构建水下事件相机仿真系统，提升机器人视觉在浑浊环境下的鲁棒性
EREBUS: End-to-end Robust Event Based Underwater Simulation
- 基于事件相机原理构建端到端水下仿真流水线
- 在低能见度与悬浮颗粒场景中实现岩石检测准确率提升
- 适用于水下机器人视觉训练，尤其适合复杂光照环境
水下环境对机器人和计算机视觉研究者构成巨大挑战，如光照不足和高动态范围场景。传统视觉方法在此类条件下表现不佳。事件相机通过逐帧追踪画面变化，可有效缓解此类问题。本文提出一种端到端的仿真流水线，用于生成安装于自主水下航行器（AUV）上的事件相机在水下环境中的真实合成数据，以训练视觉模型。我们以低能见度和悬浮颗粒条件下的岩石检测任务验证了该方法的有效性，该方法亦可推广至其他水下任务。
原文摘要 · Abstract (English)
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.
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