**5. Conclusive remarks**

**Figure 19.** Position estimation error (Dataset 2): EKF (red), NLO-Mag (blue dashed), NLO-Vel (green).

132 Recent Advances in Robotic Systems

**Figure 20.** Gyro bias estimation (Dataset 2): EKF (red), NLO-Mag (blue dashed), NLO-Vel (green).

yaw estimation is seen to have a systematic difference.

In summary, according to **Table 3** and previous figures, the EKF and nonlinear observers are seen to have similar performance during both compared flights. The differences can be assumed negligible, and real flight conditions are considered. The attitude estimates shown in **Figures 15** and **18** are very alike and correspond well to the reference, although the nonlinear Two methods for INS/GNSS integration have been investigated and compared: an extended Kalman filter using a 12-state vector and a nonlinear observer. The advantages and drawbacks of the methods have been presented and experimentally verified on flight data from a fixedwing UAV. A reference system consisting of three-antenna GNSS receiver with the antennas placed at the tail and each wing tip was use for performance comparison of the presented state estimators.

The inertial sensors used in the integration schemes are considered low-cost variants with respect to the reference system utilized. As the performance of the presented methods estimates the position, linear velocity, and attitude reasonably close to the reference, it is concluded that the methods are able to overcome the vibrations, disturbances, and bias drift connected to low-cost sensors in reasonable manner and thus provide sufficiently stable and accurate navigation solution.
