Open Access

ARTICLE

Development of Multi-Agent-Based Indoor 3D Reconstruction

by Hoi Chuen Cheng, Frederick Ziyang Hong, Babar Hussain, Yiru Wang, Chik Patrick Yue^*

Optical Wireless Lab, Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong, China

* Corresponding Author: Chik Patrick Yue. Email:

(This article belongs to the Special Issue: Intelligent Manufacturing, Robotics and Control Engineering)

Computers, Materials & Continua 2024, 81(1), 161-181. https://doi.org/10.32604/cmc.2024.053079

Received 24 April 2024; Accepted 01 August 2024; Issue published 15 October 2024

Abstract

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.

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Keywords

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