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首页> 外文期刊>International Journal of Offshore and Polar Engineering >Experiment on Simultaneous Localization and Mapping Based on Unscented Kalman Filter for Unmanned Underwater Vehicles
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Experiment on Simultaneous Localization and Mapping Based on Unscented Kalman Filter for Unmanned Underwater Vehicles

机译：基于无味卡尔曼滤波的无人水下航行器同时定位与制图实验

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摘要

This paper proposes a simultaneous localization and mapping (SLAM) scheme applicable to the autonomous navigation of unmanned underwater vehicles (UUV). A SLAM scheme is an alternative navigation method for measuring the environment through which the vehicle is passing and providing the relative position of the unmanned vehicle. An unscented Kalman filter (UKF) is utilized in order to develop a SLAM that is suitable for estimating the locations of the UUV and the surrounding objects when the UUV's motion is highly nonlinear. A range sonar is used as a sensor for collecting the data of the spatial information of the environment in which the UUV navigates. The proposed UKF-SLAM scheme was tested in experiments that used various 3 degrees-of-freedom motion conditions with a real UUV under a tank environment. The results of these experiments showed that the proposed SLAM algorithm was capable of estimating the position of the UUV and the surrounding objects in real environments, and that the algorithm will perform well in various conditions.
机译：本文提出了一种同时定位和制图（SLAM）方案，适用于无人水下航行器（UUV）的自主导航。 SLAM方案是一种替代性导航方法，用于测量车辆经过的环境并提供无人驾驶车辆的相对位置。利用无味卡尔曼滤波器（UKF）来开发SLAM，该SLAM适用于在UUV运动高度非线性时估算UUV和周围物体的位置。范围声纳用作传感器，用于收集UUV导航环境的空间信息数据。拟议的UKF-SLAM方案已在坦克环境下使用各种3自由度运动条件和真实UUV的实验中进行了测试。这些实验的结果表明，所提出的SLAM算法能够估计UUV和周围物体在真实环境中的位置，并且该算法在各种条件下都能很好地执行。

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