论文标题
在流形（UKF-M）上无意义的卡尔曼过滤的代码
A Code for Unscented Kalman Filtering on Manifolds (UKF-M)
论文作者
论文摘要
本文介绍了一种新颖的方法，用于对歧管的无味卡尔曼过滤（UKF），该方法扩展了作者在UKF上对Lie组的先前工作。除了过滤性能外，该方法的主要兴趣是其多功能性，因为该方法适用于许多状态估计问题，以及它对不一定熟悉流形和谎言组的从业者实施的简单性。我们已经在两个独立的开源Python和MATLAB框架上开发了该方法，我们称为UKF-M，用于快速实施和测试该方法。在线存储库包含教程，文档和各种相关机器人示例，用户可以轻松地复制然后调整这些示例，以进行快速原型和基准测试。该代码可在https://github.com/caor-mines-paristech/ukfm上找到。
The present paper introduces a novel methodology for Unscented Kalman Filtering (UKF) on manifolds that extends previous work by the authors on UKF on Lie groups. Beyond filtering performance, the main interests of the approach are its versatility, as the method applies to numerous state estimation problems, and its simplicity of implementation for practitioners not being necessarily familiar with manifolds and Lie groups. We have developed the method on two independent open-source Python and Matlab frameworks we call UKF-M, for quickly implementing and testing the approach. The online repositories contain tutorials, documentation, and various relevant robotics examples that the user can readily reproduce and then adapt, for fast prototyping and benchmarking. The code is available at https://github.com/CAOR-MINES-ParisTech/ukfm.
