STTR Phase I: Handheld Mobile-based Dynamic 4D Mapping and Indoor Space Reconstruction
- Institution
- NEARABL INC., NY
- Principal investigator
- Jin Chen
- Funding
- $275K
- Duration
- 2024–2025
Moonbase score
Still being scored
Abstract
The broader/commercial impact of this Small Business Technology Transfer (STTR) Phase I project includes enabling infrastructure owners, real estate developers and trade workers to optimize their operations and reduce rework, leading to a reduction in their environmental footprint and contributing to a more sustainable future. Furthermore, the project aims to decrease costs associated with digitizing infrastructure by 40-60% and lower socioeconomic workforce barriers for knowledge workers, facilitating increased construction and renovation, ultimately transforming buildings into "smart buildings" in a cost-effective manner. Additionally, the project will make indoor spaces more inclusive and accessible for individuals with disabilities by including customizable wayfinding and accessibility options; increase the safety and efficiency of construction and maintenance workers with on-ground mixed reality based work instructions; and educate the first responders and provide essential navigation to speed up the evacuation. This Small Business Technology Transfer (STTR) Phase I project focuses on innovating mobile-based 4D progression of indoor places - with a dynamic three-dimensional (3D) mapping and reconstruction process over the time dimension, capable of augmenting the physical structure of buildings. The proposed research will develop an attention-based modeling and reconstruction mechanism to enhance 3D reconstruction, coupled with a user-friendly AI-enhanced Mixed Reality guide to improve data collection by users, aiming to achieve precise 3D modeling with data collected from mobile devices, despite their inherently low quality. To minimize the required rescan work, given the ever-changing nature of construction and maintenance projects, a knowledge-based dynamic updates mechanism will be introduced, leveraging prior knowledge of the physical infrastructure to be modeled. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
Research team
- Jin ChenPI
- Zhigang Zhu
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