장애물 정보를 포함한 SLAM 기반 실시간 환경 맵핑 알고리즘의 개발 및 구현

In indoor environments, such as buildings or residential homes, autonomous robot operation requires accurate mapping of the environment and precise localization of the robot within that space. Simultaneous Localization and Mapping (SLAM) is a technique that simultaneously constructs a map of an unknown environment while estimating the robot’s position within it. Traditional 2D-SLAM, which relies on 2D LiDAR for spatial perception and map generation, faces limitations in comprehending the complexities of 3D environments. To address these challenges, this study proposes an enhanced SLAM algorithm that integrates 3D Depth camera technology for advanced obstacle detection. By incorporating an object recognition architecture, the proposed method aims to improve the robot’s environmental perception and mapping accuracy, thereby overcoming the constraints of conventional 2D-SLAM in complex indoor spaces.