We propose a probabilistic framework for attitude estimation using the matrix Fisher
distribution on the special orthogonal group SO (3). To be specific, a deep neural network is …

Image summary, an abridged version of the original visual content, can be used to represent
the scene. Thus, tasks such as scene classification, identification, indexing, etc., can be …

In this paper, several estimation algorithms called the variational unscented Kalman filters
(UKF-Vs) are proposed for matrix Lie groups. The proposed filters are inspired by the …

Distributional approximation is a fundamental problem in machine learning with numerous
applications across all fields of science and engineering and beyond. The key challenge in …

In this article, a generalized maneuvering attitude error-state Kalman filter (GMAESKF) is
proposed to mitigate the attitude measurement noise and fulfill the requirement of angular …

The attitude of a rigid body evolves on the three-dimensional (3-D) special orthogonal
group, and it is often estimated by measuring reference directions, such as gravity or …

In this paper, we propose a new probability density function, referred to as the matrix Fisher-
Gaussian (MFG) distribution, on the product of the special orthogonal group and the …

In this paper, we study density estimation for mixed Euclidean and non-Euclidean variables
that are subject to measurement errors. This problem is largely unexplored in statistics. We …