Abstract
This paper proposes a simple method for rockfall detection from terrestrial LiDAR point clouds. The method consists of four steps: registration, subtraction, clutter removal, and spatial clustering. The paper contributes a straightforward method for clutter removal based on grid density, which is computational complexity inexpensive compared to the standard method based on nearest neighbor distance. Experimental results show that both are comparable in terms of identifying rockfall events. The proposed method can detect 21 events from 27 events from our simulations, and a conventional method can detect 23 events. The false-positive events of the proposed and conventional methods are 1 and 15, respectively. In contrast, for 52,000 points, the proposed method is about 16 times faster. Also, this paper suggests a simple means to estimate the parameters used in the spatial clustering algorithm.
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Acknowledgement
This research is supported by Thailand Advanced Institute of Science and Technology (TAIST), National Science and Technology Development Agency (NSTDA), and Tokyo Institute of Technology under the TAIST-Tokyo Tech program. Also, this work has been partially supported by the ASEAN Committee on Science, Technology and Innovation (COSTI) under the ASEAN Plan of Action on Science, Technology and Innovation (APASTI) funding scheme and by e-Asia JRP funding scheme.
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Dillon, P., Karnjana, J., Aimmanee, P. (2021). Rockfall Detection from Terrestrial LiDAR Point Clouds by Using DBSCAN with Clutter Removal Based on Grid Density. In: Suhaili, W.S.H., Siau, N.Z., Omar, S., Phon-Amuaisuk, S. (eds) Computational Intelligence in Information Systems. CIIS 2021. Advances in Intelligent Systems and Computing, vol 1321. Springer, Cham. https://doi.org/10.1007/978-3-030-68133-3_13
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DOI: https://doi.org/10.1007/978-3-030-68133-3_13
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