Indoor 3D Modeling Using Consumer Drones and Neural Simultaneous Localization and Mapping (SLAM) for Virtual Reality

This thesis explores an accessible indoor mapping system utilizing consumer-grade drones, addressing inefficiencies in traditional indoor spatial capture methods. The research demonstrates high-quality mapping accuracy with consumer hardware, creates a modular architecture for simplified operation, and validates the system through real-world deployments. Novel contributions include a cloud-based framework integrating visual odometry and neural simultaneous localization and mapping (SLAM), enabling real-time 3D reconstruction from drone video feeds without global positioning systems. The system achieves 21.06 frames per second on NVIDIA RTX 4070 Ti while maintaining trajectory accuracy with 6.61 cm root mean squared error (RMSE) on the Replica benchmark. The modular architecture supports both web-based visualization and virtual reality through Oculus Quest 3, using WebSocket protocols for low- latency data streaming. Experimental evaluations across diverse indoor environments confirm robust dense reconstruction capabilities while reducing set-up time and technical barriers. These advancements democratize indoor spatial capture for behavioral research, facility management, and emergency planning applications.

Name thesis:degree_name

Master of Applied Science (M.App.Sc.)

Level thesis:degree_level

Master's

Discipline thesis:degree_discipline

Engineering, Electrical and Computer

Grantor dc:publisher

Carleton University

Year dc:date.issued

2026

Author dc:creator

• Sloan, Thomas Charles

Statement dc:rights

• Copyright © 2026 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, distribution to students, research and scholarship. Theses may only be shared by linking to the Carleton University Institutional Repository and no part may be copied without proper attribution to the author; no part may be used for commercial purposes directly or indirectly via a for-profit platform; no adaptation or derivative works are permitted without consent from the copyright owner.

Language dc:language.iso

en

DOI

10.22215/etd/2026-16927

OAI identifier oai:identifier

oai:carleton.scholaris.ca:20.500.14718/45084