Giancarlo Rufino

TL;DR: A review of state-of-the-art techniques and algorithms developed in the last decades for cooperative and uncooperative pose determination by processing data provided by electro-optical sensors is presented.

TL;DR: In this article, an analytical study of the error introduced by the centroiding algorithm is presented and it is shown that a systematic contribution and a random one exist, which result from image processing assumptions and photometric measure uncertainty, respectively.

TL;DR: A three dimensional approach is pursued in which the point cloud generated by a LIDAR is exploited for pose estimation, and results demonstrate algorithm capability of operating with sparse point clouds and large pose variations, while achieving sub-degree and sub-centimeter accuracy in relative attitude and position, respectively.

TL;DR: This paper presents the performance assessment of innovative model-based algorithms developed for pose estimation of uncooperative targets by processing sparse three-dimensional point clouds and demonstrates algorithms' effectiveness over a wide range of relative pose conditions and dealing with targets of variable size and shape.

TL;DR: An application of the European Remote Sensing satellites' radar data to digital elevation model (DEM) generation and reports show that only tandem pairs allow an efficient interferometric processing to be performed, thanks to their short-time baseline, whereas correlation adequate for differential interferometry could not be achieved.