Does it support ROS2? Yes. The official development environment uses ROS2 Humble and includes ROS2-related tutorials and examples. Does it support macOS? The manufacturer's official VM-based development solution does not support macOS. Is the project fully open source? No. Most example and application code is available for learning and development, but the ESP32 communication-chip firmware is not open source. Can universities request a formal quotation? Yes. Universities, laboratories, research institutions, and companies can use the Request a Quote function for quotation and institutional procurement requirements.
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