Institute of Advanced Materials and Sustainable Manufacturing, Tecnologico de Monterrey, Mexico City 14380, Mexico.
Centro de Innovación y Desarrollo Tecnológico en Cómputo, Instituto Politécnico Nacional, Unidad Profesional Adolfo López Mateos, Juan de Dios Bátiz s/n esq. Miguel Othón de Mendizábal, Mexico City 07700, Mexico.
Sensors (Basel). 2022 Dec 2;22(23):9411. doi: 10.3390/s22239411.
Interactive technologies such as augmented reality have grown in popularity, but specialized sensors and high computer power must be used to perceive and analyze the environment in order to obtain an immersive experience in real time. However, these kinds of implementations have high costs. On the other hand, machine learning has helped create alternative solutions for reducing costs, but it is limited to particular solutions because the creation of datasets is complicated. Due to this problem, this work suggests an alternate strategy for dealing with limited information: unpaired samples from known and unknown surroundings are used to generate a path on embedded devices, such as smartphones, in real time. This strategy creates a path that avoids virtual elements through physical objects. The authors suggest an architecture for creating a path using imperfect knowledge. Additionally, an augmented reality experience is used to describe the generated path, and some users tested the proposal to evaluate the performance. Finally, the primary contribution is the approximation of a path produced from a known environment by using an unpaired dataset.
交互技术，如增强现实，已经越来越受欢迎，但为了实时获得身临其境的体验，必须使用专门的传感器和高计算能力来感知和分析环境。然而，这些实现方式成本很高。另一方面，机器学习有助于创造替代解决方案来降低成本，但它仅限于特定的解决方案，因为数据集的创建很复杂。由于这个问题，这项工作提出了一种处理有限信息的替代策略：使用已知和未知环境的未配对样本在嵌入式设备（如智能手机）上实时生成路径。该策略通过物理对象创建避开虚拟元素的路径。作者提出了一种使用不完美知识创建路径的架构。此外，还使用增强现实体验来描述生成的路径，并且一些用户测试了该提案以评估性能。最后，主要贡献是使用未配对数据集从已知环境中近似生成路径。
