# Deep Transformer Network for Monocular Pose Estimation of Shipborne Unmanned Aerial Vehicle

arXiv: https://arxiv.org/abs/2406.09260 · HTML: https://mlsanity.com/paper/2406.09260/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 32 references that are in the map.
- First posted on arXiv 13 Jun 2024.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Describing Textures in the Wild](https://mlsanity.com/paper/1311.3618/) (2013)
- [Decoupled Weight Decay Regularization](https://mlsanity.com/paper/1711.05101/) (2017)
- [End-to-end people detection in crowded scenes](https://mlsanity.com/paper/1506.04878/) (2015)
- [Deep Drone Racing: From Simulation to Reality with Domain Randomization](https://mlsanity.com/paper/1905.09727/) (2019)
- [YOLOPose: Transformer-based Multi-Object 6D Pose Estimation using Keypoint Regression](https://mlsanity.com/paper/2205.02536/) (2022)
- [Bayesian Attitude Estimation with the Matrix Fisher Distribution on SO(3)](https://mlsanity.com/paper/1710.03746/) (2017)
- [A Region Based Easy Path Wavelet Transform For Sparse Image Representation](https://mlsanity.com/paper/1702.01961/) (2017)

## Built on by (most cited first, 1 of 1)

- [Hardware- and Vision-in-the-Loop Validation of Deep Monocular Pose Estimation for Autonomous Maritime UAV Flight](https://mlsanity.com/paper/2606.19176/) (2026)

## Abstract (arXiv)

This paper introduces a deep transformer network for estimating the relative 6D pose of a Unmanned Aerial Vehicle (UAV) with respect to a ship using monocular images. A synthetic dataset of ship images is created and annotated with 2D keypoints of multiple ship parts. A Transformer Neural Network model is trained to detect these keypoints and estimate the 6D pose of each part. The estimates are integrated using Bayesian fusion. The model is tested on synthetic data and in-situ flight experiments, demonstrating robustness and accuracy in various lighting conditions. The position estimation error is approximately 0.8% and 1.0% of the distance to the ship for the synthetic data and the flight experiments, respectively. The method has potential applications for ship-based autonomous UAV landing and navigation.