Martínez-Barberá Humberto, Bernal-Polo Pablo, Herrero-Pérez David
Facultad de Informática, University of Murcia, 30100 Murcia, Spain.
Technical University of Cartagena, Campus Muralla del Mar, 30202 Cartagena, Murcia, Spain.
Sensors (Basel). 2021 Feb 23;21(4):1549. doi: 10.3390/s21041549.
This paper presents a framework for processing, modeling, and fusing underwater sensor signals to provide a reliable perception for underwater localization in structured environments. Submerged sensory information is often affected by diverse sources of uncertainty that can deteriorate the positioning and tracking. By adopting uncertain modeling and multi-sensor fusion techniques, the framework can maintain a coherent representation of the environment, filtering outliers, inconsistencies in sequential observations, and useless information for positioning purposes. We evaluate the framework using cameras and range sensors for modeling uncertain features that represent the environment around the vehicle. We locate the underwater vehicle using a Sequential Monte Carlo (SMC) method initialized from the GPS location obtained on the surface. The experimental results show that the framework provides a reliable environment representation during the underwater navigation to the localization system in real-world scenarios. Besides, they evaluate the improvement of localization compared to the position estimation using reliable dead-reckoning systems.
本文提出了一个用于处理、建模和融合水下传感器信号的框架，以便在结构化环境中为水下定位提供可靠的感知。水下传感信息常常受到各种不确定性来源的影响，这些不确定性会使定位和跟踪变差。通过采用不确定性建模和多传感器融合技术，该框架可以维持对环境的连贯表示，过滤掉异常值、序列观测中的不一致性以及对定位无用的信息。我们使用相机和距离传感器来评估该框架，以对表示车辆周围环境的不确定特征进行建模。我们使用从水面获得的GPS位置初始化的序贯蒙特卡罗（SMC）方法来定位水下航行器。实验结果表明，在现实场景中，该框架在水下导航期间为定位系统提供了可靠的环境表示。此外，他们评估了与使用可靠航位推算系统的位置估计相比，定位的改进情况。
