Monocular visual autonomous landing system for quadcopter drones using software in the loop

TL;DR

This paper introduces a simulation-based monocular visual system for quadcopter autonomous landing, enabling safe testing and parameter tuning before physical deployment, and demonstrates its effectiveness on an actual drone platform.

Contribution

It presents a novel software-in-the-loop approach for monocular vision-based autonomous landing, reducing risks and facilitating design validation prior to physical implementation.

Findings

Successful simulation of autonomous landing in Gazebo

Effective real-world implementation on an F450 quadcopter

System enables safe parameter tuning and design testing

Abstract

Autonomous landing is a capability that is essential to achieve the full potential of multi-rotor drones in many social and industrial applications. The implementation and testing of this capability on physical platforms is risky and resource-intensive; hence, in order to ensure both a sound design process and a safe deployment, simulations are required before implementing a physical prototype. This paper presents the development of a monocular visual system, using a software-in-the-loop methodology, that autonomously and efficiently lands a quadcopter drone on a predefined landing pad, thus reducing the risks of the physical testing stage. In addition to ensuring that the autonomous landing system as a whole fulfils the design requirements using a Gazebo-based simulation, our approach provides a tool for safe parameter tuning and design testing prior to physical implementation. Finally, the proposed monocular vision-only approach to landing pad tracking made it possible to effectively implement the system in an F450 quadcopter drone with the standard computational capabilities of an Odroid XU4 embedded processor.