In this thesis, the key concepts of independent autonomous Unmanned Aircraft Systems (UAS) are explored including obstacle detection, dynamic obstacle state estimation, and avoidance strategy. This area is explored in pursuit of determining the viability of UAS Sense and Avoid (SAA) in static and dynamic operational environments. This exploration is driven by dynamic simulation and post-processing of real-world data. A sensor suite comprised of a 3D Light Detection and Ranging (LIDAR) sensor, visual camera, and 9 Degree of Freedom (DOF) Inertial Measurement Unit (IMU) was found to be beneficial to autonomous UAS SAA in urban environments. Promising results are based on to the broadening of available information about a dynamic or fixed obstacle via pixel-level LIDAR point cloud fusion and the combination of inertial measurements and LIDAR point clouds for localization purposes. However, there is still a significant amount of development required to optimize a data fusion method and SAA guidance method.
","abstract_html":"<p>In this thesis, the key concepts of independent autonomous Unmanned Aircraft Systems (UAS) are explored including obstacle detection, dynamic obstacle state estimation, and avoidance strategy. This area is explored in pursuit of determining the viability of UAS Sense and Avoid (SAA) in static and dynamic operational environments. This exploration is driven by dynamic simulation and post-processing of real-world data. A sensor suite comprised of a 3D Light Detection and Ranging (LIDAR) sensor, visual camera, and 9 Degree of Freedom (DOF) Inertial Measurement Unit (IMU) was found to be beneficial to autonomous UAS SAA in urban environments. Promising results are based on to the broadening of available information about a dynamic or fixed obstacle via pixel-level LIDAR point cloud fusion and the combination of inertial measurements and LIDAR point clouds for localization purposes. However, there is still a significant amount of development required to optimize a data fusion method and SAA guidance method.</p>","abstract_has_math":false,"creators":["Buchholz, Jonathan Mark"],"institution":null,"degree_name":"Master of Science in Unmanned and Autonomous Systems Engineering","degree_level":"Thesis - Open Access","degree_discipline":"Electrical, Computer, Software, and Systems Engineering","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-12-01T08:00:00Z","date_published":"2019-12-01T08:00:00Z","updated_at":"2026-07-27T19:26:34Z","subjects":["UAS","drones","multirotor","sensor fusion","Automotive Engineering"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://commons.erau.edu/edt/496","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Buchholz, Jonathan Mark"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical, Computer, Software, and Systems Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis - Open Access"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science in Unmanned and Autonomous Systems Engineering"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["UAS","drones","multirotor","sensor fusion","Automotive Engineering"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://commons.erau.edu/edt/496"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["In this thesis, the key concepts of independent autonomous Unmanned Aircraft Systems (UAS) are explored including obstacle detection, dynamic obstacle state estimation, and avoidance strategy. This area is explored in pursuit of determining the viability of UAS Sense and Avoid (SAA) in static and dynamic operational environments. This exploration is driven by dynamic simulation and post-processing of real-world data. A sensor suite comprised of a 3D Light Detection and Ranging (LIDAR) sensor, visual camera, and 9 Degree of Freedom (DOF) Inertial Measurement Unit (IMU) was found to be beneficial to autonomous UAS SAA in urban environments. Promising results are based on to the broadening of available information about a dynamic or fixed obstacle via pixel-level LIDAR point cloud fusion and the combination of inertial measurements and LIDAR point clouds for localization purposes. However, there is still a significant amount of development required to optimize a data fusion method and SAA guidance method.
"]},{"key":"dc:title","label":"Title","values":["Multirotor UAS Sense and Avoid with Sensor Fusion"]}]}],"canonical_facts":{"dc:creator":["Buchholz, Jonathan Mark"],"dc:description.abstract":["In this thesis, the key concepts of independent autonomous Unmanned Aircraft Systems (UAS) are explored including obstacle detection, dynamic obstacle state estimation, and avoidance strategy. This area is explored in pursuit of determining the viability of UAS Sense and Avoid (SAA) in static and dynamic operational environments. This exploration is driven by dynamic simulation and post-processing of real-world data. A sensor suite comprised of a 3D Light Detection and Ranging (LIDAR) sensor, visual camera, and 9 Degree of Freedom (DOF) Inertial Measurement Unit (IMU) was found to be beneficial to autonomous UAS SAA in urban environments. Promising results are based on to the broadening of available information about a dynamic or fixed obstacle via pixel-level LIDAR point cloud fusion and the combination of inertial measurements and LIDAR point clouds for localization purposes. However, there is still a significant amount of development required to optimize a data fusion method and SAA guidance method.
"],"dc:identifier":["https://commons.erau.edu/edt/496"],"dc:subject":["UAS","drones","multirotor","sensor fusion","Automotive Engineering"],"dc:title":["Multirotor UAS Sense and Avoid with Sensor Fusion"],"thesis:degree_discipline":["Electrical, Computer, Software, and Systems Engineering"],"thesis:degree_level":["Thesis - Open Access"],"thesis:degree_name":["Master of Science in Unmanned and Autonomous Systems Engineering"]},"updated_at":"2026-07-27T19:26:34Z"}