Dr. Francesco Tufano Profile

Dr. Francesco Tufano

at Ctr Italiano Ricerche Aerospaziali

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Proceedings Article | 26 October 2022 Presentation + Paper

On the field geometrical characterization of illegal micro-dumps by means of RPAS survey with depth camera

Donato Amitrano, Luca Cicala, Mariana Poderico, Francesco Tufano

Proceedings Volume 12268, 1226803 (2022) https://doi.org/10.1117/12.2638503

KEYWORDS: 3D image reconstruction, UAV imaging systems, Inertial navigation systems, Cameras, Sensor fusion, Navigation systems, Motion measurement, Sensors

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Satellite remote sensing is key for large scale monitoring of urban and suburban areas with high revisit time and spatial resolution. However, in some applications involving targets characterized by limited extension and unknown spectral response and spatial distribution, data acquired from space could be insufficient. This is the case of micro-dumps mapping, which represent a highly diffuse environmental hazard in Southern Italy and, in general, in many suburban areas all around the world. Their intrinsic heterogeneity hampers the usage of descriptors based on the spectral response of the targets. The limited extension makes it imaged in few pixels, even using very high-resolution data. In such cases, the exploitation of proximal sensing becomes fundamental. This paper presents a new architecture for real-time characterization of illegal micro-dumps. It is composed, at hardware level, by a customized remotely piloted aerial platform equipped with an innovative payload able to deliver the necessary data for online three-dimensional processing. In particular, a depth camera coupled with a tracking sensor and the drone autopilot (connected with a GPS receiver) is exploited to retrieve a sparse point-cloud, which is geo-localized thanks to positioning information provided by a sensor fusion algorithm. The software component is designed to synchronize, align, segment and extract a volume estimate from the available point cloud with a real time processing. Experimental results show that the developed system is able to deliver accurate volume estimates compared to manual measurements and values obtained via classic offline structure from motion techniques.

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