Vision-based indoor localization of nano drones in controlled environment with its applications

TL;DR

This paper presents a cost-effective vision-based indoor localization method for nano drones using off-board computing and monocular cameras, achieving 3.1cm accuracy in controlled environments for applications like drone control education.

Contribution

It introduces a novel off-board monocular camera approach with PID controllers for nano drone localization, suitable for low-cost educational and research applications.

Findings

Achieved 3.1cm localization error

Setup cost of 50 USD

Validated with multiple applications

Abstract

Navigating unmanned aerial vehicles in environments where GPS signals are unavailable poses a compelling and intricate challenge. This challenge is further heightened when dealing with Nano Aerial Vehicles (NAVs) due to their compact size, payload restrictions, and computational capabilities. This paper proposes an approach for localization using off-board computing, an off-board monocular camera, and modified open-source algorithms. The proposed method uses three parallel proportional-integral-derivative controllers on the off-board computer to provide velocity corrections via wireless communication, stabilizing the NAV in a custom-controlled environment. Featuring a 3.1cm localization error and a modest setup cost of 50 USD, this approach proves optimal for environments where cost considerations are paramount. It is especially well-suited for applications like teaching drone control in academic institutions, where the specified error margin is deemed acceptable. Various applications are designed to validate the proposed technique, such as landing the NAV on a moving ground vehicle, path planning in a 3D space, and localizing multi-NAVs. The created package is openly available at https://github.com/simmubhangu/eyantra_drone to foster research in this field.