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

TL;DR

This paper introduces a bio-inspired, minimalist active vision approach enabling quadrotors to detect and fly through unknown gaps using only a monocular camera, without 3D scene reconstruction, achieving high success rates in real-world tests.

Contribution

It presents the first method for gap detection of unknown shapes and locations using monocular vision and onboard sensing, inspired by biological agents.

Findings

Achieved 85% success rate in real-world gap navigation.

Operates at 2.5 m/s with only 5cm tolerance.

First approach addressing unknown gap shape and position with monocular camera.

Abstract

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.