{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/67752"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/67752","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Autonomous quadrotor unmanned aerial vehicle for culvert inspection","abstract":"This document presents work done to lay the foundation for an Unmanned Aerial Vehicle (UAV) system for inspecting culverts. By expanding upon prior progress creating an autonomous indoor quadrotor, many basic hardware and software issues are solved. The main new functionality needed for the culvert inspection task was to utilize the Global Positioning System (GPS) available outdoors to make up for the relative scarcity of objects visible to the Light Detection And Ranging sensor (LIDAR). The GPS data is fused in a new state estimator, which also incorporates data from the scan matcher running on the LIDAR data, as well as the data from the quadrotor's Inertial Measurement Unit (IMU). This data is combined into a single estimate of the current state (position, orientation, velocity, angular velocity, and acceleration) of the quadrotor by an Extended Kalman Filter (EKF). This new state estimate enables autonomous outdoor navigation and operation of this micro-UAV.","abstract_html":"This document presents work done to lay the foundation for an Unmanned Aerial Vehicle (UAV) system for inspecting culverts. By expanding upon prior progress creating an autonomous indoor quadrotor, many basic hardware and software issues are solved. The main new functionality needed for the culvert inspection task was to utilize the Global Positioning System (GPS) available outdoors to make up for the relative scarcity of objects visible to the Light Detection And Ranging sensor (LIDAR). The GPS data is fused in a new state estimator, which also incorporates data from the scan matcher running on the LIDAR data, as well as the data from the quadrotor's Inertial Measurement Unit (IMU). This data is combined into a single estimate of the current state (position, orientation, velocity, angular velocity, and acceleration) of the quadrotor by an Extended Kalman Filter (EKF). This new state estimate enables autonomous outdoor navigation and operation of this micro-UAV.","abstract_has_math":false,"creators":["Serrano, Nathan E"],"institution":"Massachusetts Institute of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. 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