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Fast covariance recovery in incremental nonlinear least square solvers

Ila, Viorela; Polok, Lucas; Solony, Marek; Smrz, Pavel; Zemcik, Pavel

Description

Many estimation problems in robotics rely on efficiently solving nonlinear least squares (NLS). For example, it is well known that the simultaneous localisation and mapping (SLAM) problem can be formulated as a maximum likelihood estimation (MLE) and solved using NLS, yielding a mean state vector. However, for many applications recovering only the mean vector is not enough. Data association, active decisions, next best view, are only few of the applications that require fast state covariance...[Show more]

dc.contributor.authorIla, Viorela
dc.contributor.authorPolok, Lucas
dc.contributor.authorSolony, Marek
dc.contributor.authorSmrz, Pavel
dc.contributor.authorZemcik, Pavel
dc.coverage.spatialSeattle, USA
dc.date.accessioned2016-06-14T23:20:07Z
dc.date.createdMay 26-30 2015
dc.identifier.isbn9781479969234
dc.identifier.urihttp://hdl.handle.net/1885/103211
dc.description.abstractMany estimation problems in robotics rely on efficiently solving nonlinear least squares (NLS). For example, it is well known that the simultaneous localisation and mapping (SLAM) problem can be formulated as a maximum likelihood estimation (MLE) and solved using NLS, yielding a mean state vector. However, for many applications recovering only the mean vector is not enough. Data association, active decisions, next best view, are only few of the applications that require fast state covariance recovery. The problem is not simple since, in general, the covariance is obtained by inverting the system matrix and the result is dense. The main contribution of this paper is a novel algorithm for fast incremental covariance update, complemented by a highly efficient implementation of the covariance recovery. This combination yields to two orders of magnitude reduction in computation time, compared to the other state of the art solutions. The proposed algorithm is applicable to any NLS solver implementation, and does not depend on incremental strategies described in our previous papers, which are not a subject of this paper.
dc.publisherIEEE
dc.relation.ispartofseries2015 IEEE International Conference on Robotics and Automation, ICRA 2015
dc.sourceProceedings - IEEE International Conference on Robotics and Automation
dc.titleFast covariance recovery in incremental nonlinear least square solvers
dc.typeConference paper
local.description.notesImported from ARIES
local.description.refereedYes
dc.date.issued2015
local.identifier.absfor090602 - Control Systems, Robotics and Automation
local.identifier.ariespublicationU3488905xPUB5649
local.type.statusPublished Version
local.contributor.affiliationIla, Viorela, College of Engineering and Computer Science, ANU
local.contributor.affiliationPolok, Lucas, Brno University of Technology,
local.contributor.affiliationSolony, Marek, Brno University of Technology
local.contributor.affiliationSmrz, Pavel, Brno University of Technology
local.contributor.affiliationZemcik, Pavel, Brno University of Technology
local.description.embargo2037-12-31
local.bibliographicCitation.startpage4636
local.bibliographicCitation.lastpage4643
local.identifier.doi10.1109/ICRA.2015.7139841
dc.date.updated2016-06-14T08:45:47Z
local.identifier.scopusID2-s2.0-84938262568
CollectionsANU Research Publications

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