Real-time monocular obstacle avoidance using Underwater Dark Channel Prior
Real-time monocular obstacle avoidance using Underwater Dark Channel Prior
复制标题
使用水下暗通道先验进行实时单目避障
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
M. Campos
中科院分区：
文献类型：
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作者：
Paulo L. J. Drews;E. Hernández;A. Elfes;Erickson R. Nascimento;M. Campos
在本文中，我们提出了一种新的基于视觉的避障策略，该策略使用水下暗通道先验（UDCP），可应用于任何配备简单单目相机和最小机载处理能力的无人潜航器（UUV）。对于每个传入图像，我们的方法首先计算一个相对深度图来估计附近的障碍物。然后，对地图进行分割，并确定最有希望的感兴趣区域（RoI）。最后，在RoI范围内计算出逃逸方向，并执行相应的控制动作以避开障碍物。我们在自然环境中的视频序列上测试了我们的方法，并将其与最先进的方法进行了比较，显示出更好的性能，特别是在光线变化的条件下。我们还提供在受控环境下的低成本远程操作车辆（ROV）的在线结果。
In this paper we propose a new vision-based obstacle avoidance strategy using the Underwater Dark Channel Prior (UDCP) that can be applied to any Unmanned Underwater Vehicle (UUV) equipped with a simple monocular camera and minimal on-board processing capabilities. For each incoming image, our method first computes a relative depth map to estimate the obstacles nearby. Then, the map is segmented and the most promising Region of Interest (RoI) is identified. Finally, an escape direction is computed within the RoI and a control action is performed accordingly to avoid the obstacles. We tested our approach on a video sequence in a natural environment and compared it against a state-of-the-art method showing better performance, specially in light changing conditions. We also provide online results on a low-cost Remotely Operated Vehicle (ROV) in a controlled environment.