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

arXiv: https://arxiv.org/abs/2108.06616 · HTML: https://mlsanity.com/paper/2108.06616/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 39 references that are in the map.
- First posted on arXiv 14 Aug 2021.

## Builds on (most cited first, 3 of 3)

- [Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World](https://mlsanity.com/paper/1703.06907/) (2017)
- [ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras](https://mlsanity.com/paper/1610.06475/) (2016)
- [Local Feature Detectors, Descriptors, and Image Representations: A Survey](https://mlsanity.com/paper/1607.08368/) (2016)

## Built on by (most cited first, 2 of 2)

- [Monocular Robot Navigation with Self-Supervised Pretrained Vision Transformers](https://mlsanity.com/paper/2203.03682/) (2022)
- [Reinforcement Learning-Based Monocular Vision Approach for Autonomous UAV Landing](https://mlsanity.com/paper/2505.06963/) (2025)

## Abstract (arXiv)

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.