2026-09-20T11:23:12Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/121986 2024-03-01 col_2142_5131 col_2142_8888 com_2142_5130 com_2142_8887 com_2142_234

Driggs-Campbell, Katherine R 2023-12 application/pdf en text Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-03-01 without embargo terms The student, Andre Schreiber, accepted the attached license on 2023-11-13 at 18:00. The student, Andre Schreiber, submitted this Thesis for approval on 2023-11-13 at 18:11. This Thesis was approved for publication on 2023-11-14 at 10:57. DSpace SAF Submission Ingestion Package generated from Vireo submission #19918 on 2024-03-01 at 13:14:31 Weakly-supervised traversability estimation for mobile robots using sparse point annotation Schreiber, Andre Maurice Mobile Robots Field Robotics Traversability Weak Supervision 2023-11-14 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. Text eng https://hdl.handle.net/2142/121986 Copyright 2023 Andre Maurice Schreiber M.S. Thesis Electrical & Computer Engr University of Illinois at Urbana-Champaign Electrical & Computer Eng