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    • achim.j.lilienthal@tum.de
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    ORCID
    0000-0003-0217-9326
    Achim J. Lilienthal
    Perception for Intelligent Systems, Technical University of Munich, Munich, Germany
    Summary
    Achim J. Lilienthal received the Ph.D. degree in computer science from Tübingen University, Tübingen, Germany, in 2005. He is currently a professor of Computer Science at TU Munich, Germany, where he leads the chair ``Perception for Intelligent Systems''. He is coordinator of the Horizon 2020 project DARKO, manager of the project ``KI.Fabrik'' at the German Technology Museum in Munich and guest professor at the University of Örebro, Sweden, where he established the Mobile Robotics and Olfaction lab. His core research interest is in perception for intelligent systems. Typically based on approaches that leverage domain knowledge and AI. He has authored or coauthored more than 250 refereed conference papers and journal articles and evaluator for several national funding agencies and the EU. his research work addresses mobile robot olfaction, rich 3-D perception, navigation of autonomous transport robots, human–robot interaction, and mathematics education research.
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    Free Access | Research Article | Feature Paper | 10 June 2024 | Cited: 1
    Extraction of Motion Information from Occupancy Grid Map Using Keystone Transform
    by
    Hongqi Fan
    ,
    Dawei Lu
    ,
    Yanwen Jiang
    ,
    Achim J. Lilienthal
    Chinese Journal of Information Fusion | Volume 1, Issue 1: 63-78, 2024 | DOI:10.62762/CJIF.2024.361892
    Abstract
    Considering the tractability of OGM (Occupancy Grid Map) and its wide use in the dynamic environment representation of mobile robotics, the extraction of motion information from successive OGMs are very important for many tasks, such as SLAM (Simultaneously Localization And Mapping) and DATMO (Detection and Tracking of Moving Object). In this paper, we propose a novel motion extraction method based on the signal transform, called as S-KST (Spatial Keystone Transform), for the motion detection and estimation from successive noisy OGMs. It extends the KST in radar imaging or motion compensation to 1D spatial case (1DS-KST) and 2D spatial case (2DS-KST) combined multiple hypotheses about poss... More >
    Graphical Abstract
    Extraction of Motion Information from Occupancy Grid Map Using Keystone Transform