Weakly-supervised traversability estimation for mobile robots using sparse point annotation
Schreiber, Andre Maurice
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https://hdl.handle.net/2142/121986
Description
Title
Weakly-supervised traversability estimation for mobile robots using sparse point annotation
Author(s)
Schreiber, Andre Maurice
Issue Date
2023-11-14
Director of Research (if dissertation) or Advisor (if thesis)
Driggs-Campbell, Katherine R
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Mobile Robots
Field Robotics
Traversability
Weak Supervision
Language
eng
Abstract
The task of traversability estimation is of key importance for mobile robots operating in unstructured environments. Such mobile robots need to be able to correctly predict where they can and cannot travel in order to ensure that they do not damage their environment or themselves. However, existing methods for estimating traversability in mobile robotics applications have significant limitations, as they can involve significant labeling tedium or require a robot to experience conditions of interest in order to label them. To overcome some of these challenges, we propose a weakly-supervised framework for traversability estimation in unstructured environments. Our framework uses sparse annotations to reduce labeling tedium, and we combine prior methods for weakly-supervised learning with a neural network-guided sampling strategy in order to improve results on our traversability prediction task. Our method demonstrates accuracy approaching that of the strongly-supervised approach, while significantly reducing labeling burden. Even though our initial proposed sparse annotation strategy demonstrates performance comparable to the strongly-supervised method with significantly reduced labeling effort, it can fail in cases of isolated, small obstacles. To address this issue, we propose a sampling strategy that explicitly emphasizes traversable regions and obstacles, and we demonstrate that this method can significantly improve prediction quality for small but untraversable obstacles.
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