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An Improvement in the Observation Model for Monte Carlo Localization
Automatic Control Group at Computer Science, Electrical and Space Engineering, Luleå, University of Technology, Luleå, Sweden.ORCID iD: 0000-0001-6868-2210
Automatic Control Group at Computer Science, Electrical and Space Engineering, Luleå, University of Technology, Luleå, Sweden.
Automatic Control Group at Computer Science, Electrical and Space Engineering, Luleå, University of Technology, Luleå, Sweden.ORCID iD: 0000-0002-0079-9049
2014 (English)In: Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO / [ed] Joaquim Filipe, Oleg Gusikhin, Kurosh Madani and Jurek Sasiadek, SciTePress , 2014, p. 498-505Chapter in book (Refereed)
Abstract [en]

Accurate and robust mobile robot localization is very important in many robot applications. Monte Carlo localization (MCL) is one of the robust probabilistic solutions to robot localization problems. The sensor model used in MCL directly influence the accuracy and robustness of the pose estimation process. The classical beam models assumes independent noise in each individual measurement beam at the same scan. In practice, the noise in adjacent beams maybe largely correlated. This will result in peaks in the likelihood measurement function. These peaks leads to incorrect particles distribution in the MCL. In this research, an adaptive sub-sampling of the measurements is proposed to reduce the peaks in the likelihood function. The sampling is based on the complete scan analysis. The specified measurement is accepted or not based on the relative distance to other points in the 2D point cloud. The proposed technique has been implemented in ROS and stage simulator. The result shows that selecting suitable value of distance between accepted scans can improve the localization error and reduce the required computations effectively.

Place, publisher, year, edition, pages
SciTePress , 2014. p. 498-505
National Category
Control Engineering
Research subject
Control Engineering
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URN: urn:nbn:se:oru:diva-82183DOI: 10.5220/0005065604980505ISBN: 978-989-758-040-6 (print)OAI: oai:DiVA.org:oru-82183DiVA, id: diva2:1433168
Available from: 2020-05-29 Created: 2020-05-29 Last updated: 2020-08-26Bibliographically approved

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Alhashimi, AnasHostettler, RolandGustafsson, Thomas

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