Vision-in-the-loop Simulation for Deep Monocular Pose Estimation of UAV in Ocean Environment

TL;DR

This paper introduces a realistic virtual simulation environment for testing deep monocular pose estimation of UAVs in ocean settings, reducing reliance on costly real-world testing and enabling comprehensive validation of vision-based control systems.

Contribution

It presents a novel photo-realistic 3D simulation using Gaussian splatting for UAV pose estimation validation in ocean environments, integrating real-world images for cost-effective testing.

Findings

Enables indoor testing of UAV flight maneuvers in a virtual ocean environment.

Validates deep neural network-based pose estimation in a realistic simulation.

Reduces operational costs and logistical challenges of real-world testing.

Abstract

This paper proposes a vision-in-the-loop simulation environment for deep monocular pose estimation of a UAV operating in an ocean environment. Recently, a deep neural network with a transformer architecture has been successfully trained to estimate the pose of a UAV relative to the flight deck of a research vessel, overcoming several limitations of GPS-based approaches. However, validating the deep pose estimation scheme in an actual ocean environment poses significant challenges due to the limited availability of research vessels and the associated operational costs. To address these issues, we present a photo-realistic 3D virtual environment leveraging recent advancements in Gaussian splatting, a novel technique that represents 3D scenes by modeling image pixels as Gaussian distributions in 3D space, creating a lightweight and high-quality visual model from multiple viewpoints. This approach enables the creation of a virtual environment integrating multiple real-world images collected in situ. The resulting simulation enables the indoor testing of flight maneuvers while verifying all aspects of flight software, hardware, and the deep monocular pose estimation scheme. This approach provides a cost-effective solution for testing and validating the autonomous flight of shipboard UAVs, specifically focusing on vision-based control and estimation algorithms.