Heredia Conde, Miguel
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Publication Access status: Open Access , ATHENA Research BookThe ATHENA European University is an alliance of nine Higher Education Institutions with the mission of fostering excellence in research and innovation by facilitating international cooperation. The ATHENA acronym stands for Advanced Technologies in Higher Education Alliance. The partner institutions are from France, Germany, Greece, Italy, Lithuania, Portugal, and Slovenia: the University of Orléans, the University of Siegen, the Hellenic Mediterranean University, the Niccolò Cusano University, the Vilnius Gediminas Technical University, the Polytechnic Institute of Porto, and the University of Maribor. In 2022 institutions from Poland and Spain joined the alliance: the Maria Curie-Skłodowska University and the University of Vigo. This research book presents a selection of the ATHENA university partners' research activities. It incorporates peer-reviewed original articles, reprints and student contributions. The ATHENA Research Book provides a platform that promotes joint and interdisciplinary research projects of both advanced and early-career researchers.Source Type:Book1 404 - Some of the metrics are blocked by yourconsent settings
Publication Access status: Metadata only , Performance Evaluation of State-of-the-Art High-Resolution Time-of-Flight CamerasIt has been about 20 years since the disruptive appearance of the first Time-of-Flight (ToF) cameras. Since then, ToF imaging has progressively evolved. Nowadays, ToF sensors have broken the barrier of the 1 MPix resolution and a significant number of high-resolution ToF cameras has appeared in the market. In order to provide a better understanding of their performance and applications, we experimentally evaluate three state-of-the-art high-resolution ToF cameras such as Azure Kinect, Helios2, and S100D, together with the solid-state LiDAR L515. We perform various experiments to examine some key parameters, such as warm-up times, accuracy, precision, lateral and axial resolutions, edge noise, unsteady scenes, and modulated waveform and optical power. Our evaluation draws various conclusions: S100D shows fluctuations within 1 mm after being powered up while the others require warm-up times. Azure Kinect, Helios2, and L515 can achieve precision within 2 mm in a measuring range of 0.5 m to 3 m. Helios2 and S100D are more severely affected by dynamic scenes. Finally, the point clouds generated for a white panel at a distance of 1.5 m show that flying pixels are present in all cameras, being this problem less acute for the L515.Source Type:Article5 - Some of the metrics are blocked by yourconsent settings
Publication Access status: Metadata only , Sparsity-Aware 3-D ToF Sensing<p>In this work, we propose several time-of-flight (ToF) sensing schemes which tackle the challenge of covering very-wide areas and long ranges in nearly real time, with relatively simple implementation and low associated computational load. We thoroughly describe two methodologies for the resolution of the inverse problem. First, we extend a greedy algorithm, such as orthogonal matching pursuit (OMP), by considering an initial refinement of the spatial domain in which the signal recovery is performed. Then, we propose various nonadaptive techniques for the construction of the sensing matrices, relying on the optimization of coherence and density. We further develop them by including an additional verification step which accounts for the non-instantaneous transitions from one element to another of the code and avoids any possible coincidence between rising and falling edges which may degrade the coherence. We also investigate the upper super-resolution limit, the over-sampling rate which yields unitary coherence, by considering the instrument response function (IRF) of the ToF sensor. Second, we expand an adaptive sensing scheme in which the rows of the sensing matrix are generated accounting for the information from previous measurements, such as adaptive progressive edge growth algorithm(APEG), by considering several groups of signals during the adaptation of the sensing matrices. The signals are then individually recovered for each pixel via OMP over the identified joint signal support. We validate the proposed methodologies by numerical simulations over datasets from stereo and ToF cameras.</p>Source Type:Article2
