{"id":{"repo_id":"carleton","oai_identifier":"oai:carleton.scholaris.ca:20.500.14718/43643"},"canonical_url":"https://search.dev.ndltd.org/etd/carleton/oai:carleton.scholaris.ca:20.500.14718/43643","repository":{"repo_id":"carleton","name":"Carleton University","base_url":"https://carleton.scholaris.ca/server/oai/request"},"display":{"title":"Autonomous Aerial Drone Landing Site Selection on a Maritime Vessel","abstract":"This thesis focuses on the development of an autonomous system capable of iden- tifying, tracking, and landing on suitable sites aboard a moving ship. Leveraging modifications to the Hazard-Aware Landing Optimization (HALO) algorithm, origi- nally designed for static terrain, the system integrates robust mapping, point cloud registration, and site selection algorithms to enable reliable performance in dynamic maritime conditions. A simulation environment was developed, utilizing Microsoft AirSim and ShipMo3D. This simulation incorporated a quadrotor equipped with Light Detec- tion and Ranging (LiDAR) to map ship decks and evaluate potential landing sites. Key innovations included dynamic point cloud registration using FilterReg and the integration of a modified Landing Period Indicator (LPI) algorithm. The results demonstrated the system’s ability to autonomously map ship decks, identify suitable landing sites, and execute landings on a ship moving under diffi- cult sea conditions. This work establishes a foundation for further development in autonomous maritime operations.","abstract_html":"This thesis focuses on the development of an autonomous system capable of iden- tifying, tracking, and landing on suitable sites aboard a moving ship. Leveraging modifications to the Hazard-Aware Landing Optimization (HALO) algorithm, origi- nally designed for static terrain, the system integrates robust mapping, point cloud registration, and site selection algorithms to enable reliable performance in dynamic maritime conditions. A simulation environment was developed, utilizing Microsoft AirSim and ShipMo3D. This simulation incorporated a quadrotor equipped with Light Detec- tion and Ranging (LiDAR) to map ship decks and evaluate potential landing sites. Key innovations included dynamic point cloud registration using FilterReg and the integration of a modified Landing Period Indicator (LPI) algorithm. The results demonstrated the system’s ability to autonomously map ship decks, identify suitable landing sites, and execute landings on a ship moving under diffi- cult sea conditions. This work establishes a foundation for further development in autonomous maritime operations.","abstract_has_math":false,"creators":["Giroux, Eric Daniel"],"institution":"Carleton University","degree_name":"Master of Applied Science (M.App.Sc.)","degree_level":"Master's","degree_discipline":"Engineering, Aerospace","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025","date_published":"2025","updated_at":"2026-07-24T01:34:45Z","subjects":[],"languages":["en"],"rights":["Copyright © 2024 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. 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