EKF Sensor Fusion for Robots

Source: ros2-copilot-skills EKF sensor fusion skill

Why This Matters

The EKF is a practical default for many mobile robots because it balances model-based fusion with operational simplicity. It still fails badly when the measurement set, covariances, or frame semantics are wrong.

Distilled Takeaways

• EKF fusion works well when the robot model and measurements are reasonably close to the assumptions being made.
• Its success depends more on input discipline than on exotic parameter tricks.
• An EKF should fuse the information that improves the state estimate, not every available field.
• Most EKF problems trace back to measurement semantics, frames, or unrealistic confidence.

Practical Guidance

• Start with the smallest trustworthy measurement set that solves the needed problem.
• Validate each measurement stream before putting it into the filter.
• Use covariances to express uncertainty honestly.
• Compare fused output against known motions and failure cases, not just stationary behavior.

Corroborating References

• robot_localization repository
• Nav2 state estimation concepts

When to Read the Original Source

Go to the original skill when you want the EKF-specific reasoning and the concrete warnings around measurement selection and filter behavior.