Lopez Paredes, Alvaro
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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: Open Access , Efficient Very-wide-area Time-of-Flight 3D Imaging by means of Adaptive Compressive Sensing(2025)Since the birth of Science, understanding, capturing and systematically reproducing the reality that surrounds us has represented an aspiration as well as a technological challenge for Engineers and Researchers. In this sense, 3D imaging technologies have remarkably evolved over the past decades, boosted by the increase of computational capabilities, the development of more efficient algorithms and the reduction of hardware requirements. Nowadays, our homes, offices and streets are ubiquitously appareled by 3D imaging devices. Thanks to them, we are able to guarantee the secure access to our data by enabling face recognition systems, reduce to a minimum the need of human intervention in routine or unsafe tasks, improve the productivity of our businesses, ensure the safety of our travels with more accurate navigation systems, or reproduce realities that were previously inaccessible for us. In this respect, Time-of-Flight 3D imaging has become an attractive and continuous pole of attention for researchers, practitioners and commercial users over the past years. Time-of-Flight cameras are cheap, manageable in size and are characterized by a relatively low-power consumption. These imaging systems provide a dense 3D representation of the observed scene by estimating the return-trip time of an intensity-modulated light signal from the camera to the surrounding objects. Despite their tremendous potential, the use of Time-of-Flight cameras is not fully exploited as, in practice, the reachable angular range is restricted by the optical system, while the lateral resolution is bound by the number of pixels and artifacts may appear in unsteady environments. In this Thesis, we present a novel computational sensor to surpass such limitations, i. e., to uncouple the lateral resolution from the pixel count and the angular range from the field of view of the camera. Our approach relies on the introduction of a controlled rotation during the sensing of the scene alongside the implementation of Compressive Sensing techniques. We demonstrate that our scheme may achieve depth resolutions close to milimeter-range, whilst approaching real-time operation. On the sensing front, we develop a novel near-to-optimal construction methodology of the sensing functions, which implements a combinatorial approach plus an evaluation step to avoid the self-intersection of the coding curves. Moreover, we introduce an original metric to predict and, eventually, correct the error derived from conventional sensing schemes, such as the ones based on random (0,1)-binary matrices of Scrambled Hadamard Ensembles. With regards to the 3D scene reconstruction, we thoroughly describe a set of original Compressive Sensing-based recovery algorithms, by exploring several concepts such as spatial correlations, spatio-temporal super-resolution, and generalization of multiple-path depth retrieval to helicoidally-coded 3D sensing functions. Finally, we present a PB-ToF camera prototype as a proof of concept of our computational sensor with the ultimate objective of becoming a cost-effective and practical alternative to 360º LIDAR sensors in navigation and mapping systems. In conclusion, in this manuscript the reader will get a profound understanding of Compressive Sensing fundamentals and their practical application to a real Time-of-Flight 3D camera.Source Type:Doctoral Thesis26 102 - 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