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’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.

This paper focuses on the experimental characterization of the polarization behavior of light backscattered through fog. A polarimetric orthogonal state contrast imager and an active, purely polarized white illuminator system are used to evaluate both linear and circular polarization signals. The experiments are carried out in a macro-scale fog chamber under controlled artificial fog conditions. We explore the effect of backscattering in each imaging channel, and the persistence of both polarization signals as a function of meteorological visibility. We confirm the presence of the polarization memory effect with circularly polarized light, and, as a consequence, the maintenance of helicity in backscattering. Moreover, the circular cross-polarized channel is found to be the imaging channel less affected by fog backscattering. These results are useful and should be taken into account when considering active polarimetric imaging techniques for outdoor applications under foggy conditions.

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.

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.

Fog, haze, or smoke are usual atmospheric phenomena that dramatically compromise the overall visibility of any scene, critically affecting features such as 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 them very relevant for decision-making for the security or autonomous vehicle industries. Several dehazing methods have been proposed, however, to the best of our knowledge, all proposed metrics compare the defogged image to its ground truth for evaluation of the defogging algorithms, or need to estimate parameters through physical models. This fact hinders progress in the field as obtaining proper ground truth images is costly and time-consuming, and physical parameters greatly depend on the scene conditions. This paper aims to tackle this issue by proposing a contour-based metric for image defogging evaluation that does not need a ground truth image. The proposed metric only requires the original hazy RGB image and the RGB image after the defogging procedure. A comparison of the proposed metric with metrics currently used in the NTIRE 2018 defogging challenge is performed to prove its effectiveness in a general situation, showing comparable results to conventional metrics.

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