ROS SLAM Quadruped Robot Dog – AI Vision Robot with LiDAR, HD Camera, ESP32, Supports ROS1/ROS2 SLAM, Object Detection, Path Planning, Jetson/Raspberry Pi Expansion | Ideal for STEM Robotics & AI Research

ROS SLAM Corgi AI Vision Quadruped Robot Dog | Autonomous Navigation and Mapping Robot with LiDAR Sensor and HD Camera | Supports ROS 1 and ROS 2 for SLAM Localization and Path Planning | AI Vision and Object Recognition for STEM Education and AI Research | ESP32 Control with Jetson Nano and Raspberry Pi Expansion | Ideal for Autonomous Robotics Learning and Computer Vision Training

1️⃣ LiDAR SLAM Mapping and Sensor Fusion

LiDAR mapping drives accurate SLAM performance by scanning surroundings and updating maps in real time. SLAM algorithm integrates LiDAR distance points and IMU orientation to generate precise 2D and 3D grids. ROS framework visualizes these maps through RViz for teaching spatial computation and path planning. Sensor fusion combines camera data with laser input to improve object detection and environment perception. Autonomous robot navigation leverages this fusion to avoid obstacles and plan routes dynamically in changing conditions. STEM learners gain practical understanding of data acquisition, Kalman filtering, and probabilistic mapping used in AI navigation research. LiDAR technology teaches distance calibration, angular resolution, and 3D visualization principles applied to modern AI robotics.

2️⃣ ROS 1 and ROS 2 Dual Compatibility

ROS architecture supports both ROS 1 and ROS 2 for maximum educational coverage. Students learn how to publish topics, create nodes, and develop custom packages for real-time data transfer and motion control. ROS 2 communication uses DDS protocol for high-performance networking and multi-robot systems. Integration with Gazebo simulation allows users to test autonomous algorithms before deploying to hardware. This dual compatibility supports STEM curriculums and research projects in autonomous navigation, AI motion planning, and SLAM development. The robot teaches software architecture, topic synchronization, and real-time feedback loop programming for engineering students.

3️⃣ AI Vision and Object Recognition

AI vision processing enables the Corgi robot to recognize objects, colors, and faces through its HD camera. Computer vision pipeline uses OpenCV for image acquisition, segmentation, and feature extraction. TensorFlow integration allows deep-learning models for object classification and gesture identification. ROS node publishes visual data to mapping modules for semantic understanding. STEM education projects utilize this function to simulate real-world applications like autonomous driving, surveillance, and smart factory inspection. AI robot vision teaches camera calibration, color thresholding, and edge detection concepts essential for AI research.

4️⃣ Voice Interaction and IoT Connectivity

Voice recognition system responds to verbal commands and enables two-way interaction between human and robot. IoT Wi-Fi connection transmits telemetry data to remote dashboards and mobile apps for cloud control. AIoT integration introduces students to the Internet of Robotic Things concept used in smart automation. Voice commands like “Move,” “Stop,” or “Scan” illustrate natural language interface development. STEM experiments combine speech recognition and robot control for interactive AI education. IoT communication adds real-time monitoring and remote supervision features used in modern autonomous systems.

5️⃣ Open-Source STEM Platform for AI Learning

Open-source hardware design allows students to customize sensors and algorithms freely. Jetson Nano and Raspberry Pi expand processing power for AI vision and machine learning experiments. ESP32 microcontroller handles low-level control, making the platform scalable for beginner and advanced users. Educational curriculum focuses on real ROS programming, hardware integration, and robot design. STEM innovation labs use Corgi robot to teach autonomous systems, AI navigation, and control logic. This robot bridges mechanics, electronics, and software in one hands-on AI ecosystem.