Camera-Based Tracking of Floating Objects using Fixed-wing UAVs

This article concerns tracking of floating objects using fixed-wing UAVs with a monocular thermal camera. Target tracking from an agile aerial vehicle is challenging because uncertainty in the UAV pose negatively affects the accuracy of the measurements obtained through thermal images. Consequently, the accuracy of the tracking estimates is degraded if navigation uncertainty is neglected. This is especially relevant for the estimated target covariance since inconsistency is a likely consequence. A tracking system based on the Schmidt-Kalman filter is proposed to mitigate navigation uncertainty. Images gathered with an uncertain UAV pose are weighted less than images captured with a reliable pose. The UAV pose is estimated independently in a multiplicative extended Kalman filter where the estimated covariance matrix is a measure of the uncertainty. The method is compared experimentally with two traditional alternatives based on the extended Kalman filter. The results show that the proposed method performs better with respect to consistency and accuracy.