Abstract
The use of autonomous surface vehicles (ASVs) is an efficient alternative to the traditional manual or static sensor network sampling for large-scale monitoring of marine and aquatic environments. However, navigating natural and narrow waterways is challenging for low-cost ASVs due to possible obstacles and limited precision global positioning system (GPS) data. Visual information coming from a camera can be used for collision avoidance, and digital image stabilization is a fundamental step for achieving this capability. This work presents an implementation of an image stabilization algorithm for a heterogeneous low-power board (i.e., NVIDIA Jetson TX1). In particular, the paper shows how such an embedded vision application has been configured to best exploit the CPU and the GPU processing elements of the board in order to obtain both computation performance and energy efficiency. We present qualitative and quantitative experiments carried out on two different environments for embedded vision software development (i.e., OpenCV and OpenVX), using real data to find a suitable solution and to demonstrate its effectiveness. The data used in this study is publicly available.
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senseplatypus.com.
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opencv.org.
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References
Bedard, D., Lim, M.Y., Fowler, R., Porterfield, A.: Powermon: fine-grained and integrated power monitoring for commodity computer systems. In: IEEE SoutheastCon, pp. 479–484 (2010)
Bloisi, D., Pennisi, A., Iocchi, L.: Background modeling in the maritime domain. Mach. Vis. Appl. 25(5), 1257–1269 (2014)
Codiga, D.L.: A marine autonomous surface craft for long-duration, spatially explicit, multidisciplinary water column sampling in coastal and estuarine systems. J. Atmos. Ocean. Technol. 32(3), 627–641 (2015)
Dekkiche, D., Vincke, B., Merigot, A.: Investigation and performance analysis of OpenVX optimizations on computer vision applications. In: International Conference on Control, Automation, Robotics and Vision, pp. 1–6 (2016)
Dunbabin, M., Grinham, A.: Quantifying spatiotemporal greenhouse gas emissions using autonomous surface vehicles. J. Field Robot. 34(1), 151–169 (2017)
El-Gaaly, T., Tomaszewski, C., Valada, A., Velagapudi, P., Kannan, B., Scerri, P.: Visual obstacle avoidance for autonomous watercraft using smartphones. In: Autonomous Robots and Multirobot Systems workshop (2013)
Elliott, G.A., Yang, K., Anderson, J.H.: Supporting real-time computer vision workloads using OpenVX on Multicore+GPU platforms. In: Real-Time Systems Symposium, pp. 273–284 (2015)
Fefilatyev, S., Goldgof, D., Lembke, C.: Tracking ships from fast moving camera through image registration. In: International Conference on Pattern Recognition, pp. 3500–3503 (2010)
Ferri, G., Manzi, A., Fornai, F., Ciuchi, F., Laschi, C.: The HydroNet ASV, a small-sized autonomous catamaran for real-time monitoring of water quality: from design to missions at sea. IEEE J. Ocean. Eng. 40(3), 710–726 (2015)
Giordano, F., Mattei, G., Parente, C., Peluso, F., Santamaria, R.: Integrating sensors into a marine drone for bathymetric 3D surveys in shallow waters. Sensors 16(1) (2016)
Huntsberger, T., Aghazarian, H., Howard, A., Trotz, D.C.: Stereo vision-based navigation for autonomous surface vessels. J. Field Robot. 28(1), 3–18 (2011)
Kalwa, J., Carreiro-Silva, M., Tempera, F., Fontes, J., Santos, R.S., Fabri, M.C., Brignone, L., Ridao, P., Birk, A., Glotzbach, T., Caccia, M., Alves, J., Pascoal, A.: The morph concept and its application in marine research. In: MTS/IEEE OCEANS, pp. 1–8 (2013)
Pisa, S., Bernardi, P., Cavagnaro, M., Pittella, E., Piuzzi, E.: Monitoring of cardio-pulmonary activity with UWB radar: A circuital model. In: Asia-Pacific Symposium on Electromagnetic Compatibility and 19th Int. Zurich Symposium on Electromagnetic Compatibility, pp. 224–227 (2008)
Rankin, A., Matthies, L.: Daytime water detection based on color variation. In: International Conference on Intelligent Robots and Systems, pp. 215–221 (2010)
Santana, P., Mendona, R., Barata, J.: Water detection with segmentation guided dynamic texture recognition. In: International Conference on Robotics and Biomimetics, pp. 1836–1841 (2012)
Scerri, P., Kannan, B., Velagapudi, P., Macarthur, K., Stone, P., Taylor, M., Dolan, J., Farinelli, A., Chapman, A., Dias, B., Kantor, G.: Flood Disaster Mitigation: A Real-World Challenge Problem for Multi-agent Unmanned Surface Vehicles, pp. 252–269. Springer (2012)
Smith, B.M., Zhang, L., Jin, H., Agarwala, A.: Light field video stabilization. In: International Conference on Computer Vision, pp. 341–348 (2009)
Tagliavini, G., Haugou, G., Marongiu, A., Benini, L.: Adrenaline: an OpenVX environment to optimize embedded vision applications on many-core accelerators. In: International Symposium on Embedded Multicore/Many-core Systems-on-Chip, pp. 289–296 (2015)
Wang, H., Wei, Z., Wang, S., Ow, C.S., Ho, K.T., Feng, B.: A vision-based obstacle detection system for unmanned surface vehicle. In: International Conference on Robotics, Automation and Mechatronics, pp. 364–369 (2011)
Yang, K., Elliott, G.A., Anderson, J.H.: Analysis for supporting real-time computer vision workloads using OpenVX on Multicore+GPU platforms. In: International Conference on Real Time and Networks Systems, RTNS ’15, pp. 77–86 (2015)
Acknowledgements
This work is partially funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 689341.
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Aldegheri, S., Bloisi, D.D., Blum, J.J., Bombieri, N., Farinelli, A. (2018). Fast and Power-Efficient Embedded Software Implementation of Digital Image Stabilization for Low-Cost Autonomous Boats. In: Hutter, M., Siegwart, R. (eds) Field and Service Robotics. Springer Proceedings in Advanced Robotics, vol 5. Springer, Cham. https://doi.org/10.1007/978-3-319-67361-5_9
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DOI: https://doi.org/10.1007/978-3-319-67361-5_9
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