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We present UrbanFly: an uncertainty-aware real-time planning framework forquadrotor navigation in urban high-rise environments. A core aspect of UrbanFlyis its ability to robustly plan directly on the sparse point clouds generatedby a Monocular Visual Inertial SLAM (VINS) backend. It achieves this by usingthe sparse point clouds to build an uncertainty-integrated cuboidrepresentation of the environment through a data-driven monocular planesegmentation network. Our chosen world model provides faster distance queriesthan the more common voxel-grid representation, and UrbanFly leverages thiscapability in two different ways leading to two trajectory optimizers. Thefirst optimizer uses a gradient-free cross-entropy method to computetrajectories that minimize collision probability and smoothness cost. Oursecond optimizer is a simplified version of the first and uses a sequentialconvex programming optimizer initialized based on probabilistic safetyestimates on a set of randomly drawn trajectories. Both our trajectoryoptimizers are made computationally tractable and independent of the nature ofunderlying uncertainty by embedding the distribution of collision violations inReproducing 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 withsynthetic and real-world dataset scenes.

UrbanFly: Uncertainty-Aware Planning for Navigation Amongst High-Rises with Monocular Visual-Inertial SLAM Maps