Technology Category
Intelligent Robots: State EstimationReasoning under Uncertainty: Stochastic OptimizationMachine Learning: Calibration & Uncertainty Quantification
Cascade IPG Observer for Underwater Robot State Estimation
🤖 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
Application Category
📝 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
Research questions and friction points this paper is trying to address.
Estimates robot state without external localization
Improves accuracy during sensor outages
Outperforms EKF and InEKF in positional accuracy
Innovation
Methods, ideas, or system contributions that make the work stand out.
Cascade nonlinear observer for state estimation
IPG algorithm for quaternion-based observer
IMU preintegration model inputs
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