This repository provides raw data (roll/pitch angles) of an unmanned aerial vehicle obtained from a multi-sensor array, and compared against a reference sensor. These datasets are structured to evaluate sensor fusion algorithms under varying dynamic disturbances.
Controlled scenario test sessions for evaluating sensor fusion and state estimation algorithms under baseline dynamic conditions.
Aerial flight test sessions providing empirical data under active flight maneuvers and high dynamic atmospheric disturbances.
Complete C++ implementation code for the Federated Kalman Filter (FKF) observer algorithm used for real-time roll-pitch attitude estimation.
Download all controlled and aerial datasets along with the C++ implementation code in a single compressed file.
Download All Datasets & Code (.ZIP)