FusionCore: ROS 2 sensor fusion that outperforms robot_localization

In plain words

FusionCore is a ROS 2 sensor fusion package that combines IMU, wheel encoders, and GPS data for mobile robot localization. It uses a 22-state unscented Kalman filter that works directly in ECEF coordinates, eliminating the need for coordinate projection or extra transformation nodes. The system estimates IMU bias, automatically adapts noise covariance from sensor innovations, and filters outliers using chi-squared tests. Benchmarks against robot_localization on real-world dataset sequences show FusionCore outperforming comparable alternatives in most scenarios.

written from the facts on this page · September 2026

From the sources

FusionCore is a production-grade ROS 2 sensor fusion library. A 23-state UKF fuses GPS, IMU, and wheel odometry with adaptive noise estimation (no manual Q tuning), chi-squared outlier rejection that catches GPS multipath spikes before they move the estimate, inertial coast through GPS dropouts, and ECEF-native GPS that works anywhere on Earth. Benchmarked against robot_localization on the NCLT outdoor dataset: ahead on 10 of 12 sequences. ROS 2, Apache 2.0.

ROS 2 localization for outdoor robots: IMU, wheel encoders and GPS fused in a 23-state UKF at 100 Hz. When the estimate goes wrong it reports which sensor and why, instead of drifting silently. Apache 2.0. - manankharwar/fusioncore

In the maker’s words, at launch

I built sensor fusion for a mobile robot and reached for robot_localization like everyone does. After spending too long fighting navsat_transform, UTM zone boundaries, and YAML covariance tuning, I wrote my own. FusionCore is a 22 state UKF that fuses IMU, wheel encoders, and GPS in ECEF directly (no coordinate…

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