Fast Uncertainty Quantification for Active Graph SLAM
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by
Julio A. Placed, José A. Castellanos
2022
Abstract
Quantifying uncertainty is a key stage in autonomous robotic exploration,
since it allows to identify the most informative actions to execute. However,
dealing with full Fisher Information matrices (FIM) is computationally heavy
and may become intractable for online systems. In this work, we study the
paradigm of Active graph SLAM formulated over SE(n), and propose a
general relationship between the full FIM and the Laplacian matrix of the
underlying pose-graph. Therefore, the optimal set of actions can be estimated
by maximizing optimality criteria of the weighted Laplacian instead of that of
the FIM. Experimental validation proves our method leads to equivalent results
in a fraction of the time traditional methods require. Based on the former, we
present an online Active graph SLAM system capable of selecting D-optimal
actions and that outperforms other state-of-the-art methods that rely on slower
computations. Also, we propose the use of such indices as stopping criterion,
making our system capable of autonomously determining when the exploration
strategy is no longer adding information to the graph SLAM algorithm and it
should be either changed or terminated.
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