Yahboom MicroROS Robot Car AI SLAM Mapping & Navigation with ESP32 AI

Yahboom MicroROS Robot Car ROS2 AI SLAM Navigation & Visual Recognition with ESP32 AI Module (No Mac Support)

Name: Yahboom MicroROS Robot Car ROS2 AI SLAM Navigation & Visual Recognition with ESP32 AI Module (No Mac Support)
Brand: Yahboom
SKU: B0D5QTST2N
Price: 319.99 EUR
Availability: InStock

Brand : Yahboom

Discover the power of robots using virtual machines as their core control system to run ROS2 applications efficiently with microROS on resource-limited hardware. This cost-effective solution bypasses expensive devices like Jetson or Raspberry Pi, making it ideal for learning ROS2, enhancing algorithm performance, and inspiring DIY innovation. The microROS robot kit transmits real-time chassis data to a PC virtual machine enabling advanced features such as LiDAR mapping navigation, path planning, obstacle avoidance, tracking, and multi-robot coordination. It supports Rviz simulation for intuitive visual feedback, aiding users in understanding LiDAR operation, map creation, and navigation—perfect for mastering ROS robot programming. Built with a sleek, fully enclosed anodized aluminum alloy chassis, the robot is durable and visually clean. Equipped with ORBBEC MS200 TOF LiDAR for precise indoor/outdoor mapping, an ESP32S3 dual-core expansion board, and a 7.4V 2000mAh battery for 5-hour runtime, it provides a reliable, high-performance platform. Yahboom offers comprehensive courses, practical tutorials, and expert support covering hardware drivers, robot control, Linux, Docker, ROS2, and microROS environments—ensuring users quickly advance their skills with confidence.

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Description
Reviews (3)

Yahboom MicroROS Robot Car ROS2 AI SLAM Mapping Navigation AI Visual Recognition with ESP32 AI Module (AI Vision Edition) VM Software Not Support MAC

The Yahboom MicroROS Robot Car combines advanced ROS2 AI SLAM mapping and navigation capabilities with AI-powered visual recognition, enhanced by the powerful ESP32 AI Module. Designed for hobbyists and developers who want cutting-edge robotics solutions, this AI Vision Edition enables seamless interaction with virtual machine software. Please note, VM software compatibility excludes Mac systems.

4wd mapping navigation virtual machine robot

Experience advanced 4-wheel drive mapping and navigation technology with this cutting-edge robot car. Its sophisticated design enables efficient SLAM (Simultaneous Localization and Mapping) capabilities, making it ideal for autonomous robotics projects and intelligent navigation tasks.

The robot is engineered to work seamlessly alongside virtual machine environments, providing developers with a versatile platform to test and develop AI navigation algorithms. Its robust build supports precise control and real-time environmental mapping.

Equipped with powerful on-board sensors and processing units, this robot car excels at environment perception for accurate mapping and path planning. Its flexibility allows seamless integration in varied robotics projects including AI-driven exploration.

Its rugged chassis supports durable operation across diverse terrains, combined with advanced AI navigation capabilities powered by ROS2 frameworks. Ideal for education, prototyping, and AI research with real-world applications.

Integrated ROS2 AI SLAM technology facilitates real-time localization and mapping, turning the robot car into an autonomous vehicle capable of navigating complex environments with precision and efficiency.

Featuring the ESP32 AI Vision Module, this edition incorporates advanced AI visual recognition capabilities. It enables the robot to identify, interpret, and react to its surroundings, adding a new dimension to autonomous robotics applications.

Close-up view of the intelligent robot car highlighting its compact, durable construction and integrated sensors. This image showcases the intricate design and high-quality build optimized for advanced robotics development.

Another perspective showcasing the robot’s AI module and sensor array, emphasizing its state-of-the-art visual capabilities for SLAM and navigation. Ideal for researchers and makers looking to push autonomous tech boundaries.

Brand Yahboom

Reference : B0D5QTST2N

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Size	S	M	L	XL
Men	7-10	10-13	13-15	15-17
Women	7-9	10-12	13-14	15-16

Example Shown : SIZE XL, Hem to Hem 84cm OR 33 ,SIZE L, Hem to Hem 84cm OR 33 ,SIZE M, Hem to Hem 84cm OR 33 ,SIZE S, Hem to Hem 84cm OR 33

Rated 5.00 on the scale 1 - 5 based on 3 customer reviews

Alex P.

10/02/2025

From Hesitant Beginner to Confident Creator: My Journey with the microROS Robot Kit

As someone eager to explore ROS2 but put off by the high price of Jetson or Raspberry Pi boards, I bought this microROS robot kit for its affordability. Before, learning ROS2 felt overwhelming and limited by my hardware; after setting up the kit and connecting it to my PC’s virtual machine, I was mapping rooms and planning paths within days. The visual Rviz feedback and hands-on tutorials not only clarified complex concepts but also inspired me to experiment with multi-robot projects, turning my curiosity into real technical skill and excitement.

Kevin B.

10/02/2025

Game-Changer for ROS2 Learning on a Budget

The robot kit’s neatly packed anodized aluminum chassis felt surprisingly premium as I lifted it from the box, promising both durability and style right from the start. Setting up the ESP32S3 board with microROS was straightforward, and I loved that it paired seamlessly with my PC’s virtual ROS2 environment—no pricey Pi or Jetson needed. The inclusion of the ORBBEC MS200 TOF LiDAR made real-time mapping in Rviz both precise and visually engaging, helping me clearly understand robot movement and navigation during my indoor mapping tests.

Melissa T.

10/02/2025

Impressive Capabilities for Budget-Conscious Learners

This microROS robot kit offers an affordable way to explore advanced ROS2 features without the need for pricey hardware like Jetson or Raspberry Pi. Its real-time data transmission and robust support for LiDAR mapping, navigation, and multi-robot coordination make it a versatile learning platform, while the included courses and tutorials help ease newcomers into complex topics. However, users with minimal hardware experience might face a learning curve when setting up the PC virtual machine environment. Overall, it's an excellent, cost-effective choice for DIY enthusiasts and students wanting to advance their robotics skills.

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