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Found 6 papers, 3 papers with code
Invariant Smoothing with low process noise
In this paper we address smoothing-that is, optimisation-based-estimation techniques for localisation problems in the case where motion sensors are very accurate.
The Geometry of Navigation Problems
While many works exploiting an existing Lie group structure have been proposed for state estimation, in particular the Invariant Extended Kalman Filter (IEKF), few papers address the construction of a group structure that allows casting a given system into the framework of invariant filtering.
Denoising IMU Gyroscopes with Deep Learning for Open-Loop Attitude Estimation
This paper proposes a learning method for denoising gyroscopes of Inertial Measurement Units (IMUs) using ground truth data, and estimating in real time the orientation (attitude) of a robot in dead reckoning.
RINS-W: Robust Inertial Navigation System on Wheels
This paper proposes a real-time approach for long-term inertial navigation based only on an Inertial Measurement Unit (IMU) for self-localizing wheeled robots.
Robotics
An EKF-SLAM algorithm with consistency properties
In this paper we address the inconsistency of the EKF-based SLAM algorithm that stems from non-observability of the origin and orientation of the global reference frame.
Robotics Systems and Control
Stochastic gradient descent on Riemannian manifolds
Stochastic gradient descent is a simple approach to find the local minima of a cost function whose evaluations are corrupted by noise.
