InterpolationSLAM: A Novel Robust Visual SLAM System in Rotating Scenes

TL;DR

InterpolationSLAM introduces an interpolation-based approach to enhance the robustness and accuracy of visual SLAM systems in rotating scenes, addressing a key challenge in complex environments.

Contribution

It is the first to integrate an interpolation network into a Visual SLAM system to improve performance in rotating scenes.

Findings

Outperforms standard SLAM baselines in accuracy

Effective in both Monocular and RGB-D configurations

Demonstrates robustness in rotating scenes on KITTI and TUM datasets

Abstract

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.