Planetary exploration is one of the main goals that humankind has established as a must for space exploration in order to be prepared for colonizing new places and provide scientific data for a better understanding of the formation of our solar system. In order to provide a safe approach, several safety measures must be undertaken to guarantee not only the success of the mission but also the safety of the crew. One of these safety measures is the Autonomous Hazard, Detection, and Avoidance (HDA) sub-system for celestial body landers that will enable different spacecraft to complete solar system exploration. The main objective of the HDA sub-system is to assemble a map of the local terrain during the descent of the spacecraft so that a safe landing site can be marked down. This thesis will be focused on a passive method using a monocular camera as its primary detection sensor due to its form factor and weight, which enables its implementation alongside the proposed HDA algorithm in the Intuitive Machines lunar lander NOVA-C as part of the Commercial Lunar Payload Services technological demonstration in 2021 for the NASA Artemis program to take humans back to the moon. This algorithm is implemented by including two different sources for making decisions, a two-dimensional (2D) vision-based HDA map and a three-dimensional (3D) HDA map obtained through a Structure from Motion process in combination with a plane fitting sequence. These two maps will provide different metrics in order to provide the lander a better probability of performing a safe touchdown. These metrics are processed to optimize a cost function.
","abstract_html":"<p>Planetary exploration is one of the main goals that humankind has established as a must for space exploration in order to be prepared for colonizing new places and provide scientific data for a better understanding of the formation of our solar system. In order to provide a safe approach, several safety measures must be undertaken to guarantee not only the success of the mission but also the safety of the crew. One of these safety measures is the Autonomous Hazard, Detection, and Avoidance (HDA) sub-system for celestial body landers that will enable different spacecraft to complete solar system exploration. The main objective of the HDA sub-system is to assemble a map of the local terrain during the descent of the spacecraft so that a safe landing site can be marked down. This thesis will be focused on a passive method using a monocular camera as its primary detection sensor due to its form factor and weight, which enables its implementation alongside the proposed HDA algorithm in the Intuitive Machines lunar lander NOVA-C as part of the Commercial Lunar Payload Services technological demonstration in 2021 for the NASA Artemis program to take humans back to the moon. This algorithm is implemented by including two different sources for making decisions, a two-dimensional (2D) vision-based HDA map and a three-dimensional (3D) HDA map obtained through a Structure from Motion process in combination with a plane fitting sequence. These two maps will provide different metrics in order to provide the lander a better probability of performing a safe touchdown. These metrics are processed to optimize a cost function.</p>","abstract_has_math":false,"creators":["Posada, Daniel"],"institution":null,"degree_name":"Master of Science in Aerospace Engineering","degree_level":"Thesis - Open Access","degree_discipline":"Aerospace Engineering","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020-07-01T07:00:00Z","date_published":"2020-07-01T07:00:00Z","updated_at":"2026-07-27T19:25:29Z","subjects":["algorithm","hazard detection","space vehicles","celestial body","landing","Aerospace Engineering","Navigation, Guidance, Control and Dynamics"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://commons.erau.edu/edt/534","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Posada, Daniel"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Aerospace Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis - Open Access"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science in Aerospace Engineering"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["algorithm","hazard detection","space vehicles","celestial body","landing","Aerospace Engineering","Navigation, Guidance, Control and Dynamics"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://commons.erau.edu/edt/534"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Planetary exploration is one of the main goals that humankind has established as a must for space exploration in order to be prepared for colonizing new places and provide scientific data for a better understanding of the formation of our solar system. In order to provide a safe approach, several safety measures must be undertaken to guarantee not only the success of the mission but also the safety of the crew. One of these safety measures is the Autonomous Hazard, Detection, and Avoidance (HDA) sub-system for celestial body landers that will enable different spacecraft to complete solar system exploration. The main objective of the HDA sub-system is to assemble a map of the local terrain during the descent of the spacecraft so that a safe landing site can be marked down. This thesis will be focused on a passive method using a monocular camera as its primary detection sensor due to its form factor and weight, which enables its implementation alongside the proposed HDA algorithm in the Intuitive Machines lunar lander NOVA-C as part of the Commercial Lunar Payload Services technological demonstration in 2021 for the NASA Artemis program to take humans back to the moon. This algorithm is implemented by including two different sources for making decisions, a two-dimensional (2D) vision-based HDA map and a three-dimensional (3D) HDA map obtained through a Structure from Motion process in combination with a plane fitting sequence. These two maps will provide different metrics in order to provide the lander a better probability of performing a safe touchdown. These metrics are processed to optimize a cost function.
"]},{"key":"dc:title","label":"Title","values":["An Open Source, Autonomous, Vision-Based Algorithm for Hazard Detection and Avoidance for Celestial Body Landing"]}]}],"canonical_facts":{"dc:creator":["Posada, Daniel"],"dc:description.abstract":["Planetary exploration is one of the main goals that humankind has established as a must for space exploration in order to be prepared for colonizing new places and provide scientific data for a better understanding of the formation of our solar system. In order to provide a safe approach, several safety measures must be undertaken to guarantee not only the success of the mission but also the safety of the crew. One of these safety measures is the Autonomous Hazard, Detection, and Avoidance (HDA) sub-system for celestial body landers that will enable different spacecraft to complete solar system exploration. The main objective of the HDA sub-system is to assemble a map of the local terrain during the descent of the spacecraft so that a safe landing site can be marked down. This thesis will be focused on a passive method using a monocular camera as its primary detection sensor due to its form factor and weight, which enables its implementation alongside the proposed HDA algorithm in the Intuitive Machines lunar lander NOVA-C as part of the Commercial Lunar Payload Services technological demonstration in 2021 for the NASA Artemis program to take humans back to the moon. This algorithm is implemented by including two different sources for making decisions, a two-dimensional (2D) vision-based HDA map and a three-dimensional (3D) HDA map obtained through a Structure from Motion process in combination with a plane fitting sequence. These two maps will provide different metrics in order to provide the lander a better probability of performing a safe touchdown. These metrics are processed to optimize a cost function.
"],"dc:identifier":["https://commons.erau.edu/edt/534"],"dc:subject":["algorithm","hazard detection","space vehicles","celestial body","landing","Aerospace Engineering","Navigation, Guidance, Control and Dynamics"],"dc:title":["An Open Source, Autonomous, Vision-Based Algorithm for Hazard Detection and Avoidance for Celestial Body Landing"],"thesis:degree_discipline":["Aerospace Engineering"],"thesis:degree_level":["Thesis - Open Access"],"thesis:degree_name":["Master of Science in Aerospace Engineering"]},"updated_at":"2026-07-27T19:25:29Z"}