MAD-C: Multi-stage Approximate Distributed Cluster-Combining for Obstacle Detection and Localization
Paper i proceeding, 2018

Efficient distributed multi-sensor monitoring is a key feature of upcoming digitalized infrastructures. We address the problem of obstacle detection, having as input multiple point clouds, from a set of laser-based distance sensors; the latter generate high-rate data and can rapidly exhaust baseline analysis methods, that gather and cluster all the data. We propose MAD-C, a distributed approximate method: it can build on any appropriate clustering, to process disjoint subsets of the data distributedly; MAD-C then distills each resulting cluster into a data-summary. The summaries, computable in a continuous way, in constant time and space, are combined, in an order-insensitive, concurrent fashion, to produce approximate volumetric representations of the objects. MAD-C leads to (i) communication savings proportional to the number of points, (ii) multiplicative decrease in the dominating component of the processing complexity and, at the same time, (iii) high accuracy (with RandIndex >0.95), in comparison to its baseline counterpart. We also propose MAD-C-ext, building on the MAD-C’s output, by further combining the original data-points, to improve the outcome granularity, with the same asymptotic processing savings as MAD-C.

Point cloud processing

Approximations

Fog computing

Författare

Amir Keramatian

Chalmers, Data- och informationsteknik, Nätverk och system

Vincenzo Massimiliano Gulisano

Chalmers, Data- och informationsteknik, Nätverk och system

Marina Papatriantafilou

Chalmers, Data- och informationsteknik, Nätverk och system

Philippas Tsigas

Chalmers, Data- och informationsteknik, Nätverk och system

Ioannis Nikolakopoulos

Chalmers, Data- och informationsteknik, Nätverk och system

Vol. 11339 312-324

Parallel Processing Workshops. Euro-Par 2018.
Turin, Italy,

Molnbaserade produkter och produktion (FiC)

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

Styrkeområden

Informations- och kommunikationsteknik

Building Futures

Ämneskategorier

Annan teknik

Systemvetenskap

Datavetenskap (datalogi)

Datorseende och robotik (autonoma system)

DOI

10.1007/978-3-030-10549-5_25

Mer information

Skapat

2019-01-16