Bi-objective Optimization for Robust RGB-D Visual Odometry
This work addresses robustness in visual odometry for robotics or AR/VR applications, but it is incremental as it builds on existing optimization techniques.
The paper tackles robust RGB-D visual odometry by proposing a new bi-objective optimization formulation, showing it yields more accurate motion estimates and better robustness in feature-poor scenarios compared to existing methods on the TUM RGB-D dataset.
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.