卡尔曼滤波比较车辆定位数据对齐

ICAR '05. Proceedings., 12th International Conference on Advanced Robotics, 2005. Pub Date : 2005-07-18 DOI:10.1109/ICAR.2005.1507410

B. Mourllion, D. Gruyer, A. Lambert, S. Glaser

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引用次数: 6

摘要

本文的目的是对几种用于非线性系统的卡尔曼滤波算法进行比较。在介绍了目前最流行的几种卡尔曼滤波器及其局限性的基础上，介绍了几种新的卡尔曼滤波器，并对它们在车辆定位问题上的应用进行了比较。这种比较是基于与车辆定位中可能发生的最坏情况相对应的预测步骤。通常，当我们实现车辆跟踪时，如果被跟踪的车辆是隐藏的，则校正数据不可用，因此校正步骤被禁用(时间数据对齐)。

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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Kalman filters comparison for vehicle localization data alignment

The aim of this paper is to carry out a comparison between several algorithms of the Kalman filters family for nonlinear systems. Alter having presented the most popular of them and showed its limitations, we introduce some new Kalman filters and compare them for the vehicle localization problem. This comparison is based on the predictive step what corresponds to the worst case that it can occur in vehicle localization. Typically, when we achieve a vehicle tracking, if the tracked vehicle is hidden, corrective data are unavailable and therefore the corrective step is disable (time data alignment)

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ICAR '05. Proceedings., 12th International Conference on Advanced Robotics, 2005.

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