InterpolationSLAM: A Novel Robust Visual SLAM System in Rotating Scenes
This addresses robustness issues in visual SLAM for applications like robotics and autonomous vehicles, but it is incremental as it builds on ORB-SLAM2.
The paper tackles the problem of increased mapping errors and tracking loss in visual SLAM systems during rotating scenes by proposing InterpolationSLAM, which detects rotations and performs interpolation to improve pose estimation, resulting in outperforming standard baselines on KITTI and TUM datasets.
In recent years, visual SLAM has achieved great progress and development, but in complex scenes, especially rotating scenes, the error of mapping will increase significantly, and the slam system is easy to lose track. In this article, we propose an InterpolationSLAM framework, which is a visual SLAM framework based on ORB-SLAM2. InterpolationSLAM is robust in rotating scenes for Monocular and RGB-D configurations. By detecting the rotation and performing interpolation processing at the rotated position, pose of the system can be estimated more accurately at the rotated position, thereby improving the accuracy and robustness of the SLAM system in the rotating scenes. To the best of our knowledge, it is the first work combining the interpolation network into a Visual SLAM system to improve SLAM system robustness in rotating scenes. We conduct experiments both on KITTI Monocular and TUM RGB-D datasets. The results demonstrate that InterpolationSLAM outperforms the accuracy of standard Visual SLAM baselines.