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• W2064169939 startingPage "1181" @default.
• W2064169939 abstract "Solving the SLAM (simultaneous localization and mapping) problem is one way to enable a robot to explore, map, and navigate in a previously unknown environment. Smoothing approaches have been investigated as a viable alternative to extended Kalman filter (EKF)-based solutions to the problem. In particular, approaches have been looked at 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. This paper presents the theory underlying these methods, along with an interpretation of factorization in terms of the graphical model associated with the SLAM problem. Both simulation results and actual SLAM experiments in large-scale environments are presented that underscore the potential of these methods as an alternative to EKF-based approaches." @default.
• W2064169939 created "2016-06-24" @default.
• W2064169939 creator A5014216861 @default.
• W2064169939 creator A5087336025 @default.
• W2064169939 date "2006-12-01" @default.
• W2064169939 modified "2023-10-14" @default.
• W2064169939 title "Square Root SAM: Simultaneous Localization and Mapping via Square Root Information Smoothing" @default.
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• W2064169939 doi "https://doi.org/10.1177/0278364906072768" @default.
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