The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Download
Publications Copernicus
Download
Citation
Articles | Volume XL-7/W3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-7/W3, 1291–1298, 2015
https://doi.org/10.5194/isprsarchives-XL-7-W3-1291-2015
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-7/W3, 1291–1298, 2015
https://doi.org/10.5194/isprsarchives-XL-7-W3-1291-2015

  30 Apr 2015

30 Apr 2015

VIRTUALIZING SUPER-COMPUTATION ON-BOARD UAS

E. Salami, J. A. Soler, R. Cuadrado, C. Barrado, and E. Pastor E. Salami et al.
  • Technical University of Catalonia (UPC), Computer Architecture Department, 08860 Castelldefels, Spain

Keywords: UAS, remote sensing, super-computing, parallelization, image processing, benchmarking, virtualization

Abstract. Unmanned aerial systems (UAS, also known as UAV, RPAS or drones) have a great potential to support a wide variety of aerial remote sensing applications. Most UAS work by acquiring data using on-board sensors for later post-processing. Some require the data gathered to be downlinked to the ground in real-time. However, depending on the volume of data and the cost of the communications, this later option is not sustainable in the long term. This paper develops the concept of virtualizing super-computation on-board UAS, as a method to ease the operation by facilitating the downlink of high-level information products instead of raw data. Exploiting recent developments in miniaturized multi-core devices is the way to speed-up on-board computation. This hardware shall satisfy size, power and weight constraints. Several technologies are appearing with promising results for high performance computing on unmanned platforms, such as the 36 cores of the TILE-Gx36 by Tilera (now EZchip) or the 64 cores of the Epiphany-IV by Adapteva. The strategy for virtualizing super-computation on-board includes the benchmarking for hardware selection, the software architecture and the communications aware design. A parallelization strategy is given for the 36-core TILE-Gx36 for a UAS in a fire mission or in similar target-detection applications. The results are obtained for payload image processing algorithms and determine in real-time the data snapshot to gather and transfer to ground according to the needs of the mission, the processing time, and consumed watts.