Showing papers by "Uwe D. Hanebeck published in 2023"

Continuous-Time Ultra-Wideband-Inertial Fusion

Abstract: 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 parameterizing motion states continuously over time. Systematic derivations of analytic kinematic interpolations and spatial differentiations are further provided. Based thereon, a new sliding-window spline fitting scheme is established for asynchronous multi-sensor fusion and online calibration. We conduct a dedicated validation of the quaternion spline fitting method, and evaluate the proposed system, SFUISE (spline fusion-based ultra-wideband-inertial state estimation), in real-world scenarios using public data set and experiments. The proposed sensor fusion system is real-time capable and delivers superior performance over state-of-the-art discrete-time schemes. We release the source code and own experimental data at https://github.com/KIT-ISAS/SFUISE.

Improving Accuracy of Optical Sorters Using Closed-Loop Control of Material Recirculation

Abstract: Optical sorting is a key technology for the circular economy and is widely applied in the food, mineral, and recycling industries. Despite its widespread use, one typically resorts to expensive means of adjusting the accuracy, e.g., by reducing the mass flow or changing mechanical or software parameters, which typically requires manual tuning in a lengthy, iterative process. To circumvent these drawbacks, we propose a new layout for optical sorters along with a controller that allows re-feeding of controlled fractions of the sorted mass flows. To this end, we build a dynamic model of the sorter, analyze its static behavior, and show how material recirculation affects the sorting accuracy. Furthermore, we build a model predictive controller (MPC) employing the model and evaluate the closed-loop sorting system using a coupled discrete element–computational fluid dynamics (DEM–CFD) simulation, demonstrating improved accuracy.

The Kernel-SME Filter with Adaptive Kernel Widths for Association-free Multi-target Tracking

Abstract: Different objectives and paradigms exist for tracking multiple targets when measurements do not contain information about the target identities (IDs). The Symmetric Measurement Equation (SME) filter can be used when one is agnostic to the labels and does not attempt to assign different IDs to the different targets. We present an extension of the Kernel-SME filter that, unlike the original variant, uses adaptive kernel widths that depend on the respective uncertainty. In our evaluation, it outperformed existing SMEbased approaches, while it is only second to a more complex global nearest neighbor tracker.

Bayesian Cramér-Rao Lower Bounds for Magnetic Field-based Train Localization

Abstract: In this paper, the theoretically achievable accuracy of magnetic field-based localization in railway environments is analyzed. The analysis is based on the Bayesian Cramér-Rao lower bound (BCRLB) that bounds the mean squared error of an estimator from below. The derivation of the BCRLB for magnetic field-based localization is not straightforward because the magnetic field cannot be described by an analytical equation but must be derived from measurements. In this paper we show how the BCRLB can be calculated by fitting a Gaussian process (GP) to magnetometer measurements to obtain an analytical expression of the magnetic field along a railway line. The proposed GP-based BCRLB is evaluated with the magnetic field of a 1 km long track segment. Furthermore, a comparison between the bound and the estimation error of a particle filter shows the sub-optimality of the particle filter for magnetic railway localization.

Distributed Range-Only Localisation That Preserves Sensor and Navigator Privacies

Abstract: Distributed state estimation and localisation methods have become increasingly popular with the rise of ubiquitous computing, and have led naturally to an increased concern regarding data and estimation privacy. Traditional distributed sensor navigation methods typically involve the leakage of sensor or navigator information by communicating measurements or estimates and thus do not preserve participants' privacy. The existing approaches that do provide such guarantees fail to address sensor and navigator privacy in the common application of model-based range-only localisation, consequently forfeiting broad applicability. In this work, we define a notion of privacy-preserving linear combination aggregation and use it to derive a modified Extended Kalman Filter using range measurements such that navigator location, sensors' locations, and sensors' measurements are kept private during navigation. Additionally, a formal cryptographic backing is presented to guarantee our method's privacy as well as an implementation to evaluate its performance. The novel, provably secure, range-based localisation method has applications in a variety of environments where sensors may not be trusted or estimates are considered sensitive, such as autonomous vehicle localisation or air traffic navigation.

Progressive Bayesian Particle Flows based on Optimal Transport Map Sequences

Abstract: 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 particles progressively flow from prior to posterior. This is achieved by splitting the filter step into a series of sub-steps. In each sub-step, optimal resampling is done by a map that replaces non-equally weighted particles with equally weighted ones. Inversions of the maps or monotonicity constraints are not required, greatly simplifying the procedure. The parameters of the mapping network are optimized w.r.t.\ to a particle set distance. This distance is differentiable, and compares non-equally and equally weighted particles. Composition of the map sequence provides a final mapping from prior to posterior particles. Radial basis function neural networks are used as maps. It is important that no intermediate continuous density representation is required. The entire flow works directly with particle representations. This avoids costly density estimation.

Simultaneous Localization and Calibration (SLAC) Methods for a Train-Mounted Magnetometer

Abstract:

Summary

Magnetic field localization is based on the fact that the Earth’s magnetic field is distorted in the vicinity of ferromagnetic objects. When ferromagnetic objects are in fixed positions, the distortions are also fixed and, thus, contain location information. In our prior work, we proposed a simultaneous localization and calibration (SLAC) algorithm based on a Rao-Blackwellized particle filter that enables magnetic train localization using only uncalibrated magnetometer measurements. In this paper, a lower-complexity version of the SLAC algorithm is proposed that only estimates a subset of calibration parameters. An evaluation compares the full and reduced SLAC approach to a particle filter in which the magnetometer is pre-calibrated with a fixed set of parameters. The results show a clear advantage for both SLAC approaches and that the SLAC algorithm with a reduced set of calibration parameters achieves the same performance as the one with a full set of parameters.

Simulation study and experimental validation of a neural network-based predictive tracking system for sensor-based sorting

Abstract: Abstract Sensor-based sorting offers cutting-edge solutions for separating granular materials. The line-scanning sensors currently in use in such systems only produce a single observation of each object and no data on its movement. According to recent studies, using an area-scan camera has the potential to reduce both characterization and separation error in a sorting process. A predictive tracking approach based on Kalman filters makes it possible to estimate the followed paths and parametrize a unique motion model for each object using a multiobject tracking system. While earlier studies concentrated on physically-motivated motion models, it has been demonstrated that novel machine learning techniques produce predictions that are more accurate. In this paper, we describe the creation of a predictive tracking system based on neural networks. The new algorithm is applied to an experimental sorting system and to a numerical model of the sorter. Although the new approach does not yet fully reach the achieved sorting quality of the existing approaches, it allows the use of the general method without requiring expert knowledge or a fundamental understanding of the parameterization of the particle motion model.