sensor-integration

Sensor fusion combines multiple sensor inputs like IMU and depth data into unified state estimates. This Skill generates Pyodide-compatible Python code for browser-based fusion using Kalman and complementary filters, enabling educational demonstrations of multi-sensor integration without server infrastructure.

Yes. This Skill produces production-quality Python code for simulated IMU processing—accelerometer, gyroscope, magnetometer with noise models—and implements Kalman and complementary filters, all executable in Pyodide within the browser environment.

LiDAR simulation here covers 2D depth perception generation and 3D visualization using NumPy, SciPy, and Matplotlib. The Skill creates browser-runnable Python demonstrations with calibration examples and tactile sensing fusion for teaching perception principles.

No. This Skill excludes ROS, real-time sensor libraries, and hardware drivers, focusing instead on simulated sensor data processing and educational demonstrations. Use it for browser-based learning, not production hardware integration.

The Skill generates code using Matplotlib for 2D and 3D visualization of fused sensor data directly in the browser via Pyodide. Visualizations include calibration outputs, state estimates, and noise model demonstrations with comprehensive docstrings for educational clarity.

NumPy, SciPy, and Matplotlib are the core dependencies for this Skill's Pyodide-compatible code. The Skill provides type hints, error handling, and educational templates compatible with these libraries, excluding file I/O and system-level sensor access.