Liu Shuang, Xu Hongli, Lin Yang, Gao Lei
State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China.
Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China.
Sensors (Basel). 2019 Apr 20;19(8):1889. doi: 10.3390/s19081889.
Autonomous underwater vehicles (AUVs) play very important roles in underwater missions. However, the reliability of the automated recovery of AUVs has still not been well addressed. We propose a vision-based framework for automatically recovering an AUV by another AUV in shallow water. The proposed framework contains a detection phase for the robust detection of underwater landmarks mounted on the docking station in shallow water and a pose-estimation phase for estimating the pose between AUVs and underwater landmarks. We propose a Laplacian-of-Gaussian-based coarse-to-fine blockwise (LCB) method for the detection of underwater landmarks to overcome ambient light and nonuniform spreading, which are the two main problems in shallow water. We propose a novel method for pose estimation in practical cases where landmarks are broken or covered by biofouling. In the experiments, we show that our proposed LCB method outperforms the state-of-the-art method in terms of remote landmark detection. We then combine our proposed vision-based framework with acoustic sensors in field experiments to demonstrate its effectiveness in the automated recovery of AUVs.
自主水下航行器（AUV）在水下任务中发挥着非常重要的作用。然而，AUV自动回收的可靠性仍未得到很好的解决。我们提出了一种基于视觉的框架，用于在浅水区由另一艘AUV自动回收AUV。所提出的框架包括一个检测阶段，用于在浅水区对安装在对接站上的水下地标进行鲁棒检测，以及一个姿态估计阶段，用于估计AUV与水下地标之间的姿态。我们提出了一种基于高斯拉普拉斯的从粗到细逐块（LCB）方法来检测水下地标，以克服浅水区的两个主要问题，即环境光和不均匀散射。我们提出了一种在实际情况下地标被生物污垢破坏或覆盖时进行姿态估计的新方法。在实验中，我们表明，我们提出的LCB方法在远程地标检测方面优于现有方法。然后，我们在现场实验中将所提出的基于视觉的框架与声学传感器相结合，以证明其在AUV自动回收中的有效性。
