2026-09-19T07:28:49Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1216372026-06-06T00:48:40Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Nicholas Roy.
Rouben, Yousif Farid Dawood.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2019-07-15T20:29:58Z
2019-07-15T20:29:58Z
2018
2018
https://hdl.handle.net/1721.1/121637
1098179735
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 87-96).
As hardware improves, so does the ability to run more complex algorithms. Improvements in the portability and performance of computing units has allowed for more compact robotic systems such as Micro Aerial Vehicles (MAVs), while developments in camera technologies has allowed for the generation of denser robot perception, such as depth and semantics, derived from environmental cues such as colour, shape and texture. Additionally, with the advent of commercial civilian satellites and initiatives like the Google Maps project, most of the earth's outdoor urban environments have been captured through satellite imagery. These overhead images contain useful information that can aid with outdoor robot navigation and localisation, by, for example, being used as prior maps in a localisation pipeline. This thesis presents a method for generating semantically consistent robot localisation updates on-board a MAV. The key contribution is a process that fuses local ground and global satellite image information through geometric alignment at the semantic level. This method uses a semantically segmented collapsed point cloud from a camera system on-board a MAV, without any notion of mapping, referencing only a potentially stale global overhead image. A particle filter is used to enable multi-hypothesis data association and allow for efficient recovery from motion uncertainty.
by Yousif Farid Dawood Rouben.
M.Eng.
M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
96 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
Electrical Engineering and Computer Science.
Pose estimation using overhead imagery and semantics
Thesis
application/pdf
2019-07-15T20:29:56Z
Publication
Master
EECS
unknown
unknown
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<Abstract>As hardware improves, so does the ability to run more complex algorithms. Improvements in the portability and performance of computing units has allowed for more compact robotic systems such as Micro Aerial Vehicles (MAVs), while developments in camera technologies has allowed for the generation of denser robot perception, such as depth and semantics, derived from environmental cues such as colour, shape and texture. Additionally, with the advent of commercial civilian satellites and initiatives like the Google Maps project, most of the earth's outdoor urban environments have been captured through satellite imagery. These overhead images contain useful information that can aid with outdoor robot navigation and localisation, by, for example, being used as prior maps in a localisation pipeline. This thesis presents a method for generating semantically consistent robot localisation updates on-board a MAV. The key contribution is a process that fuses local ground and global satellite image information through geometric alignment at the semantic level. This method uses a semantically segmented collapsed point cloud from a camera system on-board a MAV, without any notion of mapping, referencing only a potentially stale global overhead image. A particle filter is used to enable multi-hypothesis data association and allow for efficient recovery from motion uncertainty.</Abstract>
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