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experiments on augmenting 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]

In this paper we study some modifications of the CONDENSATION algorithm. The case studied is feature based mobile robot localization in a large scale environment. The required sample set size for making the CONDENSATION  algorithm converge properly can in many cases require too much computation. This is often the case when observing features in symmetric environments like for instance doors in long corridors. In such areas a large sample set is required to resolve the generated multi-hypotheses problem. To manage with a sample set size which in the normal case would cause the CONDENSATION algorithm to break down, we study two modifications. The first strategy, called "CONDENSATION with random sampling", takes part of the sample set and spreads it randomly over the environment the robot operates in. The second strategy, called "CONDENSATION with planned sampling", places part of the sample set at planned positions based on the detected features. From the experiments we conclude that the second strategy is the best and can reduce the sample set size by at least a factor of 40.

Place, publisher, year, edition, pages
2000. 2518-2524 p.
National Category
Computer and Information Science
URN: urn:nbn:se:kth:diva-69627OAI: diva2:485642
IEEE International Conference on Robotics and Automation
NR 20140805Available from: 2012-01-29 Created: 2012-01-29Bibliographically approved

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