Abstract
Situational awareness is getting traction in the field of autonomous inland vessels. Large amounts of data needs to be shared in order to set up this awareness. This ranges from relatively small positional updates, to consistent streams of sensory data. Point clouds, captured by LiDAR sensors, are heavily used by inland vessels as they give a detailed sense of range. This sharing is not a major complexity when vessels are in close proximity with each other; dedicated networks could handle this consistent stream of data. However, when vessels are farther away from each other, long range networks are needed, at the cost of high bandwidth capabilities. Therefore, the sensor message size should be reduced while retaining a reasonable quality. In this paper, we investigate the trade-off between lossless and lossy point cloud compression with Google Draco and its resulting quality. The results show that a considerable size reduction can be applied while the point cloud maintains acceptable quality.
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Acknowledgements
This work was performed within the SSAVE (Shared Situational Awareness for VEssels) - project. The project was realised with the financial support of Flanders Innovation & Entrepreneurship (VLAIO) and the Blue Cluster.
This research also received funding from the Flemish Government under the “Onderzoeksprogramma Artificiële Intelligentie (AI) Vlaanderen” programme.
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de Hoog, J., Ahmed, A.N., Anwar, A., Latré, S., Hellinckx, P. (2022). Quality-Aware Compression of Point Clouds with Google Draco. In: Barolli, L. (eds) Advances on P2P, Parallel, Grid, Cloud and Internet Computing. 3PGCIC 2021. Lecture Notes in Networks and Systems, vol 343. Springer, Cham. https://doi.org/10.1007/978-3-030-89899-1_23
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DOI: https://doi.org/10.1007/978-3-030-89899-1_23
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