9-state EKF for inertial navigation fusing GPS, IMU, and barometer
The project provides a compact extended Kalman filter that fuses GPS, IMU, and barometric data to estimate position, velocity, and attitude in NED coordinates. It uses fixed‑size, Eigen‑free matrix types so the entire filter runs on the stack, making it suitable for resource‑constrained embedded systems. The code includes a CMake build, a demo executable, and a test suite that validates accuracy on simulated flight profiles. It is aimed at robotics, UAV, and embedded developers needing a lightweight, ready‑to‑use sensor‑fusion library.
View on GitHub →akhilvanka/kalman-filter