  We investigate how to utilize predictive models for selecting appropriate
motion planning strategies based on perception uncertainty estimation for agile
unmanned aerial vehicle (UAV) navigation tasks. Although there are variety of
motion planning and perception algorithms for such tasks, the impact of
perception uncertainty is not explicitly handled in many of the current motion
algorithms, which leads to performance loss in real-life scenarios where the
measurement are often noisy due to external disturbances. We develop a novel
framework for embedding perception uncertainty to high level motion planning
management, in order to select the best available motion planning approach for
the currently estimated perception uncertainty. We estimate the uncertainty in
visual inputs using a deep neural network (CovNet) that explicitly predicts the
covariance of the current measurements. Next, we train a high level machine
learning model for predicting the lowest cost motion planning algorithm given
the current estimate of covariance as well as the UAV states. We demonstrate on
both real-life data and drone racing simulations that our approach, named
uncertainty driven motion planning switcher (UDS) yields the safest and fastest
trajectories among compared alternatives. Furthermore, we show that the
developed approach learns how to trade-off safety with agility by switching to
motion planners that leads to more agile trajectories when the estimated
covariance is high and vice versa.
