현실 환경에서 증강현실과 딥러닝을 활용한 M&S 모델 증강가시화

Abstract

This paper proposes a new method to effectively visualize modeling & simulation (M&S) results in a real environment using augmented reality (AR) and deep learning. The proposed approach makes it possible to dynamically generate an M&S analysis space of the real environment, to recognize real objects by using a deep learning technique, and to place the analyzed M&S results onto them. In order to construct an M&S space dynamically, we perform area learning on the real space using a smart device supporting RGB-D camera. In addition, real objects are recognized through deep learning-based object detection. Spatial mapping and user interaction are conducted to match the recognized real object with corresponding M&S model in the mobile AR environment. A proof-of-concept system was developed to show the advantage and feasibility of the proposed method. Therefore, the proposed approach can be used for seamlessly integrating M&S models into various real spaces and for reviewing M&S results more consistently and effectively.