IMU Integration for Mobile Robots

Source: ros2-copilot-skills IMU integration skill

Why This Matters

An IMU can stabilize heading estimates and improve local motion estimation, but only if it is integrated with realistic expectations. Blindly fusing every field often makes localization worse, not better.

Distilled Takeaways

• IMUs are usually strongest as part of a fusion stack, not as a standalone truth source.
• Yaw rate, orientation, and acceleration should be fused selectively and deliberately.
• The IMU should complement wheel odometry or other motion sources rather than fight them.
• Integration quality depends on calibration, frame orientation, and realistic covariance.

Practical Guidance

• Start by deciding which IMU outputs you actually trust.
• Fuse only dimensions that improve the filter rather than maximizing data volume.
• Compare fused output against known simple motions like straight driving and in-place rotation.
• Revisit integration after mount changes, firmware changes, or driver replacement.

Corroborating References

• robot_localization repository
• REP 105 coordinate frame conventions

When to Read the Original Source

Go to the original skill when you want more specific guidance on how IMU outputs interact with wheel odometry and filter configuration in typical Jazzy mobile robots.