  Estimating the pose of an unmanned aerial vehicle (UAV) or drone is a
challenging task. It is useful for many applications such as navigation,
surveillance, tracking objects on the ground, and 3D reconstruction. In this
work, we present a LiDAR-camera-based relative pose estimation method between a
drone and a ground vehicle, using a LiDAR sensor and a fisheye camera on the
vehicle's roof and another fisheye camera mounted under the drone. The LiDAR
sensor directly observes the drone and measures its position, and the two
cameras estimate the relative orientation using indirect observation of the
surrounding objects. We propose a dynamically adaptive kernel-based method for
drone detection and tracking using the LiDAR. We detect vanishing points in
both cameras and find their correspondences to estimate the relative
orientation. Additionally, we propose a rotation correction technique by
relying on the observed motion of the drone through the LiDAR. In our
experiments, we were able to achieve very fast initial detection and real-time
tracking of the drone. Our method is fully automatic.
