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.
翻译:近些年来,视觉 SLM 在不同场景中取得了巨大的进步和发展,然而,仍然有许多问题有待解决,SLM 系统不仅受到外部场景的限制,而且还受到其移动模式的影响,例如移动速度、旋转运动等。由于最优秀的内插网络、Sepconv-slomo和EDSC的代表可以预测上一个框架和当前框架之间的高质量中间框架,框架内插技术可以直觉地丰富图像序列的信息,其数量受相机框架率的限制,从而降低SLAM系统失灵率的概率。在本篇文章中,我们提议建立一个INDIOLationSLAM框架。 IntergationSLAM在单体和RGB-D配置的轮动运动中非常活跃。通过在旋转位置上探测轮调和进行内插处理,可以更准确地估计系统构成,从而提高SLM 系统在轮动中的准确性和稳健性。