Vision-in-the-loop Simulation for Deep Monocular Pose Estimation of UAV in Ocean Environment
This provides a cost-effective solution for testing autonomous flight of shipboard UAVs, specifically for vision-based control and estimation, though it is incremental as it builds on existing simulation and pose estimation methods.
The paper tackles the challenge of validating deep monocular pose estimation for UAVs in ocean environments by proposing a vision-in-the-loop simulation using Gaussian splatting to create a photo-realistic 3D virtual environment, enabling cost-effective indoor testing of flight maneuvers and algorithms.
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.