pi-Lisco: parallel and incremental stream-based point-cloud clustering
Paper in 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.

Data-stream processing

Point-cloud analysis



Hannaneh Najdataei

Network and Systems

Vincenzo Massimiliano Gulisano

Network and Systems

Philippas Tsigas

Network and Systems

Marina Papatriantafilou

Network and Systems

Proceedings of the ACM Symposium on Applied Computing

9781450387132 (ISBN)

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

Future factories in the Cloud (FiC)

Swedish Foundation for Strategic Research (SSF) (GMT14-0032), 2016-01-01 -- 2020-12-31.

Subject Categories

Other Computer and Information Science

Media Engineering

Computer Vision and Robotics (Autonomous Systems)

Areas of Advance

Information and Communication Technology



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