A modular hybrid SLAM for the 3D mapping of large scale environments
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 informat...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Conference Paper |
| Published: |
IEEE
2012
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| Online Access: | http://hdl.handle.net/20.500.11937/22812 |
| _version_ | 1848750976017629184 |
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| author | Le Cras, Jared Paxman, Jonathan |
| author2 | Danwei Wang |
| author_facet | Danwei Wang Le Cras, Jared Paxman, Jonathan |
| author_sort | Le Cras, Jared |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T07:45:23Z |
| format | Conference Paper |
| id | curtin-20.500.11937-22812 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:45:23Z |
| publishDate | 2012 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-228122017-09-13T15:59:26Z A modular hybrid SLAM for the 3D mapping of large scale environments Le Cras, Jared Paxman, Jonathan Danwei Wang mining localization omnivision sensor fusion 3D mapping SLAM 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. 2012 Conference Paper http://hdl.handle.net/20.500.11937/22812 10.1109/ICARCV.2012.6485300 IEEE fulltext |
| spellingShingle | mining localization omnivision sensor fusion 3D mapping SLAM Le Cras, Jared Paxman, Jonathan A modular hybrid SLAM for the 3D mapping of large scale environments |
| title | A modular hybrid SLAM for the 3D mapping of large scale environments |
| title_full | A modular hybrid SLAM for the 3D mapping of large scale environments |
| title_fullStr | A modular hybrid SLAM for the 3D mapping of large scale environments |
| title_full_unstemmed | A modular hybrid SLAM for the 3D mapping of large scale environments |
| title_short | A modular hybrid SLAM for the 3D mapping of large scale environments |
| title_sort | modular hybrid slam for the 3d mapping of large scale environments |
| topic | mining localization omnivision sensor fusion 3D mapping SLAM |
| url | http://hdl.handle.net/20.500.11937/22812 |