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首页> 外文会议>IEEE/ASME International Conference on Advanced Intelligent Mechatronics >Effects of Iteration in Kalman Filters Family for Improvement of Estimation Accuracy in Simultaneous Localization and Mapping
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Effects of Iteration in Kalman Filters Family for Improvement of Estimation Accuracy in Simultaneous Localization and Mapping

机译：Kalman滤波器系列迭代的影响提​​高了同时定位和映射的估计精度

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摘要

In this paper we investigate the role of iteration in kalman filters family for improvement of the estimation accuracy of states in Simultaneous Localization and Mapping (SLAM). The linearized error propagation existing in kalman filters family can result in large errors and inconsistency in the SLAM problem. One approach to alleviate this situation is the use of iteration in Extended Kalman Filter (EKF) and Sigma Point Kalman Filter (SPKF). We will describe that the iterated versions of kalman filters can increase the estimation accuracy and robustness of these filters against linear error propagation. Simulation results are presented to validate this improvement of state estimate convergence through repetitive linearization of the nonlinear model in EKFSLAM and SPKFSLAM algorithms.
机译：在本文中，我们调查迭代在卡尔曼滤波器中的作用，以提高同时定位和映射（SLAM）中各国的估计准确性。卡尔曼滤波器系列中存在的线性化错误传播可能导致SLAM问题中的误差和不一致。缓解这种情况的一种方法是在扩展卡尔曼滤波器（EKF）和Sigma点卡尔曼滤波器（SPKF）中使用迭代。我们将描述卡尔曼滤波器的迭代版本可以提高这些滤波器对线性误差传播的估计精度和鲁棒性。提出了仿真结果以通过EKFSLAM和SPKFSLAM算法中的非线性模型的重复线性化来验证状态估计收敛的这种改进。

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