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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Martin, F. Munoz, M.L. Garrido, S. Blanco, D. Moreno, L. |
| Copyright Year | 2009 |
| Description | Author affiliation: Facultad de Informática, Universidad Politécnica, Madrid, Spain (Munoz, M.L.) || Robotic's Laboratory, Universidad Carlos III, Madrid, Spain (Martin, F.; Garrido, S.; Blanco, D.; Moreno, L.) |
| Abstract | Global localization methods deal with the estimation of a mobile robot's pose assuming no prior state information about it and a complete a priori knowledge of the environment where the mobile robot is going to be localized. Most existent algorithms are based on the minimization of a L2-norm loss function. However, the use of a L1-norm offers some alternative advantages. The present work explores the use of a L1-norm together with an Evolutive Localization Filter to determine its efficiency when applied to the global localization problem. The algorithm has been tested subject to different noise levels to demonstrate the accuracy, effectiveness, robustness and computational efficiency of the L1-norm approach. |
| Starting Page | 229 |
| Ending Page | 234 |
| File Size | 214836 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781424450572 |
| DOI | 10.1109/WISP.2009.5286559 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-08-26 |
| Publisher Place | Hungary |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | L1-norm Optimization methods Information filtering Mobile robots Intelligent robots Monte Carlo methods Global Localization Robot kinematics Signal processing algorithms Mobile Robots Probability density function Information filters Differential Evolution Filter State estimation |
| Content Type | Text |
| Resource Type | Article |
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