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Processing LiDAR Data from a Virtual Logistics Space

Authors Jaakko Harjuhahto, Anton Debner, Vesa Hirvisalo

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Subject Classification

ACM Subject Classification
  • Computer systems organization → Embedded and cyber-physical systems
  • Computing methodologies → Modeling and simulation
  • Computing methodologies → Distributed computing methodologies
Keywords
  • simulation
  • hybrid systems
  • new control applications
  • fog computing

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Abstract

We study computing solutions that can be used close to the network edge in I2oT systems (Industrial Internet of Things). As a specific use case, we consider a factory warehouse with AGVs (Automated Guided Vehicles). The computing services for such systems should be dependable, yield high performance, and have low latency. For understanding such systems, we have constructed a hybrid system that consists of a simulator yielding virtual LiDAR sensor data streams in real-time and a sensor data processor on a real cluster that acts as a fog computing node close to the warehouse. The processing merges the observations done from the individual sensor streams without using the vehicle-to-vehicle communication links for the complicated computing. We present our experimental results, which show the feasibility of the computing solution.

Cite As Get BibTex

Jaakko Harjuhahto, Anton Debner, and Vesa Hirvisalo. Processing LiDAR Data from a Virtual Logistics Space. In 2nd Workshop on Fog Computing and the IoT (Fog-IoT 2020). Open Access Series in Informatics (OASIcs), Volume 80, pp. 4:1-4:12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020) https://doi.org/10.4230/OASIcs.Fog-IoT.2020.4

Author Details

Jaakko Harjuhahto
  • Aalto University, Department of Computer Science, Espoo, Finland
Anton Debner
  • Aalto University, Department of Computer Science, Espoo, Finland
Vesa Hirvisalo
  • Aalto University, Department of Computer Science, Espoo, Finland

Funding

This work has been financially supported by the Technology Industries of Finland Centennial Foundation.

Acknowledgements

We would like to thank research assistant Matias Hyyppä for his work on the dataset creation tools and the anonymous reviewers of this paper for their valuable comments.

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