Reference : Supervised linear feature extraction for mobile robot localization
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
Engineering, computing & technology : Electrical & electronics engineering
http://hdl.handle.net/10993/11083
Supervised linear feature extraction for mobile robot localization
English
Vlassis, Nikos mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Motomura, Y. [> >]
Krose, B. [> >]
2000
Proc. IEEE Int. Conf. on Robotics and Automation
2979 - 2984
Yes
IEEE Int. Conf. on Robotics and Automation
2000
[en] We are seeking linear projections of supervised high-dimensional robot observations and an appropriate environment model that optimize the robot localization task. We show that an appropriate risk function to minimize is the conditional entropy of the robot positions given the projected observations. We propose a method of iterative optimization through a probabilistic model based on kernel smoothing. To obtain good starting optimization solutions we use canonical correlation analysis. We apply our method on a real experiment involving a mobile robot equipped with an omnidirectional camera in an office setup.
http://hdl.handle.net/10993/11083

File(s) associated to this reference

Fulltext file(s):

FileCommentaryVersionSizeAccess
Open access
download.pdfAuthor postprint631.4 kBView/Open

Bookmark and Share SFX Query

All documents in ORBilu are protected by a user license.