EREBUS: End-to-end Robust Event Based Underwater Simulation
This addresses the challenge of training vision models for underwater robotics and computer vision, where traditional methods struggle due to adverse conditions, but it is incremental as it builds on existing event-based camera simulation techniques.
The paper tackles the problem of generating realistic synthetic data for event-based cameras in underwater environments, demonstrating its effectiveness for rock detection tasks with poor visibility and suspended particulate matter.
The underwater domain presents a vast array of challenges for roboticists and computer vision researchers alike, such as poor lighting conditions and high dynamic range scenes. In these adverse conditions, traditional vision techniques struggle to adapt and lead to suboptimal performance. Event-based cameras present an attractive solution to this problem, mitigating the issues of traditional cameras by tracking changes in the footage on a frame-by-frame basis. In this paper, we introduce a pipeline which can be used to generate realistic synthetic data of an event-based camera mounted to an AUV (Autonomous Underwater Vehicle) in an underwater environment for training vision models. We demonstrate the effectiveness of our pipeline using the task of rock detection with poor visibility and suspended particulate matter, but the approach can be generalized to other underwater tasks.