# Hardware- and Vision-in-the-Loop Validation of Deep Monocular Pose Estimation for Autonomous Maritime UAV Flight

arXiv: https://arxiv.org/abs/2606.19176 · HTML: https://mlsanity.com/paper/2606.19176/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 14 references that are in the map.
- First posted on arXiv 17 Jun 2026.

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

- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [Deep Transformer Network for Monocular Pose Estimation of Shipborne Unmanned Aerial Vehicle](https://mlsanity.com/paper/2406.09260/) (2024)
- [Vision-in-the-loop Simulation for Deep Monocular Pose Estimation of UAV in Ocean Environment](https://mlsanity.com/paper/2502.05409/) (2025)

## Abstract (arXiv)

Autonomous UAV operations on ships require reliable vision-based relative pose estimation, yet at-sea validation is costly, weather-dependent, and risky. This paper presents a hardware-validated vision-in-the-loop framework that enables fully autonomous indoor flight while emulating photorealistic maritime environments. Rendered maritime views are processed onboard by a deep transformer-based monocular pose estimator. Delayed vision measurements are fused with high-rate IMU data using a delayed Kalman filter to provide consistent state estimates for geometric control. The system captures critical embedded effects, including perception latency, asynchronous updates, and computational constraints, that are absent in pure simulation. Autonomous takeoff, trajectory tracking, and landing experiments demonstrate stable closed-loop flight. The results establish a safe and hardware-realistic intermediate stage for developing maritime UAV autonomy prior to shipboard deployment.