about me

I am a postdoctoral researcher in the Intelligent Sensor-Actuator-Systems (ISAS) laboratory at the Karlsruhe Institute of Technology (KIT), Germany. I have obtained my Ph.D. from Karlsruhe Institute of Technology under the supervision of Prof. Uwe D. Hanebeck (KIT) and Dr. Simon J. Julier (UCL, UK). On this website, you can find information about my work and research interests.

from observation to information

My research in the field of state estimation theory includes robust Kalman filtering, distributed and decentralized information processing for sensor networks, and event-based filtering. With state estimation, usable information can be extracted from sensor data — even under severe uncertainties. Quantifying uncertainty in predictions and estimates is essential for the assessment and design of robust filtering techniques. Further important challenges in developing filtering techniques arise from nonlinear process and sensor models and from spatially distributed systems. In view of these challenges, I’m interested in consistent and robust solutions.

research overview

news

  • November 26, 2016 Our Automatica article “Decentralized Data Fusion with Inverse Covariance Intersection” has been accepted.
  • July 18, 2016 Four papers for the MFI conference in Baden-Baden, Germany, have been accepted.
  • July 01, 2016 GAČR-DFG project “Cooperative Approaches to Design of Nonlinear Filters” with the Department of Computer Science and Engineering at the University of West Bohemia starts today.
  • May 09, 2016 Two papers for the FUSION conference in Heidelberg, Germany, have been accepted.
  • January 01, 2016 BMWi project “Fertigung und Entwicklung von Tanks unter Industrie4.0-Bedingungen” starts today.
  • December 16, 2015 Article for tm - technisches messen (teme) has been accepted.
  • November 24, 2015 Book chapter in Event-Based Control and Signal Processing is out.
  • September 01, 2015 IGF project “Inside Schüttgut” starts today.

recent publications

  1. Simulation-based Evaluation of Predictive Tracking for Sorting Bulk Materials

    Florian Pfaff, Christoph Pieper, Georg Maier, Benjamin Noack, Harald Kruggel-Emden, Robin Gruna, Uwe D. Hanebeck, Siegmar Wirtz, Viktor Scherer, Thomas Längle, and Jürgen Beyerer

    Proceedings of the 2015 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2016), Baden-Baden, Germany

    September, 2016

    Abstract:

    Multitarget tracking problems arise in many real-world applications. The performance of the utilized algorithm strongly depends both on how the data association problem is handled and on the suitability of the motion models employed. Especially the motion models can be hard to validate. Previously, we have proposed to use multitarget tracking to improve optical belt sorters. In this paper, we evaluate both the suitability of our model and the tracking and then of our entire system incorporating the image processing component via the use of highly realistic numerical simulations. We first assess the model using noise-free measurements generated by the simulation and then evaluate the entire system by using synthetically generated image data.

    @inproceedings{MFI16_Pfaff,
      title = {{Simulation-based Evaluation of Predictive Tracking for Sorting Bulk Materials}},
      author = {Pfaff, Florian and Pieper, Christoph and Maier, Georg and Noack, Benjamin and Kruggel-Emden, Harald and Gruna, Robin and Hanebeck, Uwe D. and Wirtz, Siegmar and Scherer, Viktor and L{\"{a}}ngle, Thomas and Beyerer, J{\"{u}}rgen},
      booktitle = {Proceedings of the 2015 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2016)},
      year = {2016},
      address = {Baden-Baden, Germany},
      month = {sep},
    }
    
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  2. Algebraic Analysis of Data Fusion with Ellipsoidal Intersection

    Benjamin Noack, Joris Sijs, and Uwe D. Hanebeck

    Proceedings of the 2015 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2016), Baden-Baden, Germany

    September, 2016

    Abstract:

    For decentralized fusion problems, ellipsoidal intersection has been proposed as an efficient fusion rule that provides less conservative results as compared to the well-know covariance intersection method. Ellipsoidal intersection relies on the computation of a common estimate that is shared by the estimates to be fused. In this paper, an algebraic reformulation of ellipsoidal intersection is discussed that circumvents the computation of the common estimate. It is shown that ellipsoidal intersection corresponds to an internal ellipsoidal approximation of the intersection of covariance ellipsoids. An interesting result is that ellipsoidal intersection can be computed with the aid of the Bar-Shalom/Campo fusion formulae. This is achieved by assuming a specific correlation structure between the estimates to be fused.

    @inproceedings{MFI16_Noack,
      title = {{Algebraic Analysis of Data Fusion with Ellipsoidal Intersection}},
      author = {Noack, Benjamin and Sijs, Joris and Hanebeck, Uwe D.},
      booktitle = {Proceedings of the 2015 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2016)},
      year = {2016},
      address = {Baden-Baden, Germany},
      month = {sep},
    }
    
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  3. Fast Multitarget Tracking via Strategy Switching for Sensor-Based Sorting

    Georg Maier, Florian Pfaff, Christoph Pieper, Robin Gruna, Benjamin Noack, Harald Kruggel-Emden, Thomas Längle, Uwe D. Hanebeck, Siegmar Wirtz, Viktor Scherer, and Jürgen Beyerer

    Proceedings of the 2015 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2016), Baden-Baden, Germany

    September, 2016

    Abstract:

    State-of-the-art sensor-based sorting systems provide solutions to sort various products according to quality aspects. Such systems face the challenge of an existing delay between perception and separation of the material. To reliably predict an object’s position when reaching the separation stage, information regarding its movement needs to be derived. Multitarget tracking offers approaches through which this can be achieved. However, processing time is typically limited since the sorting decision for each object needs to be derived sufficiently early before it reaches the separation stage. In this paper, an approach for multitarget tracking in sensor-based sorting is proposed which supports establishing an upper bound regarding processing time required for solving the measurement to track association problem. To demonstrate the success of the proposed method, experiments are conducted for data-sets obtained via simulation of a sorting system. This way, it is possible to not only demonstrate the impact on required runtime but also on the quality of the association.

    @inproceedings{MFI16_Maier,
      title = {{Fast Multitarget Tracking via Strategy Switching for Sensor-Based Sorting}},
      author = {Maier, Georg and Pfaff, Florian and Pieper, Christoph and Gruna, Robin and Noack, Benjamin and Kruggel-Emden, Harald and L{\"{a}}ngle, Thomas and Hanebeck, Uwe D. and Wirtz, Siegmar and Scherer, Viktor and Beyerer, J{\"{u}}rgen},
      booktitle = {Proceedings of the 2015 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2016)},
      year = {2016},
      address = {Baden-Baden, Germany},
      month = {sep},
    }
    
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  4. Camera- and IMU-based Pose Tracking for Augmented Reality

    Florian Faion, Antonio Zea, Benjamin Noack, Jannik Steinbring, and Uwe D. Hanebeck

    Proceedings of the 2015 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2016), Baden-Baden, Germany

    September, 2016

    Abstract:

    In this paper, we propose an algorithm for tracking mobile devices (such as smartphones, tablets, or smartglasses) in a known environment for augmented reality applications. For this purpose, we interpret the environment as an extended object with a known shape, and design likelihoods for different types of image features, using association models from extended object tracking. Based on these likelihoods, and together with sensor information of the inertial measurement unit of the mobile device, we design a recursive Bayesian tracking algorithm. We present results of our first prototype and discuss the lessons we learned from its implementation. In particular, we set up a “pick-by-vision” scenario, where the location of objects in a shelf is to be highlighted in a camera image. Our experiments confirm that the proposed tracking approach achieves accurate and robust tracking results even in scenarios with fast motion.

    @inproceedings{MFI16_Faion,
      title = {{Camera- and IMU-based Pose Tracking for Augmented Reality}},
      author = {Faion, Florian and Zea, Antonio and Noack, Benjamin and Steinbring, Jannik and Hanebeck, Uwe D.},
      booktitle = {Proceedings of the 2015 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2016)},
      year = {2016},
      address = {Baden-Baden, Germany},
      month = {sep},
    }
    
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  5. Numerical Investigation of Optical Sorting using the Discrete Element Method

    Christoph Pieper, Harald Kruggel-Emden, Siegmar Wirtz, Viktor Scherer, Florian Pfaff, Benjamin Noack, Uwe D . Hanebeck, Georg Maier, Robin Gruna, Thomas Längle, and Jürgen Beyerer

    Proceedings of the 7th International Conference on Discrete Element Methods (DEM7), Dalian, China

    August, 2016

    Abstract:

    Automated optical sorting systems are important devices in the growing field of bulk solids handling. The initial sorter calibration and the precise optical sorting of many materials is still very time consuming and difficult. A numerical model of an automated optical belt sorter is presented in this study. The sorter and particle interaction is described with the Discrete Element Method (DEM) while the separation phase is considered in a post processing step. Different operating parameters and their influence on sorting quality are investigated. In addition, two models for detecting and predicting the particle movement between the detection point and the separation step are presented and compared, namely a conventional line scan camera model and a new approach combining an area scan camera model with particle tracking.

    @inproceedings{DEM16_Pieper,
      title = {{Numerical Investigation of Optical Sorting using the Discrete Element Method}},
      author = {Pieper, Christoph and Kruggel-Emden, Harald and Wirtz, Siegmar and Scherer, Viktor and Pfaff, Florian and Noack, Benjamin and Hanebeck, Uwe D . and Maier, Georg and Gruna, Robin and L{\"{a}}ngle, Thomas and Beyerer, J{\"{u}}rgen},
      booktitle = {Proceedings of the 7th International Conference on Discrete Element Methods (DEM7)},
      year = {2016},
      address = {Dalian, China},
      month = {aug},
    }
    
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