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. Lecture Notes in Computer Science, 4830, pp. 682-686.
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 SLAM (Simultaneous Localization and Mapping) 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. This representation is critical for the initialization of landmark positions in bearing-only SLAM systems. 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,alpha) of a landmark position expressed in polar coordinates when r and alpha are independent, and the marginal probability p(r) of the PDF is constrained to be uniform. Existing methods that approximate the PDF of a landmark position with a mixture of Gaussians do not satisfy this uniformity requirement. We also show how to use the proposed PDFs for a 2D bearing-only SLAM system that relies only on the landmark bearings measured by a panoramic camera.
Citation countsare sourced monthly fromand citation databases.
These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.
Citations counts from theindexing service can be viewed at the linked Google Scholar™ search.
Full-text downloadsdisplays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.
|Item Type:||Journal Article|
|Additional Information:||AI 2007: Advances in Artificial Intelligence: 20th Australian Joint Conference on Artificial Intelligence, Gold Coast, Australia, December 2-6, 2007. Proceedings|
|Keywords:||SLAM, Probability Density Function|
|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)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000)
|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 (please consult author)|
|Deposited On:||10 Sep 2007|
|Last Modified:||14 Oct 2013 14:39|
Repository Staff Only: item control page