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Robust Asynchronous Temporal Event Mapping

Schill, Felix; Zimmer, Uwe

Description

Localisation and mapping relies on the representation and recognition of features or patterns detected in sensor data. An important aspect is the temporal relationship of observations in sensor data streams. This article proposes a new approach for simultaneous localisation and mapping based on temporal relations in the flow of characteristic events in the sensor data channels. A dynamical system is employed to acquire these correlations between simultaneous and sequential events from different...[Show more]

dc.contributor.authorSchill, Felix
dc.contributor.authorZimmer, Uwe
dc.coverage.spatialLausanne Switzerland
dc.date.accessioned2015-12-13T23:23:53Z
dc.date.available2015-12-13T23:23:53Z
dc.date.createdSeptember 30 2002
dc.identifier.isbn0780373987
dc.identifier.urihttp://hdl.handle.net/1885/91966
dc.description.abstractLocalisation and mapping relies on the representation and recognition of features or patterns detected in sensor data. An important aspect is the temporal relationship of observations in sensor data streams. This article proposes a new approach for simultaneous localisation and mapping based on temporal relations in the flow of characteristic events in the sensor data channels. A dynamical system is employed to acquire these correlations between simultaneous and sequential events from different sources, to map causal sequences, while considering time spans, and to recognise previously observed patterns (localisation). While this system is applicable to sensor modalities with different characteristics and timing behaviours, it is especially suitable for distributed computing. Mapping and localisation take place simultaneously in an life-long unsupervised distributed on-line learning process. The dynamical system has been implemented as a distributed realtime system with symmetric processes. A real-time clustering network reduces the dimension of raw sensor data; cluster transitions are used as input for the dynamical mapping system. Results from physical experiments with one sensor modality are presented.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE Inc)
dc.relation.ispartofseriesIEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2002)
dc.sourceProceedings of the 2002 IEEE/ RSJ International Conference on Intelligent Robots and Systems
dc.subjectKeywords: Correlation methods; Distributed computer systems; Image sensors; Pattern recognition; Sensor data fusion; Temporal correlation; Temporal event mapping; Mobile robots
dc.titleRobust Asynchronous Temporal Event Mapping
dc.typeConference paper
local.description.notesImported from ARIES
local.description.refereedYes
dc.date.issued2002
local.identifier.absfor080101 - Adaptive Agents and Intelligent Robotics
local.identifier.ariespublicationMigratedxPub22903
local.type.statusPublished Version
local.contributor.affiliationSchill, Felix, College of Engineering and Computer Science, ANU
local.contributor.affiliationZimmer, Uwe, College of Engineering and Computer Science, ANU
local.bibliographicCitation.startpage190
local.bibliographicCitation.lastpage195
dc.date.updated2015-12-12T09:18:04Z
local.identifier.scopusID2-s2.0-0036450639
CollectionsANU Research Publications

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