FusionCore: ROS 2 sensor fusion that outperforms robot_localization
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 projection, no extra node). It estimates IMU bias, adapts its noise covariance automatically from the innovation sequence, and gates outliers with a chi squared test on every sensor. I benchmarked it against robot_localization EKF on 6 sequences from the NCLT public dataset…
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
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 projection, no extra node). It estimates IMU bias, adapts its noise covariance automatically from the innovation sequence, and gates outliers with a chi squared test on every sensor. I benchmarked it against robot_localization EKF on 6 sequences from the NCLT public dataset (University of Michigan, real robot, real GPS, RTK ground truth). It wins 5 of 6. On the 6th sequence (fall, degraded GPS over a long period) it loses badly. RL UKF diverged to NaN on all six. Configs, methodology, and full reproduce instructions are in the benchmarks/ folder.
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