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Efficient Appearance-Based Topological Mapping and Navigation with Omnidirectional Vision

Biomedical Sciences Research Institute Computer Science Research Institute Environmental Sciences Research Institute Nanotechnology & Advanced Materials Research Institute

Burbridge, Christopher and Condell, Joan (2010) Efficient Appearance-Based Topological Mapping and Navigation with Omnidirectional Vision. In: TAROS 2010 - Towards Autonomous Robotic Systems 2010, University of Plymouth, Devon, UK. University of Plymouth. 8 pp. [Conference contribution]

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Abstract

Because of a mobile robot’s ability to move in itsenvironment, one of the most important and common tasks formobile robots is, arguably, the task of navigation. This has tobe carried out in practically every mobile robot, and it hasto be carried out quickly (real-time constraints) and cheaply(computational constraints). SLAM [7] is a reliable and veryaccurate method which is widely used now in mobile robotics.However, these algorithms are not computationally cheap. Inthis paper we therefore investigate a computationally efficientalgorithm for simultaneous mapping and navigation that issuitable for application in simple mobile robots. We apply selforganisingmaps in a novel way to image data compression andindexing of topological map nodes at the same time.Our proposed system builds a dense topological map using onlythe visual appearance of the environment, with no need for anyfeature extraction or matching. This is made possible throughthe novel use of a self-organising map and a relaxed attitudetowards loop closure and metric consistency. The inconsistenciesand uncertainties within the map are not considered duringmapping, but rather only during navigation at which point aBayesian approach is taken to allow accurate navigation. Thepaper concludes by presenting mobile robot experiments

Item Type:Conference contribution (Paper)
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Intelligent Systems Research Centre
ID Code:17383
Deposited By:Dr Joan Condell
Deposited On:26 May 2011 15:36
Last Modified:26 May 2011 15:36

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