Robust 3D object detection and feature extraction for cooperative multi-robot tasks

• Main Author: Kananka Liyanage, Arunasiri
• Language: English
• Published: University of British Columbia 2010
• Online Access: http://hdl.handle.net/2429/26185

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 Columbia. Rigorous experiments are conducted in a typical unstructured environment. Features such as invariance of scale, rotation, illumination, and occlusion are tested with different types of objects, for various methods. Generally good results have been obtained thereby validating the developed vision system for use in the multi-robot application.