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 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.

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.

Source Title

Proceedings of the 2015 27th Chinese Control and Decision Conference (CCDC)

DOI

10.1109/CCDC.2015.7162218

ISBN

9781479970162

School

Department of Mathematics and Statistics

Remarks

Copyright © 2015 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.

URI

http://hdl.handle.net/20.500.11937/21650

Collection

Curtin Research Publications