Abstract
This paper presents a novel cascade nonlinear observer framework for inertial state estimation. It tackles the problem of intermediate state estimation when external localization is unavailable or in the event of a sensor outage. The proposed observer comprises two nonlinear observers based on a recently developed iteratively preconditioned gradient descent (IPG) algorithm. It takes the inputs via an IMU preintegration model where the first observer is a quaternion-based IPG. The output for the first observer is the input for the second observer, estimating the velocity and, consequently, the position. The proposed observer is validated on a public underwater dataset and a real-world experiment using our robot platform. The estimation is compared with an extended Kalman filter (EKF) and an invariant extended Kalman filter (InEKF). Results demonstrate that our method outperforms these methods regarding better positional accuracy and lower variance.
Abstract (translated)
本文提出了一种新的级联非线性观测器框架，用于惯性状态估计。该方法解决了在外部定位不可用或传感器故障情况下中间状态估计的问题。所提出的观测器由两个基于最近开发的迭代预条件梯度下降（IPG）算法的非线性观测器组成。它通过IMU（惯性测量单元）预积分模型接收输入，其中第一个观测器是基于四元数的IPG。第一个观测器的输出作为第二个观测器的输入，用于估计速度和位置。该提出的观测器在公开的水下数据集和我们机器人平台上的真实世界实验中进行了验证，并与扩展卡尔曼滤波（EKF）和不变扩展卡尔曼滤波（InEKF）方法进行比较。结果显示，我们的方法在位置精度和方差方面优于这些方法。
URL
https://arxiv.org/abs/2504.15235
