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FusionCore
ROS 2 UKF sensor fusion: GPS+IMU+wheels, zero manual tuning
FusionCore is a production-grade ROS 2 sensor fusion library. It runs a 22-state UKF fusing GPS, IMU, and wheel odometry with adaptive noise estimation (no manual Q tuning), Mahalanobis outlier rejection for GPS spikes and IMU glitches, and ECEF-native GPS handling that works at any scale. Benchmarked against robot_localization on the NCLT outdoor driving dataset: FusionCore wins on 5 of 6 sequences, up to 4 × lower ATE. Apache 2.0.
I built FusionCore after spending weeks fighting robot_localization on a mobile robot. The filter kept diverging, the docs said "tune your Q matrix" with zero guidance, and there was no way to know *why* it was failing.
FusionCore is a 22-state UKF that fuses GPS, IMU, and wheel odometry for ROS 2. Three things I did differently:
**Adaptive noise estimation**: the filter tunes its own measurement noise online from a sliding innovation window. You don't touch a Q matrix.
**Mahalanobis outlier rejection**: bad GPS fixes and IMU spikes are detected and dropped before corrupting the state. On the NCLT outdoor driving dataset, this is the difference between 5.6 m ATE and total divergence.
**ECEF-native GPS**: no UTM zone crossings, no ENU approximation errors at long range. Works anywhere on Earth.
I benchmarked against robot_localization EKF on 6 sequences of the NCLT dataset (90 km of outdoor driving). FusionCore wins on 5 of 6, up to 12.9× better ATE. RL-UKF diverges to NaN on every sequence... known numerical instability.
Apache 2.0, fully open source. If you're doing robot navigation, localization, or autonomous vehicles in ROS 2, I'd love your feedback.
About FusionCore on Product Hunt
“ROS 2 UKF sensor fusion: GPS+IMU+wheels, zero manual tuning”
FusionCore was submitted on Product Hunt and earned 4 upvotes and 1 comments, placing #127 on the daily leaderboard. FusionCore is a production-grade ROS 2 sensor fusion library. It runs a 22-state UKF fusing GPS, IMU, and wheel odometry with adaptive noise estimation (no manual Q tuning), Mahalanobis outlier rejection for GPS spikes and IMU glitches, and ECEF-native GPS handling that works at any scale. Benchmarked against robot_localization on the NCLT outdoor driving dataset: FusionCore wins on 5 of 6 sequences, up to 4 × lower ATE. Apache 2.0.
On the analytics side, FusionCore competes within Open Source, Robots, Developer Tools and GitHub — topics that collectively have 632.7k followers on Product Hunt. The dashboard above tracks how FusionCore performed against the three products that launched closest to it on the same day.
Who hunted FusionCore?
FusionCore was hunted by Manan. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of FusionCore including community comment highlights and product details, visit the product overview.
Manan