Pre-integrated dynamics factors and a dynamical agile visual-inertial dataset for UAV perception
Massachusetts Institute of Technology. Department of Mechanical Engineering.
Sertac Karaman and John Leonard.
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For the past few years, the rapid development of unmanned aerial vehicle (UAV) technology has been met by increased interest in these platforms for a wide range of applications. Particularly, the autonomous navigation of these vehicles is of great interest for applications such as surveillance, mapping, searching, agriculture, and film-making, to name a few. But autonomous UAV research has a long way to go to meet the capabilities and robustness required in many of these applications. This work presents two contributions towards closing that gap: pre-integrated dynamics factors for factor graph visual-inertial odometry (VIO), and a large-scale dataset with a great variety of visual, inertial, and dynamical sensor data from a quadrotor platform. The pre-integrated dynamics factors were tested on a challenging subset of the dataset and showed an improvement in robustness of a VIO system. The size and variety of the dataset make it a valuable tool for evaluating and testing visual-inertial estimation algorithms, as shown with the dynamics factors. Both contributions facilitate the development of more robust autonomous UAV navigation systems.
Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2018.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 75-78).
DepartmentMassachusetts Institute of Technology. Department of Mechanical Engineering.; Massachusetts Institute of Technology. Department of Mechanical Engineering
Massachusetts Institute of Technology