Bi-objective Optimization for Robust RGB-D Visual Odometry
| dc.contributor.author | Han, T. | |
| dc.contributor.author | Xu, C. | |
| dc.contributor.author | Loxton, Ryan | |
| dc.contributor.author | Xie, L. | |
| dc.date.accessioned | 2017-01-30T12:26:33Z | |
| dc.date.available | 2017-01-30T12:26:33Z | |
| dc.date.created | 2015-12-10T04:26:02Z | |
| dc.date.issued | 2015 | |
| dc.identifier.citation | Han, T. and Xu, C. and Loxton, R. and Xie, L. 2015. Bi-objective Optimization for Robust RGB-D Visual Odometry, in Proceedings of the 2015 27th Chinese Control and Decision Conference (CCDC), pp. 1843-1850. Qingdao, China: IEEE. | |
| dc.identifier.uri | http://hdl.handle.net/20.500.11937/21650 | |
| dc.identifier.doi | 10.1109/CCDC.2015.7162218 | |
| dc.description.abstract |
This paper considers a new bi-objective optimization formulation for robust RGB-D visual odometry. We investigate two methods for solving the proposed bi-objective optimization problem: the weighted sum method (in which the objective functions are combined into a single objective function) and the bounded objective method (in which one of the objective functions is optimized and the value of the other objective function is bounded via a constraint). Our experimental results for the open source TUM RGB-D dataset show that the new bi-objective optimization formulation is superior to several existing RGB-D odometry methods. In particular, the new formulation yields more accurate motion estimates and is more robust when textural or structural features in the image sequence are lacking. | |
| dc.publisher | IEEE | |
| dc.title | Bi-objective Optimization for Robust RGB-D Visual Odometry | |
| dc.type | Conference Paper | |
| dcterms.source.startPage | 1843 | |
| dcterms.source.endPage | 1850 | |
| dcterms.source.title | Proceedings of the 2015 27th Chinese Control and Decision Conference (CCDC) | |
| dcterms.source.series | Proceedings of the 2015 27th Chinese Control and Decision Conference (CCDC) | |
| dcterms.source.isbn | 9781479970162 | |
| dcterms.source.conference | 2015 27th Chinese Control and Decision Conference (CCDC) | |
| dcterms.source.place | Singapore | |
| curtin.note |
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| curtin.department | Department of Mathematics and Statistics | |
| curtin.accessStatus | Open access |