Please use this identifier to cite or link to this item: https://doi.org/10.1109/OCEANS-Bergen.2013.6608000
Title: Bayesian multi-hypothesis scan matching
Authors: Brekke, E. 
Chitre, M. 
Issue Date: 2013
Source: Brekke, E.,Chitre, M. (2013). Bayesian multi-hypothesis scan matching. OCEANS 2013 MTS/IEEE Bergen: The Challenges of the Northern Dimension : -. ScholarBank@NUS Repository. https://doi.org/10.1109/OCEANS-Bergen.2013.6608000
Abstract: This paper proposes a multi-hypothesis solution to the simplified problem of simultaneous localization and mapping (SLAM) that arises when only two measurement frames are available. The proposed solution calculates hypothesis probabilities according to modeling based on standard multitarget tracking (MTT). State estimation is carried out by a hybrid technique consisting of extended Kalman filtering (EKF) and natural gradient (NG) optimization. The search for promising candidate hypotheses is carried out by Bron & Kerbosh' clique detection algorithm. Both Monte-Carlo simulations and implementation on real-world sonar data show that the proposed approach has desirable robustness properties. © 2013 IEEE.
Source Title: OCEANS 2013 MTS/IEEE Bergen: The Challenges of the Northern Dimension
URI: http://scholarbank.nus.edu.sg/handle/10635/69495
ISBN: 9781479900015
DOI: 10.1109/OCEANS-Bergen.2013.6608000
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