Avoiding Degeneracy for Monocular Visual SLAM with Point and Line Features
This addresses robustness issues in visual SLAM for low-texture or variable illumination environments, though it is incremental as it builds on prior work with line features.
The paper tackles degeneracy in monocular visual SLAM when using point and line features by proposing a method to identify degenerate lines and a structural constraint to avoid them, resulting in more accurate localization and mapping compared to state-of-the-art algorithms on the EuRoC dataset.
In this paper, a degeneracy avoidance method for a point and line based visual SLAM algorithm is proposed. Visual SLAM predominantly uses point features. However, point features lack robustness in low texture and illuminance variant environments. Therefore, line features are used to compensate the weaknesses of point features. In addition, point features are poor in representing discernable features for the naked eye, meaning mapped point features cannot be recognized. To overcome the limitations above, line features were actively employed in previous studies. However, since degeneracy arises in the process of using line features, this paper attempts to solve this problem. First, a simple method to identify degenerate lines is presented. In addition, a novel structural constraint is proposed to avoid the degeneracy problem. At last, a point and line based monocular SLAM system using a robust optical-flow based lien tracking method is implemented. The results are verified using experiments with the EuRoC dataset and compared with other state-of-the-art algorithms. It is proven that our method yields more accurate localization as well as mapping results.