Invariant Kalman Filtering
Invariant Kalman Filtering
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DOI：
10.1146/annurev-control-060117-105010
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发表时间：
2018-01-01
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影响因子：
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通讯作者：
Bonnabel, Silvere
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文献类型：
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作者：
Barrau, Axel;Bonnabel, Silvere
卡尔曼滤波器-或者更准确地说，扩展卡尔曼滤波器（EKF）是一种基本的工程工具，广泛用于控制和机器人以及自治系统中的各种估计任务。最近开发的不变扩展卡尔曼滤波领域使用状态空间的几何结构和动态来改进EKF，特别是在数学保证方面。该方法基本上应用于定位、导航和同时定位与地图绘制（SLAM）领域。虽然它是最近才创建的，但其卓越的鲁棒性已经激励了航空航天领域的真实的工业实施。这篇评论的目的是提供一个方便的介绍不变卡尔曼滤波的方法，并让读者深入了解该方法的相关性，以及它与传统的EKF的重要区别。对于那些对数学理论的实际应用感兴趣的读者，以及那些有兴趣为定位、导航和SLAM（特别是自主车辆导航）寻找鲁棒、易于实现的滤波器的读者，这应该是感兴趣的。
The Kalman filter- or, more precisely, the extended Kalman filter (EKF)is a fundamental engineering tool that is pervasively used in control and robotics and for various estimation tasks in autonomous systems. The recently developed field of invariant extended Kalman filtering uses the geometric structure of the state space and the dynamics to improve the EKF, notably in terms of mathematical guarantees. The methodology essentially applies in the fields of localization, navigation, and simultaneous localization and mapping (SLAM). Although it was created only recently, its remarkable robustness properties have already motivated a real industrial implementation in the aerospace field. This review aims to provide an accessible introduction to the methodology of invariant Kalman filtering and to allow readers to gain insight into the relevance of the method as well as its important differences with the conventional EKF. This should be of interest to readers intrigued by the practical application of mathematical theories and those interested in finding robust, simple-to-implement filters for localization, navigation, and SLAM, notably for autonomous vehicle guidance.