提出级联非线性观测器，提升水下机器人无外部定位时的状态估计精度。
Cascade IPG Observer for Underwater Robot State Estimation
- 两级级联设计：先用四元数IPG估姿态，再估速度与位置。
- 在公开数据集和实机实验中，位置误差更小、方差更低。
- 适合水下机器人、无人潜航器等依赖惯性的导航场景。
本文提出一种新颖的级联非线性观测器框架，用于惯性状态估计。当外部定位不可用或传感器失效时，解决中间状态估计问题。该观测器基于最近提出的迭代预条件梯度下降（IPG）算法，由两个非线性观测器构成。输入通过IMU预积分模型，第一观测器为基于四元数的IPG，输出作为第二观测器的输入，用于估计速度和位置。所提方法在公开水下数据集及真实机器人平台上验证，并与扩展卡尔曼滤波（EKF）和不变扩展卡尔曼滤波（InEKF）对比。结果表明，本方法在位置精度和方差方面均优于对比方法。
原文摘要 · Abstract (English)
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.
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