Square Root SAM: Simultaneous localization and mapping via square root information smoothing

Square Root SAM: Simultaneous localization and mapping via square root information smoothing (2006)

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by Frank Dellaert , Michael Kaess

Venue:	International Journal of Robotics Reasearch
Citations:	39 - 11 self

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Datum	Value	Source
TITLE	Square Root SAM: Simultaneous localization and mapping via square root information smoothing	INFERENCE
AUTHOR NAME	Frank Dellaert	SVM HeaderParse 0.2
AUTHOR AFFIL	Center for Robotics and Intelligent Machines, College of Computing; Georgia Institute of Technology, Atlanta	SVM HeaderParse 0.2
AUTHOR ADDR	GA 30332-0280	SVM HeaderParse 0.2
AUTHOR NAME	Michael Kaess	SVM HeaderParse 0.2
AUTHOR AFFIL	Center for Robotics and Intelligent Machines, College of Computing; Georgia Institute of Technology, Atlanta	SVM HeaderParse 0.2
AUTHOR ADDR	GA 30332-0280	SVM HeaderParse 0.2
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. 1	SVM HeaderParse 0.2
YEAR	2006	INFERENCE
VENUE	International Journal of Robotics Reasearch	INFERENCE
VENUE TYPE	JOURNAL	INFERENCE
PAGES	2006	INFERENCE
VOLUME	25	INFERENCE
TECH	Technical Report GIT-GVU-05-11, Georgia Tech	INFERENCE
CITATIONS	82 found	ParsCit 1.0