  LiDAR has become one of the primary sensors in robotics and autonomous system
for high-accuracy situational awareness. In recent years, multi-modal LiDAR
systems emerged, and among them, LiDAR-as-a-camera sensors provide not only 3D
point clouds but also fixed-resolution 360{\deg}panoramic images by encoding
either depth, reflectivity, or near-infrared light in the image pixels. This
potentially brings computer vision capabilities on top of the potential of
LiDAR itself. In this paper, we are specifically interested in utilizing LiDARs
and LiDAR-generated images for tracking Unmanned Aerial Vehicles (UAVs) in
real-time which can benefit applications including docking, remote
identification, or counter-UAV systems, among others. This is, to the best of
our knowledge, the first work that explores the possibility of fusing the
images and point cloud generated by a single LiDAR sensor to track a UAV
without a priori known initialized position. We trained a custom YOLOv5 model
for detecting UAVs based on the panoramic images collected in an indoor
experiment arena with a MOCAP system. By integrating with the point cloud, we
are able to continuously provide the position of the UAV. Our experiment
demonstrated the effectiveness of the proposed UAV tracking approach compared
with methods based only on point clouds or images. Additionally, we evaluated
the real-time performance of our approach on the Nvidia Jetson Nano, a popular
mobile computing platform.
