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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 matrix into square root form. Such techniques have several significant advantages over the EKF: they are faster yet exact, they can be used indoi:10.15607/rss.2005.i.024 dblp:conf/rss/Dellaert05 fatcat:5zowkkponrgvdp2x7b72f5j4ge