GapFlyt: Active Vision Based Minimalist Structure-less Gap Detection For Quadrotor Flight.

Although quadrotors, and aerial robots in general, are inherently active agents, their perceptual capabilities in literature so far have been mostly passive in nature. Researchers and practitioners today use traditional computer vision algorithms with the aim of building a representation of general applicability: a 3-D reconstruction of the scene. Using this representation, planning tasks are constructed and accomplished to allow the quadrotor to demonstrate autonomous behavior. These methods are inefficient as they are not task driven and such methodologies are not utilized by flying insects and birds. Such agents have been solving the problem of navigation and complex control for ages without the need to build a 3-D map and are highly task driven. In this letter, we propose this framework of bioinspired perceptual design for quadrotors. We use this philosophy to design a minimalist sensorimotor framework for a quadrotor to fly through unknown gaps without an explicit 3-D reconstruction of the scene using only a monocular camera and onboard sensing. We successfully evaluate and demonstrate the proposed approach in many real-world experiments with different settings and window shapes, achieving a success rate of 85% at 2.5 ms $^{-1}$ even with a minimum tolerance of just 5 cm. To best of our knowledge, this is the first letter that addresses the problem of gap detection of an unknown shape and location with a monocular camera and onboard sensing.