2026-09-18T23:01:33Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1225172021-07-05T14:03:20Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Sertac Karaman.
Venturelli Cavalheiro, Guilherme.
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics.
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
2019-10-11T21:59:53Z
2019-10-11T21:59:53Z
2019
2019
https://hdl.handle.net/1721.1/122517
1121277435
Thesis: S.M., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2019
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 57-62).
Recent advances in technology indicate that autonomous vehicles and self-driving cats in particular may become commonplace in the near future. This thesis contributes to that scenario by studying the problem of depth perception based on sequences of camera images. We start by presenting a sensor fusion framework that achieves state-of-the-art performance when completing depth from sparse LiDAR measurements and a camera. Then, we study how the system performs under a variety of modifications of the sparse input until we ultimately replace LiDAR measurements with triangulations from a typical sparse visual odometry pipeline. We are then able to achieve a small improvement over the single image baseline and chart guidelines to assist in designing a system with even more substantial gains.
by Guilherme Venturelli Cavalheiro.
S.M.
S.M. Massachusetts Institute of Technology, Department of Aeronautics and Astronautics
62 pages
eng
Massachusetts Institute of Technology
MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.
http://dspace.mit.edu/handle/1721.1/7582
Aeronautics and Astronautics.
Fusing visual odometry and depth completion
Thesis
application/pdf
2019-10-11T21:59:52Z
Publication
Master
Aero
unknown
unknown
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