Publications

Delay Estimation in Dense Multipath Environments using Time Series Segmentation
Sebastian Kram, Christopher Kraus, Maximilian Stahlke, Tobias Feigl, Jörn Thielecke, Christopher Mutschler
IEEE Wireless Communications and Networking Conference (WCNC)
We propose a pipeline built upon the U-Net convolutional neural network architecture to extract an arbitrary number of delays without prior knowledge and that does not rely on computationally demanding operations (such as eigenvalue decomposition). We evaluate the presented method with synthetic data of different noise configurations and signal bandwidths and a publicly available dataset, achieving considerable performance gains w.r.t. detection performance and tracking accuracy.
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Joint Classification and Trajectory Regression of Online Handwriting using a Multi-Task Learning Approach
Felix Ott, David Rügamer, Lucas Heublein, Bernd Bischl, Christopher Mutschler
Winter Conference on Applications of Computer Vision (WACV)
We propose a novel neural network architecture for multivariate time series that notably improves the classification and trajectory regression performance in online handwriting recognition. We achieve this by jointly learning the cross-entropy loss in combination with distance and similarity losses.
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Can You Trust your Autonomous Car? Interpretable and Verifiably Safe Reinforcement Learning (Best Paper Award)
Lukas Schmidt, Georgios Kontes, Axel Plinge, Christopher Mutschler
32nd IEEE Intelligent Vehicles Symposium
We propose a novel imitation learning pipeline that generates safe and efficient behavior policies. We combine an RL step that solves for safe behavior through the introduction of safety distances with a subsequent innovative safe extraction of decision tree policies. The resulting decision tree is not only easy to interpret, it is also safer than the neural network policy trained for safety.
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Benchmarking Online Sequence-to-Sequence and Character-based Handwriting Recognition from IMU-Enhanced Pens
Felix Ott, David Rügamer, Lucas Heublein, Tim Hamann, Jens Barth, Bernd Bischl, Christopher Mutschler
submitted to Datasets and Benchmarks Track (NeurIPS 2021)
We present data and benchmark models . We propose a variety of datasets including equations and words for both the writer-dependent and writer-independent real-time sequence-to-sequence (seq2seq) learning and single character-based recognition. We provide an evaluation benchmark for the seq2seq and single character-based HWR using recurrent and temporal convolutional networks and Transformers combined with a connectionist temporal classification (CTC) loss. Our methods do not resort to language or lexicon models.
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Estimating TOA Reliability with Variational Autoencoders
Maximilian Stahlke, Sebastian Kram, Felix Ott, Tobias Feigl, Christopher Mutschler
IEEE Sensors Journal
We propose an out-of-distribution detection on channel impulse responses based on generative deep learning models that predicts an anomaly score for the channel of a TOF-based Ultra-wideband (UWB) system. Our approach generalizes well to new environments and reliably detects non-line-of-sight CIRs. Our anomaly score integrated into a TOF-based extended Kalman filter improves tracking performance by over 25%.
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Contact Tracing with the Exposure Notification Framework in the German Corona Warn-App
Steffen Meyer, Thomas Windisch, Adrian Perl, Daniel Dzibela, Robert Marzilger, Nicolas Witt, Justus Benzler, Göran Kirchner, Tobias Feigl, Christopher Mutschler
11th International Conference on Indoor Positioning and Indoor Navigation
We describe how we derived optimal parameters for a decentralized CT from extensive measurement campaigns that we carried out together with Deutsche Telekom (DT) and SAP under the supervision of the Robert Koch Institut (RKI). With centimeter accurate optical reference systems we show that optimal parameters are application-specific. and cause impractical high resource costs. In contrast, optimized general parameters offer a compromise between energy costs, applicability, accuracy, and reliability.
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Accuracy-aware Compression of Channel Impulse Responses using Deep Learning
Thomas Altstidl, Sebastian Kram, Oskar Hermann, Tobias Feigl, Christopher Mutschler
11th International Conference on Indoor Positioning and Indoor Navigation
We compress channel impulse responses with neural networks containing encoding (compressing) and decoding (reconstructing) components and compare them to the state-of-the-art compression techniques. Our results prove that the reconstructed CSI can be used for positioning with only mild performance deterioration at a compression of >97% and even when trained on a different environment.
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Robust ToA-Estimation using Convolutional Neural Networks on Randomized Channel Models
Tobias Feigl, Ernst Eberlein, Sebastian Kram, Christopher Mutschler
11th International Conference on Indoor Positioning and Indoor Navigation
We propose a 1D Convolutional Neural Network to identify optimal FDPoAs as ToA directly from the raw CSI. We train our model with realistic 5G channel model data synthesized by QuaDRiGa. Metrics such as Delay Spread (DS), k-Factor (kF), and SNR are appropriate to cover most LoS-NLoS constellations. Our approach consistently outperforms the state of the art by about 17% for SNRs below -10 dB.
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Deep Siamese Metric Learning: A Highly Scalable Approach to Searching Unordered Sets of Trajectories
Christoffer Löffler, Luca Reeb, Daniel Dzibela, Robert Marzilger, Nicolas Witt, Björn Eskofier, Christopher Mutschler
ACM Transactions on Intelligent Systems and Technology
We present a novel representation learning approach for fast similarity-based scene retrieval of unstructured ensembles of trajectory data using Siamese Metric Learning that approximates a distance preserving low-dimensional representation. 

Off-line Evaluation of Indoor Positioning Systems in Different Scenarios: The Experiences from IPIN 2020 Competition
Francesco Potori, Joaquin Torres-Sospedra, …, Sebastian Kram, Maximilian Stahlke, Christopher Mutschler, …, Hong Lye Oh
IEEE Sensors Journal
The IPIN competition aims at evaluating real-world indoor localisation systems by testing them in a realistic environment, with realistic movement, using the EvAAL framework. The competition provided a unique overview of the state-of-the-art of systems, technologies, and methods for indoor positioning and navigation purposes. In 2020, the competition included 5 diverse off-site off-site Tracks, each resembling real use cases and challenges for indoor positioning.
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IALE: Imitating Active Learner Ensembles
Christoffer Löffler, Christopher Mutschler
preprint
We use imitation learning to learn a policy for pool-based active learning (AL). We let a set of given AL heuristics work on a dataset and train a policy that imitates the best heuristic at the current stage in the training course. We apply the policy on a similar  domain and show that we successfully learn to imitate a set of hard-coded experts.
| bibtex | code | arxiv | pdf |

Policy Adaptation via Self-Supervised State Translation
Christopher Mutschler, Sebastian Pokutta
preprint
We show how to adapt an existing policy when the environment representation changes. We transfer the original policy by translating the environment representation back into its original encoding by sampling observations from both the environment and a dynamics model trained from prior experience. This allows us to bootstrap a neural network model for state translation without using extrinsic rewards.
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Real-Time Gait Reconstruction for Virtual Reality Using a Single Sensor
Tobias Feigl, Lisa Gruner, Christopher Mutschler, Daniel Roth
Proceedings of the International Symposium of Mixed and Augmented Reality (ISMAR) – Adjunct
We propose an approach to reconstruct gait motions from a single head-mounted accelerometer. We train our models to map head motions to corresponding ground truth gait phases. To reconstruct leg motion, the models predict gait phases to trigger equivalent synthetic animations.
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A Sense of Quality for Augmented Reality Assisted Process Guidance
Anes Redzepagic, Christoffer Löffler, Tobias Feigl, Christopher Mutschler
Proceedings of the International Symposium of Mixed and Augmented Reality (ISMAR) – Adjunct
We show how to combine inertial sensors, mounted on work tools, with AR headsets to enrich modern assistance systems with a sense of process quality. An ML classifier predicts quality metrics from a 9-DoF inertial measurement unit, while we simultaneously guide and track the work processes with a HoloLens AR system.
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The OnHW Dataset: Online Handwriting Recognition from IMU-Enhanced Ballpoint Pens with Machine Learning
Felix Ott, Mohamad Wehbi, Tim Hamann, Jens Barth, Björn Eskofier, Christopher Mutschler
Proceedings of the ACM on Interactive, Wearable and Ubiquitous Technologies
We release a novel dataset for online handwriting recognition with 31,275 upper- and lower-case English letters (52 classes) from 119 participants together with CNN, LSTM, and BLSTM baseline deep learning models for the writer-dependent and writer-independent recognition tasks. 
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RNN-aided Human Velocity Estimation from a Single IMU
Tobias Feigl, Sebastian Kram, Philipp Woller, Ramiz Siddiqui, Michael Philippsen, Christopher Mutschler
Sensors 
We propose a hybrid filter that combines a CNN/BLSTM with a Bayesian filter to estimate a person’s velocity on rotation-invariant signals. Our experiments show the robustness against different movement states and changes in orientation, even with high dynamics. With a single IMU we outperform the state of the art in terms of velocity and traveled distance, and generalize well to other movement speeds.
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Automated Quality Assurance for Hand-held Tools via Embedded Classification and AutoML
Christoffer Löffler, Christian Nickel, Christopher Sibel, Daniel Dzibela, Jonathan Braat Benjamin Gruhler, Philipp Woller, Nicolas Witt, Christopher Mutschler
European Conference on Machine Learning and Practice of Knowledge Discovery in Databases (ECML-PKDD)
We propose process monitoring system that uses inertial, magnetic field and audio sensors that we attach as add-ons to hand-held tools. Embedded classifiers analyse the sensor data and we directly provide feedback to workers during the execution of work processes. We show how our system automatically trains and deploys new machine learning models based on new user data.
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High-Speed Collision Avoidance using Deep Reinforcement Learning and Domain Randomization for Autonomous Vehicles
Georgios Kontes, Daniel Scherer, Tim Nisslbeck, Janina Fischer, Christopher Mutschler
13th International IEEE Conference on Intelligent Transportation Systems (ITSC)
We use domain randomization to study simulation-to-reality transfer for a high-speed collision avoidance scenario. We train the policy not only on a single version of the setup but on several variations. Our experiments show that the resulting policy is able to generalize much better to different values for the vehicle speed and distance from the obstacle compared to policies trained in the non-randomized version of the setup.
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ViPR: Visual-Odometry-aided Pose Regression for 6DoF Camera Localization
Felix Ott, Tobias Feigl, Christoffer Löffler, Christopher Mutschler
CVPR Workshops (Joint Workshop on Long-Term Visual Localization, Visual Odometry and Geometric and Learning-based SLAM)
We propose an architecture for long-term 6DoF visual odometry that leverages synergies between absolute pose estimates (from PoseNet-like architectures) and relative pose estimates (from FlowNet-like architectures) by combining both through recurrent layers. Experiments on public datasets and on our own Industry dataset show that our novel design outperforms existing techniques in long-term navigation tasks.
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NLOS Detection using UWB Channel Impulse Responses and Convolutional Neural Networks
Maximilian Stahlke, Sebastian Kram, Christopher Mutschler, Thomas Mahr
International Conference on Localization an GNSS (ICL-GNSS)
With a realistic measurement campaign we evaluate different convolutional neural network architectures on the NLOS detection task. We show that most models highly outperform ML-based baselines even with low network complexities and that they also generalize to unseen  receivers and environments.
| bibtex | AI-based Positioning |

Localization Limitations of ARCore, ARKit, and Hololens in Dynamic Large-Scale Industry Environments
Tobias Feigl, Andreas Porada, Steve Steiner, Christoffer Löffler, Christopher Mutschler, Michael Philippsen
15th Intl. Conf. on Computer Graphics Theory and Applications (GRAPP)
We study the applicability of popular AR systems (Apple ARKit, Google ARCore, and Microsoft Hololens) in industrial contexts. With an elaborate measurement campaign we show that for such a context, i.e., when a reliable and accurate tracking of a user matters, the Simultaneous Localization and Mapping (SLAM) techniques of these AR systems are a showstopper. While added natural features help, the tracking reliability can often not be improved enough.
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Deep Reinforcement Learning for Motion Planning of Mobile Robots
Leonid Butyrev, Thorsten Edelhäußer, Christopher Mutschler
arXiv
This paper presents a novel motion and trajectory planning algorithm for nonholonomic mobile robots that uses recent advances in deep reinforcement learning. Starting from a random initial state, i.e., position, velocity and orientation, the robot reaches an arbitrary target state while taking both kinematic and dynamic constraints into account. Our deep rein- forcement learning agent not only processes a continuous state space it also executes continuous actions, i.e., the acceleration of wheels and the adaptation of the steering angle. We evaluate our motion and trajectory planning on a mobile robot with a differential drive in a simulation environment.
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Sick Moves! Motion Parameters as Indicators of Simulator Sickness
Tobias Feigl, Daniel Roth, Stefan Gradl, Markus Wirth, Marc Erich Latoschik, Bjoern M. Eskofier, Michael Philippsen, Christopher Mutschler
IEEE Transactions on Visualization and Computer Graphics (TVCG)
We explore motion parameters, more specifically gait parameters, as an objective indicator to assess simulator sickness in Virtual Reality (VR). We used two different pose estimation methods for the evaluation of motion tasks in a large-scale VR environment: a simple model and an optimised model that allows for a more accurate and natural mapping of human senses. The results show that both models affect the gait parameters and simulator sickness. We further trained a classifier that extracts the non-linear correlation of gait parameters to assess simulator sickness from gait parameters alone. 
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A Framework for Location-Based VR Applications
Jean-Luc Lugrin, Constantin Kleinbeck, Daniel Roth, Christian Daxer, Tobias Feigl, Christopher Mutschler, Marc Erich Latoschik
16. Workshop der GI-Fachgruppe VR/VR
This paper presents a framework to develop and investigate location-based Virtual Reality (VR) applications. We demonstrate our framework by introducing a novel type of VR museum, designed to support a large number of simultaneous co-located users. These visitors are walking in a hangar-scale tracking zone (600m2), while sharing a ten times bigger virtual space (7000m2). Co-located VR applications like this one are opening novel VR perspectives. However, sharing a limitless virtual world using a large, but limited, tracking space is also raising numerous challenges: from financial considerations and technical implementation to interactions and evaluations (e.g., user’s representation, navigation, health & safety, monitoring). How to design, develop and evaluate such a VR system is still an open question. Here, we describe a fully implemented framework with its specific features and performance optimizations. We also illustrate our framework’s viability with a first VR application and discuss its potential benefits for education and future evaluation.
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A Bidirectional LSTM for Estimating Dynamic Human Velocities from a Single IMU
Tobias Feigl, Sebastian Kram, Philipp Woller, Ramiz H. Siddiqui, Michael Philippsen, Christopher Mutschler
9th International Conference on Indoor Positioning and Indoor Navigation (IPIN)

We use machine learning (ML) and deep learning (DL) to estimate a human’s velocity under varying dynamics (as they are present for instance in sports applications) such as abrupt and unpredictable changes. Our approach is robust to varying motion states and orientation changes in dynamic situations. On data from a single uncalibrated IMU our novel recurrent model not only outperforms the state of the art on instantaneous velocity (<0.1m/s) and on traveled distance (< 29m/km). It can also generalise to different and varying rates of motion and provides accurate and precise velocity estimates.
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A Deep Learning Approach to Position Estimation from Channel Impulse Responses
Arne Niitsoo, Thorsten Edelhäußer, Ernst Eberlein, Niels Hadaschik, Christopher Mutschler
Sensors 2019, 19(5), 1064
Radio-based locating systems allow for a robust and continuous tracking in industrial environments and are a key enabler for the digitalization of processes in many areas such as production, manufacturing, and warehouse management. Time difference of arrival (TDoA) systems estimate the time-of-flight (ToF) of radio burst signals with a set of synchronized antennas from which they trilaterate accurate position estimates of mobile tags. However, in industrial environments where multipath propagation is predominant it is difficult to extract the correct ToF of the signal. This article shows how deep learning (DL) can be used to estimate the position of mobile objects directly from the raw channel impulse responses (CIR) extracted at the receivers. Our experiments show that our DL-based position estimation not only works well under harsh multipath propagation but also outperforms state-of-the-art approaches in line-of-sight situations.
| bibtex | AI-based Positioning |

Evaluation Criteria for Inside-Out Indoor Positioning Systems based on Machine Learning
Christoffer Löffler, Sascha Riechel, Janina Fischer, Christopher Mutschler
8th International Conference on Indoor Positioning and Indoor Navigation (IPIN)

[bibtex] [project page]

Recurrent Neural Networks on Drifting Time-of-Flight Measurements
Tobias Feigl, Thorsten Nowak, Michael Philippsen, Thorsten Edelhäußer, Christopher Mutschler
8th International Conference on Indoor Positioning and Indoor Navigation (IPIN)

We train recurrent neural networks on ToFs both in simulation and reality that successfully processes noisy measurements that are not zero-mean Gaussian distributed. We show that our methods outperforms conventional methods based on Kalman filters that are currently considered to be state of the art.

[bibtex] [project page]

Convolutional Neural Networks for Position Estimation in TDoA-based Locating Systems (Best Paper Award)
Arne Niitsoo, Thorsten Edelhäußer, Christopher Mutschler
8th International Conference on Indoor Positioning and Indoor Navigation

[bibtex] [AI-based Positioning]

Supervised Learning for Yaw Orientation Estimation
Tobias Feigl, Christopher Mutschler, Michael Philippsen
8th International Conference on Indoor Positioning and Indoor Navigation (IPIN)

Beyond Replication: Augmenting Social Behaviors in Multi-User Virtual Realities
Daniel Roth, Constantin Kleinbeck, Tobias Feigl, Christopher Mutschler, Marc Erich Latoschik
IEEE VR

Head-to-Body-Pose Classification in No-Pose VR Tracking Systems
Tobias Feigl, Christopher Mutschler, Michael Philippsen
IEEE VR

Reliable Speculative Processing of Out-of-Order Event Streams in Generic Publish/Subscribe Middlewares
Christopher Mutschler, Michael Philippsen
7th ACM Internation Conference on Distributed Event-Based Systems