MAD-C: Multi-stage Approximate Distributed Cluster-Combining for Obstacle Detection and Localization

  • Amir Keramatian
  • Vincenzo Gulisano
  • Marina PapatriantafilouEmail author
  • Philippas Tsigas
  • Yiannis Nikolakopoulos
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11339)


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 



Work supported by SSF grant “FiC: Future Factories in the Cloud” (GMT14-0032) and VR grants “HARE: Self-deploying and Adaptive Data Streaming Analytics in Fog Architectures” (2016-03800) and “Models and Techniques for Energy-Efficient Concurrent Data Access Designs” (2016-05360).


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Chalmers University of TechnologyGothenburgSweden

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