# Reinforcement Learning-Based Monocular Vision Approach for Autonomous UAV Landing

arXiv: https://arxiv.org/abs/2505.06963 · HTML: https://mlsanity.com/paper/2505.06963/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 13 references that are in the map.
- First posted on arXiv 11 May 2025.

## Builds on (most cited first, 4 of 4)

- [Autonomous Quadrotor Landing using Deep Reinforcement Learning](https://mlsanity.com/paper/1709.03339/) (2017)
- [Learning to Assist Drone Landings](https://mlsanity.com/paper/2011.13146/) (2020)
- [Reinforcement Learning for Shared Autonomy Drone Landings](https://mlsanity.com/paper/2202.02927/) (2022)
- [Monocular visual autonomous landing system for quadcopter drones using software in the loop](https://mlsanity.com/paper/2108.06616/) (2021)

## Abstract (arXiv)

This paper introduces an innovative approach for the autonomous landing of Unmanned Aerial Vehicles (UAVs) using only a front-facing monocular camera, therefore obviating the requirement for depth estimation cameras. Drawing on the inherent human estimating process, the proposed method reframes the landing task as an optimization problem. The UAV employs variations in the visual characteristics of a specially designed lenticular circle on the landing pad, where the perceived color and form provide critical information for estimating both altitude and depth. Reinforcement learning algorithms are utilized to approximate the functions governing these estimations, enabling the UAV to ascertain ideal landing settings via training. This method's efficacy is assessed by simulations and experiments, showcasing its potential for robust and accurate autonomous landing without dependence on complex sensor setups. This research contributes to the advancement of cost-effective and efficient UAV landing solutions, paving the way for wider applicability across various fields.