{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/32751"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/32751","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"A Novel Method to Locate Targets Using Active Vision and Robotic Inertial Navigation Data","abstract":"Unmanned vehicles are increasingly being used for mobile sensing missions. These missions can range from target acquisition to chemical and biological sensing. The reason why these vehicles are increasingly being used is because they can carry many different types of sensors and can function as a cheap platform for carrying these sensors. The sensing that will be explained in this thesis is target acquisition. Target acquisition is the act of locating the exact position of an \"area of interest.\" Currently this task can be completed with different types of complex range sensors. This thesis presents a type of target acquisition scheme for unmanned vehicles that will use a combination of cheap, simple vision sensors and robot inertial navigation data in order to accurately measure the location of a target in real world coordinates. This thesis will first develop an accurate waypoint driving algorithm that will either use dead reckoning or GPS/ compass sensors. We will then develop a robust target extraction algorithm that will be able to pick out a target in an image. After this is completed we will develop an algorithm that will be used to find the distance to the target from the robot. This algorithm will be based on a type of active vision system. Finally we will integrate all of these algorithms together in order to develop a target extraction technique that will be able to accurately find the distance to the target. With the distance we can then find the real world location of the target.","abstract_html":"Unmanned vehicles are increasingly being used for mobile sensing missions. These missions can range from target acquisition to chemical and biological sensing. The reason why these vehicles are increasingly being used is because they can carry many different types of sensors and can function as a cheap platform for carrying these sensors. The sensing that will be explained in this thesis is target acquisition. Target acquisition is the act of locating the exact position of an "area of interest." Currently this task can be completed with different types of complex range sensors. This thesis presents a type of target acquisition scheme for unmanned vehicles that will use a combination of cheap, simple vision sensors and robot inertial navigation data in order to accurately measure the location of a target in real world coordinates. This thesis will first develop an accurate waypoint driving algorithm that will either use dead reckoning or GPS/ compass sensors. We will then develop a robust target extraction algorithm that will be able to pick out a target in an image. After this is completed we will develop an algorithm that will be used to find the distance to the target from the robot. This algorithm will be based on a type of active vision system. Finally we will integrate all of these algorithms together in order to develop a target extraction technique that will be able to accurately find the distance to the target. With the distance we can then find the real world location of the target.","abstract_has_math":false,"creators":["Simone, Matthew James"],"institution":"Virginia Tech","degree_name":"Master of Science","degree_level":"masters","degree_discipline":"Electrical and Computer Engineering","degree_department":"Electrical and Computer Engineering","school":null,"contributors":[],"advisors":[],"committee_chairs":["Kachroo, Pushkin"],"committee_members":["Wicks, Alfred L.","Reinholtz, Charles F."],"year":2006,"date_issued":"2006-05-11","date_published":"2006-05-11","updated_at":"2026-07-22T22:20:38Z","subjects":["target acquisition","waypoint navigation","autonomous robots","pattern matching","image processing"],"languages":[],"rights":["In Copyright"],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["etd-05142006-185516"],"render_values":[{"text":"etd-05142006-185516","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/10919/32751","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Kachroo, Pushkin"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Wicks, Alfred L.","Reinholtz, Charles F."]},{"key":"dc:contributor.department","label":"Department","values":["Electrical and Computer Engineering"]},{"key":"dc:creator","label":"Author","values":["Simone, Matthew James"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2014-03-14T20:36:47Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2014-03-14T20:36:47Z","2006-07-06"]},{"key":"dc:date.issued","label":"Date","values":["2006-05-11"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical and Computer Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["target acquisition","waypoint navigation","autonomous robots","pattern matching","image processing"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["In Copyright"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["etd-05142006-185516"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/10919/32751"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Unmanned vehicles are increasingly being used for mobile sensing missions. These missions can range from target acquisition to chemical and biological sensing. The reason why these vehicles are increasingly being used is because they can carry many different types of sensors and can function as a cheap platform for carrying these sensors. The sensing that will be explained in this thesis is target acquisition. Target acquisition is the act of locating the exact position of an \"area of interest.\" Currently this task can be completed with different types of complex range sensors. This thesis presents a type of target acquisition scheme for unmanned vehicles that will use a combination of cheap, simple vision sensors and robot inertial navigation data in order to accurately measure the location of a target in real world coordinates. This thesis will first develop an accurate waypoint driving algorithm that will either use dead reckoning or GPS/ compass sensors. We will then develop a robust target extraction algorithm that will be able to pick out a target in an image. After this is completed we will develop an algorithm that will be used to find the distance to the target from the robot. This algorithm will be based on a type of active vision system. Finally we will integrate all of these algorithms together in order to develop a target extraction technique that will be able to accurately find the distance to the target. With the distance we can then find the real world location of the target."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Master of Science"]},{"key":"dc:title","label":"Title","values":["A Novel Method to Locate Targets Using Active Vision and Robotic Inertial Navigation Data"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Kachroo, Pushkin"],"dc:contributor.committeemember":["Wicks, Alfred L.","Reinholtz, Charles F."],"dc:contributor.department":["Electrical and Computer Engineering"],"dc:creator":["Simone, Matthew James"],"dc:date.accessioned":["2014-03-14T20:36:47Z"],"dc:date.available":["2014-03-14T20:36:47Z","2006-07-06"],"dc:date.issued":["2006-05-11"],"dc:description.abstract":["Unmanned vehicles are increasingly being used for mobile sensing missions. These missions can range from target acquisition to chemical and biological sensing. The reason why these vehicles are increasingly being used is because they can carry many different types of sensors and can function as a cheap platform for carrying these sensors. The sensing that will be explained in this thesis is target acquisition. Target acquisition is the act of locating the exact position of an \"area of interest.\" Currently this task can be completed with different types of complex range sensors. This thesis presents a type of target acquisition scheme for unmanned vehicles that will use a combination of cheap, simple vision sensors and robot inertial navigation data in order to accurately measure the location of a target in real world coordinates. This thesis will first develop an accurate waypoint driving algorithm that will either use dead reckoning or GPS/ compass sensors. We will then develop a robust target extraction algorithm that will be able to pick out a target in an image. After this is completed we will develop an algorithm that will be used to find the distance to the target from the robot. This algorithm will be based on a type of active vision system. Finally we will integrate all of these algorithms together in order to develop a target extraction technique that will be able to accurately find the distance to the target. With the distance we can then find the real world location of the target."],"dc:description.degree":["Master of Science"],"dc:identifier.other":["etd-05142006-185516"],"dc:identifier.uri":["http://hdl.handle.net/10919/32751"],"dc:publisher":["Virginia Tech"],"dc:rights":["In Copyright"],"dc:rights.uri":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["target acquisition","waypoint navigation","autonomous robots","pattern matching","image processing"],"dc:title":["A Novel Method to Locate Targets Using Active Vision and Robotic Inertial Navigation Data"],"dc:type":["Thesis"],"thesis:degree_discipline":["Electrical and Computer Engineering"],"thesis:degree_level":["masters"],"thesis:degree_name":["Master of Science"],"thesis:institution_name":["Virginia Polytechnic Institute and State University"]},"updated_at":"2026-07-22T22:20:38Z"}