Square Root SAM Simultaneous Localization and Mapping via Square Root Information Smoothing

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Please use this identifier to cite or link to this item: http://hdl.handle.net/1853/38684

Title: Square Root SAM Simultaneous Localization and Mapping via Square Root Information Smoothing
Author: Dellaert, Frank ; Kaess, Michael
Abstract: Solving the SLAM problem is one way to enable a robot to explore, map, and navigate in a previously unknown environment. We investigate smoothing approaches as a viable alternative to extended Kalman filter-based solutions to the problem. In particular, we look at approaches that factorize either the associated information matrix or the measurement Jacobian into square root form. Such techniques have several significant advantages over the EKF: they are faster yet exact, they can be used in either batch or incremental mode, are better equipped to deal with non-linear process and measurement models, and yield the entire robot trajectory, at lower cost for a large class of SLAM problems. In addition, in an indirect but dramatic way, column ordering heuristics automatically exploit the locality inherent in the geographic nature of the SLAM problem. In this paper we present the theory underlying these methods, along with an interpretation of factorization in terms of the graphical model associated with the SLAM problem. We present both simulation results and actual SLAM experiments in large-scale environments that underscore the potential of these methods as an alternative to EKF-based approaches.
Type: Article
URI: http://hdl.handle.net/1853/38684
ISSN: 0278-3649
Citation: Kaess, M., & Dellaert, F. (2011) “Square Root SAM Simultaneous Localization and Mapping via Square Root Information Smoothing”. To appear in the International Journal of Robotics Research.
Date: 2006
Contributor: Georgia Institute of Technology. Center for Robotics and Intelligent Machines
Georgia Institute of Technology. College of Computing
Publisher: Georgia Institute of Technology
SAGE Publications
Subject: Graphical models
Mobile robots
SLAM

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