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Experiments on augmenting condensation for mobile robot localization
2000 (English)Conference paper (Refereed)
Abstract [en]

In this paper we study some modifications of the


algorithm. The case studied is feature

based mobile robot localization in a large scale environment.

The required sample set size for making



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


algorithm to break down,

we study two modifications. The first strategy, called


with random sampling", takes part

of the sample set and spreads it randomly over the

environment the robot operates in. The second strategy,



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-52978OAI: diva2:468337
Proc. of the IEEE International Conference on Robotics and Automation (ICRA’00)
NR 20140805Available from: 2011-12-20 Created: 2011-12-20Bibliographically approved

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