@conference {cLazaro24, title = {6G-EWOC: Crowdsourced SLAM Data Fusion for Safe and Efficient ADAS Driving}, booktitle = {IEEE Future Networks World Forum: Symposium on Future Networks for Connected and Automated Mobility }, year = {2024}, month = {10/2024}, publisher = {IEEE}, organization = {IEEE}, address = {Dubai}, abstract = {

The development of transport infrastructures for Advanced Driver Assistance Systems (ADAS) and autonomous vehicles operating efficiently and safely in congestion-free traffic flows is a major challenge for telecommunications technologies.\  Simultaneous Localization and Mapping (SLAM) plays a crucial role in ensuring uninterrupted journeys for emergency vehicles and increasing the safety of vulnerable road users in complex traffic scenarios. Accurate SLAM mapping for ADAS systems requires data from different sensor technologies {\textendash}such as high-resolution cameras or Radio/Light Detection and Ranging (RaDAR/LiDAR){\textendash} to be effectively combined or fused. Sensor fusion results in high data throughput and low latency requirements. However, optimal mapping outcomes occur when processing systems fuse data from sensors positioned at different locations within the traffic scene. By crowdsourcing diverse sensors, we can multiply the view angles, mitigate occlusions and improve the overall scene coverage. Yet, this approach introduces additional challenges for communication systems within both the vehicles and the infrastructure. Addressing these challenges is essential for seamless development of safe and efficient ADAS driving techniques.

}, doi = {10.1109/FNWF63303.2024.11028711}, url = {https://ieeexplore.ieee.org/document/11028711}, author = {Jos{\'e} Antonio L{\'a}zaro and Casas, J. and Ruiz-Hidalgo, J. and Cortada-Garcia, M. and Mart{\'\i}n-Pey, G. and Salavedra-Pujol, J. and Wang, M. and Theodoropoulou, E. and Lyberopoulos, G. and Pablo Garc{\'\i}a-G{\'o}mez and S. Royo and Jordi Riu and Marcus, C. and Eriksson, O. and Schrenk, B. and F{\`a}brega, J.M.} } @conference {cSchrenk24, title = {6G-EWOC: Optical Wireless Communication in Support of Autonomous Driving}, booktitle = {IEEE Future Networks World Forum: Symposium on Future Networks for Connected and Automated Mobility }, year = {2024}, month = {10/2024}, publisher = {IEEE}, organization = {IEEE}, address = {Dubai}, abstract = {

The road towards an efficient transport infrastructure, which safely operates fully autonomous vehicles in congestion-free traffic flows, is subject to several technological challenges that reach also into the realm of telecommunications. Future 6G networks are required to handle huge amounts of sensor information, both in terms of low-latency communication and real-time data processing. The 6G-EWOC project contributes to this challenge through a concerted use of optical wireless communication and sensing with radio-frequency based cellular technology. Three optical-wireless scenarios are discussed, including (i) instant data exchange between vehicles leveraging their lighting assets for low-latency communication, (ii) the provision of vehicular data offloading to a fiber-based 6G infrastructure through beamsteering-assisted optical remote radio heads to enable data fusion at the edge cloud, and (iii) the seamless wireless extension of fiber-grade capacity to isolated network segments situated in fiber-scarce brownfields, which is accomplished through transparent optical air interfaces that ensure fiber-grade continuity by preventing a translucent electro-optic termination of the single-mode fiber channel. Another important contribution aims to advance light- and radio-based sensing technologies towards networked versions that offer simultaneous communication and detecting capabilities.

}, doi = {10.1109/FNWF63303.2024.11028710}, url = {https://ieeexplore.ieee.org/document/11028710}, author = {Schrenk, B. and Theodoropoulou, E. and Lyberopoulos, G. and Marcus, C. and Eriksson, O. and Jos{\'e} Antonio L{\'a}zaro and Casas, J. and Federico Dios and Pablo Garc{\'\i}a-G{\'o}mez and S. Royo and Jordi Riu and F{\`a}brega, J.M.} } @conference {cTheodoropoulou24, title = {Towards AI-Enhanced Fibre-Wireless Optical 6G Network in Support of Connected Mobility}, booktitle = {IEEE 29th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD)}, year = {2024}, month = {10/2024}, publisher = {IEEE}, organization = {IEEE}, address = {Athens}, abstract = {

The 6G-EWOC project seeks to create an AI-enhanced fiber-wireless optical 6G network to support advanced use cases for Fully Autonomous Vehicles (FAVs) by establishing a new access network incorporating the integration of both fixed/wireless optical and wireless/RF technologies. The access network will be supported by a fast, reconfigurable, highly dynamic and customizable optical fiber-based fronthaul infrastructure which will minimize optoelectronic transitions through tunable and programmable devices, as well as low energy photonic switching of (packet/optical) spectrum and spatial resources, managed by AI-enabled SDN. End-to-end connectivity between AI-based edge computation units will enable advanced Connected and Automated Mobility (CAM) services over a rapid, adaptable network architecture.

}, doi = {10.1109/CAMAD62243.2024.10942755}, url = {https://ieeexplore.ieee.org/document/10942755}, author = {Theodoropoulou, E. and Lyberopoulos, G. and Setaki, Foteini and Pablo Garc{\'\i}a-G{\'o}mez and S. Royo and Marcus, C. and Eriksson, O. and F{\`a}brega, J.M. and Ricardo Mart{\'\i}nez and Ramon Casellas and Schrenk, B. and Casas, J. and Jos{\'e} Antonio L{\'a}zaro} } @article {ade-Mas-Gimenez23, title = {Gradient-Based Metrics for the Evaluation of Image Defogging}, journal = {World Electric Vehicle Journal}, volume = {14}, year = {2023}, month = {09/2023}, chapter = {254}, abstract = {

Fog, haze, or smoke are standard atmospheric phenomena that dramatically compromise the overall visibility of any scene, critically affecting features such as the illumination, contrast, and contour detection of objects. The decrease in visibility compromises the performance of computer vision algorithms such as pattern recognition and segmentation, some of which are very relevant to decision-making in the field of autonomous vehicles. Several dehazing methods have been proposed that either need to estimate fog parameters through physical models or are statistically based. But physical parameters greatly depend on the scene conditions, and statistically based methods require large datasets of natural foggy images together with the original images without fog, i.e., the ground truth, for evaluation. Obtaining proper fog-less ground truth images for pixel-to-pixel evaluation is costly and time-consuming, and this fact hinders progress in the field. This paper aims to tackle this issue by proposing gradient-based metrics for image defogging evaluation that do not require a ground truth image without fog or a physical model. A comparison of the proposed metrics with metrics already used in the NTIRE 2018 defogging challenge as well as several state-of-the-art defogging evaluation metrics is performed to prove its effectiveness in a general situation, showing comparable results to conventional metrics and an improvement in the no-reference scene. A Matlab implementation of the proposed metrics has been developed and it is open-sourced in a public GitHub repository.

}, doi = {10.3390/wevj14090254}, url = {https://www.mdpi.com/2032-6653/14/9/254}, author = {Gerard de-Mas-Gim{\'e}nez and Pablo Garc{\'\i}a-G{\'o}mez and Casas, J. and S. Royo} } @article {aPlasencia23, title = {A Preliminary Study of Deep Learning Sensor Fusion for Pedestrian Detection}, journal = {Sensors}, volume = {23}, year = {2023}, month = {04/2023}, chapter = {4167}, abstract = {

Most pedestrian detection methods focus on bounding boxes based on fusing RGB with lidar. These methods do not relate to how the human eye perceives objects in the real world. Furthermore, lidar and vision can have difficulty detecting pedestrians in scattered environments, and radar can be used to overcome this problem. Therefore, the motivation of this work is to explore, as a preliminary step, the feasibility of fusing lidar, radar, and RGB for pedestrian detection that potentially can be used for autonomous driving that uses a fully connected convolutional neural network architecture for multimodal sensors. The core of the network is based on SegNet, a pixel-wise semantic segmentation network. In this context, lidar and radar were incorporated by transforming them from 3D pointclouds into 2D gray images with 16-bit depths, and RGB images were incorporated with three channels. The proposed architecture uses a single SegNet for each sensor reading, and the outputs are then applied to a fully connected neural network to fuse the three modalities of sensors. Afterwards, an up-sampling network is applied to recover the fused data. Additionally, a custom dataset of 60 images was proposed for training the architecture, with an additional 10 for evaluation and 10 for testing, giving a total of 80 images. The experiment results show a training mean pixel accuracy of 99.7\% and a training mean intersection over union of 99.5\%. Also, the testing mean of the IoU was 94.4\%, and the testing pixel accuracy was 96.2\%. These metric results have successfully demonstrated the effectiveness of using semantic segmentation for pedestrian detection under the modalities of three sensors. Despite some overfitting in the model during experimentation, it performed well in detecting people in test mode. Therefore, it is worth emphasizing that the focus of this work is to show that this method is feasible to be used, as it works regardless of the size of the dataset. Also, a bigger dataset would be necessary to achieve a more appropiate training. This method gives the advantage of detecting pedestrians as the human eye does, thereby resulting in less ambiguity. Additionally, this work has also proposed an extrinsic calibration matrix method for sensor alignment between radar and lidar based on singular value decomposition.

}, keywords = {autonomous driving, Convolutional Neural Networks, sensor calibration, sensor fusion}, doi = {10.3390/s23084167}, url = {https://www.mdpi.com/1424-8220/23/8/4167}, author = {Alfredo Ch{\'a}vez Plasencia and Pablo Garc{\'\i}a-G{\'o}mez and Eduardo Bernal P{\'e}rez and Gerard de-Mas-Gim{\'e}nez and Casas, J. and S. Royo} } @conference {cde-Mas-Gimenez23, title = {Single image defogging and evaluation with deep neural networks without the need of ground truth}, booktitle = {SPIE Automated Visual Inspection and Machine Vision V}, year = {2023}, month = {05/2023}, publisher = {Proc. SPIE 12623}, organization = {Proc. SPIE 12623}, address = {Munich}, abstract = {

Fog dramatically compromises the overall visibility of any scene, critically affecting features such as objects{\textquoteright} illumination, contrast, and contours. The decrease in visibility compromises the performance of Computer Vision algorithms such as pattern recognition and segmentation, some of them very relevant to decision-making in the rise of autonomous-driven vehicles. Many dehazing methods have been proposed. However, to the best of our knowledge, all currently used metrics do compare the defogged image to its ground truth, usually the same scene on a non-foggy day, or estimate physical parameters from the scene. This hinders progress in the field, as obtaining proper ground truth images is not always possible and becomes costly and time-consuming because physical parameters greatly depend on the scene conditions. This work aims to tackle this issue by proposing a real-time operating defogging network that only takes an RGB image of the fogged scene as input, performs the defogging, and uses a contour-based metric for Single Image Defogging evaluation even when the ground truth is not available, which is the most common situation. The proposed metric only requires the original hazy image and the image after the defogging procedure. We trained our network using a novel two-stage pipeline with the DENSE dataset and compared our method and metric with currently used metrics and other defogging techniques with the NTIRE 2018 defogging challenge to prove their effectiveness.

}, doi = {10.1117/12.2673442}, author = {Gerard de-Mas-Gim{\'e}nez and Pablo Garc{\'\i}a-G{\'o}mez and Casas, J. and S. Royo} } @conference {cGarcia-Gomez22, title = {Multimodal imaging System based on s{\`o}lid-State LiDAR for Advanced perception applications}, booktitle = {10th International Symposium on Optronics in defence \& security}, year = {2022}, month = {06/2022}, publisher = {3AF OPTRO2022}, organization = {3AF OPTRO2022}, address = {Versailles, France}, url = {https://www.3af-optro.com/}, author = {Pablo Garc{\'\i}a-G{\'o}mez and S. Royo and Noel Rodrigo and Casas, J. and Jordi Riu} } @phdthesis {dGarcia-Gomez21, title = {Development of a multimodal imaging system based on LIDAR}, year = {2021}, school = {Universitat Polit{\`e}cnica de Catalunya}, type = {phd}, abstract = {

Perception of the environment is an essential requirement for the fields of autonomous vehicles and robotics, that claim for high amounts of data to make reliable decisions. With this aim, many different sensors are often combined in a process known as data fusion for obtaining the largest possible amount of information from the environment, even if redundant. In this context, Light Detection And Ranging (LiDAR) imaging sensors have become crucial sensors for such applications due to their 3D geometry sensing capability that overtake Radio Detection And Ranging (RADAR) devices in terms of spatial resolution. This thesis aims to develop a unique data fusion device consisting of different and complementary imaging modes with a 3D solid-state LiDAR sensor as its core sensor. We aim to reduce the usual parallax error committed during the fusion of sensors situated along large enclosures and to provide a feasible multimodal imaging device easy to integrate with the approach of mechanically reducing the multi-sensor framework inside a single device. Consequently, this thesis reviews and discusses the current state of the sensors{\textquoteright} technology and data fusion. In particular, it focuses on reviewing the different LiDAR techniques and the most used camera model in Computer Vision (CV). Based on that, it provides a discussion about the imaging modes of interest in the perception unit, which includes a pulsed Micro-Electrical-Mechanical System (MEMS) mirror solid-state LiDAR as its principal sensor. Then, this thesis analytically describes the particular MEMS LiDAR system using Snell{\textquoteright}s law. Furthermore, it provides a general model for the imaging system that may be used on any other LiDAR for predicting and correcting the variable angular resolution across the Field-Of-View (FOV) that distorts the Point Cloud. Analogously, it mathematically derives the necessary transformations for data fusion between sensor pairs. Accordingly, this thesis proposes a novel calibration method for LiDAR and fusion systems. It presents the methodology and the materials, that include a multimodal calibration pattern and an automatic guide system, necessary for accurately and automatically calibrating all the sensors included in the unit. The novelty of using the LiDAR depth information for better estimating the location of the target have yielded the calibration to improve the up-to-date calibration methods in the literature. Finally, this thesis presents the development of a multimodal perception unit based on the above which is aimed to become a TRL8 product, together with some of its potential applications in the field of CV.

}, doi = {10.5821/dissertation-2117-420270}, url = {http://hdl.handle.net/2117/420270}, author = {Pablo Garc{\'\i}a-G{\'o}mez}, editor = {S. Royo and Casas, J.} } @conference {cGarcia-Gomez21, title = {Multimodal solid-state LiDAR for advanced perception applications}, booktitle = {OPTOEL}, year = {2021}, month = {06/2021}, abstract = {

Perception of the environment is an essential requirement for the fields of autonomous vehicles and robotics. Consequently, LiDAR imaging sensors have become crucial sen-sors for such applications due to their 3D geometry sensing capability. However, auton-omous systems claim for high amounts of data to make reliable decisions so many dif-ferent sensors are often combined. In this context, we present a multimodal imaging sys-tem based on a solid-state LiDAR combined with three other imaging sensors that pro-vides multimodal information with low parallax fusion error.

}, keywords = {artificial intelligence, autonomous navigation, computer Vision, enhanced perception, robotics, sensor fusion, solid-state LiDAR}, author = {Pablo Garc{\'\i}a-G{\'o}mez and Noel Rodrigo and Jordi Riu and Casas, J. and S. Royo} } @article {aGarcia-Gomez20, title = {Geometric Model and Calibration Method for a Solid-State LiDAR}, journal = {Sensors}, volume = {20}, year = {2020}, month = {05/2020}, pages = {2898}, abstract = {

This paper presents a novel calibration method for solid-state LiDAR devices based on a geometrical description of their scanning system, which has variable angular resolution. Determining this distortion across the entire Field-of-View of the system yields accurate and precise measurements which enable it to be combined with other sensors. On the one hand, the geometrical model is formulated using the well-known Snell{\textquoteright}s law and the intrinsic optical assembly of the system, whereas on the other hand the proposed method describes the scanned scenario with an intuitive camera-like approach relating pixel locations with scanning directions. Simulations and experimental results show that the model fits with real devices and the calibration procedure accurately maps their variant resolution so undistorted representations of the observed scenario can be provided. Thus, the calibration method proposed during this work is applicable and valid for existing scanning systems improving their precision and accuracy in an order of magnitude.

}, keywords = {solid-state LiDAR; LiDAR calibration; distortion correction; FOV mapping}, issn = {1424-8220}, doi = {10.3390/s20102898}, url = {https://www.mdpi.com/1424-8220/20/10/2898}, author = {Pablo Garc{\'\i}a-G{\'o}mez and S. Royo and Noel Rodrigo and Casas, J.} } @conference {cGarcia-Gomez19, title = {Self-registered lidar and polarimetric images in real-time: application to detection of small objects at sea}, booktitle = {7th Workshop on Active Imaging}, year = {2019}, month = {11/2019}, publisher = {French-German Research Institute of Saint-Louis (ISL)}, organization = {French-German Research Institute of Saint-Louis (ISL)}, address = {Saint-Louis, France}, author = {Pablo Garc{\'\i}a-G{\'o}mez and Jordi Riu and Casas, J. and S. Royo} } @conference {cCaminal18, title = {SLAM-based 3D outdoor reconstructions from LIDAR data}, booktitle = {IC3D}, year = {2018}, month = {12/2018}, publisher = {IEEE}, organization = {IEEE}, address = {Brussels, Belgium}, abstract = {

The use of depth (RGBD) cameras to reconstruct large outdoor environments is not feasible due to lighting conditions and low depth range. LIDAR sensors can be used instead. Most state of the art SLAM methods are devoted to indoor environments and depth (RGBD) cameras. We have adapted two SLAM systems to work with LIDAR data. Quantitative evaluations have been performed with LIDAR and RGBD data allowing to compare the systems. Results show that the best method with LIDAR is RTAB-Map with clear difference. Additionally, RTAB-Map has been used to create 3D reconstructions with and without the use of a visible color camera. This proves the potential of Lidar sensors for the reconstructionof outdoor environments for immersion or audiovisual production applications.

}, keywords = {3D imaging, Lidar cameras, mapping, point-cloud processing, SLAM, time-of-flight}, url = {http://www.stereopsia.com/international-conference-3d-immersion-ic3d}, author = {I. Caminal and Casas, J. and S. Royo} }