  This paper proposes a multi-sensor based approach to detect, track, and
localize a quadcopter unmanned aerial vehicle (UAV). Specifically, a pipeline
is developed to process monocular RGB and thermal video (captured from a fixed
platform) to detect and track the UAV in our FoV. Subsequently, a 2D planar
lidar is used to allow conversion of pixel data to actual distance
measurements, and thereby enable localization of the UAV in global coordinates.
The monocular data is processed through a deep learning-based object detection
method that computes an initial bounding box for the UAV. The thermal data is
processed through a thresholding and Kalman filter approach to detect and track
the bounding box. Training and testing data are prepared by combining a set of
original experiments conducted in a motion capture environment and publicly
available UAV image data. The new pipeline compares favorably to existing
methods and demonstrates promising tracking and localization capacity of sample
experiments.
