This paper presents CrossPlace, an innovative method for cross-modal place recognition between heterogeneous sensor modalities, particularly between fisheye cameras and LiDAR. Place recognition is the fundamental capability of mobile robots to determine their most likely location within a database, based on sensory input queries. In cross-modal place recognition, the goal is to localize using a different sensor from the one originally used to construct the database. The core contribution of this paper is a unified feature space that integrates intensity, depth and semantic information. Both the database entries and the queries are obtained by embedding sensor readings through the same CrossPlace model, ensuring a consistent representation across modalities. Consequently, a database constructed from LiDAR can be queried with fisheye images, and vice versa, using a single shared architecture. Furthermore, a comprehensive data transformation and preprocessing pipeline is presented. Specifically, CrossPlace is constituted by three independent branches, each one for processing intensity, depth and semantic information. Each branch consists of a CosPlace model for image embedding with shared weights across sensor modalities. Late fusion through concatenation of the intensity, depth and semantic embeddings provides optimal global performance. We conduct an exhaustive evaluation on the KITTI-360 dataset, where CrossPlace surpasses state-of-the-art techniques across all metrics, establishing a new standard for cross-modal place recognition in urban and highway environments. The results demonstrate the effectiveness of our unified approach for place recognition across different sensor modalities while maintaining a robust performance under various operating environments.

Photovoltaic (PV) energy plays a key role in addressing the growing global energy demand. Organic solar cells (OSCs) represent a promising alternative to silicon-based PVs due to their low cost, lightweight, and sustainable production. Despite achieving power conversion efficiencies (PCEs) over 20 %, OSCs still face challenges in stability and efficiency. Recent advances in manufacturing, artificial intelligence and machine learning (ML) achieve optimized and screened OSCs for greater sustainability and commercial viability, thus potentially reducing costs while ensuring stable and long term performance. This work presents optimal ML models to represent the temporal degradation on the PCE of polymeric OSCs with structure ITO/PEDOT:PSS/P3HT:PCBM/Al. First, we generated a database with 166 entries with measurements of 5 OSCs, and up to 7 variables regarding the manufacturing and environmental conditions for more than 180 days. Then, we relied on a software framework that provides a conglomeration of automated ML protocols that execute sequentially against our database by simply command-line interface. This easily permits hyper-optimizing the ML models through exhaustive benchmarking so that optimal models are obtained. The accuracy for predicting PCE over time reaches values of the coefficient determination widely exceeding 0.90, whereas the root mean squared error, sum of squared error, and mean absolute error are significantly low. Additionally, we assessed the predictive ability of the models using an unseen OSC as an external set. For comparative purposes, classical Bayesian regression fitting are also presented, which only perform sufficiently for univariate cases of single OSCs.

An optimal solution to the localization problem is essential for developing autonomous robotic systems. Apart from autonomous vehicles, precision agriculture is one of the fields that can benefit most from these systems. Although LiDAR Place Recognition (LPR) is a widely used technique in recent years to achieve accurate localization, it is mostly used in urban settings. However, the lack of distinctive features and the unstructured nature of agricultural environments make place recognition challenging. This work presents a comprehensive review of state-of-the-art deep learning applications for agricultural environments and LPR techniques. We focus on the challenges that arise in these environments. We analyze the existing approaches, datasets, and metrics used to evaluate LPR system performance and discuss the limitations and future directions of research in this field. This is the first survey that focuses on LiDAR-based localization in agricultural settings, with the aim of providing a thorough understanding and fostering further research in this specialized domain.

Abstract Full spherical views provide advantages in many applications that use visual information. Dual back-to-back fisheye cameras are receiving much attention to obtain this type of view. However, obtaining a high-quality full spherical view is very challenging. In this paper, we propose a correction step that models the relation between the pixels of the pair of fisheye images in polar coordinates. This correction is implemented during the mapping from the unit sphere to the fisheye image using the equidistant fisheye projection. The objective is that the projections of the same point in the pair of images have the same position on the unit sphere after the correction. In this way, they will also have the same position on the equirectangular coordinate system. Consequently, the discontinuity between the spherical views for blending is minimized. Throughout the manuscript, we show that the angular polar coordinates of the same scene point in the fisheye images are related by a sine function and the radial distance coordinates by a linear function. Also, we propose employing a polynomial as a geometric transformation between the pair of spherical views during the image alignment since the relationship between the matching points of pairs of spherical views is not linear, especially in the top/bottom regions. Quantitative evaluations demonstrate that using the correction step improves the quality of the full spherical view, i.e. IQ MS-SSIM, up to 7%. Similarly, using a polynomial improves the IQ MS-SSIM up to 6.29% with respect to using an affine matrix.

Feature matching is a key technique for a wide variety of computer vision and image processing applications such as visual localization. It permits finding correspondences of significant points within the environment that eventually determine the localization of a mobile agent. In this context, this work evaluates an Adaptive Probability-Oriented Feature Matching (APOFM) method that dynamically models the visual knowledge of the environment in terms of the probability of existence of features. Several improvements are proposed to achieve a more robust matching in a visual odometry framework: a study on the classification of the matching candidates, enhanced by a nearest neighbour search policy; a dynamic weighted matching that exploits the probability of feature existence in order to tune the matching thresholds; and an automatic false positive detector. Additionally, a comparison of performance is carried out, considering a publicly available dataset composed of two kinds of wide field-of-view images: catadioptric and fisheye. Overall, the results validate the appropriateness of these contributions, which outperform other well-recognized implementations within this framework, such as the standard visual odometry, a visual odometry method based on RANSAC, as well as the basic APOFM. The analysis shows that fisheye images provide more visual information of the scene, with more feature candidates. Contrarily, omnidirectional images produce fewer feature candidates, but with higher ratios of feature acceptance. Finally, it is concluded that improved precision is obtained when the location problem is solved by this method.