Processing LiDAR Data from a Virtual Logistics Space

Authors Jaakko Harjuhahto, Anton Debner, Vesa Hirvisalo

Thumbnail PDF


  • Filesize: 4.87 MB
  • 12 pages

Document Identifiers

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


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.

Cite AsGet 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)


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.

Subject Classification

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


  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    PDF Downloads


  1. Jack E. Bresenham. Algorithm for computer control of a digital plotter. IBM Systems journal, 4(1):25-30, 1965. URL:
  2. Anton Debner, Jaakko Harjuhahto, and Vesa Hirvisalo. A LiDAR dataset from a virtual warehouse. Aalto University, 2020. URL:
  3. Alexey Dosovitskiy, German Ros, Felipe Codevilla, Antonio Lopez, and Vladlen Koltun. CARLA: An open urban driving simulator. In Proceedings of the 1st Annual Conference on Robot Learning, pages 1-16, 2017. Google Scholar
  4. Gregory Dudek and Michael Jenkin. Computational principles of mobile robotics. Cambridge University Press, 2010. Google Scholar
  5. Janos Farkas, Balasz Varga, György Miklos, and Joachim Sachs. 5G-TSN Integration for Industrial Automation. Ericsson Technology Review, 07/2019. Google Scholar
  6. FlatBuffers. The FlatBuffers website, 2020. URL:
  7. OpenFog Consortium Architecture Working Group. OpenFog reference architecture for fog computing. OPFRA001, 20817:162, 2017. Google Scholar
  8. Jussi Hanhirova, Teemu Kämäräinen, Sipi Sipilä, Matti Siekkinen, Vesa Hirvisalo, and Antti Ylä-Jääski. Latency and throughput characterization of convolutional neural networks for mobile computer vision. In Proceedings of the 9th ACM Multimedia Systems Conference (MMSys'18), 2018. URL:
  9. iFogSim. A Toolkit for Modeling and Simulation of Resource Management Techniques in Internet of Things, Edge and Fog Computing Environments. URL:
  10. Vasileios Karagiannis. Compute node communication in the fog: Survey and research challenges. In Proceedings of the Workshop on Fog Computing and the IoT, pages 36-40, 2019. URL:
  11. Alexander Katriniok, Peter Kleibaum, and Martina Joševski. Distributed model predictive control for intersection automation using a parallelized optimization approach. IFAC-PapersOnLine, 50(1):5940-5946, 2017. URL:
  12. Dorin Maxim and Ye-Qiong Song. Delay Analysis of AVB traffic in Time-Sensitive Networks (TSN). In Proceedings Real-Time Networks and Systems (RTNS'17), 2017. URL:
  13. Ruben Mayer and Hans-Arno Jacobsen. Scalable Deep Learning on Distributed Infrastructures: Challenges, Techniques, and Tools. ACM Compututing Surveys, 53(1), February 2020. URL:
  14. Mohamed W. Mehrez, Tobias Sprodowski, Karl Worthmann, George K.I. Mann, Raymond G. Gosine, Juliana K. Sagawa, and Jürgen Pannek. Occupancy grid based distributed MPC for mobile robots. In 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 4842-4847, September 2017. URL:
  15. Nima Mohajerin and Mohsen Rohani. Multi-step prediction of occupancy grid maps with recurrent neural networks. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 10592-10600, June 2019. URL:
  16. Ahmed Nasrallah, Akhilesh S. Thyagaturu, Cuixiang Wang Ziyad Alharbi, Xing Shao, Martin Reisslein, and Hesham Elbakoury. Performance Comparison of IEEE 802.1 TSN Time Aware Shaper (TAS) and Asynchronous Traffic Shaper (ATS). IEEE Access, 7, April 2019. URL:
  17. Arne Neumann, Lukasz Wisniewski, Torsten Musiol, Christian Mannweiler, Borislava Gajic, Rakash SivaSiva Ganesan, and Peter Ros. Abstraction models for 5G mobile networks integration into industrial networks and their evaluation. In In Kommunikation und Bildverarbeitung in der Automation (Technologien für die intelligente Automation) 12, 2020. URL:
  18. Giang Nguyen, Stefan Dlugolinsky, Martin Bobák, Viet Tran, Álvaro López García, Ignacio Heredia, Peter Malík, and Ladislav Hluchý. Machine learning and deep learning frameworks and libraries for large-scale data mining: a survey. Artificial Intelligence Review, 52(1):77-124, 2019. URL:
  19. Lorenzo Sabattini, Elena Cardarelli, Valerio Digani, Cristian Secchi, Cesare Fantuzzi, and Kay Fuerstenberg. Advanced sensing and control techniques for multi agv systems in shared industrial environments. In 2015 IEEE 20th Conference on Emerging Technologies Factory Automation (ETFA), pages 1-7, September 2015. URL:
  20. Shaik Mohammed Salman, Vaclav Struhar, Alessandro V. Papadopoulos, Moris Behnam, and Thomas Nolte. Fogification of industrial robotic systems: Research challenges. In Proceedings of the Workshop on Fog Computing and the IoT, IoT-Fog ’19, page 41–45, New York, NY, USA, 2019. Association for Computing Machinery. URL:
  21. The HDF Group. Hierarchical data format version 5. URL:
  22. Ashkan Yousefpour, Caleb Fung, Tam Nguyen, Krishna Kadiyala, Fatemeh Jalali, Amirreza Niakanlahiji, Jian Kong, and Jason P. Jue. All one needs to know about fog computing and related edge computing paradigms: A complete survey. Journal of Systems Architecture, 98:289-330, September 2019. URL:
Questions / Remarks / Feedback

Feedback for Dagstuhl Publishing

Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail