Search Results for author: Uwe D. Hanebeck

Found 6 papers, 1 papers with code

Progressive Bayesian Particle Flows based on Optimal Transport Map Sequences

no code implementations4 Mar 2023 Uwe D. Hanebeck

In each sub-step, optimal resampling is done by a map that replaces non-equally weighted particles with equally weighted ones.

Density Estimation

Gaussian Mixture Estimation from Weighted Samples

no code implementations9 Jun 2021 Daniel Frisch, Uwe D. Hanebeck

We consider estimating the parameters of a Gaussian mixture density with a given number of components best representing a given set of weighted samples.

Three-dimensional Simultaneous Shape and Pose Estimation for Extended Objects Using Spherical Harmonics

no code implementations25 Dec 2020 Gerhard Kurz, Florian Faion, Florian Pfaff, Antonio Zea, Uwe D. Hanebeck

We propose a new recursive method for simultaneous estimation of both the pose and the shape of a three-dimensional extended object.

Object Pose Estimation

Towards High-Performance Solid-State-LiDAR-Inertial Odometry and Mapping

2 code implementations25 Oct 2020 Kailai Li, Meng Li, Uwe D. Hanebeck

LiLi-OM (Livox LiDAR-inertial odometry and mapping) is real-time capable and achieves superior accuracy over state-of-the-art systems for both LiDAR types on public data sets of mechanical LiDARs and in experiments using the Livox Horizon.

Robotics

Directional Statistics and Filtering Using libDirectional

no code implementations28 Dec 2017 Gerhard Kurz, Igor Gilitschenski, Florian Pfaff, Lukas Drude, Uwe D. Hanebeck, Reinhold Haeb-Umbach, Roland Y. Siegwart

In this paper, we present libDirectional, a MATLAB library for directional statistics and directional estimation.

Robust Filtering and Smoothing with Gaussian Processes

no code implementations20 Mar 2012 Marc Peter Deisenroth, Ryan Turner, Marco F. Huber, Uwe D. Hanebeck, Carl Edward Rasmussen

We propose a principled algorithm for robust Bayesian filtering and smoothing in nonlinear stochastic dynamic systems when both the transition function and the measurement function are described by non-parametric Gaussian process (GP) models.

Gaussian Processes

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