Monocular Simultaneous Localization and Mapping (SLAM), Visual Odometry (VO), and Structure from Motion (SFM) are techniques that have emerged recently to address the problem of reconstructing objects or environments using monocular cameras. Monocular pure visual techniques have become attractive solutions for 3D reconstruction tasks due to their affordability, lightweight, easy deployment, good outdoor performance, and availability in most handheld devices without requiring additional input devices. In this work, we comprehensively overview the SLAM, VO, and SFM solutions for the 3D reconstruction problem that uses a monocular RGB camera as the only source of information to gather basic knowledge of this ill-posed problem and classify the existing techniques following a taxonomy. To achieve this goal, we extended the existing taxonomy to cover all the current classifications in the literature, comprising classic, machine learning, direct, indirect, dense, and sparse methods. We performed a detailed overview of 42 methods, considering 18 classic and 24 machine learning methods according to the ten categories defined in our extended taxonomy, comprehensively systematizing their algorithms and providing their basic formulations. Relevant information about each algorithm was summarized in nine criteria for classic methods and eleven criteria for machine learning methods to provide the reader with decision components to implement, select or design a 3D reconstruction system. Finally, an analysis of the temporal evolution of each category was performed, which determined that the classical-sparse-indirect and classical-dense-indirect categories have been the most accepted solutions to the monocular 3D reconstruction problem over the last 18 years.

Pure monocular 3D reconstruction is a complex problem that has attracted the research community’s interest due to the affordability and availability of RGB sensors. SLAM, VO, and SFM are disciplines formulated to solve the 3D reconstruction problem and estimate the camera’s ego-motion; so, many methods have been proposed. However, most of these methods have not been evaluated on large datasets and under various motion patterns, have not been tested under the same metrics, and most of them have not been evaluated following a taxonomy, making their comparison and selection difficult. In this research, we performed a comparison of ten publicly available SLAM and VO methods following a taxonomy, including one method for each category of the primary taxonomy, three machine-learning-based methods, and two updates of the best methods to identify the advantages and limitations of each category of the taxonomy and test whether the addition of machine learning or updates on those methods improved them significantly. Thus, we evaluated each algorithm using the TUM-Mono dataset and benchmark, and we performed an inferential statistical analysis to identify the significant differences through its metrics. The results determined that the sparse-direct methods significantly outperformed the rest of the taxonomy, and fusing them with machine learning techniques significantly enhanced the geometric-based methods’ performance from different perspectives.

This paper proposes a heuristic procedure created to improve the collection routes obtained with the support of the ArcGIS software and its complement, Network Analyst. After a series of experiments, it was found that the software presents several inconsistencies with logistical and operational management concepts, such as the unnecessary realization of U-turns in a dead end and unnecessary access to areas with difficult access to a single customer. These are issues that a collection route planner must consider to make a good decision that considers the cost of visiting areas with difficult access and the benefits of reaching that area. In this sense, our heuristic procedure considers a set of best practices in operational and logistical strategies that could be programmed within the Network Analyst. As it is well known in the science of vehicle routing, U-turns and sub-tours in the routes travelled by vehicles increase distances and operating costs, so in our work, we propose a systematic heuristic procedure to reduce the number of U-turns performed by a municipal waste collection truck, while using the ArcGIS-Network Analyst add-on to reduce the number of sub-tours in the route under the Directed-Capacitated Arc Routing Problem approach. It is then shown how the routes improved using our conceptual heuristic procedure, which provides better quality than the original routes obtained with ArcGIS and Network Analyst. Specifically, reducing the total distances travelled by the vehicle fleet, increasing the coverage of sidewalks visited by the truck in the urban perimeter of a city, and minimizing the time used in municipal solid waste collection operations. The importance of our work lies in the fact that effective MSW management is an essential municipal service whose regulation can drive innovation, sustainability, and efficiency in the MSW sector.

This work consists of a systematised review examining recent models for agri-food supply chain management (AFSCM), focusing on artificial intelligence (AI) applications and sustainability integration. Through PRISMA methodology, we conducted a descriptive statistical analysis of 183 articles published between 2019 and 2023. The dimensional analysis revealed that strategic modelling represented 55% of approaches, while perishable products were considered in 58% of studies. Sustainability principles were integrated into 74% of the models, while AI applications showed a limited 14% adoption across AFSCM. The analysis of AI implementations showed distribution in agricultural production management at 27%, transportation and logistics at 8%, and sustainability planning at 19%. A novel AFSCM taxonomy was developed through systematic classification, combining quantitative bibliometric analysis, qualitative thematic analysis and expert interpretation. The results identified gaps in mixed methods approaches and tactical or operational modelling, providing a structured framework for future research directions in AI integration and sustainable practices across agri-food supply chains.

This study explored the incidence of metaphorical virtual classrooms and interactive learning objects in the interaction of students in online mode. The main objective was to analyze how these digital tools, driven by a set of strategies to promote their use, affect the interaction of students in the virtual classroom system and their derived effects. To this end, the latest version of Moodle was implemented in conjunction with gamification plugins and interactive tools in the higher education institution used as a case study. The methodology consisted of data collection through ordinal instruments applied to the teachers and student performance metrics gathered using a plugin developed to extract accurate metrics of each student’s usage and performance through direct queries to the Moodle database and its processing through a neural network. This facilitated the collection of standardized data on the actual metrics of each virtual classroom at the end of the teaching of each subject from both the previous LMS and the newly implemented one. This data was then analyzed using advanced statistical techniques, including Mahalanobis distances, confirmatory factor analysis, and the Wilcoxon signed-rank test. These methods provided a compelling comparison between the old and new systems, revealing significant improvements in the metrics and factors evaluated. The results showed a significant improvement in teachers’ perceptions of the usability of the virtual classroom system and an increase in students’ academic performance, interaction, progress, and time spent learning in virtual contexts. These results provide solid empirical evidence of the added value of these educational tools as effective strategies for improving student interaction, performance, and motivation in online education.