{"id":{"repo_id":"carleton","oai_identifier":"oai:carleton.scholaris.ca:20.500.14718/45084"},"canonical_url":"https://search.dev.ndltd.org/etd/carleton/oai:carleton.scholaris.ca:20.500.14718/45084","repository":{"repo_id":"carleton","name":"Carleton University","base_url":"https://carleton.scholaris.ca/server/oai/request"},"display":{"title":"Indoor 3D Modeling Using Consumer Drones and Neural Simultaneous Localization and Mapping (SLAM) for Virtual Reality","abstract":"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. 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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.","abstract_has_math":false,"creators":["Sloan, Thomas Charles"],"institution":"Carleton University","degree_name":"Master of Applied Science (M.App.Sc.)","degree_level":"Master's","degree_discipline":"Engineering, Electrical and Computer","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2026,"date_issued":"2026","date_published":"2026","updated_at":"2026-07-24T01:34:22Z","subjects":[],"languages":["en"],"rights":["Copyright © 2026 the author(s). 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