扩展和无气味卡尔曼滤波器在细胞覆盖环境重建中的应用

Luigi D’Alfonso, Antonio Grano, P. Muraca, P. Pugliese
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引用次数: 2

摘要

我们比较了两种广泛用于非线性系统的滤波器,即扩展卡尔曼滤波器(EKF)和无气味卡尔曼滤波器(UKF)在重建移动机器人运动的未知环境中的有效性。重建是通过一种新颖的细胞覆盖算法获得的,该算法仅使用机器人机载声纳传感器的距离测量值。我们表明,尽管UKF具有优越的理论特性,但两种滤波器的性能相当好,并且所提出的算法提供了良好的定位性能和可靠的环境重建。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Extended and Unscented Kalman Filters in a cells-covering method for environment reconstruction
We compare the effectiveness of two widely used filters for nonlinear systems, i.e., the Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF), in reconstructing the unknown environment where a mobile robot moves. The reconstruction is obtained by a novel cells-covering algorithm that only uses the distance measurements taken from the robot’s on-board sonar sensors. We show that, despite the superior theoretical properties of the UKF, both filters perform comparably well, and that the proposed algorithm provides good localization performance and a reliable environment reconstruction.
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