Robust 3D object detection and feature extraction for cooperative multi-robot tasks
department or school: Applied Science, Faculty of
department or school: Mechanical Engineering, Department of
data provider: DSpace
degree campus: UBCV
creator: Kananka Liyanage, Arunasiri
issued: 2010-07-07T20:47:27Z
issued: 2010
related degree: Master of Applied Science - MASc
degree grantor: University of British Columbia
description: Computer vision uses image processing, image understanding, and feature extraction, which is vital in robotic tasks. This research is an integral part of a larger project on human rescue robotics where the goal is to quickly locate objects in an emergency scenario by a group of heterogeneous robots and assemble them into a useful device. Hence, the vision system should be fast and capable of working in an unstructured, dynamic, and unknown environment. Since there may be a number of variations with regard to the objects and the environment, the robustness is crucial. A novel vision system architecture is proposed and developed in this research to fulfill the vision requirements of a multi-robot system. Appropriate approaches, techniques, and structures are proposed and implemented together with appropriate existing methods and their enhancements. An approach of object modeling is proposed and used to generate object models. These models are used with a proposed object detection method to identify objects and determine useful features and parameters. Another object detection method is proposed to detect regular geometrical shaped objects. The proposed methods is able to detect multiple objects with varying object properties and environmental factors. Different types of object detection methods are employed in the proposed system according to the requirement of a robot by utilizing a real-time method selection technique, which is developed in the thesis. Achieving the expected level of performance involves a trade-off between speed and accuracy, by managing the execution of the processing steps in the developed method. Properties of expected objects need to be defined as facts and constraints based on the requirements of the robots. The performance of the vision system can be enhanced, by providing more facts and constraints. The developed methodologies are implemented in an experimental system in the Industrial Automation Laboratory of the University of British Colum…
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/26185
note: ROBUST 3D OBJECT DETECTION AND FEATURE EXTRACTION FOR COOPERATIVE MULTI-ROBOT TASKS by Arunasiri Kananka Liyanage B.Tech.(Eng.), The Open University of Sri Lanka, 2005 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF APPLIED SCIENCE in The Faculty of Graduate Studies (Mechanical Engineering) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) June 2010 © Arunasiri Kananka Liyanage, 2010 ii Abstract Computer vision uses image processing, image understanding, and feature extraction, which is vital in robotic tasks. This research is an integral part of a larger project on human rescue robotics where the goal is to quickly locate objects in an emergency scenario by a group of heterogeneous robots and assemble them into a useful device. Hence, the vision system should be fast and capable of working in an unstructured, dynamic, and unknown environment. Since there may be a number of variations with regard to the objects and the environment, the robustness is crucial. A novel vision system architecture is proposed and developed in this research to fulfill the vision requirements of a multi-robot system. Appropriate approaches, techniques, and structures are proposed and implemented together with appropriate existing methods and their enhancements. An approach of object modeling is proposed and used to generate object models. These models are used with a proposed object detection method to identify objects and determine useful features and parameters. Another object detection method is proposed to detect regular geometrical shaped objects. The proposed methods is able to detect multiple objects with varying object properties and environmental factors. Different types of object detection methods are employed in the proposed system according to the requirement of a robot by utilizing a real-time method selection technique, which is developed in the thesis. Achieving the expected level of performance involves a tradeoff between speed and acc…
has type: Thesis/Dissertation
date issued: 2010-11
is shown at: 10.14288/1.0071036
language: eng
degree discipline: Mechanical Engineering
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
type: Text
identifier URI: http://hdl.handle.net/2429/26185