pi-Lisco: parallel and incremental stream-based point-cloud clustering
Paper i proceeding, 2022

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.

Clustering

Point-cloud analysis

Data-stream processing

Författare

Hannaneh Najdataei

Nätverk och System

Vincenzo Massimiliano Gulisano

Nätverk och System

Philippas Tsigas

Nätverk och System

Marina Papatriantafilou

Nätverk och System

Proceedings of the ACM Symposium on Applied Computing

460-469
9781450387132 (ISBN)

37th ACM/SIGAPP Symposium on Applied Computing
Virtual, ,

Molnbaserade produkter och produktion (FiC)

Stiftelsen för Strategisk forskning (SSF) (GMT14-0032), 2016-01-01 -- 2020-12-31.

Ämneskategorier

Annan data- och informationsvetenskap

Mediateknik

Datorseende och robotik (autonoma system)

Styrkeområden

Informations- och kommunikationsteknik

DOI

10.1145/3477314.3507093

Mer information

Senast uppdaterat

2023-10-26