The computational bottleneck in all informationbased
algorithms for SLAM is the recovery of the state mean and
covariance. The mean is needed to evaluate model Jacobians and
the covariance is needed to generate data association hypotheses.
Recovering the state mean and covariance requires the inversion
of a matrix of the size of the state. Current state recovery methods
use sparse linear algebra tools that have quadratic cost, either
in memory or in time. In this paper, we present an approach to
state estimation that is worst case linear both in execution time
and in memory footprint at loop closure, and constant otherwise.
The approach relies on a state representation that combines the
Kalman and the information-based state representations. The
strategy is valid for any SLAM system that maintains constraints
between robot poses at different time slices. This includes both
Pose SLAM, the variant of SLAM where only the robot trajectory
is estimated, and hierarchical techniques in which submaps are
registered with a network of relative geometric constraints.
CitationIla, V.; Porta, J.; Andrade-Cetto, J. Amortized constant time state estimation in SLAM using a mixed Kalman-information filter. A: European Conference on Mobile Robots. "European Conference on Mobile Robots (ECMR) 4th". Mlini: 2009, p. 211-216.
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