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    Volume 1, Issue 1, Chinese Journal of Information Fusion
    Volume 1, Issue 1, 2024
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    You He
    You He
    Tsinghua University, China
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    Chinese Journal of Information Fusion, 2024, Volume 1, Issue 1: 63-78

    Free to Read | Research Article | Feature Paper | 10 June 2024
    Extraction of Motion Information from Occupancy Grid Map Using Keystone Transform
    by
    IECE Author Hongqi Fan 1
    IECE Author Dawei Lu 1 *
    IECE Author Yanwen Jiang 1
    IECE Author Achim J. Lilienthal 2
    1 National Key Laboratory of Automatic Target Recognition (ATR), National University of Defense Technology, Changsha 410073, China
    2 Perception for Intelligent Systems, Technical University of Munich, Munich, Germany
    * Corresponding Author: Dawei Lu, [email protected]
    DOI: https://doi.org/10.62762/CJIF.2024.361892
    Received: 17 March 2024, Accepted: 06 June 2024, Published: 10 June 2024  
    Cited by: 2  (Source: Web of Science) , 3  (Source: Google Scholar)
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    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 possible directions of moving obstacles. Meanwhile, the fast algorithm of 2DS-KST based on Chirp Z-Transform (CZT) is also given, which includes five steps, i.e. spatial FFT, directional filtering, CZT, spatial IFFT and Maximal Power Detector (MPD) merging and its computational complexity is proportional to the 2D-FFT. Simulation test results for the point objects and the extended objects show that SKST has a good performance on the extraction of sub-pixel motions in very noisy environment, especially for those slowly moving obstacles.
    Graphical Abstract
    Extraction of Motion Information from Occupancy Grid Map Using Keystone Transform
    Keywords
    Mobile Robotics
    Occupancy Grid Map
    Moving Object
    Keystone Transform
    2DS-KST
    Velocity Estimation
    Funding
    This work was partially supported by the National Natural Science Foundation of China (62303478), ATR Foundation (2035250204) and Key Lab. Foundation (220302).
    Cite This Article
    APA Style
    Fan, H., Lu, D., Jiang, Y., & Lilienthal, A. J. (2024). Extraction of Motion Information from Occupancy Grid Map Using Keystone Transform. Chinese Journal of Information Fusion, 1(1), 63–78. https://doi.org/10.62762/CJIF.2024.361892
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    Chinese Journal of Information Fusion

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