A SLAM algorithm based on the central difference Kalman filter

Jihua Zhu, Nanning Zheng, Zejian Yuan, Qiang Zhang, Xuetao Zhang, Yongjian He
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引用次数: 52

Abstract

This paper presents an central difference Kalman filter (CDKF) based Simultaneous Localization and Mapping (SLAM) algorithm, which is an alternative to the classical extended Kalman filter based SLAM solution (EKF-SLAM). EKF-SLAM suffers from two important problems, which are the calculation of Jacobians and the linear approximations to the nonlinear models. They can lead the filter to be inconsistent. To overcome the serious drawbacks of the previous frameworks, Sterling's polynomial interpolation method is employed to approximate nonlinear models. Combined with the Kanlman filter framework, CDKF is proposed to solve the probabilistic state-space SLAM problem. The proposed approach improves the filter consistency and state estimation accuracy. Both simulated experiments and bench mark data set are used to demonstrating the superiority of the proposed algorithm.
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一种基于中心差分卡尔曼滤波的SLAM算法
本文提出了一种基于中心差分卡尔曼滤波(CDKF)的同步定位与映射(SLAM)算法,作为经典的基于扩展卡尔曼滤波的同步定位与映射(SLAM)算法的替代方案。EKF-SLAM存在两个重要问题,即雅可比矩阵的计算和非线性模型的线性逼近。它们会导致过滤器不一致。为了克服以往框架的严重缺陷,采用斯特林多项式插值方法逼近非线性模型。结合Kanlman滤波框架,提出CDKF来解决概率状态空间SLAM问题。该方法提高了滤波一致性和状态估计精度。通过仿真实验和基准数据集验证了该算法的优越性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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