  A major challenge in deploying the smallest of Micro Aerial Vehicle (MAV)
platforms (< 100 g) is their inability to carry sensors that provide
high-resolution metric depth information (e.g., LiDAR or stereo cameras).
Current systems rely on end-to-end learning or heuristic approaches that
directly map images to control inputs, and struggle to fly fast in unknown
environments. In this work, we ask the following question: using only a
monocular camera, optical odometry, and offboard computation, can we create
metrically accurate maps to leverage the powerful path planning and navigation
approaches employed by larger state-of-the-art robotic systems to achieve
robust autonomy in unknown environments? We present MonoNav: a fast 3D
reconstruction and navigation stack for MAVs that leverages recent advances in
depth prediction neural networks to enable metrically accurate 3D scene
reconstruction from a stream of monocular images and poses. MonoNav uses
off-the-shelf pre-trained monocular depth estimation and fusion techniques to
construct a map, then searches over motion primitives to plan a collision-free
trajectory to the goal. In extensive hardware experiments, we demonstrate how
MonoNav enables the Crazyflie (a 37 g MAV) to navigate fast (0.5 m/s) in
cluttered indoor environments. We evaluate MonoNav against a state-of-the-art
end-to-end approach, and find that the collision rate in navigation is
significantly reduced (by a factor of 4). This increased safety comes at the
cost of conservatism in terms of a 22% reduction in goal completion.
