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Achim Lilienthal

Position

Professor of Computer Science at Örebro University

Expertise

Robot Perception, Mobile Robotics, Artificial Intelligence, Mobile Robot Olfaction

Achim J. Lilienthal is professor of Computer Science and head of the Mobile Robotics and Olfaction (MRO) Lab at Örebro University. His core research interests are in perception systems in unconstrained, dynamic environments. Typically based on approaches that leverage domain knowledge and Artificial Intelligence, his research work addresses mobile robot olfaction, rich 3D perception, navigation of autonomous transport robots, human robot interaction and mathematics education research. Achim J. Lilienthal obtained his Ph.D. in computer science from Tübingen University. The Ph.D. thesis addresses gas distribution mapping and gas source localisation with mobile robots. He is author/co-author of more than 250 refereed conference papers and journal articles.

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Selected publications

  • Bi-directional Navigation Intent Communication using Spatial Augmented Reality and Eye-Tracking Glasses for Improved Safety in Human Robot Interaction. R. T. Chadalavada, H. Andreasson, M. Schindler, R. Palm, A. J. Lilienthal. Robotics and Computer-Integrated Manufacturing (RCIM), Volume 61, February 2020, to appear.
  • The Auto-Complete Graph: Merging and Mutual Correction of Sensor and Prior Maps. M. Mielle, M. Magnusson, and A. J. Lilienthal. Robotics 2019, 8(2), 40 (Special Issue “Robotics in Extreme Environments”)
  • Model-based Gas Source Localization Strategy for a Cooperative Multi-Robot System – A Probabilistic Approach and Experimental Validation Incorporating Physical Knowledge and Model Uncertainties, T. Wiedemann, D. Shutin, A. J. Lilienthal, Robotics and Autonomous Systems (RAS), 118, 2019, pp. 66-79.
  • Multi-Domain Airflow Modelling and Ventilation Characterization Using Mobile Robots, Stationary Sensors and Machine Learning. V. Hernandez Bennetts, K. Kamarudin, T. Wiedemann, T. P. Kucner, S. Lokesh Somisetty, A. J. Lilienthal. Sensors 2019, 19(5), pp. 1119.
  • A Cluster Analysis Approach Based on Exploiting Density Peaks for Gas Discrimination with Electronic Noses in Open Environments, H. Fan, E. Schaffernicht, V. Hernandez Bennetts, A. J. Lilienthal, Sensors & Actuators: B. Chemical, 259, 2018, pp. 183-203.
  • Learning to Detect Misaligned Point Clouds. H. Almqvist, M. Magnusson, T. Kucner, A. J. Lilienthal, Journal of Field Robotics (JFR), 35, 2018, pp. 662-677.
  • GADEN: A 3D Gas Dispersion Simulator for Mobile Robot Olfaction in Realistic Environments, J. Monroy, V. Hernandez Bennetts, H. Fan, A. J. Lilienthal, J. Gonzalez-Jimenez, Sensors, 17(7), pp. 1479 – 1494, 2017
  • Probabilistic Air Flow Modelling Using Turbulent and Laminar Characteristics for Ground and Aerial Robots. V. Hernandez Bennetts, T. P. Kucner, E. Schaffernicht, P. P. Neumann, H. Fan and A. J. Lilienthal, IEEE Robotics and Automation Letters (RA-L), 2:2, 2017, pp. 1117 – 1123.