InterpolationSLAM: A Novel Robust Visual SLAM System in Rotational Motion

TL;DR

InterpolationSLAM introduces a frame interpolation-based approach to enhance the robustness and accuracy of visual SLAM systems during rotational movements by leveraging high-quality intermediate frame prediction.

Contribution

The paper proposes a novel InterpolationSLAM framework that improves visual SLAM performance in rotational motion by integrating frame interpolation techniques for better pose estimation.

Findings

Enhanced SLAM accuracy during rotational movements

Reduced failure rate of SLAM in challenging motions

Effective in both Monocular and RGB-D configurations

Abstract

In recent years, visual SLAM has achieved great progress and development in different scenes, however, there are still many problems to be solved. The SLAM system is not only restricted by the external scenes but is also affected by its movement mode, such as movement speed, rotational motion, etc. As the representatives of the most excellent networks for frame interpolation, Sepconv-slomo and EDSC can predict high-quality intermediate frame between the previous frame and the current frame. Intuitively, frame interpolation technology can enrich the information of images sequences, the number of which is limited by the camera's frame rate, and thus decreasing the probability of SLAM system's failure rate. In this article, we propose an InterpolationSLAM framework. InterpolationSLAM is robust in rotational movement 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, thereby improving the accuracy and robustness of the SLAM system in the rotational movement.