sensor-fusion

Purpose

This skill enables the fusion of IoT sensor data using algorithms like Kalman filters to enhance accuracy and reliability in real-time applications. It processes inputs from multiple sensors, applies fusion techniques, and outputs refined data streams for downstream use.

When to Use

Use this skill when dealing with noisy or inconsistent sensor data, such as in autonomous vehicles for obstacle detection, smart home systems for environmental monitoring, or industrial IoT for predictive maintenance. Apply it in scenarios requiring real-time data smoothing, like merging GPS and accelerometer data, or when sensor redundancy improves decision-making.

Key Capabilities

• Implements Kalman and Extended Kalman filters for state estimation and noise reduction.
• Supports data fusion from up to 10 sensor types (e.g., temperature, humidity, motion) via JSON input streams.
• Handles real-time processing with configurable update rates (e.g., 10-100 Hz).
• Provides output in standardized formats like CSV or JSON for easy integration.
• Includes adaptive thresholding to detect and ignore faulty sensor readings.
• Offers visualization hooks for debugging fused data outputs.

Usage Patterns

To use this skill, first set the environment variable for authentication: export OPENCLAW_API_KEY=your_api_key. Then, invoke via CLI or API, providing sensor configurations in a JSON file. For CLI, pipe sensor data directly; for API, send POST requests with data payloads. Always specify the fusion algorithm and sensors in the command or request body to avoid defaults.