论文标题
不确定性意识到的咸卡尔曼过滤器：具有不确定后卫的混合系统的状态估计
The Uncertainty Aware Salted Kalman Filter: State Estimation for Hybrid Systems with Uncertain Guards
论文作者
论文摘要
在本文中，我们提出了一种通过与不确定的表面接触来更新机器人状态信念的方法，并将此更新应用于卡尔曼过滤器，以进行更准确的状态估计。在检查后卫表面不确定性如何影响每种模式的时间时，我们得出了一个护罩盐矩阵 - 在混合事件之前将其绘制在扰动之前，以驱动到扰动 - 考虑了所得状态的其他变化。此外，我们建议使用参数化的重置函数 - 捕获未知参数如何将状态从一种模式映射到另一种模式的方式 - 雅各比式的jacobian说明了所得状态的额外不确定性。通过不确定的过渡事件模拟采样分布并比较所得的协方差，可以显示这些映射的准确性。最后，我们将这些附加术语集成到“不确定性意识到的盐罐”，UASKF，并在各种测试条件和系统上显示平均估计误差的峰值降低24-60％。
In this paper we present a method for updating robotic state belief through contact with uncertain surfaces and apply this update to a Kalman filter for more accurate state estimation. Examining how guard surface uncertainty affects the time spent in each mode, we derive a guard saltation matrix - which maps perturbations prior to hybrid events to perturbations after - accounting for additional variation in the resulting state. Additionally, we propose the use of parameterized reset functions - capturing how unknown parameters change how states are mapped from one mode to the next - the Jacobian of which accounts for the additional uncertainty in the resulting state. The accuracy of these mappings is shown by simulating sampled distributions through uncertain transition events and comparing the resulting covariances. Finally, we integrate these additional terms into the "uncertainty aware Salted Kalman Filter", uaSKF, and show a peak reduction in average estimation error by 24-60% on a variety of test conditions and systems.
