This image shows Florian Pfaff

Florian Pfaff

Jun.-Prof. Dr.-Ing.

Institute of Industrial Automation and Software Engineering

Contact

+49 711 685 69182
 +49 711 685 67302

Pfaffenwaldring 47
70569 Stuttgart
Germany
Room: 2.366

Subject

Florian Pfaff is currently holding the professorship  on "Cognitive Sensor Technology for the Mobility of the Future". His journey in academia began with exceptional academic achievements, including completing his Ph.D. in Computer Science at the Karlsruhe Institute of Technology with the highest distinction, summa cum laude.

His professional experience is marked by a series of notable positions and research endeavors, including postdoctoral research and leadership roles at the Karlsruhe Institute of Technology, where he led projects in the context of optical belt sorting. His expertise and contributions were further recognized during a research stay at University College London (UCL) and a Visiting Professorship at the University of Oxford.

Florian Pfaff's work has garnered widespread recognition, earning him several prestigious awards and honors. Among these are the ISIF Young Investigator Award, the SICK Science Award for the best Ph.D. thesis, and inclusion in the "Ausgezeichnete Informatikdissertation" collection, highlighting the best Ph.D. theses in the German-speaking world. His research has also led to notable best paper awards.

In addition to his research accomplishments, Florian Pfaff is an active member of the academic community, delivering invited talks at esteemed institutions and conferences, and sharing his knowledge through tutorials on robust Kalman filtering. His dedication to advancing the field and mentoring the next generation of researchers is evident in his supervision of award-winning student projects and his active engagement in academic discourse.

Journals and Conferences:

  1. 2024

    1. O. Dünkel, T. Salzmann, and F. Pfaff, “Normalizing Flows on the Product Space of SO(3) Manifolds for Probabilistic Human Pose Modeling.” 2024.

  2. 2023

    1. M. Reith-Braun, F. Pfaff, J. Thummy, and U. Hanebeck, “Approximate First-Passage Time Distributions for Gaussian Motion and Transportation Models,” 2023, pp. 1–8.
    2. M. Reith-Braun, A. Bauer, M. Staab, F. Pfaff, G. Maier, R. Gruna, T. Längle, J. Beyerer, H. Kruggel-Emden, and U. Hanebeck, “GridSort: Image-based Optical Bulk Material Sorting Using Convolutional LSTMs,” IFAC-PapersOnLine, vol. 56, pp. 4620–4626, Jan. 2023.
    3. J. Vieth, M. Reith-Braun, A. Bauer, F. Pfaff, G. Maier, R. Gruna, T. Längle, H. Kruggel-Emden, and U. Hanebeck, “Improving Accuracy of Optical Sorters Using Closed-Loop Control of Material Recirculation,” 2023, pp. 3257–3263.
    4. E. Ernst, F. Pfaff, M. Baum, and U. Hanebeck, “Multitarget–Multidetection Tracking Using the Kernel SME Filter,” 2023, pp. 1–7.
    5. G. Maier, M. Reith-Braun, A. Bauer, R. Gruna, F. Pfaff, H. Kruggel-Emden, T. Längle, U. D. Hanebeck, and J. Beyerer, “Simulation study and experimental validation of a neural network-based predictive tracking system for sensor-based sorting,” tm - Technisches Messen, vol. 90, pp. 489–499, 2023.
    6. E. Ernst, F. Pfaff, U. Hanebeck, and M. Baum, “The Kernel-SME Filter with Adaptive Kernel Widths for Association-free Multi-target Tracking,” 2023, pp. 355–361.

  3. 2022

    1. A. Bauer, G. Maier, M. Reith-Braun, H. Kruggel-Emden, F. Pfaff, R. Gruna, U. Hanebeck, and T. Längle, “Benchmarking a DEM‐CFD Model of an Optical Belt Sorter by Experimental Comparison,” Chemie Ingenieur Technik, vol. 95, Oct. 2022.
    2. K. Li, F. Pfaff, and U. Hanebeck, “Circular Discrete Reapproximation,” 2022.
    3. G. Maier, M. Reith-Braun, A. Bauer, R. Gruna, F. Pfaff, H. Kruggel-Emden, T. Längle, U. D. Hanebeck, and J. Beyerer, “Machine learning based multiobject tracking for sensor based sorting,” in Forum Bildverarbeitung 2022. Ed.: T. Längle, Karlsruhe, Deutschland, 2022, pp. 115–126.
    4. F. Pfaff, K. Li, and U. Hanebeck, The State Space Subdivision Filter for SE(2). 2022.
    5. F. Pfaff, K. Li, and U. Hanebeck, “The State Space Subdivision Filter for SE(3),” 2022.

  4. 2021

    1. F. Pfaff, K. Li, and U. D. Hanebeck, “Conditional Densities and Likelihoods for Hypertoroidal Densities Based on Trigonometric Polynomials,” in Proceedings of the 2021 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2021), Karlsruhe, Deutschland, 2021.
    2. P. Koepernik and F. Pfaff, “Consistency of Gaussian Process Regression in Metric Spaces,” Journal of Machine Learning Research, vol. 22, pp. 1–27, Oct. 2021.
    3. F. Pfaff, K. Li, and U. Hanebeck, “Deep Likelihood Learning for 2-D Orientation Estimation Using a Fourier Filter,” 2021.
    4. G. Maier, F. Pfaff, C. Pieper, R. Gruna, B. Noack, H. Kruggel-Emden, T. Längle, U. D. Hanebeck, and J. Beyerer, “Experimental Evaluation of a Novel Sensor-Based Sorting Approach Featuring Predictive Real-Time Multiobject Tracking,” IEEE transactions on industrial electronics, vol. 68, pp. 1548–1559, 2021.
    5. J. Thumm, M. Reith-Braun, F. Pfaff, U. Hanebeck, M. Flitter, G. Maier, R. Gruna, T. Längle, A. Bauer, and H. Kruggel-Emden, “Mixture of Experts of Neural Networks and Kalman Filters for Optical Belt Sorting,” IEEE Transactions on Industrial Informatics, vol. PP, p. 1, Sep. 2021.
    6. K. Li, F. Pfaff, and U. Hanebeck, “Progressive von Mises-Fisher Filtering Using Isotropic Sample Sets for Nonlinear Hyperspherical Estimation,” Sensors, vol. 21, Apr. 2021.

  5. 2020

    1. F. Pfaff, K. Li, and U. Hanebeck, “A Hyperhemispherical Grid Filter for Orientation Estimation,” 2020.
    2. G. Maier, F. Pfaff, A. Bittner, R. Gruna, B. Noack, H. Kruggel-Emden, U. Hanebeck, T. Längle, and J. Beyerer, “Characterizing material flow in sensor-based sorting systems using an instrumented particle,” at - Automatisierungstechnik, vol. 68, pp. 256–264, Apr. 2020.
    3. K. Li, F. Pfaff, and U. Hanebeck, “Dual Quaternion Sample Reduction for SE(2) Estimation,” 2020.
    4. F. Pfaff, K. Li, and U. Hanebeck, “Estimating Correlated Angles Using the Hypertoroidal Grid Filter,” 2020.
    5. D. Pollithy, M. Reith-Braun, F. Pfaff, and U. D. Hanebeck, “Estimating Uncertainties of Recurrent Neural Networks in Application to Multitarget Tracking,” in 2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), 2020, pp. 229–236.
    6. K. Li, F. Pfaff, and U. Hanebeck, “Grid-Based Quaternion Filter for SO(3) Estimation,” 2020.
    7. K. Li, F. Pfaff, and U. Hanebeck, “Hyperspherical Unscented Particle Filter for Nonlinear Orientation Estimation,” IFAC-PapersOnLine, vol. 53, pp. 2347–2353, Jan. 2020.
    8. K. Li, F. Pfaff, and U. Hanebeck, “Nonlinear von Mises-Fisher Filtering Based on Isotropic Deterministic Sampling,” 2020.
    9. F. Pfaff, C. Pieper, G. Maier, B. Noack, R. Gruna, H. Kruggel-Emden, U. D. Hanebeck, S. Wirtz, V. Scherer, T. Längle, and J. Beyerer, “Predictive tracking with improved motion models for optical belt sorting,” Automatisierungstechnik, vol. 68, pp. 239–255, 2020.
    10. F. Pfaff and U. Hanebeck, “Sensor-based sorting,” at - Automatisierungstechnik, vol. 68, pp. 229–230, Apr. 2020.
    11. F. Pfaff, K. Li, and U. Hanebeck, “The Spherical Grid Filter for Nonlinear Estimation on the Unit Sphere,” 2020.
    12. K. Li, F. Pfaff, and U. Hanebeck, “Unscented Dual Quaternion Particle Filter for SE(3) Estimation,” vol. 5, pp. 647–652, Jun. 2020.

  6. 2019

    1. F. Pfaff, K. Li, and U. D. Hanebeck, “Association likelihoods for directional estimation,” in 2019 IEEE International Conference on Industrial Cyber Physical Systems, ICPS 2019; Howards Plaza HotelTaipei; Taiwan; 6 May 2019 through 9 May 2019, 2019, pp. 211–217.
    2. G. Kurz, I. Gilitschenski, F. Pfaff, L. Drude, U. D. Hanebeck, R. Haeb-Umbach, and R. Y. Siegwart, “Directional Statistics and Filtering Using libDirectional,” Journal of Statistical Software, vol. 89, 2019.
    3. T. Kronauer, F. Pfaff, B. Noack, W. Tian, G. Maier, and U. D. Hanebeck, “Feature-Aided Multitarget Tracking for Optical Belt Sorters.” 2019.
    4. F. Pfaff, K. Li, and U. Hanebeck, “Fourier Filters, Grid Filters, and the Fourier-Interpreted Grid Filter,” 2019.
    5. K. Li, F. Pfaff, and U. Hanebeck, “Geometry-Driven Stochastic Modeling of SE(3) States Based on Dual Quaternion Representation,” 2019.
    6. K. Li, F. Pfaff, and U. Hanebeck, “Hyperspherical Deterministic Sampling Based on Riemannian Geometry for Improved Nonlinear Bingham Filtering,” 2019.
    7. F. Pfaff, “Multitarget Tracking Using Orientation Estimation for Optical Belt Sorting,” 2019.

  7. 2018

    1. G. Maier, F. Pfaff, C. Pieper, R. Gruna, B. Noack, H. Kruggel-Emden, T. Längle, U. D. Hanebeck, S. Wirtz, V. Scherer, and J. Beyerer, “Application of Area-Scan Sensors in Sensor-Based Sorting,” in 8th Sensor-Based Sorting & Control, SBSC 2018 : Aachen, 6-7 March 2018. Ed.: T. Pretz, Aachen, Deutschland, 2018, pp. 73–82.
    2. G. Kurz, F. Pfaff, and U. D. Hanebeck, “Application of Discrete Recursive Bayesian Estimation on Intervals and the Unit Circle to Filtering on SE(2),” IEEE Transactions on Industrial Informatics, vol. 14, pp. 1197–1206, 2018.
    3. G. Maier, F. Pfaff, F. Becker, C. Pieper, R. Gruna, B. Noack, H. Kruggel-Emden, T. Längle, U. D. Hanebeck, S. Wirtz, and others, Improving material characterization in sensor-based sorting by utilizing motion information. KIT Scientific Publishing, 2018.
    4. C. Pieper, F. Pfaff, G. Maier, H. Kruggel-Emden, S. Wirtz, B. Noack, R. Gruna, V. Scherer, U. Hanebeck, T. Längle, and J. Beyerer, “Numerical modelling of an optical belt sorter using a DEM–CFD approach coupled with particle tracking and comparison with experiments,” Powder Technology, vol. 340, Sep. 2018.

  8. 2017

    1. G. Kurz, F. Pfaff, and U. Hanebeck, “Discretization of SO(3) Using Recursive Tesseract Subdivision,” 2017.
    2. F. Pfaff, G. Kurz, and U. D. Hanebeck, “Filtering on the unit sphere using spherical harmonics,” in Proceedings of the International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2017, Daegu, South Korea, 16th - 18th November 2017, Daegu, Südkorea, 2017, pp. 124–130.
    3. F. Pfaff, B. Noack, U. Hanebeck, F. Govaers, and W. Koch, “Information form distributed Kalman filtering (IDKF) with explicit inputs,” 2017, pp. 1–8.
    4. G. Maier, F. Pfaff, F. Becker, C. Pieper, R. Gruna, B. Noack, H. Kruggel-Emden, T. Längle, U. Hanebeck, S. Wirtz, V. Scherer, and J. Beyerer, “Motion-based material characterization in sensor-based sorting,” tm - Technisches Messen, vol. 85, Jan. 2017.
    5. G. Kurz, F. Pfaff, and U. D. Hanebeck, “Nonlinear toroidal filtering based on bivariate wrapped normal distributions,” in 20th International Conference on Information Fusion, Fusion 2017; Xi’an; China; 10 July 2017 through 13 July 2017, 2017, p. Art. Nr.: 8009831.
    6. C. Pieper, H. Kruggel-Emden, S. Wirtz, V. Scherer, F. Pfaff, B. Noack, U. D. Hanebeck, G. Maier, R. Gruna, T. Längle, and J. Beyerer, “Numerical investigation of optical sorting using the discrete element method,” in 7th International Conference on Discrete Element Methods, DEM7 2016, Dalian, China, 2016, 1 - 4 August, 2017, vol. 188, pp. 1105–1113.
    7. C. Pieper, S. Wirtz, V. Scherer, G. Maier, R. Gruna, T. Langle, J. Beyerer, F. Pfaff, B. Noack, U. D. Hanebeck, and H. Kruggel-Emden, “Numerical modelling of the separation of complex shaped particles in an optical belt sorter using a dem-cfd approach and comparison with experiments,” in 5th International Conference on Particle-Based Methods - Fundamentals and Applications, PARTICLES 2017, Hannover, Germany, 26th - 28th September 2017, 2017, pp. 373–384.
    8. F. Pfaff, G. Maier, M. Aristov, B. Noack, R. Gruna, U. Hanebeck, T. Längle, J. Beyerer, C. Pieper, H. Kruggel-Emden, S. Wirtz, and V. Scherer, “Real-time motion prediction using the chromatic offset of line scan cameras,” at - Automatisierungstechnik, vol. 65, Jun. 2017.
    9. G. Maier, F. Pfaff, M. Wagner, C. Pieper, R. Gruna, B. Noack, H. Kruggel-Emden, T. Längle, U. Hanebeck, S. Wirtz, V. Scherer, and J. Beyerer, “Real-time multitarget tracking for sensor-based sorting: A new implementation of the auction algorithm for graphics processing units,” Journal of Real-Time Image Processing, vol. 16, Nov. 2017.

  9. 2016

    1. G. Kurz, F. Pfaff, and U. Hanebeck, Discrete Recursive Bayesian Filtering on Intervals and the Unit Circle. 2016.
    2. G. Maier, F. Pfaff, C. Pieper, R. Gruna, B. Noack, H. Kruggel-Emden, T. Längle, U. Hanebeck, S. Wirtz, V. Scherer, and J. Beyerer, “Fast multitarget tracking via strategy switching for sensor-based sorting,” 2016, pp. 505–510.
    3. F. Pfaff, C. Pieper, G. Maier, B. Noack, H. Kruggel-Emden, R. Gruna, U. Hanebeck, S. Wirtz, V. Scherer, T. Längle, and J. Beyerer, “Improving Optical Sorting of Bulk Materials Using Sophisticated Motion Models,” tm - Technisches Messen, vol. 83, Mar. 2016.
    4. G. Kurz, F. Pfaff, and U. Hanebeck, Kullback–Leibler Divergence and Moment Matching for Hyperspherical Probability Distributions. 2016.
    5. F. Pfaff, G. Kurz, and U. Hanebeck, “Multivariate angular filtering using fourier series,” Journal of Advances in Information Fusion, vol. 11, pp. 206–226, Dec. 2016.
    6. F. Pfaff, G. Kurz, and U. Hanebeck, “Nonlinear Prediction for Circular Filtering Using Fourier Series,” 2016.
    7. C. Pieper, G. Maier, F. Pfaff, H. Kruggel-Emden, S. Wirtz, R. Gruna, B. Noack, V. Scherer, T. Längle, J. Beyerer, and U. Hanebeck, “Numerical modelllng of an automated optical belt sorter using the Discrete Element Method,” Powder Technology, vol. 301, pp. 805–814, Jul. 2016.
    8. J. Steinbring, C. Mandery, F. Pfaff, F. Faion, T. Asfour, and U. Hanebeck, “Real-Time Whole-Body Human Motion Tracking Based on Unlabeled Markers,” 2016.
    9. F. Pfaff, C. Pieper, G. Maier, B. Noack, H. Kruggel-Emden, R. Gruna, U. D. Hanebeck, S. Wirtz, V. Scherer, T. Langle, and J. Beyerer, “Simulation-based evaluation of predictive tracking for sorting bulk materials,” in 2016 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), Baden-Baden, Germany, 19–21 September 2016, 2016, pp. 511–516.
    10. B. Noack, F. Pfaff, M. Baum, and U. D. Hanebeck, “State estimation considering negative information with switching Kalman and ellipsoidal filtering,” in 2016 19th International Conference on Information Fusion (FUSION), 2016, pp. 1945–1952.

  10. 2015

    1. F. Pfaff, G. Kurz, and U. Hanebeck, “Multimodal Circular Filtering Using Fourier Series,” 2015.
    2. F. Pfaff, M. Baum, B. Noack, U. Hanebeck, R. Gruna, T. Längle, and J. Beyerer, TrackSort: Predictive tracking for sorting uncooperative bulk materials. 2015, pp. 7–12.

  11. 2013

    1. F. Pfaff, B. Noack, and U. Hanebeck, “Data Validation in the Presence of Stochastic and Set-membership Uncertainties,” in Proceedings of the 16th International Conference on Information Fusion, FUSION 2013, 2013.

  12. 2012

    1. B. Noack, F. Pfaff, and U. D. Hanebeck, “Combined stochastic and set-membership information filtering in multisensor systems,” in 2012 15th International Conference on Information Fusion, 2012, pp. 1218–1224.
    2. B. Noack, F. Pfaff, and U. Hanebeck, “Optimal Kalman Gains for Combined Stochastic and Set-Membership State Estimation,” in Proceedings of the IEEE Conference on Decision and Control, 2012.

  13. 2011

    1. A. Arias, H. P. Et, F. Pfaff, and U. Hanebeck, “The plenhaptic guidance function for intuitive navigation in extended range telepresence scenarios,” 2011, pp. 475–480.

Starting in the summer term of 2024, Florian Pfaff will take over the lecture "Grundlagen der Softwaresysteme" at the University of Stuttgart. Previously, he held lectures on "Fuzzy Sets" and "Information Processing in Sensor Networks" at the Karlsruhe Institute of Technology (KIT).

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