We present a novel dual quaternion filter for recursive estimation of rigid body motions.
Based on the sequential Monte Carlo scheme, particles are deployed on the manifold of unit …

A novel discrete Bayesian filtering scheme is proposed on the manifold of unit quaternions
for rotation estimation. Existing quaternion filters rely on specific distributions (typically the …

Bayesian estimation is a vital tool in robotics as it allows systems to update the robot state
belief using incomplete information from noisy sensors. To render the state estimation …

We propose a novel scheme for efficient Dirac mixture modeling of distributions on unit
hyperspheres. A so-called hyperspherical localized cumulative distribution (HLCD) is …

Uncertainty quantification and state estimation of random variables in directional domains
play important roles in ubiquitous application scenarios, in particular, robotic perception …

Estimating orientations of objects in Euclidean space is an omnipresent challenge in
robotics and autonomous systems. A useful representation of orientations involves unit …

We extend the core operation of non-parametric belief propagation (NBP), also known as
multi-scale sequential Gibbs sampling, to approximate products of kernel density estimated …

We present a novel sample reduction scheme for random variables belonging to the SE (2)
group by means of Dirac mixture approximation. For this, dual quaternions are employed to …

For manifolds with topologies that strongly differ from the standard topology of R n, using
common filters created for linear domains can yield misleading results. While there is a lot of …

We present a novel nonparametric scheme for modeling circular random variables. For that,
the circular Cramer-von Mises distance (CCvMD) is proposed to measure the statistical …