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pi-Lisco: parallel ...
pi-Lisco: parallel and incremental stream-based point-cloud clustering
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- Najdataei, Hannaneh, 1988 (författare)
- Chalmers tekniska högskola,Chalmers University of Technology
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- Gulisano, Vincenzo Massimiliano, 1984 (författare)
- Chalmers tekniska högskola,Chalmers University of Technology
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- Tsigas, Philippas, 1967 (författare)
- Chalmers tekniska högskola,Chalmers University of Technology
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- Papatriantafilou, Marina, 1966 (författare)
- Chalmers tekniska högskola,Chalmers University of Technology
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(creator_code:org_t)
- 2022-05-06
- 2022
- Engelska.
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Ingår i: Proceedings of the ACM Symposium on Applied Computing. - New York, NY, USA : ACM. ; , s. 460-469
- Relaterad länk:
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https://research.cha...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- Point-cloud clustering is a key task in applications like autonomous vehicles and digital twins, where rotating LiDAR sensors commonly generate point-cloud measurements in data streams. The state-of-the-art algorithms, Lisco and its parallel equivalent P-Lisco, define a single-pass distance-based clustering. However, while outperforming other batch-based techniques, they cannot incrementally cluster point-clouds from consecutive LiDAR rotations, as they cannot exploit result-similarity between rotations. The simplicity of Lisco, along with the potential of improvements through utilization of computational overlaps, form the motivation of a more challenging objective studied here. We propose Parallel and Incremental Lisco (pi-Lisco), which, with a simple yet efficient approach, clusters LiDAR data in streaming sliding windows, reusing the results from overlapping portions of the data, thus, enabling single-window (i.e., in-place) processing. Moreover, pi-Lisco employs efficient work-sharing among threads, facilitated by the ScaleGate data structure, and embeds a customised version of the STINGER concurrent data structure. Through an orchestration of these key ideas, pi-Lisco is able to lead to significant performance improvements. We complement with an evaluation of pi-Lisco, using the Ford Campus real-world extensive data-set, showing (i) the computational benefits from incrementally processing the consecutive point-clouds; and (ii) the fact that pi-Lisco' parallelization leads to continuously increasing sustainable rates with increasing number of threads, shifting the saturation point of the baseline.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Annan data- och informationsvetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Other Computer and Information Science (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Annan teknik -- Mediateknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Other Engineering and Technologies -- Media Engineering (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)
Nyckelord
- Clustering
- Point-cloud analysis
- Data-stream processing
Publikations- och innehållstyp
- kon (ämneskategori)
- ref (ämneskategori)