TY  - EJOU
AU  - Cheng, Hoi Chuen 
AU  - Hong, Frederick Ziyang 
AU  - Hussain, Babar 
AU  - Wang, Yiru 
AU  - Yue, Chik Patrick 

TI  - Development of Multi-Agent-Based Indoor 3D Reconstruction
T2  - Computers, Materials \& Continua

PY  - 2024
VL  - 81
IS  - 1
SN  - 1546-2226

AB  - Large-scale indoor 3D reconstruction with multiple robots faces challenges in core enabling technologies. This work contributes to a framework addressing localization, coordination, and vision processing for multi-agent reconstruction. A system architecture fusing visible light positioning, multi-agent path finding via reinforcement learning, and 360° camera techniques for 3D reconstruction is proposed. Our visible light positioning algorithm leverages existing lighting for centimeter-level localization without additional infrastructure. Meanwhile, a decentralized reinforcement learning approach is developed to solve the multi-agent path finding problem, with communications among agents optimized. Our 3D reconstruction pipeline utilizes equirectangular projection from 360° cameras to facilitate depth-independent reconstruction from posed monocular images using neural networks. Experimental validation demonstrates centimeter-level indoor navigation and 3D scene reconstruction capabilities of our framework. The challenges and limitations stemming from the above enabling technologies are discussed at the end of each corresponding section. In summary, this research advances fundamental techniques for multi-robot indoor 3D modeling, contributing to automated, data-driven applications through coordinated robot navigation, perception, and modeling.
KW  - Multi-agent system; multi-robot human collaboration; visible light communication; visible light positioning; 3D reconstruction; reinforcement learning; multi-agent path finding

DO  - 10.32604/cmc.2024.053079