Comparison of Dynamic and Kinematic Model Driven Extended Kalman Filters (EKF) for the Localization of Autonomous Underwater Vehicles
Comparison of Dynamic and Kinematic Model Driven Extended Kalman Filters (EKF) for the Localization of Autonomous Underwater Vehicles
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用于自主水下航行器定位的动态和运动学模型驱动扩展卡尔曼滤波器 (EKF) 的比较
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发表时间：
2021
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通讯作者：
W. Norris
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作者：
Sharan Balasubramanian;Ayushi Rajput;Rodra W. Hascaryo;Chirag Rastogi;W. Norris
自主水下航行器(AUV)和遥控航行器(ROV)被用于与勘探和科学研究有关的各种任务。这些系统的成功导航需要一个好的定位系统。自20世纪60年代初以来，基于卡尔曼滤波的定位技术一直很流行，并在开发和设计中进行了广泛的研究。已经发现，在卡尔曼滤波中使用动力学模型(而不是运动学模型)可以得到更准确的预测，因为动力学模型考虑了作用在AUV上的力。提出了一种基于简化动力学模型的AUV运动预测扩展卡尔曼滤波算法。首先推导了REXROV的动力学模型，然后对REXROV进行了简化，REXROV是一种用于简单的水下勘探、海底结构、管道和沉船检查的潜艇。在一个开源的航行器模拟器UUV模拟器中实现了该滤波器，并与地面真实情况进行了比较。结果表明，动态滤波器具有良好的预测精度，但在EKF能够实时使用之前还需要改进。对实际实施提出了一些看法和讨论，以显示这一概念需要采取的下一步步骤。
Autonomous Underwater Vehicles (AUVs) and Remotely Operated Vehicles (ROVs) are used for a wide variety of missions related to exploration and scientific research. Successful navigation by these systems requires a good localization system. Kalman filter based localization techniques have been prevalent since the early 1960s and extensive research has been carried out using them, both in development and in design. It has been found that the use of a dynamic model (instead of a kinematic model) in the Kalman filter can lead to more accurate predictions, as the dynamic model takes the forces acting on the AUV into account. Presented in this paper is a motion-predictive extended Kalman filter (EKF) for AUVs using a simplified dynamic model. The dynamic model is derived first and then it was simplified for a RexROV, a type of submarine vehicle used in simple underwater exploration, inspection of subsea structures, pipelines and shipwrecks. The filter was implemented with a simulated vehicle in an open-source marine vehicle simulator called UUV Simulator and the results were compared with the ground truth. The results show good prediction accuracy for the dynamic filter, though improvements are needed before the EKF can be used on real-time. Some perspective and discussion on practical implementation is presented to show the next steps needed for this concept.