  In recent years, unmanned aerial vehicles (UAVs) are used for numerous
inspection and video capture tasks. Manually controlling UAVs in the vicinity
of obstacles is challenging, however, and poses a high risk of collisions. Even
for autonomous flight, global navigation planning might be too slow to react to
newly perceived obstacles. Disturbances such as wind might lead to deviations
from the planned trajectories.
  In this work, we present a fast predictive obstacle avoidance method that
does not depend on higher-level localization or mapping and maintains the
dynamic flight capabilities of UAVs. It directly operates on LiDAR range images
in real time and adjusts the current flight direction by computing angular
potential fields within the range image. The velocity magnitude is subsequently
determined based on a trajectory prediction and time-to-contact estimation.
  Our method is evaluated using Hardware-in-the-Loop simulations. It keeps the
UAV at a safe distance to obstacles, while allowing higher flight velocities
than previous reactive obstacle avoidance methods that directly operate on
sensor data.
