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Chinese Journal of Information Fusion, 2024, Volume 1, Issue 1: 63-78

Free Access | Research Article | Feature Paper | 10 June 2024 | Cited: 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: [email protected]
Received: 17 March 2024, Accepted: 06 June 2024, Published: 10 June 2024  

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

References

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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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