Abstract: | Accurate localization is a critical element for the successful and safe operation of Urban Air Mobility (UAM). In this study, we present a method for UAM place recognition that utilizes point cloud map (PCM) data and a virtual LiDAR sensor model. The PCM-based approach enables the creation of a virtual descriptor database (VDD) for place recognition. To generate descriptors invariant to translation and rotation, we introduce a region of interest sampling method and a feature point detection approach, effectively minimizing altitude influence. We also outline a technique for creating translation and rotation invariant descriptors through the integration of robust feature extraction methods. Furthermore, we conduct an experiment utilizing a game engine-based UAM simulator to validate the proposed method. PCM and VDD are generated through the simulator, and a quantitative analysis of descriptors and place recognition is subsequently carried out. |
Published in: |
Proceedings of the 2024 International Technical Meeting of The Institute of Navigation January 23 - 25, 2024 Hyatt Regency Long Beach Long Beach, California |
Pages: | 651 - 657 |
Cite this article: | Im, Ji-Ung, Lee, Yong-Ha, Won, Jong-Hoon, "LiDAR Point Cloud Descriptor for UAM Place Recognition with Point Cloud Map," Proceedings of the 2024 International Technical Meeting of The Institute of Navigation, Long Beach, California, January 2024, pp. 651-657. https://doi.org/10.33012/2024.19521 |
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