Perception systems for robust autonomous navigation in natural environments
| dc.contributor.author | Trabelsi, Ameni, author | |
| dc.contributor.author | Beveridge, Ross J., advisor | |
| dc.contributor.author | Blanchard, Nathaniel, committee member | |
| dc.contributor.author | Anderson, Chuck, committee member | |
| dc.contributor.author | King, Emily, committee member | |
| dc.date.accessioned | 2022-05-30T10:22:24Z | |
| dc.date.available | 2022-05-30T10:22:24Z | |
| dc.date.issued | 2022 | |
| dc.description.abstract | As assistive robotics continues to develop thanks to the rapid advances of artificial intelligence, smart sensors, Internet of Things, and robotics, the industry began introducing robots to perform various functions that make humans' lives more comfortable and enjoyable. While the principal purpose of deploying robots has been productivity enhancement, their usability has widely expanded. Examples include assisting people with disabilities (e.g., Toyota's Human Support Robot), providing driver-less transportation (e.g., Waymo's driver-less cars), and helping with tedious house chores (e.g., iRobot). The challenge in these applications is that the robots have to function appropriately under continuously changing environments, harsh real-world conditions, deal with significant amounts of noise and uncertainty, and operate autonomously without the intervention or supervision of an expert. To meet these challenges, a robust perception system is vital. This dissertation casts light on the perception component of autonomous mobile robots and highlights their major capabilities, and analyzes the factors that affect their performance. In short, the developed approaches in this dissertation cover the following four topics: (1) learning the detection and identification of objects in the environment in which the robot is operating, (2) estimating the 6D pose of objects of interest to the robot, (3) studying the importance of the tracking information in the motion prediction module, and (4) analyzing the performance of three motion prediction methods, comparing their performances, and highlighting their strengths and weaknesses. All techniques developed in this dissertation have been implemented and evaluated on popular public benchmarks. Extensive experiments have been conducted to analyze and validate the properties of the developed methods and demonstrate this dissertation's conclusions on the robustness, performance, and utility of the proposed approaches for intelligent mobile robots. | |
| dc.format.medium | born digital | |
| dc.format.medium | doctoral dissertations | |
| dc.identifier | Trabelsi_colostate_0053A_17016.pdf | |
| dc.identifier.uri | https://hdl.handle.net/10217/235264 | |
| dc.language | English | |
| dc.language.iso | eng | |
| dc.publisher | Colorado State University. Libraries | |
| dc.relation.ispartof | 2020- | |
| dc.rights | Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright. | |
| dc.subject | computer vision | |
| dc.subject | perception systems | |
| dc.subject | machine learning | |
| dc.subject | autonomous navigation systems | |
| dc.title | Perception systems for robust autonomous navigation in natural environments | |
| dc.type | Text | |
| dcterms.rights.dpla | This Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). | |
| thesis.degree.discipline | Computer Science | |
| thesis.degree.grantor | Colorado State University | |
| thesis.degree.level | Doctoral | |
| thesis.degree.name | Doctor of Philosophy (Ph.D.) |
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