Bi-objective Optimization for Robust RGB-D Visual Odometry

TL;DR

This paper introduces a bi-objective optimization approach for RGB-D visual odometry, improving accuracy and robustness over existing methods, especially in low-texture scenarios.

Contribution

It proposes a novel bi-objective optimization framework with two solving methods, demonstrating superior performance on standard datasets.

Findings

More accurate motion estimates

Enhanced robustness in low-texture conditions

Outperforms existing RGB-D odometry methods

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.