Title:
Square Root SAM: Simultaneous Localization and Mapping via Square Mapping Root Information Smoothing
Square Root SAM: Simultaneous Localization and Mapping via Square Mapping Root Information Smoothing
Author(s)
Dellaert, Frank
Kaess, Michael
Kaess, Michael
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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.
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Date Issued
2005-06
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Proceedings