We show that a distributed network of robots or other devices which make measurements of
each other can collaborate to globally localize via efficient ad hoc peer-to-peer …

In this article, we first derive a general intrinsic Barankin bound (IBB) for unknown
parameters lying on Lie groups (LGs), and its intrinsic McAulay–Seidman bound (IMSB) …

In this paper we propose a simultaneous localization and mapping (SLAM) back-end
solution called the exactly sparse delayed state filter on Lie groups (LG-ESDSF). We derive …

Motivated by the rapid progress of aerospace and robotics engineering, the navigation and
control systems on matrix Lie groups have been actively studied in recent years. For rigid …

An extended Kalman filter (EKF) for systems with configuration given by matrix Lie groups is
presented. The error dynamics are given by the logarithm of the Lie group and are based on …

With the rise of advanced driver assistance systems and highly automated driving, the
research of highly reliable and accurate tracking algorithms received special attention within …

An unscented Kalman filter (UKF) for matrix Lie groups is proposed where the time
propagation of the state is formulated on the Lie algebra. This is done with the kinematic …

Attitude estimation is a basic task for most spacecraft missions in aerospace engineering
and many Kalman type attitude estimators have been applied to the guidance and …

This article addresses the problem of computing a Cramér-Rao bound when the likelihood
of Euclidean observations is parameterized by both unknown Lie group (LG) parameters …

We propose the idea of using Kuramoto models (including their higher-dimensional
generalizations) for machine learning over non-Euclidean data sets. These models are …