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An improved probability density function for representing landmark positions in bearing-only SLAM systems

Huang, Henry, Maire, Frederic D., & Keeratipranon, Narongdech (2007) An improved probability density function for representing landmark positions in bearing-only SLAM systems. In Orgun, Mehmet A. & Thornton, John (Eds.) 20th Australian Joint Conference on Artificial Intelligence - AI 2007: Advances in Artificial Intelligence, 2-6 December, 2007, Gold Coast, Queensland, Australia.

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Abstract

To navigate successfully, a mobile robot must be able to estimate the spatial relationships of the objects of interest in its environment accurately. The main advantage of a bearing-only Simultaneous Localization and Mapping (SLAM) system is that it requires only a cheap vision sensor to enable a mobile robot to gain knowledge of its environment and navigate. In this paper, we focus on the representation of the spatial uncertainty of landmarks caused by sensor noise. We follow a principled approach for computing the Probability Density Functions (PDFs) of landmark positions when an initial observation is made. We characterize the PDF p(r,a) of a landmark position expressed in polar coordinates when r and a are independent, and the marginal probability p(r) of the PDF is constrained to be uniform.

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ID Code: 12687
Item Type: Conference Paper
Keywords: Bearing, only SLAM, Probability Density Function
DOI: 10.1007/978-3-540-76928-6_75
ISBN: 9783540769262
ISSN: 1611-3349
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Adaptive Agents and Intelligent Robotics (080101)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100)
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Past > QUT Faculties & Divisions > Faculty of Science and Technology
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2007 Springer
Copyright Statement: This is the author-version of the work. Conference proceedings published, by Springer Verlag, will be available via SpringerLink. http://www.springer.de/comp/lncs/ Lecture Notes in Computer Science
Deposited On: 27 Feb 2008
Last Modified: 14 Oct 2013 14:46

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