MAD-C: Multi-stage Approximate Distributed Cluster-Combining for Obstacle Detection and Localization
Paper in proceedings, 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.

Fog computing

Approximations

Point cloud processing

Author

Amir Keramatian

Chalmers, Computer Science and Engineering (Chalmers), Networks and Systems (Chalmers)

Vincenzo Massimiliano Gulisano

Chalmers, Computer Science and Engineering (Chalmers), Networks and Systems (Chalmers)

Marina Papatriantafilou

Chalmers, Computer Science and Engineering (Chalmers), Networks and Systems (Chalmers)

Philippas Tsigas

Chalmers, Computer Science and Engineering (Chalmers), Networks and Systems (Chalmers)

Ioannis Nikolakopoulos

Chalmers, Computer Science and Engineering (Chalmers), Networks and Systems (Chalmers)

Vol. 11339 312-324

24th International Conference on Parallel and Distributed Computing. Euro-Par 2018.
Turin, Italy,

Future factories in the Cloud (FiC)

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

Areas of Advance

Information and Communication Technology

Building Futures (2010-2018)

Subject Categories

Other Engineering and Technologies

Information Science

Computer Science

Computer Vision and Robotics (Autonomous Systems)

DOI

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

More information

Latest update

3/12/2019