  We present UrbanFly: an uncertainty-aware real-time planning framework for
quadrotor navigation in urban high-rise environments. A core aspect of UrbanFly
is its ability to robustly plan directly on the sparse point clouds generated
by a Monocular Visual Inertial SLAM (VINS) backend. It achieves this by using
the sparse point clouds to build an uncertainty-integrated cuboid
representation of the environment through a data-driven monocular plane
segmentation network. Our chosen world model provides faster distance queries
than the more common voxel-grid representation, and UrbanFly leverages this
capability in two different ways leading to two trajectory optimizers. The
first optimizer uses a gradient-free cross-entropy method to compute
trajectories that minimize collision probability and smoothness cost. Our
second optimizer is a simplified version of the first and uses a sequential
convex programming optimizer initialized based on probabilistic safety
estimates on a set of randomly drawn trajectories. Both our trajectory
optimizers are made computationally tractable and independent of the nature of
underlying uncertainty by embedding the distribution of collision violations in
Reproducing Kernel Hilbert Space. Empowered by the algorithmic innovation,
UrbanFly outperforms competing baselines in metrics such as collision rate,
trajectory length, etc., on a high-fidelity AirSim simulator augmented with
synthetic and real-world dataset scenes.
