Robust Relocalization Based on Active Loop Closure for Real-Time Monocular SLAM

Abstract

Remarkable performance has been achieved using the state-of-the-art monocular Simultaneous Localization and Mapping (SLAM) algorithms. However, tracking failure is still a challenging problem during the monocular SLAM process, and it seems to be even inevitable when carrying out long-term SLAM in large-scale environments. In this paper, we propose an active loop closure based relocalization system, which enables the monocular SLAM to detect and recover from tracking failures automatically even in previously unvisited areas where no keyframe exists. We test our system by extensive experiments including using the most popular KITTI dataset, and our own dataset acquired by a hand-held camera in outdoor large-scale and indoor small-scale real-world environments where man-made shakes and interruptions were added. The experimental results show that the least recovery time (within 5 ms) and the longest success distance (up to 46 m) were achieved comparing to other relocalization systems. Furthermore, our system is more robust than others, as it can be used in different kinds of situations, i.e., tracking failures caused by the blur, sudden motion and occlusion. Besides robots or autonomous vehicles, our system can also be employed in other applications, like mobile phones, drones, etc.