Environmental Sensing Using Land-Based Spectrally-Selective Cameras and a Quadcopter
We investigate the reconstruction of an environmental scalar field using robotic mobility and heterogeneous sensing. Using two land-based, immobile, co-located spectrally selective cameras, and a non-contact infraredbased temperature sensor on a quadcopter, we study the problem of reconstructing the surface temperature of the ground under survey. Both land units — a thermographic camera for low-resolution thermal images and a commercial digital camera for high resolution truecolor images — are mounted on an elevated camera rig. We explore methods for field reconstruction using a combination of the three imaging sensors. First, we show that the quadcopter data is correlated with the synoptic snapshots obtained by the thermal imaging camera. Next, we demonstrate upsampling of the low-resolution thermal camera data with truecolor images. This results in high-resolution reconstruction of the temperature field. Finally, we discuss adaptive sampling techniques that utilize the mobility of the quadcopter to ‘fill the gaps’ in data acquired by the thermal imaging camera. Our work experimentally demonstrates the feasibility of heterogeneous sensing and mobility to effectively reconstruct environmental fields.
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- 1.Nadeau, D., Parlange, M.: Atmospheric Boundary Layer Dynamics of Transitional Flows over Complex Terrain. Ph.D. dissertation, EPFL (2011)Google Scholar
- 2.Bryson, M., Reid, A., Hung, C., Ramos, F., Sukkarieh, S.: Cost-Effective Mapping Using Unmanned Aerial Vehicles in Ecology Monitoring Applications. In: Khatib, O., Kumar, V., Sukhatme, G. (eds.) Experimental Robotics. STAR, vol. 79, pp. 509–523. Springer, Heidelberg (2012)Google Scholar
- 4.Zhang, B., Sukhatme, G.S.: Adaptive Sampling for Estimating a Scalar Field using a Robotic Boat and a Sensor Network. In: IEEE International Conference on Robotics and Automation, pp. 3673–3680 (2007)Google Scholar
- 5.Binney, J., Krause, A., Sukhatme, G.S.: Informative Path Planning for an Autonomous Underwater Vehicle. In: IEEE International Conference on Robotics and Automation, pp. 4791–4796 (2010)Google Scholar
- 7.Rasmussen, C.E.: Gaussian Processes for Machine Learning. MIT Press (2006)Google Scholar