Hybrid Metric-topological Mapping for Large Scale Monocular SLAM

Simultaneous Localization and Mapping (SLAM) is a central problem for autonomous mobile robotics. Monocular SLAM is one of the ways to tackle the problem, where the only input information are the images from a moving camera. Current approaches for this problem have achieved a good balance between accuracy and density of the map, however, they are not suited for large scale. In this paper, we present a dynamic mapping strategy where the metric map is divided into regions with highly connected observations, resulting in a topological structure which permits the efficient augmentation and optimization of the map. For that, a graph representation where the nodes represent keyframes, and their connections are a measure of their overlapping, is continuously rearranged. The experiments show that this hybrid metric-topological approach outperforms the efficiency and scalability of previous approaches.