Relative multiplicative extended Kalman filter for observable GPS-denied navigation
Relative multiplicative extended Kalman filter for observable GPS-denied navigation
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DOI：
10.1177/0278364920903094
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发表时间：
2020-06-23
影响因子：
9.2
通讯作者：
Brink, Kevin M.
中科院分区：
文献类型：
--
作者：
Koch, Daniel P.;Wheeler, David O.;Brink, Kevin M.
这项工作提出了一种乘法扩展卡尔曼滤波器 (MEKF)，用于估计在 GPS 拒绝环境中运行的多旋翼飞行器的相对状态。该滤波器将来自惯性测量单元和高度计的数据与来自基于关键帧的视觉里程计或激光扫描匹配算法的相对姿势更新融合在一起。由于在没有 GPS 等全局测量的情况下无法观察车辆的全局位置和航向状态，因此本文中的过滤器根据与里程计关键帧位于同一位置的本地帧来估计状态。因此，里程计更新提供了几乎直接的相对车辆姿态测量，使这些状态可观察。最近的出版物严格记录了这种可观测参数化的理论优势，包括提高的一致性、准确性和系统鲁棒性，并证明了这种方法在长时间多旋翼飞行测试中的有效性。本文通过提供相对 MEKF 的完整、独立的教程推导来补充之前的工作，该推导已被彻底激发，但迄今为止仅进行了简要描述。本文介绍了滤波器的一些改进和扩展，同时明确定义了所使用的所有四元数约定和属性，包括与误差四元数及其欧拉角分解相关的几个新的有用属性。最后，本文推导了针对惯性框架定义的传统动力学和针对车辆车身框架定义的以机器人为中心的动力学的滤波器，并深入了解了两种公式之间出现的细微差异。
This work presents a multiplicative extended Kalman filter (MEKF) for estimating the relative state of a multirotor vehicle operating in a GPS-denied environment. The filter fuses data from an inertial measurement unit and altimeter with relative-pose updates from a keyframe-based visual odometry or laser scan-matching algorithm. Because the global position and heading states of the vehicle are unobservable in the absence of global measurements such as GPS, the filter in this article estimates the state with respect to a local frame that is colocated with the odometry keyframe. As a result, the odometry update provides nearly direct measurements of the relative vehicle pose, making those states observable. Recent publications have rigorously documented the theoretical advantages of such an observable parameterization, including improved consistency, accuracy, and system robustness, and have demonstrated the effectiveness of such an approach during prolonged multirotor flight tests. This article complements this prior work by providing a complete, self-contained, tutorial derivation of the relative MEKF, which has been thoroughly motivated but only briefly described to date. This article presents several improvements and extensions to the filter while clearly defining all quaternion conventions and properties used, including several new useful properties relating to error quaternions and their Euler-angle decomposition. Finally, this article derives the filter both for traditional dynamics defined with respect to an inertial frame, and for robocentric dynamics defined with respect to the vehicle's body frame, and provides insights into the subtle differences that arise between the two formulations.