Zero-Shot Based Spatial AI Algorithm for Up-to-Date 3D Vision Map Generations in Highly Complex Indoor Environments

This paper proposes a zero-shot based spatial recognition AI algorithm by fusing and developing multi-dimensional vision identification technology adapted to the situation in large indoor and underground spaces. With the expansion of large shopping malls and underground urban spaces (UUS), there is an increasing need for new technologies that can quickly identify complex indoor structures and changes such as relocation, remodeling, and construction for the safety and management of citizens through the provision of the up-to-date indoor 3D site maps. The proposed algorithm utilizes data collected by an unmanned robot to create a 3D site map of the up-to-date indoor site and recognizes complex indoor spaces based on zero-shot learning. This research specifically addresses two major challenges: the difficulty of detecting walls and floors due to complex patterns and the difficulty of spatial perception due to unknown obstacles. The proposed algorithm addresses the limitations of the existing foundation model, detects floors and obstacles without expensive sensors, and improves the accuracy of spatial recognition by combining floor detection, vanishing point detection, and fusion obstacle detection algorithms. The experimental results show that the algorithm effectively detects the floor and obstacles in various indoor environments, with F1 scores of 0.96 and 0.93 in the floor detection and obstacle detection experiments, respectively.