Fusing visual odometry and depth completion
Name
1121277435-MIT.pdf
Size
4.91 MB
Format
Adobe PDF
Checksum (MD5)
6931cc5e62d6fc34f8e6f00f525689f0
Author(s)
Venturelli Cavalheiro, Guilherme.
Advisor(s)
Sertac Karaman.
Date Issued
2019
Publisher
Massachusetts Institute of Technology
Abstract
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.
Description
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).
Subjects
Aeronautics and Astronautics.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Terms of Use
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.
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