Bi-objective Optimization for Robust RGB-D Visual Odometry

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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.

Tao Han, Chao Xu, Ryan Loxton, Lei Xie · 2014

Related benchmarks

Task	Dataset	Result
Visual Odometry	TUM RGB-D poor structure, rich texture, far (sequences 5-8)	RMSE (m/s)0.078033	4
Visual Odometry	TUM RGB-D rich structure poor texture far (sequences 5-8)	RMSE (m/s)0.066008	4
Visual Odometry	TUM RGB-D poor structure, poor texture, far (sequences 5-8)	RMSE (m/s)0.123848	4
Visual Odometry	TUM RGB-D rich structure rich texture far (sequences 5-8)	RMSE (m/s)0.014284	4
Visual Odometry	TUM RGB-D poor structure, rich texture, close (1st-4th sequences)	RMSE (m/s)0.032715	4
Visual Odometry	TUM RGB-D rich structure, poor texture, close (1st-4th sequences)	RMSE (m/s)0.088853	4
Visual Odometry	TUM RGB-D rich structure rich texture close (sequences 1-4)	RMSE (m/s)0.015101	4
Visual Odometry	TUM RGB-D poor structure, poor texture, close (1st-4th sequences)	RMSE (m/s)0.178571	4
RGB-D odometry	TUM RGB-D Structure vs. Texture category	Runtime22.99	4