Bi-objective Optimization for Robust RGB-D Visual Odometry
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 b...
| Main Authors: | , , , |
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| Format: | Conference Paper |
| Published: |
IEEE
2015
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| Online Access: | http://hdl.handle.net/20.500.11937/21650 |
| _version_ | 1848750649102041088 |
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| author | Han, T. Xu, C. Loxton, Ryan Xie, L. |
| author_facet | Han, T. Xu, C. Loxton, Ryan Xie, L. |
| author_sort | Han, T. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T07:40:11Z |
| format | Conference Paper |
| id | curtin-20.500.11937-21650 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:40:11Z |
| publishDate | 2015 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-216502017-09-13T16:01:25Z Bi-objective Optimization for Robust RGB-D Visual Odometry Han, T. Xu, C. Loxton, Ryan Xie, L. 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. 2015 Conference Paper http://hdl.handle.net/20.500.11937/21650 10.1109/CCDC.2015.7162218 IEEE fulltext |
| spellingShingle | Han, T. Xu, C. Loxton, Ryan Xie, L. Bi-objective Optimization for Robust RGB-D Visual Odometry |
| title | Bi-objective Optimization for Robust RGB-D Visual Odometry |
| title_full | Bi-objective Optimization for Robust RGB-D Visual Odometry |
| title_fullStr | Bi-objective Optimization for Robust RGB-D Visual Odometry |
| title_full_unstemmed | Bi-objective Optimization for Robust RGB-D Visual Odometry |
| title_short | Bi-objective Optimization for Robust RGB-D Visual Odometry |
| title_sort | bi-objective optimization for robust rgb-d visual odometry |
| url | http://hdl.handle.net/20.500.11937/21650 |