The Kalman filter—or, more precisely, the extended Kalman filter (EKF)—is a fundamental
engineering tool that is pervasively used in control and robotics and for various estimation …

Equivariance is a common and natural property of many nonlinear control systems,
especially those associated with models of mechatronic and navigation systems. Such …

Legged robots require knowledge of pose and velocity in order to maintain stability and
execute walking paths. Current solutions either rely on vision data, which is susceptible to …

The recently introduced matrix group provides a 5 5 matrix representation for the orientation,
velocity, and position of an object in the 3-D space, a triplet we call “extended pose.” In this …

In this article, in order to improve the performance of the micro inertial measurement unit
(MIMU) based on low-accuracy navigation system under the condition of initial large …

In this paper, we first consider a simple Bayesian fusion problem in a matrix Lie group, and
propose to tackle it using the unscented transform. The method is then leveraged to derive …

The present paper introduces a novel methodology for Unscented Kalman Filtering (UKF) on
manifolds that extends previous work by the authors on UKF on Lie groups. Beyond filtering …

In this paper, we formulate the active SLAM paradigm in terms of model-free Deep
Reinforcement Learning, embedding the traditional utility functions based on the Theory of …

Combining visual information with inertial measurements is a popular approach to achieve
robust and autonomous navigation in robotics, specifically in GPS-denied environments. In …

In mobile robotics, scan matching of point clouds using Iterative Closest Point (ICP) allows
estimating sensor displacements. It may prove important to assess the associated …