Unmanned Aerial Vehicle Assisted Localization using Multi-Sensor Fusion and Ground Vehicle Approach

An accurate localization of unmanned aerial vehicles (UAVs) is crucial for the execution of its growing applications such as surveillance and rescue missions. Previous researches have extensively studied the usage of sensor fusion algorithms to combine the sensors on board of the UAV to improve its localization. However, application of collaborative localization techniques in UAV navigation has not been investigated thus far. These novel algorithms stand to improve the stability and accuracy of UAV localization approaches through incorporation of additional sensors from other moving targets such as an unmanned ground vehicle (UGV). It is believed that the accuracy of the UAV localization will be further improved with help of multi-sensor Kalman filter (MS-KF) and this collaborative sensor fusion approach leads to a better accuracy than that of the single-sensor Kalman filter (SS-KF) approach. The obtained results in this study show promising improvements of both position and attitude with MS-KF. In comparison, the mean square error (MSE) for position is 0.005 and 0.026 for the developed MS-KF and SS-KF, respectively. Meanwhile, MSE for attitude is 2.396e-5 and 8.11e-4 for the developed MSKF and SS-KF, respectively. Based on these findings, the positive potential of collaborative sensor fusion approach has been aptly highlighted.