DeblurSLAM: A Novel Visual SLAM System Robust in Blurring Scene

Interest point extraction is a crucial technique for visual simultaneous localization and mapping, which can affect the accuracy of the whole system's localization. Feature point extraction is closely related to image quality. In real life, the motion blur phenomenon is always unavoidable in the process of camera shooting. For example, the fast motion of the camera and the rapid movement of the object will cause the motion blur of the image, which will lead to the inaccurate extraction location of interest points. In this paper we propose a DeblurSLAM framework, which is a Visual SLAM system building on ORB-SLAM2 [1]. DeblurSLAM is robust in blurring scenarios for RGB-D and stereo configurations. We are capable of detecting the blur and removing blur interference. To our knowledge, it is the first work combining the deblurring network into a Visual SLAM system. We conduct experiments both on TUM RGB-D and KITTI stereo datasets. The results demonstrate that DeblurSLAM outperforms the accuracy of standard Visual SLAM baselines.