Fast Uncertainty Quantification for Active Graph SLAM release_jstep3nf2nb2hin6fk6kkjqjmu

by Julio A. Placed, José A. Castellanos

Released as a article .

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.
In text/plain format

Archived Content

There are no accessible files associated with this release. You could check other releases for this work for an accessible version.

"Dark" Preservation Only
Save Paper Now!

Know of a fulltext copy of on the public web? Submit a URL and we will archive it

Type  article
Stage   submitted
Date   2022-02-22
Version   v2
Language   en ?
arXiv  2110.01289v2
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: f2189459-84ee-4d6d-bfce-88190cc20b24
API URL: JSON