  The vision of unmanned aerial vehicles is very significant for UAV-related
applications such as search and rescue, landing on a moving platform, etc. In
this work, we have developed an integrated system for the UAV landing on the
moving platform, and the UAV object detection with tracking in the complicated
environment. Firstly, we have proposed a robust LoG-based deep neural network
for object detection and tracking, which has great advantages in robustness to
object scale and illuminations compared with typical deep network-based
approaches. Then, we have also improved based on the original Kalman filter and
designed an iterative multi-model-based filter to tackle the problem of unknown
dynamics in real circumstances of motion estimations. Next, we implemented the
whole system and do ROS Gazebo-based testing in two complicated circumstances
to verify the effectiveness of our design. Finally, we have deployed the
proposed detection, tracking, and motion estimation strategies into real
applications to do UAV tracking of a pillar and obstacle avoidance. It is
demonstrated that our system shows great accuracy and robustness in real
applications.
