2026-09-18T23:43:42Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1216572021-07-05T14:03:20Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Daniela Rus.
Naser, Felix Maximilian.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2019-07-15T20:31:27Z
2019-07-15T20:31:27Z
2019
2019
https://hdl.handle.net/1721.1/121657
1102050932
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 85-89).
Moving obstacles occluded by corners are a potential source for collisions in mobile robotics applications such as autonomous vehicles. In this work, we address the problem of anticipating such potential collisions by proposing a vision-based detection algorithm for obstacles which are outside of a vehicle's direct line of sight. Our method detects shadows of obstacles hidden around corners and automatically classifies these unseen obstacles as "dynamic" or "static". We evaluate our proposed detection algorithm on real-world corners and a large variety of simulated environments to assess generalizability in different challenging surface and lighting conditions. For the image registration step we compare a visual odometry method (i.e. DSO) with a fiducial marker system (i.e. AprilTags). The mean classification accuracy on simulated data is around 80% and on real-world corners approximately for both image registration methods 70%. Additionally, we integrate our detection system on a full-scale autonomous wheelchair and demonstrate its feasibility as an additional safety mechanism through real-world experiments. We release our real-time-capable implementation of the proposed ShadowCam algorithm and the dataset containing simulated and real-world data under an open-source license.
by Felix Maximilian Naser.
S.M.
S.M. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
89 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.
Detection of dynamic obstacles out of the line of sight for autonomous vehicles to increase safety based on shadows
Thesis
application/pdf
2019-07-15T20:31:25Z
Publication
Master
EECS
unknown
unknown
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<Abstract>Moving obstacles occluded by corners are a potential source for collisions in mobile robotics applications such as autonomous vehicles. In this work, we address the problem of anticipating such potential collisions by proposing a vision-based detection algorithm for obstacles which are outside of a vehicle's direct line of sight. Our method detects shadows of obstacles hidden around corners and automatically classifies these unseen obstacles as "dynamic" or "static". We evaluate our proposed detection algorithm on real-world corners and a large variety of simulated environments to assess generalizability in different challenging surface and lighting conditions. For the image registration step we compare a visual odometry method (i.e. DSO) with a fiducial marker system (i.e. AprilTags). The mean classification accuracy on simulated data is around 80% and on real-world corners approximately for both image registration methods 70%. Additionally, we integrate our detection system on a full-scale autonomous wheelchair and demonstrate its feasibility as an additional safety mechanism through real-world experiments. We release our real-time-capable implementation of the proposed ShadowCam algorithm and the dataset containing simulated and real-world data under an open-source license.</Abstract>
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