Show simple item record

dc.contributor.authorLe Cras, Jared
dc.contributor.authorPaxman, Jonathan
dc.contributor.editorDanwei Wang
dc.date.accessioned2017-01-30T12:33:45Z
dc.date.available2017-01-30T12:33:45Z
dc.date.created2013-05-22T20:00:23Z
dc.date.issued2012
dc.identifier.citationLe Cras, Jared and Paxman, Jonathan. 2012. A modular hybrid SLAM for the 3D mapping of large scale environments, in Proceedings of the 12th International Conference on Control Automation Robotics & Vision (ICARCV), Dec 5-7 2012, pp. 1036-1041. Guangzhou, China: IEEE.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/22812
dc.identifier.doi10.1109/ICARCV.2012.6485300
dc.description.abstract

Underground mining environments pose many unique challenges to the task of creating extensive, survey quality 3D maps. The extreme characteristics of such environments require a modular mapping solution which has no dependency on Global Positioning Systems (GPS), physical odometry, a priori information or motion model simplification. These restrictions rule out many existing 3D mapping approaches. This work examines a hybrid approach to mapping, fusing omnidirectional vision and 3D range data to produce an automatically registered, accurate and dense 3D map. A series of discrete 3D laser scans are registered through a combination of vision based bearing-only localization and scan matching with the Iterative Closest Point (ICP) algorithm. Depth information provided by the laser scans is used to correctly scale the bearing-only feature map, which in turn supplies an initial pose estimate for a registration algorithm to build the 3D map and correct localization drift. The resulting extensive maps require no external instrumentation or a priori information. Preliminary testing demonstrated the ability of the hybrid system to produce a highly accurate 3D map of an extensive indoor space.

dc.publisherIEEE
dc.subjectmining
dc.subjectlocalization
dc.subjectomnivision
dc.subjectsensor fusion
dc.subject3D mapping
dc.subjectSLAM
dc.titleA modular hybrid SLAM for the 3D mapping of large scale environments
dc.typeConference Paper
dcterms.source.startPage1036
dcterms.source.endPage1041
dcterms.source.titleControl Automation Robotics & Vision (ICARCV), 2012 12th International Conference on
dcterms.source.seriesControl Automation Robotics & Vision (ICARCV), 2012 12th International Conference on
dcterms.source.isbn978-1-4673-1871-6
dcterms.source.isbn9781467318709
dcterms.source.conferenceControl Automation Robotics & Vision (ICARCV), 2012 12th International Conference on
dcterms.source.conference-start-dateDec 5 2012
dcterms.source.conferencelocationGuangzhou
dcterms.source.placeGuangzhou
curtin.note

Copyright © 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

curtin.department
curtin.accessStatusOpen access


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record