Information Fusion Research

Abstract This paper presents the development and evaluation of an Indoor Navigation Application leveraging Augmented Reality (AR) technology to provide accurate and intuitive navigation within complex indoor environments. The primary objective is to enhance user experience by integrating ARCore, NavMesh, and the A* algorithm to ensure precise localization and efficient pathfinding. The methodology involves creating a comprehensive 3D model of the building using Blender, with QR codes strategically placed throughout the structure to facilitate accurate user localization. Upon scanning a QR code, the system activates the user's camera and employs ARCore for simultaneous localization and mapping (SLAM), aligning the user's position with the 3D model. Real-time navigation is then provided through AR overlays, guiding users along the optimal path determined by the A* algorithm. The evaluation metrics focus on accuracy, usability, efficiency, reliability, and scalability. The system demonstrates high accuracy in positioning, minimal latency in pathfinding, and consistent performance across diverse indoor environments. User feedback indicates a high level of usability, with intuitive interactions and clear visual cues. The modular design of the system ensures scalability and adaptability to various building configurations. In conclusion, the AR indoor navigation system offers a robust solution for indoor navigation, with potential applications in commercial complexes, educational institutions, and healthcare facilities. Future work will focus on enhancing accuracy, optimizing user experience, and integrating advanced AR features and IoT devices.

Abstract This paper provides a review of the literature on introductory programming training in universities and the use of generative artificial intelligence software (GAIS) for these purposes. During introductory programming training in universities, the tasks of developing five skills are highlighted: abstraction (determining what information about an entity/object needs to be preserved and what to ignore); generalization (formulating a solution in general terms so that it can be applied to solve various problems); decomposition (breaking complex problems into smaller subtasks that are easier to understand and solve); algorithmization (determining step by step a set of operations/actions in order to solve a problem); debugging (localization and elimination of errors). There are also many programming languages used in introductory education at universities, such as Pascal, C , Python, C#, Java. A review of the literature on the use of GAIS in teaching of programming at universities indicated the following main problems and tasks: to prevent the use of GAIS to obtain undeserved grades in the subject; develop techniques and examples of using GAIS in the educational process to personalize, increase intensity and quality; explore new areas of using GAIS, for example, developing skills in using GAIS to solve real production problems (GAIS operator). A description is given of the author's experience of using GAIS for introductory programming training for first-year students of the Faculty of Mathematics and Programming Technologies of Gomel State University named after F. Skorina. The main futures of authors approach are the following: automatic personalized training, low entry threshold, multiple entry points, personalization, intensification, freedom to choose a program development language (in current practice between five programming languages: Pascal, Python, Java, C#, C . If the need arises, new languages can be added).