AIRA: A Low-cost IR-based Approach Towards Autonomous Precision Drone Landing and NLOS Indoor Navigation

TL;DR

AIRA is a low-cost infrared-based system enabling microdrones to land precisely and navigate indoors without relying on high-resource sensors, outperforming vision-based methods in energy efficiency and operational conditions.

Contribution

The paper introduces AIRA, a novel infrared light-based platform that allows microdrones to land accurately and navigate indoors using minimal hardware and low energy, even in challenging lighting conditions.

Findings

Achieves under 10cm landing error from 11.1m distance.

Costs under 83 USD, with comparable performance to vision-based methods.

Operates effectively in low light and non line of sight scenarios.

Abstract

Automatic drone landing is an important step for achieving fully autonomous drones. Although there are many works that leverage GPS, video, wireless signals, and active acoustic sensing to perform precise landing, autonomous drone landing remains an unsolved challenge for palm-sized microdrones that may not be able to support the high computational requirements of vision, wireless, or active audio sensing. We propose AIRA, a low-cost infrared light-based platform that targets precise and efficient landing of low-resource microdrones. AIRA consists of an infrared light bulb at the landing station along with an energy efficient hardware photodiode (PD) sensing platform at the bottom of the drone. AIRA costs under 83 USD, while achieving comparable performance to existing vision-based methods at a fraction of the energy cost. AIRA requires only three PDs without any complex pattern recognition models to accurately land the drone, under $10$cm of error, from up to $11.1$ meters away, compared to camera-based methods that require recognizing complex markers using high resolution images with a range of only up to $1.2$ meters from the same height. Moreover, we demonstrate that AIRA can accurately guide drones in low light and partial non line of sight scenarios, which are difficult for traditional vision-based approaches.