State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China.
Advanced Technology Institute, Zhejiang University, Hangzhou 310027, China.
Sensors (Basel). 2021 Apr 2;21(7):2468. doi: 10.3390/s21072468.
Docking technology for autonomous underwater vehicles (AUVs) involves energy supply, data exchange and navigation, and plays an important role to extend the endurance of the AUVs. The navigation method used in the transition between AUV homing and docking influences subsequent tasks. How to improve the accuracy of the navigation in this stage is important. However, when using ultra-short baseline (USBL), outliers and slow localization updating rates could possibly cause localization errors. Optical navigation methods using underwater lights and cameras are easily affected by the ambient light. All these may reduce the rate of successful docking. In this paper, research on an improved localization method based on multi-sensor information fusion is carried out. To improve the localization performance of AUVs under motion mutation and light variation conditions, an improved underwater simultaneous localization and mapping algorithm based on ORB features (IU-ORBSALM) is proposed. A nonlinear optimization method is proposed to optimize the scale of monocular visual odometry in IU-ORBSLAM and the AUV pose. Localization tests and five docking missions are executed in a swimming pool. The localization results indicate that the localization accuracy and update rate are both improved. The 100% successful docking rate achieved verifies the feasibility of the proposed localization method.
自主水下航行器（AUV）的对接技术涉及能量供应、数据交换和导航，对延长 AUV 的续航能力起着重要作用。AUV 归航与对接过程中的导航方法影响后续任务。如何提高该阶段的导航精度是很重要的。然而，在使用超短基线（USBL）时，异常值和缓慢的定位更新率可能会导致定位误差。使用水下灯光和相机的光学导航方法容易受到环境光的影响。所有这些都可能降低成功对接的速度。本文对基于多传感器信息融合的改进定位方法进行了研究。为了提高 AUV 在运动突变和光照变化条件下的定位性能，提出了一种基于 ORB 特征的改进水下同时定位与建图算法（IU-ORBSALM）。提出了一种非线性优化方法来优化 IU-ORBSLAM 中单目视觉里程计和 AUV 姿态的尺度。在游泳池中进行了定位测试和五次对接任务。定位结果表明，定位精度和更新率都得到了提高。100%的成功对接率验证了所提出的定位方法的可行性。
