@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 {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} } @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} }