Open Access

ARTICLE

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

Sehun Lee, Taehoon Kim, Junho Ahn^*

AI Computer Engineering Department, Kyonggi University, Suwon, 16227, Republic of Korea

* Corresponding Author: Junho Ahn. Email:

(This article belongs to the Special Issue: Research on Deep Learning-based Object Detection and Its Derivative Key Technologies)

Computers, Materials & Continua 2025, 84(2), 3623-3648. https://doi.org/10.32604/cmc.2025.063985

Received 31 January 2025; Accepted 21 May 2025; Issue published 03 July 2025

Abstract

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.

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Keywords

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