Cascade IPG Observer for Underwater Robot State Estimation

2025-04-21

AI Summary

To address pose and motion-state estimation for underwater robots under external localization failure or sensor outages, this paper proposes a cascaded nonlinear observer framework. First, attitude is estimated via IMU preintegration combined with iterative preconditioned gradient descent (IPG) optimization on the quaternion Lie group; subsequently, velocity and position are estimated in a decoupled, sequential manner. This work pioneers the integration of IPG into cascaded observer design, leveraging Lie-algebraic state representation and update mechanisms to significantly enhance fault tolerance and estimation consistency. Evaluated on public underwater datasets and real-world robotic platforms, the method achieves superior localization accuracy compared to both the Extended Kalman Filter (EKF) and Invariant EKF (InEKF), reducing position estimation variance by 32% and demonstrating markedly improved robustness against sensor degradation and environmental disturbances.

Technology Category

• Intelligent Robots: State Estimation
• Reasoning under Uncertainty: Stochastic Optimization
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

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.

Problem

• Estimates robot state without external localization
• Improves accuracy during sensor outages
• Outperforms EKF and InEKF in positional accuracy

Innovation

• Cascade nonlinear observer for state estimation
• IPG algorithm for quaternion-based observer
• IMU preintegration model inputs