Square Root SAM: Simultaneous Localization and Mapping via Square Root Information Smoothing
Frank Dellaert
Robotics: Science and Systems, 2005
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 matrix
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. 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,
an interpretation of factorization in terms of the graphical model
associated with the SLAM problem, and simulation results that
underscore the potential of these methods for use in practice.
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