290 citations · 681 across the 38 of their papers we have counts for
43 papers
Fixed-structure Gaussian Mixture Filtering with Robust Measurement Updates under Outliers
Ondřej Straka, Uwe D. Hanebeck
Bayesian state estimation for discrete-time nonlinear stochastic systems is considered in the presence of measurement outliers. Building on a fixed-structure Gaussian mixture filte…
Sampling-based Model Predictive Control Using Trust Regions
Markus Walker, Marcel Reith-Braun, Daniel Frisch +1
Sampling-based model predictive control (MPC) algorithms, such as model predictive path integral (MPPI), enable approximate, gradient-free solutions to optimal control problems by…
Smooth Sampling-Based Model Predictive Control Using Deterministic Samples
Markus Walker, Marcel Reith-Braun, Tai Hoang +2
Sampling-based model predictive control (MPC) is effective for nonlinear systems but often produces non-smooth control inputs due to random sampling. To address this issue, we exte…
Sample-Efficient and Smooth Cross-Entropy Method Model Predictive Control Using Deterministic Samples
Markus Walker, Daniel Frisch, Uwe D. Hanebeck
Cross-entropy method model predictive control (CEM--MPC) is a powerful gradient-free technique for nonlinear optimal control, but its performance is often limited by the reliance o…
Newton-Flow Particle Filters based on Generalized Cramér Distance
Uwe D. Hanebeck
We propose a recursive particle filter for high-dimensional problems that inherently never degenerates. The state estimate is represented by deterministic low-discrepancy particle…
HapticGiant: A Novel Very Large Kinesthetic Haptic Interface with Hierarchical Force Control
Michael Fennel, Markus Walker, Dominik Pikos +1
Research in virtual reality and haptic technologies has consistently aimed to enhance immersion. While advanced head-mounted displays are now commercially available, kinesthetic ha…
Efficient Gaussian Mixture Filters based on Transition Density Approximation
Ondŕej Straka, Uwe D. Hanebeck
Gaussian mixture filters for nonlinear systems usually rely on severe approximations when calculating mixtures in the prediction and filtering step. Thus, offline approximations of…
Nudged Particle Filter with Optimal Resampling Applied to the Duffing Oscillator
Ryne Beeson, Uwe Hanebeck
Efficiently solving the continuous-time signal and discrete-time observation filtering problem for chaotic dynamical systems presents unique challenges in that the advected distrib…
Closed-Form Information-Theoretic Roughness Measures for Mixture Densities
Uwe D. Hanebeck
We calculate the smoothest mixture density under a variety of prescribed specifications. This includes constraints on certain moments, specifications on density values and/or its d…
Progressive Bayesian Particle Flows based on Optimal Transport Map Sequences
Uwe D. Hanebeck
We propose a method for optimal Bayesian filtering with deterministic particles. In order to avoid particle degeneration, the filter step is not performed at once. Instead, the par…
Continuous-Time Ultra-Wideband-Inertial Fusion
Kailai Li, Ziyu Cao, Uwe D. Hanebeck
We introduce a novel framework of continuous-time ultra-wideband-inertial sensor fusion for online motion estimation. Quaternion-based cubic cumulative B-splines are exploited for…
Hyperspherical Dirac Mixture Reapproximation
Kailai Li, Florian Pfaff, Uwe D. Hanebeck
We propose a novel scheme for efficient Dirac mixture modeling of distributions on unit hyperspheres. A so-called hyperspherical localized cumulative distribution (HLCD) is introdu…
Gaussian Mixture Estimation from Weighted Samples
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. We adopt a density interpre…
Deterministic Sampling on the Circle using Projected Cumulative Distributions
Daniel Frisch, Uwe D. Hanebeck
We propose a method for deterministic sampling of arbitrary continuous angular density functions. With deterministic sampling, good estimation results can typically be achieved wit…
Three-dimensional Simultaneous Shape and Pose Estimation for Extended Objects Using Spherical Harmonics
Gerhard Kurz, Florian Faion, Florian Pfaff +2
We propose a new recursive method for simultaneous estimation of both the pose and the shape of a three-dimensional extended object. The key idea of the presented method is to repr…
Towards High-Performance Solid-State-LiDAR-Inertial Odometry and Mapping
Kailai Li, Meng Li, Uwe D. Hanebeck
We present a novel tightly-coupled LiDAR-inertial odometry and mapping scheme for both solid-state and mechanical LiDARs. As frontend, a feature-based lightweight LiDAR odometry pr…
iviz: A ROS Visualization App for Mobile Devices
Antonio Zea, Uwe D. Hanebeck
In this work, we introduce iviz, a mobile application for visualizing ROS data. In the last few years, the popularity of ROS has grown enormously, making it the standard platform f…
Deterministic Sampling of Multivariate Densities based on Projected Cumulative Distributions
Uwe D. Hanebeck
We want to approximate general multivariate probability density functions by deterministic sample sets. For optimal sampling, the closeness to the given continuous density has to b…
Improved Pose Graph Optimization for Planar Motions Using Riemannian Geometry on the Manifold of Dual Quaternions
Kailai Li, Johannes Cox, Benjamin Noack +1
We present a novel Riemannian approach for planar pose graph optimization problems. By formulating the cost function based on the Riemannian metric on the manifold of dual quaterni…
A Distance-based Framework for Gaussian Processes over Probability Distributions
Maxim Dolgov, Uwe D. Hanebeck
Gaussian processes constitute a very powerful and well-understood method for non-parametric regression and classification. In the classical framework, the training data consists of…
FLUX: Progressive State Estimation Based on Zakai-type Distributed Ordinary Differential Equations
Uwe D. Hanebeck
We propose a homotopy continuation method called FLUX for approximating complicated probability density functions. It is based on progressive processing for smoothly morphing a giv…
Directional Statistics and Filtering Using libDirectional
Gerhard Kurz, Igor Gilitschenski, Florian Pfaff +4
In this paper, we present libDirectional, a MATLAB library for directional statistics and directional estimation. It supports a variety of commonly used distributions on the unit c…
High-Accuracy Real-Time Whole-Body Human Motion Tracking Based on Constrained Nonlinear Kalman Filtering
Jannik Steinbring, Christian Mandery, Nikolaus Vahrenkamp +2
We present a new online approach to track human whole-body motion from motion capture data, i.e., positions of labeled markers attached to the human body. Tracking in noisy data ca…
Infinite-horizon Linear Optimal Control of Markov Jump Systems without Mode Observation via State Feedback
Maxim Dolgov, Uwe D. Hanebeck
In this paper, we consider stochastic optimal control of Markov Jump Linear Systems with state feedback but without observation of the jumping parameter. The proposed control law i…