GapFlyt: Active Vision Based Minimalist Structure-less Gap Detection For Quadrotor Flight
This addresses the inefficiency of passive perception in quadrotor navigation by introducing a task-driven, bio-inspired method for gap detection, which is incremental as it applies existing bio-inspired concepts to a specific robotic task.
The paper tackles the problem of enabling quadrotors to fly through unknown gaps without building a 3D map, using a bio-inspired minimalist approach with only a monocular camera and onboard sensing, achieving an 85% success rate at 2.5 m/s with a 5 cm tolerance.
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 3D 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 3D map and are highly task driven. In this paper, we propose this framework of bio-inspired perceptual design for quadrotors. We use this philosophy to design a minimalist sensori-motor framework for a quadrotor to fly though unknown gaps without a 3D 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 2.5ms$^{-1}$ even with a minimum tolerance of just 5cm. To our knowledge, this is the first paper which addresses the problem of gap detection of an unknown shape and location with a monocular camera and onboard sensing.