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Feature based condensation for mobile robot localization
KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS. KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.ORCID iD: 0000-0002-1170-7162
2000 (English)Conference paper (Refereed)
Abstract [en]

Much attention has been given to CONDENSATION methods for mobile robot localization. This has resulted in somewhat of a breakthrough in representing urncertainty for mobile robots. In this paper we use CONDENSATION with planned sampling as a tool for doing feature based global localization in a large and semi-structured environment. This paper presents a comparison of four different feature types: sonar based triangulation points and point pairs, as well as lines and doors extracted using a laser scanner. We show eperimental results that highlight the information content of the different features, and point to fruitful combinations. Accuracy, computation time and the ability to narrow down the search space are among the measures used to compare the features. From the comparison of the features, some general guidelines are drawn for determining good feature types.

Place, publisher, year, edition, pages
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Computer and Information Science
URN: urn:nbn:se:kth:diva-69628OAI: diva2:485645
IEEE International Conference on Robotics and Automation
NR 20140805Available from: 2012-01-29 Created: 2012-01-29Bibliographically approved

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